CN119482974A - An operation method and system based on electric power measurement and control instrument - Google Patents
An operation method and system based on electric power measurement and control instrument Download PDFInfo
- Publication number
- CN119482974A CN119482974A CN202510058646.3A CN202510058646A CN119482974A CN 119482974 A CN119482974 A CN 119482974A CN 202510058646 A CN202510058646 A CN 202510058646A CN 119482974 A CN119482974 A CN 119482974A
- Authority
- CN
- China
- Prior art keywords
- data
- maintenance
- time
- load
- power grid
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000005259 measurement Methods 0.000 title claims abstract description 62
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000012423 maintenance Methods 0.000 claims abstract description 193
- 238000012544 monitoring process Methods 0.000 claims abstract description 82
- 238000004458 analytical method Methods 0.000 claims abstract description 40
- 230000002159 abnormal effect Effects 0.000 claims abstract description 30
- 238000013468 resource allocation Methods 0.000 claims abstract description 20
- 230000008859 change Effects 0.000 claims description 45
- 238000012795 verification Methods 0.000 claims description 36
- 238000009826 distribution Methods 0.000 claims description 19
- 230000005540 biological transmission Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 12
- 238000012216 screening Methods 0.000 claims description 10
- 230000007246 mechanism Effects 0.000 claims description 6
- 230000035945 sensitivity Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 238000003745 diagnosis Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 claims description 5
- 238000011017 operating method Methods 0.000 claims description 3
- 238000013439 planning Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 description 7
- 238000007726 management method Methods 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000012300 Sequence Analysis Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Human Computer Interaction (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
本发明涉及电力测控仪技术领域，具体为一种基于电力测控仪的操作方法及系统，包括以下步骤，基于电力测控仪，远程采集电网实时运行数据，筛选和剔除异常数据，并整合历史运行数据，分析从传感器收集的数据，得到设备故障概率指标。本发明中，通过电力测控仪远程采集的实时数据结合历史数据进行分析，使得故障预测更为精确，减少了电网中断的概率，关键监控点的实时确定与故障预测的准确性提升，优化了资源分配并加快故障处理速度，与维护周期数据的智能比较，优化了维护日程，保障了电力设备的高效运行，通过分析当前负载与预测负载的差异，有效预测未来负载变化，确保电网能够适应不同的运行条件，提高了整体电网的可靠性和效率。
The present invention relates to the technical field of electric power measurement and control instruments, and specifically to an operation method and system based on electric power measurement and control instruments, comprising the following steps: based on the electric power measurement and control instrument, remotely collect real-time operation data of the power grid, screen and eliminate abnormal data, integrate historical operation data, analyze data collected from sensors, and obtain equipment failure probability indicators. In the present invention, the real-time data collected remotely by the electric power measurement and control instrument is combined with historical data for analysis, so that fault prediction is more accurate, the probability of power grid interruption is reduced, the real-time determination of key monitoring points and the accuracy of fault prediction are improved, resource allocation is optimized and fault handling speed is accelerated, intelligent comparison with maintenance cycle data is performed, the maintenance schedule is optimized, and the efficient operation of electric power equipment is guaranteed. By analyzing the difference between the current load and the predicted load, future load changes are effectively predicted, ensuring that the power grid can adapt to different operating conditions, and improving the reliability and efficiency of the overall power grid.
Description
Technical Field
The invention relates to the technical field of electric power measurement and control instruments, in particular to an operation method and system based on an electric power measurement and control instrument.
Background
The power measurement and control instrument relates to real-time monitoring and control of a power system and comprises equipment and a method for measuring, recording and analyzing voltage, current, power and other electric parameters in a power grid, the power measurement and control instrument not only can help maintain the stability of the power grid, prevent power faults, but also can optimize energy utilization efficiency and reduce energy consumption, and under the support of intelligent power grid technology, the power measurement and control instrument can realize more complex data processing and remote control functions including load management, fault diagnosis, energy management and the like.
The operation method of the electric power measurement and control instrument relates to executing electric power monitoring and control tasks, and generally comprises the steps of setting monitoring parameters of the measurement and control instrument, reading and analyzing measurement data, and executing an automatic or manual control strategy according to data results.
The prior art mainly focuses on basic monitoring, has obvious short plates for deep data processing and remote control, can implement monitoring, but has insufficient deep analysis and predictive maintenance processing on data, often causes the response of a power grid to an emergency to be lagged, the lack of advanced analysis means that faults are difficult to predict and position, the maintenance complexity and the cost are increased, in addition, the load management strategy cannot be adjusted in real time, so that the operation of the power grid is unstable when the power demand is suddenly changed, the power grid is particularly outstanding under the condition of complicated or urgent management, the power grid is difficult to adapt to the change, and the low efficiency and the potential power grid faults are easily caused.
Disclosure of Invention
In order to solve the technical problems that the intensive data processing and the remote control have obvious short plates, the monitoring can be implemented, but the intensive analysis and the predictive maintenance processing of the data are insufficient, so that the response of a power grid to an emergency is delayed, the lack of high-level analysis means that the fault is difficult to predict and locate, the maintenance complexity and the cost are increased, in addition, the load management strategy cannot be adjusted in real time, the operation of the power grid is unstable when the power demand changes suddenly, the power grid is particularly outstanding under the condition of complicated or urgent management, the change is difficult to adapt, the inefficiency is easy to be caused, and the potential power grid fault is easy to cause, and the embodiment of the invention provides the operation method and the operation system based on the power measurement and control instrument. The technical scheme is as follows:
in one aspect, an operation method based on an electric power measurement and control instrument is provided, which comprises the following steps:
S1, based on an electric power measurement and control instrument, remotely collecting real-time operation data of a power grid, screening and eliminating abnormal data, integrating historical operation data, and analyzing data collected from a sensor to obtain an equipment failure probability index;
S2, determining key monitoring points of the power grid based on the equipment fault probability index, transmitting monitoring point data to a power grid dispatching center in real time, and carrying out fault analysis on the key monitoring data to obtain a fault moment prediction record;
S3, based on the fault moment prediction record, comparing the fault moment prediction record with the maintenance period data of the power equipment, identifying key power equipment to be maintained, and adjusting maintenance time and resource allocation to obtain an optimized maintenance schedule;
s4, based on the optimized maintenance schedule, formulating a remote maintenance task, sending an execution command, remotely verifying a maintenance task instruction, determining the correctness and the completeness of the maintenance task, and obtaining maintenance task verification information;
S5, collecting real-time load data of a power grid by using an electric power measurement and control instrument based on the maintenance task verification information, calculating the difference between the current real-time load and the historical predicted load, analyzing the load difference value, and predicting the future short-term load change to obtain a short-term load change overview;
And S6, analyzing the current running condition of the power grid based on the short-term load change overview, and remotely adjusting the load distribution parameters of the power grid through the power measurement and control instrument to obtain the adjusted running condition of the power grid.
On the other hand, the equipment fault probability index comprises a fault potential, a fault influence range and an early warning level, the fault moment prediction record comprises a prediction accuracy, fault influence equipment and a prediction basis, the optimized maintenance schedule comprises a key maintenance date, resource allocation details and maintenance task priority, the maintenance task verification information comprises a verification result, task compliance and error detection information, the short-term load change overview comprises a prediction error range, a key influence factor and a load adjustment range, and the adjusted power grid running state comprises a running efficiency index, a stability level and an adjustment feedback result.
On the other hand, based on the electric power measurement and control instrument, the real-time operation data of the power grid is collected remotely, abnormal data are screened and removed, historical operation data are integrated, data collected from a sensor are analyzed, and the step of obtaining the equipment failure probability index is specifically as follows:
S101, based on an electric power measurement and control instrument, collecting real-time operation data of a power grid remotely, screening the collected data, eliminating noise and abnormal points, and merging historical operation data to obtain a real-time power grid data set;
s102, classifying data based on the real-time power grid data set, marking equipment states corresponding to the data, and extracting equipment operation key parameters to obtain equipment state information;
And S103, calculating the abnormal operation frequency of the equipment based on the equipment state information, and evaluating the potential of the future faults of the equipment by combining the historical fault data of the equipment to obtain equipment fault probability indexes.
On the other hand, based on the equipment fault probability index, determining key monitoring points of the power grid, transmitting monitoring point data to a power grid dispatching center in real time, and carrying out fault analysis on the key monitoring data, wherein the step of obtaining a fault moment prediction record specifically comprises the following steps:
S201, identifying key monitoring points with potential faults in a power grid based on the equipment fault probability index, marking the monitoring points, and adjusting a monitoring mechanism according to the fault risk level of the monitoring points to obtain a key monitoring point catalog;
s202, setting time intervals and priorities of data transmission to a power grid dispatching center based on the key monitoring point catalogue, optimizing transmission efficiency of data streams, verifying instantaneity and accuracy of the data, and obtaining data stream state analysis results;
And S203, performing fault analysis on the key monitoring data based on the data flow state analysis result, adjusting fault identification sensitivity, and optimizing the accuracy of fault diagnosis to obtain a fault moment prediction record.
On the other hand, based on the fault time prediction record, the method compares the fault time prediction record with the maintenance period data of the power equipment, identifies the key power equipment to be maintained, adjusts the maintenance time and the resource allocation, and obtains the optimized maintenance schedule, wherein the method comprises the following steps:
S301, based on the fault moment prediction record, comparing maintenance period data of the power equipment, identifying maintenance requirement window and equipment maintenance frequency difference, and marking equipment with high maintenance priority to obtain a maintenance requirement list;
and S302, based on the maintenance requirement list, carrying out maintenance time adjustment, reallocating available technical resources and manpower, balancing maintenance tasks and resource supply, and combining schedule and resource allocation data to obtain an optimized maintenance schedule.
On the other hand, comparing the maintenance period data of the power equipment according to the formula:
Calculating maintenance cycle difference index In which, in the process,Representing the current maintenance period of the ith device,Represents the estimated maintenance period of the ith equipment according to the fault prediction, M represents the total number of the equipment,Is a weight factor determined according to the type of device and the frequency of use factor.
On the other hand, based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, remote verification is carried out on maintenance task instructions, the correctness and the integrity of the maintenance task are determined, and the steps for obtaining maintenance task verification information are specifically as follows:
S401, based on the optimized maintenance schedule, making a flow and time schedule of a remote maintenance task, and sending a key execution command to a maintenance team to obtain a maintenance operation plan;
and S402, based on the maintenance operation plan, performing remote verification, checking the accuracy of a maintenance instruction and the compliance of task parameters, adjusting the setting which does not meet the requirements, and determining the correctness and the integrity of a maintenance task to obtain maintenance task verification information.
On the other hand, based on the maintenance task verification information, collecting real-time load data of a power grid by using an electric power measurement and control instrument, calculating the difference between the current real-time load and the historical predicted load, analyzing the load difference value, predicting the future short-term load change, and obtaining a short-term load change overview specifically comprises the following steps:
s501, based on the maintenance task verification information, monitoring a power grid in real time through an electric power measurement and control instrument, collecting current load data, synchronously updating real-time data flow, and carrying out time sequence analysis on the data to obtain a real-time load monitoring record;
s502, based on the real-time load monitoring record, carrying out data comparison analysis, calculating deviation from a historical load, identifying key negative carrier moving points, and identifying key changes to obtain a load fluctuation analysis result;
And S503, based on the load fluctuation analysis result, evaluating the data change trend in a short period, and integrating the predicted data in the short period to obtain a short-period load change overview.
On the other hand, the data comparison analysis is performed according to the formula:
Calculating load deviations In which, in the process,Represents the firstReal-time load values at the moment of time,Represents the firstThe historical load value of the moment in time,Representing the total number of measurement points in the time window,Is the firstA weight factor for a time, representing the load criticality of that time,Is a compensation term.
In another aspect, an operating system based on a power measurement and control instrument is provided, and the system is applied to an operating method based on the power measurement and control instrument, and includes:
The data acquisition and analysis module is used for remotely acquiring real-time operation data of the power grid, screening and eliminating abnormal data, integrating historical operation data, and analyzing data collected from the sensor to obtain equipment fault probability indexes;
the fault monitoring and identifying module determines key monitoring points of the power grid based on the equipment fault probability index, transmits monitoring point data to a power grid dispatching center in real time, and performs fault analysis to obtain a fault moment prediction record;
the maintenance planning module is used for comparing the fault moment prediction record with the maintenance period data of the power equipment, identifying the key power equipment to be maintained, and adjusting the maintenance time and the resource allocation to obtain an optimized maintenance schedule;
The remote maintenance execution module establishes a remote maintenance task based on the optimized maintenance schedule, sends an execution command, remotely verifies a maintenance task instruction, determines the correctness and the integrity of the maintenance task, and obtains maintenance task verification information;
The power grid load optimization module collects power grid real-time load data based on the maintenance task verification information, calculates the difference between the power grid real-time load data and the historical predicted load, analyzes the load difference value, predicts the future short-term load change, evaluates the current running condition of the power grid, remotely adjusts the power grid load distribution parameters, and obtains the adjusted running condition of the power grid.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
The real-time data collected remotely through the electric power measurement and control instrument is combined with historical data to analyze, so that fault prediction is more accurate, the probability of power grid interruption is reduced, the accuracy of real-time determination and fault prediction of key monitoring points is improved, resource allocation is optimized, fault processing speed is accelerated, the maintenance schedule is optimized in comparison with the intelligent maintenance period data, efficient operation of electric power equipment is guaranteed, future load change is effectively predicted by analyzing the difference between the current load and the predicted load, the power grid is ensured to adapt to different operation conditions, and the reliability and efficiency of the whole power grid are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the main steps of the present invention;
FIG. 2 is a flow chart showing the steps of the invention S1;
FIG. 3 is a flow chart showing the steps of the method S2 of the present invention;
FIG. 4 is a flow chart showing the steps of the method S3 of the present invention;
FIG. 5 is a flow chart showing the steps of the invention S4;
FIG. 6 is a flow chart showing the steps of the method S5;
FIG. 7 is a flow chart showing the steps of the method S6;
fig. 8 is a system block diagram of the present invention.
Detailed Description
The technical scheme of the invention is described below with reference to the accompanying drawings.
In embodiments of the invention, words such as "exemplary," "such as" and the like are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, the term use of an example is intended to present concepts in a concrete fashion. Furthermore, in embodiments of the present invention, the meaning of "and/or" may be that of both, or may be that of either, optionally one of both.
In the embodiments of the present invention, "image" and "picture" may be sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized. "of", "corresponding (corresponding, relevant)" and "corresponding (corresponding)" are sometimes used in combination, and it should be noted that the meaning of the expression is consistent when the distinction is not emphasized.
In embodiments of the present invention, sometimes a subscript such as W 1 may be written in a non-subscript form such as W1, and the meaning of the expression is consistent when de-emphasizing the distinction.
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides an operation method based on an electric power measurement and control instrument, as shown in fig. 1, comprising the following steps:
S1, based on an electric power measurement and control instrument, remotely collecting real-time operation data of a power grid, screening and removing abnormal data, integrating historical operation data, and analyzing data collected from a sensor to obtain equipment failure probability indexes;
s2, determining key monitoring points of the power grid based on equipment fault probability indexes, transmitting monitoring point data to a power grid dispatching center in real time, and carrying out fault analysis on the key monitoring data to obtain a fault moment prediction record;
s3, based on the fault moment prediction record, comparing the fault moment prediction record with the maintenance period data of the power equipment, identifying the key power equipment to be maintained, and adjusting the maintenance time and the resource allocation to obtain an optimized maintenance schedule;
S4, based on an optimized maintenance schedule, formulating a remote maintenance task, sending an execution command, remotely verifying a maintenance task instruction, determining the correctness and the integrity of the maintenance task, and obtaining maintenance task verification information;
S5, collecting real-time load data of the power grid based on maintenance task verification information by using a power measurement and control instrument, calculating the difference between the current real-time load and the historical predicted load, analyzing the load difference value, and predicting the future short-term load change to obtain a short-term load change overview;
And S6, analyzing the current running condition of the power grid based on the short-term load change overview, and remotely adjusting the load distribution parameters of the power grid through the power measurement and control instrument to ensure the running efficiency and stability of the power grid and obtain the adjusted running condition of the power grid.
The equipment fault probability indexes comprise fault potential, a fault influence range and an early warning level, the fault moment prediction record comprises prediction accuracy, fault influence equipment and a prediction basis, the optimized maintenance schedule comprises a key maintenance date, resource allocation details and maintenance task priority, the maintenance task verification information comprises verification results, task compliance and error detection information, the short-term load change overview comprises a prediction error range, key influence factors and a load adjustment range, and the adjusted power grid running state comprises running efficiency indexes, stability levels and adjustment feedback results.
As shown in fig. 2, based on the electric power measurement and control instrument, the steps of remotely collecting real-time operation data of the power grid, screening and eliminating abnormal data, integrating historical operation data, analyzing data collected from the sensor, and obtaining the equipment failure probability index are specifically as follows:
S101, based on an electric power measurement and control instrument, collecting real-time operation data of a power grid remotely, screening the collected data, eliminating noise and abnormal points, and merging historical operation data to obtain a real-time power grid data set;
Screening the obtained data, removing invalid data and noise, adopting a set abnormality detection threshold to identify abnormal points in the data, calculating the deviation of each data point, marking the data points with the deviation value exceeding the set threshold as abnormal data and removing the abnormal data, combining the screened data and historical operation data according to a time sequence to ensure the consistency of time stamps, and simultaneously calculating the average value of the combined data and the variation range of data distribution to verify the data quality so as to obtain a real-time power grid data set which is available after cleaning.
S102, classifying data based on a real-time power grid data set, marking equipment states corresponding to the data, and extracting equipment operation key parameters to obtain equipment state information;
Classifying the data according to the device types, extracting a unique identifier corresponding to each device, marking each group of data as different device states according to data characteristics by combining key parameters such as voltage, current and the like, respectively extracting key characteristics of the voltage and current data including maximum value, minimum value, average value and change rate, comparing the characteristic values with device operation records to ensure the rationality and consistency of classification, and finally generating marking information containing the device operation states to form a device state data set.
S103, calculating the abnormal operation frequency of the equipment based on the equipment state information, and evaluating the potential of the future faults of the equipment by combining the historical fault data of the equipment to obtain equipment fault probability indexes;
Counting abnormal frequencies in the running process of equipment, collecting abnormal events recorded in the running process of each piece of equipment, classifying the abnormal events into different types, counting the occurrence times of various abnormal events, calculating the total abnormal frequency, evaluating the potential failure of the equipment by combining historical failure data, determining the probability of future failure of the equipment by analyzing the association trend of the change of the abnormal frequency of the current equipment and the historical failure, and outputting failure probability indexes of the equipment.
As shown in fig. 3, based on the equipment fault probability index, determining a key monitoring point of the power grid, transmitting monitoring point data to a power grid dispatching center in real time, and performing fault analysis on the key monitoring data, wherein the steps of obtaining a fault moment prediction record specifically include:
S201, identifying key monitoring points with potential faults in a power grid based on equipment fault probability indexes, marking the monitoring points, and adjusting a monitoring mechanism according to fault risk levels of the monitoring points to obtain a key monitoring point catalog;
Identifying monitoring points with faults in a power grid, extracting risk levels of the monitoring points from fault probability indexes, classifying the monitoring points according to a set fault probability threshold value, marking the monitoring points with higher risk levels as key monitoring points, then analyzing the running state and fault history of each key monitoring point, storing data into a special database for later calling, simultaneously distributing unique identification codes for each monitoring point to track running information of each monitoring point, and adjusting monitoring mechanisms aiming at the monitoring points with different risk levels, wherein the monitoring mechanisms comprise improving data acquisition frequency, enhancing capturing and real-time feedback capabilities of abnormal data, and generating a power grid key monitoring point catalog.
S202, setting time intervals and priorities of data transmission to a power grid dispatching center based on a key monitoring point catalog, optimizing transmission efficiency of data streams, verifying instantaneity and accuracy of the data, and obtaining a data stream state analysis result;
Setting a time interval of data transmission for each monitoring point in a catalog, wherein the setting of the time interval is divided according to the risk level of the monitoring point, the data transmission frequency of the high risk point is set to be short, the middle and low risk points are set to be longer, the priority is classified according to the importance of the data, the data is divided into three levels of high, medium and low, independent transmission channel allocation is carried out on the high priority data, when the real-time performance of the data is verified, the time delay in the transmission process is monitored, the transmission accuracy is recorded, the high-efficiency operation of the data flow is ensured by optimizing a transmission mechanism, and the data flow state analysis result is obtained.
S203, performing fault analysis on the key monitoring data based on the data flow state analysis result, adjusting fault recognition sensitivity, and optimizing the accuracy of fault diagnosis to obtain a fault moment prediction record;
screening key data, classifying and arranging abnormal data according to time sequence, analyzing data change trend to determine fault points, adjusting sensitivity of fault identification, setting sensitivity differentiation standard according to risk level of monitoring points, verifying accuracy of current fault analysis by utilizing archived historical fault data, adjusting diagnosis model parameters of the monitoring points according to analysis results, recording predicted fault time points and related states, and obtaining a fault moment prediction record.
As shown in fig. 4, based on the predicted record of fault time, the method compares the predicted record of fault time with the maintenance period data of the power equipment, identifies the key power equipment to be maintained, adjusts the maintenance time and the resource allocation, and obtains the optimized maintenance schedule, which specifically includes the following steps:
S301, comparing maintenance period data of the power equipment based on the fault moment prediction record, identifying maintenance requirement window and equipment maintenance frequency difference, and marking equipment with high maintenance priority to obtain a maintenance requirement list;
comparing maintenance period data of the power equipment, and according to the formula:
Calculating maintenance cycle difference index In which, in the process,Representing the current maintenance period of the ith device,Represents the estimated maintenance period of the ith equipment according to the fault prediction, M represents the total number of the equipment,Is a weight factor determined according to the type of device and the frequency of use factor, for example, a critical device used at high frequency may have a higher weight;
considering the situation of three devices, the weight factors, actual maintenance period and predicted maintenance period of the devices are as follows:
Device 1: , The time of a month is one month, Month;
Device 2: , The time of a month is one month, Month;
Device 3: , The time of a month is one month, Month;
Calculating molecules:
The denominator is the sum of weights:
Maintaining a period difference index calculation result:
The result shows that the weighted average of the importance of the equipment and the prediction deviation is considered, the average of the actual and the prediction difference of the whole maintenance period is 2 months, and the maintenance strategy of the equipment which needs to be adjusted or re-evaluated in the actual operation is reflected so as to more accurately meet the actual maintenance requirement.
S302, based on a maintenance demand list, performing maintenance time adjustment, reallocating available technical resources and manpower, balancing maintenance tasks and resource supply, and combining schedule and resource allocation data to obtain an optimized maintenance schedule;
The method comprises the steps of collecting available conditions of technical resources and manpower, dividing the resources into three types of technical tools, manpower and time, calculating available total amount of each type of resources respectively, analyzing resource allocation amount required by each maintenance task in a maintenance demand list, comparing the resource allocation with the available resources, allocating enough resources for high-priority tasks according to task priorities, simultaneously readjusting time schedules of low-priority tasks to release the resources, integrating allocation data and time schedules of all tasks, and generating an optimized maintenance schedule.
As shown in fig. 5, based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, a maintenance task instruction is remotely verified, the correctness and the integrity of the maintenance task are determined, and the steps for obtaining maintenance task verification information are specifically as follows:
S401, based on an optimized maintenance schedule, making a flow and time schedule of a remote maintenance task, and sending a key execution command to a maintenance team to obtain a maintenance operation plan;
The method comprises the steps of disassembling time schedule and resource allocation of each maintenance task in a schedule, arranging according to the priority of the task, extracting execution steps of the high-priority task, formulating a specific operation flow of each task, allocating team members and technical resources for executing the task, generating an instruction set according to task requirements, including an initialization instruction, an operation state detection instruction and a recovery instruction of maintenance equipment, arranging the instructions in sequence according to the execution steps, setting execution time and feedback time limit of the instructions, sending the instructions to related maintenance teams through a communication platform, recording the sending time and receiving state of the instructions, and generating a maintenance operation plan containing time schedule and task details.
S402, based on a maintenance operation plan, performing remote verification, checking the accuracy of a maintenance instruction and the compliance of task parameters, adjusting the setting which does not meet the requirements, and determining the correctness and the integrity of a maintenance task to obtain maintenance task verification information;
Checking the received maintenance instruction, confirming that the content of the instruction is consistent with a task specified in a plan, extracting task parameters, and comparing the task parameters with the plan parameters item by item, including the model of equipment, the starting time of the task, execution steps and the like, checking whether the parameters meet standard requirements, adjusting parameters which do not meet the requirements, synchronizing an adjustment result to a maintenance operation plan, then simulating and executing a key maintenance step to test the accuracy of the instruction, recording feedback data and results of simulation execution, verifying the accuracy and the completeness of the maintenance task through data comparison, and generating verification information of the maintenance task.
As shown in fig. 6, based on maintenance task verification information, collecting real-time load data of a power grid by using a power measurement and control instrument, calculating the difference between the current real-time load and a historical predicted load, analyzing a load difference value, predicting future short-term load changes, and obtaining a short-term load change overview specifically includes the steps of:
s501, based on maintenance task verification information, monitoring a power grid in real time through an electric power measurement and control instrument, collecting current load data, synchronously updating real-time data flow, and carrying out time sequence analysis on the data to obtain a real-time load monitoring record;
Connecting a data acquisition interface of an electric power measurement and control instrument, extracting real-time load data, synchronously calibrating a time stamp of the data, integrating the data with the existing data flow record, then carrying out time sequence analysis on the integrated real-time data flow, extracting key load parameters of each time point, including load peak value, average value and change rate, dividing data segments according to set time intervals, analyzing fluctuation trend of each segment of data, recording abnormal points and load change rate, and generating a real-time load monitoring record.
S502, based on a real-time load monitoring record, carrying out data comparison analysis, calculating deviation from a historical load, identifying a key negative carrier moving point, and identifying key changes to obtain a load fluctuation analysis result;
performing data comparison analysis according to the formula:
Calculating load deviations In which, in the process,Represents the firstReal-time load values at the moment of time,Represents the firstThe historical load value of the moment in time,Representing the total number of measurement points in the time window,Is the firstA weight factor for a time, representing the load criticality of that time,Is a compensation term based on environmental variables or other factors for adjusting the firstLoad difference at time;
In a power system, the weight factor of the peak period (e.g. 7 to 9 pm) Possibly higher, and thus the weighting factors are statistically ordered and normalized by load change sensitivity to 1 by historical data analysis, e.g., 7, 8,9 points at night are weighted 0.3, 0.4, and 0.3 in that order;
real-time load and historical load AndBy means of real-time monitoring devices and historical data record acquisition of the power system, in one specific measurement window, the real-time load values may be 1500kW, 1550kW and 1480kW, while the corresponding historical load values are 1450kW, 1500kW and 1470kW;
Compensation term According to environmental factors or unexpected event adjustment, such as temperature change or sudden event influence, identifying abnormal deviation by contemporaneous comparison with history, setting a compensation value, and setting the compensation item to be +50kW if the special activity load is abnormally increased at 7 pm;
the calculation is performed taking into account the load data and parameters at three time points:
;
weighting factor ;
Real-time loadkW;
Historical loadkW;
Compensation termkW;
The numerator of the calculation formula is:
The denominator is the sum of weights:
load deviation The calculated results of (2) are:
the result shows that the actual load considering the weight factors and the environmental compensation has an overall deviation of 53kW compared with the historical load, and the result reflects the load change after the key period and the abnormal event are considered, so that important references are provided for system adjustment and energy distribution.
S503, based on the load fluctuation analysis result, evaluating the data change trend in a short period, and integrating the predicted data in the short period to obtain a short period load change overview;
extracting time sequence data of key load change points, calculating a load change trend in a short period according to a set time window, analyzing the increase and decrease amplitude and the change rate of the trend, integrating the trend data with a recent history record, predicting a load change value in the short period, then summarizing and integrating the predicted data, outputting the change trend, the predicted load value and a corresponding time interval into a unified record file, and generating a short-period load change overview.
As shown in fig. 7, based on the short-term load change overview, the current operation condition of the power grid is analyzed, the load distribution parameters of the power grid are remotely adjusted through the power measurement and control instrument, the operation efficiency and stability of the power grid are ensured, and the steps for obtaining the adjusted operation condition of the power grid are specifically as follows:
s601, analyzing the current running condition of the power grid based on a short-term load change overview, comparing key running parameters through real-time data, identifying potential performance problems, and carrying out parameter adjustment to obtain a running state evaluation result;
Extracting key operation parameters from short-term load change records, comparing the parameters with operation data acquired in real time item by item, identifying parameter items with larger deviation, analyzing the change trend of the deviation data and positioning performance problem points, and then adjusting related parameters, including resetting load distribution proportion, correcting frequency offset value and adjusting operation voltage range, and recording key data and feedback information in the adjustment process to obtain an evaluation result of the operation state of the power grid.
S602, based on an operation state evaluation result, remotely adjusting load distribution parameters through an electric power measurement and control instrument, reconfiguring load distribution logic, optimizing power grid resource distribution, and obtaining resource optimization configuration;
And extracting a load distribution scheme in the evaluation result, comparing each load distribution task in the scheme with the current configuration, identifying a parameter range and distribution logic which need to be adjusted, utilizing a remote control interface to modify parameters one by one, reallocating the power distribution value of each load point, checking the distribution logic, ensuring that the total load quantity is consistent with the power supply capacity of the power grid, recording the change histories of all adjustment parameters, updating a system configuration file, and generating an optimized resource configuration result.
S603, based on resource optimization configuration, performing power grid load adjustment, verifying the operation efficiency and stability of the adjusted power grid, and judging whether a preset target is reached or not to obtain an adjusted power grid operation state;
Loading new load distribution configuration through the electric power measurement and control instrument, monitoring the adjusted load operation parameters in real time, collecting data, recording the change before and after adjustment, evaluating the operation efficiency and stability, including calculating the operation loss rate and the power fluctuation rate, analyzing whether the adjusted power grid operation index is in a preset range, re-optimizing the configuration and repeating the adjustment process if the adjusted power grid operation index does not reach the target, recording all verification data and adjustment steps, and outputting the adjusted power grid operation state.
As shown in fig. 8, an operating system based on a power measurement and control instrument includes:
The data acquisition and analysis module is used for remotely acquiring real-time operation data of the power grid, screening and eliminating abnormal data, integrating historical operation data, and analyzing data collected from the sensor to obtain equipment fault probability indexes;
the fault monitoring and identifying module determines key monitoring points of the power grid based on the equipment fault probability index, transmits monitoring point data to a power grid dispatching center in real time, and performs fault analysis to obtain a fault moment prediction record;
The maintenance planning module is used for comparing the fault moment prediction record with the maintenance period data of the power equipment, identifying the key power equipment to be maintained, and adjusting the maintenance time and the resource allocation to obtain an optimized maintenance schedule;
The remote maintenance execution module formulates a remote maintenance task based on the optimized maintenance schedule, sends an execution command, remotely verifies a maintenance task instruction, determines the correctness and the integrity of the maintenance task, and obtains maintenance task verification information;
the power grid load optimization module collects real-time load data of a power grid based on maintenance task verification information, calculates the difference between the load data and a historical predicted load, analyzes a load difference value, predicts future short-term load change, evaluates the current running condition of the power grid, remotely adjusts power grid load distribution parameters, and obtains the adjusted running condition of the power grid.
It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B, and may mean that a exists alone, while a and B exist alone, and B exists alone, wherein a and B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.
In the present invention, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (a, b, or c) of a, b, c, a-b, a-c, b-c, or a-b-c may be represented, wherein a, b, c may be single or plural.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another device, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510058646.3A CN119482974B (en) | 2025-01-15 | 2025-01-15 | An operation method and system based on electric power measurement and control instrument |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510058646.3A CN119482974B (en) | 2025-01-15 | 2025-01-15 | An operation method and system based on electric power measurement and control instrument |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119482974A true CN119482974A (en) | 2025-02-18 |
| CN119482974B CN119482974B (en) | 2025-03-21 |
Family
ID=94573549
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510058646.3A Active CN119482974B (en) | 2025-01-15 | 2025-01-15 | An operation method and system based on electric power measurement and control instrument |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119482974B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119670023A (en) * | 2025-02-20 | 2025-03-21 | 国家管网集团北方管道有限责任公司 | A pump unit fault monitoring method based on multi-dimensional perception fusion recognition |
| CN119846518A (en) * | 2025-03-20 | 2025-04-18 | 贵州浦源通科技有限公司 | Short-circuit parameter measurement algorithm development method and system |
| CN119892716A (en) * | 2025-03-26 | 2025-04-25 | 深圳凯升联合科技有限公司 | Electric automatization remote control system |
| CN120127648A (en) * | 2025-05-12 | 2025-06-10 | 贵州江源电力建设有限公司 | A method and system for improving power grid operation safety margin |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210221247A1 (en) * | 2018-06-22 | 2021-07-22 | Moixa Energy Holdings Limited | Systems for machine learning, optimising and managing local multi-asset flexibility of distributed energy storage resources |
| CN114330771A (en) * | 2021-12-28 | 2022-04-12 | 国家电网有限公司 | Power grid maintenance scheduling method based on load analysis |
| CN117422163A (en) * | 2023-09-04 | 2024-01-19 | 国网内蒙古东部电力有限公司供电服务监管与支持中心 | Intelligent power grid load prediction method and system based on digital park |
| CN117930815A (en) * | 2024-01-25 | 2024-04-26 | 南京工业职业技术大学 | Wind turbine generator remote fault diagnosis method and system based on cloud platform |
| CN118316188A (en) * | 2024-04-03 | 2024-07-09 | 国网陕西省电力有限公司电力科学研究院 | Asset monitoring system and method applied to power grid data |
| CN118348878A (en) * | 2024-05-17 | 2024-07-16 | 宜兴涚平软件有限公司 | Remote control distributed energy station intelligent operation and maintenance management system and method |
-
2025
- 2025-01-15 CN CN202510058646.3A patent/CN119482974B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210221247A1 (en) * | 2018-06-22 | 2021-07-22 | Moixa Energy Holdings Limited | Systems for machine learning, optimising and managing local multi-asset flexibility of distributed energy storage resources |
| CN114330771A (en) * | 2021-12-28 | 2022-04-12 | 国家电网有限公司 | Power grid maintenance scheduling method based on load analysis |
| CN117422163A (en) * | 2023-09-04 | 2024-01-19 | 国网内蒙古东部电力有限公司供电服务监管与支持中心 | Intelligent power grid load prediction method and system based on digital park |
| CN117930815A (en) * | 2024-01-25 | 2024-04-26 | 南京工业职业技术大学 | Wind turbine generator remote fault diagnosis method and system based on cloud platform |
| CN118316188A (en) * | 2024-04-03 | 2024-07-09 | 国网陕西省电力有限公司电力科学研究院 | Asset monitoring system and method applied to power grid data |
| CN118348878A (en) * | 2024-05-17 | 2024-07-16 | 宜兴涚平软件有限公司 | Remote control distributed energy station intelligent operation and maintenance management system and method |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119670023A (en) * | 2025-02-20 | 2025-03-21 | 国家管网集团北方管道有限责任公司 | A pump unit fault monitoring method based on multi-dimensional perception fusion recognition |
| CN119846518A (en) * | 2025-03-20 | 2025-04-18 | 贵州浦源通科技有限公司 | Short-circuit parameter measurement algorithm development method and system |
| CN119892716A (en) * | 2025-03-26 | 2025-04-25 | 深圳凯升联合科技有限公司 | Electric automatization remote control system |
| CN119892716B (en) * | 2025-03-26 | 2025-06-03 | 深圳凯升联合科技有限公司 | An electrical automation remote control system |
| CN120127648A (en) * | 2025-05-12 | 2025-06-10 | 贵州江源电力建设有限公司 | A method and system for improving power grid operation safety margin |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119482974B (en) | 2025-03-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119482974B (en) | An operation method and system based on electric power measurement and control instrument | |
| CN119025283A (en) | A computing power data management system and method based on distributed computing | |
| KR101960755B1 (en) | Method and apparatus of generating unacquired power data | |
| CN118779743A (en) | Method and system for predicting equipment failures in radiation monitoring stations based on small sample training models | |
| CN115086089B (en) | Method and system for network security assessment prediction | |
| CN119359287A (en) | A predictive maintenance strategy optimization method for transformer area equipment maintenance | |
| CN118896427B (en) | Block chain-based air source heat pump monitoring and regulating method and system | |
| CN118428774B (en) | Intelligent analysis method and system for energy consumption based on time sequence and load characteristics | |
| CN119276357B (en) | Cloud computing-based narrow linewidth 1200nm semiconductor laser data processing system | |
| CN113570277A (en) | A kind of power capacity management method and device | |
| JP2009086896A (en) | Failure prediction system and failure prediction method for computer | |
| CN119293706B (en) | BIM model-based intelligent operation and maintenance method for full life cycle | |
| CN119428321A (en) | A dynamic power scheduling method for charging piles | |
| CN117331790A (en) | Machine room fault detection method and device for data center | |
| CN119988350B (en) | Intelligent heat supply data resource pool establishment method | |
| CN119225320A (en) | A factory task management method and system based on Internet of Things | |
| CN118779798B (en) | Power edge optimization control method and system based on IoT cloud-edge collaboration | |
| CN119828611A (en) | Robot data processing method and system based on industrial Internet of things | |
| CN117745110B (en) | Intelligent campus restaurant operation management and control method and system based on behavior analysis | |
| CN118394739A (en) | A dynamic data migration method based on information platform | |
| JP6625839B2 (en) | Load actual data determination device, load prediction device, actual load data determination method, and load prediction method | |
| CN119915503B (en) | Linear guide rail fault diagnosis method and system based on deep learning | |
| CN117971474B (en) | Data center talent training system with self-adaptive energy efficiency and dynamic resource configuration | |
| KR102617618B1 (en) | Device, method and system for providing artificial intelligence-based decision-making service for replacement of high-efficiency equipment replacement in industrial factories | |
| CN118233288B (en) | Vehicle communication management method based on SaaS |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |