An operation method and system based on electric power measurement and control instrument

Abstract

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

[0001] The present invention relates to the technical field of electric power measurement and control instruments, and in particular to an operation method and system based on electric power measurement and control instruments. Background Art

[0002] Power measurement and control instruments involve real-time monitoring and control of power systems, including equipment and methods for measuring, recording and analyzing voltage, current, power and other electrical parameters in power grids. Power measurement and control instruments can not only help maintain grid stability and prevent power failures, but also optimize energy efficiency and reduce energy consumption. With the support of smart grid technology, power measurement and control instruments can achieve more complex data processing and remote control functions, including load management, fault diagnosis and energy management.

[0003] Among them, the operation method of the power measurement and control instrument involves performing power monitoring and control tasks, which usually includes setting the monitoring parameters of the measurement and control instrument, reading and parsing the measurement data, and how to execute automatic or manual control strategies based on the data results. It has a wide range of uses, ranging from daily operation and maintenance management of power facilities to rapid response to emergencies in the power system, so that operators can understand the operating status of the power grid more accurately and adjust the control strategy in time to ensure the stable operation of the power system.

[0004] Existing technologies mainly focus on basic monitoring, but have obvious shortcomings in in-depth data processing and remote control. Although monitoring can be implemented, in-depth analysis of data and predictive maintenance are insufficient, which often causes the power grid to respond slowly to emergencies. The lack of advanced analysis means that it is difficult to predict and locate faults, increasing maintenance complexity and costs. In addition, load management strategies fail to adjust in real time, resulting in unstable grid operation when power demand changes dramatically. This is particularly prominent under complex or urgent grid conditions, making it difficult to adapt to changes and easily leading to inefficiency and potential grid failures. Summary of the invention

[0005] In order to solve the existing problems of focusing on basic monitoring and having obvious shortcomings in in-depth data processing and remote control, although monitoring can be implemented, in-depth analysis of data and predictive maintenance processing are insufficient, which often makes the power grid respond to emergencies with lags. The lack of advanced analysis means that it is difficult to predict and locate faults, which increases the complexity and cost of maintenance. In addition, the load management strategy fails to adjust in real time, resulting in unstable power grid operation when power demand changes sharply, which is particularly prominent under complex or urgent power grid conditions, making it difficult to adapt to changes and easily causing inefficiency and potential power grid failures. The embodiment of the present invention provides an operation method and system based on a power measurement and control instrument. The technical solution is as follows:

[0006] On the one hand, an operation method based on a power measurement and control instrument is provided, comprising the following steps:

[0007] S1: Based on the power measurement and control instrument, remotely collect real-time operation data of the power grid, filter and eliminate abnormal data, integrate historical operation data, analyze the data collected from sensors, and obtain equipment failure probability indicators;

[0008] S2: Based on the equipment failure probability index, determine the key monitoring points of the power grid, transmit the monitoring point data to the power grid dispatching center in real time, and perform fault analysis on the key monitoring data to obtain the prediction record of the fault time;

[0009] S3: Based on the fault time prediction record, compare with the power equipment maintenance cycle data, identify the key power equipment that needs maintenance, and adjust the maintenance time and resource allocation to obtain an optimized maintenance schedule;

[0010] S4: Based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, the maintenance task instruction is remotely verified, the correctness and completeness of the maintenance task are determined, and maintenance task verification information is obtained;

[0011] S5: Based on the maintenance task verification information, use the power measurement and control instrument to collect real-time load data of the power grid, calculate the difference between the current real-time load and the historical predicted load, and analyze the load difference value to predict future short-term load changes and obtain an overview of short-term load changes;

[0012] S6: Based on the short-term load change overview, analyze the current operation status of the power grid, remotely adjust the load distribution parameters of the power grid through the power measurement and control instrument, and obtain the adjusted power grid operation status.

[0013] On the other hand, the equipment failure probability index includes the potential of failure, the scope of failure impact, and the warning level. The failure time prediction record includes the prediction accuracy, the equipment affected by the failure, and the prediction basis. The optimized maintenance schedule includes the key maintenance dates, resource allocation details, and the maintenance task priority. The maintenance task verification information includes the verification results, task compliance, and error detection information. The short-term load change overview includes the prediction error range, key influencing factors, and the load adjustment range. The adjusted power grid operation status includes the operation efficiency index, the stability level, and the adjustment feedback results.

[0014] On the other hand, based on the power measurement and control instrument, the real-time operation data of the power grid is remotely collected, abnormal data is screened and eliminated, and historical operation data is integrated. The data collected from the sensors is analyzed to obtain the equipment failure probability index. The specific steps are as follows:

[0015] S101: Based on the power measurement and control instrument, remotely collect the real-time operation data of the power grid, filter the collected data, remove noise and abnormal points, and merge the historical operation data to obtain the real-time power grid data set;

[0016] S102: Based on the real-time power grid data set, classify the data, mark the device status corresponding to the data, and extract key parameters of device operation to obtain device status information;

[0017] S103: Based on the device status information, the abnormal operation frequency of the device is calculated, and combined with the historical failure data of the device, the potential of future failures of the device is evaluated to obtain a device failure probability index.

[0018] On the other hand, based on the equipment failure probability index, the key monitoring points of the power grid are determined, the monitoring point data is transmitted to the power grid dispatching center in real time, and the key monitoring data is analyzed for failures. The specific steps of obtaining the failure time prediction record are as follows:

[0019] S201: Based on the equipment failure probability index, identify key monitoring points in the power grid where potential failures may occur, mark the monitoring points, and adjust the monitoring mechanism according to the failure risk level of the monitoring points to obtain a directory of key monitoring points;

[0020] S202: Based on the key monitoring point directory, set the time interval and priority for transmitting data to the power grid dispatching center, optimize the transmission efficiency of the data stream, verify the real-time and accuracy of the data, and obtain the data stream status analysis results;

[0021] S203: Based on the data stream state analysis result, perform fault analysis on key monitoring data, adjust fault identification sensitivity, and optimize the accuracy of fault diagnosis to obtain a fault time prediction record.

[0022] On the other hand, based on the fault time prediction record, compared with the power equipment maintenance cycle data, identifying the key power equipment that needs maintenance, and adjusting the maintenance time and resource allocation, the steps of obtaining the optimized maintenance schedule are specifically as follows:

[0023] S301: Based on the fault time prediction record, compare the maintenance cycle data of the power equipment, identify the maintenance requirement window and the equipment maintenance frequency difference, mark the equipment with high maintenance priority, and obtain a maintenance requirement list;

[0024] S302: Based on the maintenance requirement list, adjust the maintenance time, reallocate available technical resources and manpower, balance maintenance tasks and resource supply, and merge schedule and resource allocation data to obtain an optimized maintenance schedule.

[0025] On the other hand, comparing the maintenance cycle data of the power equipment, according to the formula:

[0026]

[0027] Calculate the maintenance cycle variance index , where represents the current maintenance cycle of the i-th device, represents the maintenance period of the i-th device based on the fault prediction, M represents the total number of devices, It is a weight factor determined based on device type and frequency of use.

[0028] On the other hand, based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, the maintenance task instruction is remotely verified, the correctness and completeness of the maintenance task are determined, and the steps of obtaining maintenance task verification information are specifically as follows:

[0029] S401: Based on the optimized maintenance schedule, a process and time arrangement of remote maintenance tasks are formulated, key execution commands are sent to the maintenance team, and a maintenance operation plan is obtained;

[0030] S402: Based on the maintenance operation plan, perform remote verification, check the accuracy of the maintenance instructions and the compliance of the task parameters, adjust the settings that do not meet the requirements, and determine the correctness and completeness of the maintenance task to obtain maintenance task verification information.

[0031] On the other hand, based on the maintenance task verification information, the power measurement and control instrument is used to collect real-time load data of the power grid, calculate the difference between the current real-time load and the historical predicted load, and analyze the load difference value to predict future short-term load changes. The steps of obtaining an overview of short-term load changes are as follows:

[0032] S501: Based on the maintenance task verification information, the power grid is monitored in real time through the power measurement and control instrument, current load data is collected, the real-time data stream is updated synchronously, and time series analysis is performed on the data to obtain real-time load monitoring records;

[0033] S502: Based on the real-time load monitoring record, perform data comparison and analysis, calculate the deviation from the historical load, identify key load fluctuation points, and mark key changes to obtain load fluctuation analysis results;

[0034] S503: Based on the load fluctuation analysis result, the short-term data change trend is evaluated, and the short-term forecast data is integrated to obtain an overview of short-term load changes.

[0035] On the other hand, the data comparison analysis is performed according to the formula:

[0036]

[0037] Calculate load deviation , where Representative The real-time load value at the moment, Representative The historical load value at the time, Represents the total number of measurement points in the time window, It is The weight factor of a moment indicates the load criticality at that moment. It is a compensation item.

[0038] On the other hand, an operating system based on a power measurement and control instrument is provided, and the system is applied to an operating method based on a power measurement and control instrument, including:

[0039] The data acquisition and analysis module is based on the power measurement and control instrument, remotely collects real-time operation data of the power grid, screens and removes abnormal data, integrates historical operation data, analyzes data collected from sensors, and obtains equipment failure probability indicators;

[0040] The fault monitoring and identification module determines the key monitoring points of the power grid based on the equipment failure probability index, transmits the monitoring point data to the power grid dispatching center in real time, and performs fault analysis to obtain the prediction record of the fault time;

[0041] The maintenance planning module compares the fault time prediction record with the power equipment maintenance cycle data, identifies the key power equipment that needs maintenance, and adjusts the maintenance time and resource allocation to obtain an optimized maintenance schedule;

[0042] The remote maintenance execution module formulates a remote maintenance task based on the optimized maintenance schedule, sends an execution command, remotely verifies the maintenance task instruction, determines the correctness and completeness of the maintenance task, and obtains maintenance task verification information;

[0043] The power grid load optimization module collects real-time power grid load data based on the maintenance task verification information, calculates the difference with the historical predicted load, analyzes the load difference value, predicts future short-term load changes, and evaluates the current operating status of the power grid, remotely adjusts the power grid load distribution parameters, and obtains the adjusted power grid operating status.

[0044] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0045] Real-time data collected remotely by power measurement and control instruments is combined with historical data for analysis, making fault prediction more accurate and reducing the probability of power grid interruption. Real-time determination of key monitoring points and accuracy of fault prediction are improved, optimizing resource allocation and speeding up fault handling. Intelligent comparison with maintenance cycle data optimizes maintenance schedules and ensures efficient operation of power equipment. By analyzing the difference between current load and predicted load, future load changes can be effectively predicted to ensure that the power grid can adapt to different operating conditions, thereby improving the reliability and efficiency of the overall power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 It is a main step flow chart of the present invention;

[0048] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0049] Figure 3 is a flow chart of the steps of S2 of the present invention;

[0050] Figure 4 is a flow chart of the steps of S3 of the present invention;

[0051] Figure 5 is a flow chart of the steps of S4 of the present invention;

[0052] Figure 6 is a flow chart of the steps of S5 of the present invention;

[0053] Figure 7 is a flow chart of the steps of S6 of the present invention;

[0054] Figure 8 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0058] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0059] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0060] The embodiment of the present invention provides an operation method based on a power measurement and control instrument, such as Figure 1 As shown, the following steps are included:

[0061] S1: Based on the power measurement and control instrument, remotely collect the real-time operation data of the power grid, filter and eliminate abnormal data, integrate historical operation data, analyze the data collected from sensors, and obtain the equipment failure probability index; the real-time operation data of the power grid refers to voltage, current, power, etc., as well as the working status of the equipment; sensors may include current sensors, voltage sensors, power sensors, temperature sensors, etc.;

[0062] S2: Based on the equipment failure probability index, determine the key monitoring points of the power grid, transmit the monitoring point data to the power grid dispatching center in real time, and perform fault analysis on the key monitoring data to obtain the prediction record of the fault time;

[0063] S3: Based on the fault time prediction records, compare with the power equipment maintenance cycle data, identify the key power equipment that needs maintenance, and adjust the maintenance time and resource allocation to obtain an optimized maintenance schedule;

[0064] S4: Based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, the maintenance task instruction is remotely verified, the correctness and completeness of the maintenance task is determined, and maintenance task verification information is obtained;

[0065] S5: Based on the maintenance task verification information, use the power measurement and control instrument to collect the real-time load data of the power grid, calculate the difference between the current real-time load and the historical predicted load, and analyze the load difference value to predict the future short-term load changes and obtain an overview of the short-term load changes; the real-time load data of the power grid refers to the load situation in the power grid, that is, the power demand currently carried by the power grid;

[0066] S6: Based on the overview of short-term load changes, analyze the current operation status of the power grid, remotely adjust the load distribution parameters of the power grid through the power measurement and control instrument, ensure the efficiency and stability of the power grid operation, and obtain the adjusted power grid operation status.

[0067] Equipment failure probability indicators include failure potential, failure impact range, and warning level. Failure time prediction records include prediction accuracy, fault-affected equipment, and prediction basis. Optimized maintenance schedule includes key maintenance dates, resource allocation details, and maintenance task priorities. Maintenance task verification information includes verification results, task compliance, and error detection information. An overview of short-term load changes includes prediction error range, key influencing factors, and load adjustment range. The adjusted power grid operation status includes operating efficiency indicators, stability level, and adjustment feedback results.

[0068] like Figure 2 As shown in the figure, based on the power measurement and control instrument, the real-time operation data of the power grid is remotely collected, abnormal data is screened and eliminated, and historical operation data is integrated. The data collected from the sensor is analyzed to obtain the equipment failure probability index. The specific steps are as follows:

[0069] S101: Based on the power measurement and control instrument, remotely collect the real-time operation data of the power grid, filter the collected data, remove noise and abnormal points, and merge the historical operation data to obtain the real-time power grid data set;

[0070] The acquired data is screened to remove invalid data and noise. The set anomaly detection threshold is used to identify anomalies in the data, and the deviation of each data point is calculated. Any data point whose deviation value exceeds the set threshold is marked as abnormal data and removed. Subsequently, the screened data is merged with the historical operation data in chronological order to ensure timestamp consistency. At the same time, the average value of the merged data and the range of change of the data distribution are calculated to verify the data quality and obtain a cleaned and usable real-time power grid data set.

[0071] S102: Based on the real-time power grid data set, classify the data, mark the device status corresponding to the data, and extract key parameters of device operation to obtain device status information;

[0072] The data is classified and processed according to the equipment category, and the unique identifier corresponding to each device is extracted. In combination with key parameters such as voltage and current, each group of data is labeled as a different equipment state according to the data characteristics. The key features of the voltage and current data are extracted separately, including the maximum value, minimum value, average value and change rate. The characteristic values ​​are then compared with the equipment operation records to ensure the rationality and consistency of the classification. Finally, the annotation information containing the equipment operation status is generated to form a set of equipment status data.

[0073] S103: Based on the equipment status information, the abnormal operation frequency of the equipment is calculated, and combined with the historical failure data of the equipment, the potential of future failures of the equipment is evaluated to obtain the equipment failure probability index;

[0074] Statistics are collected on the abnormal frequency during the operation of the equipment, and the abnormal events recorded during the operation of each device are collected and classified into different types. The number of occurrences of each type of abnormal event is counted, and the total abnormal frequency is calculated. Combined with historical failure data, the potential failure of the equipment is evaluated. By analyzing the correlation trend between the abnormal frequency changes of the current equipment and historical failures, the probability of future equipment failure is determined, and the failure probability index of the equipment is output.

[0075] like Figure 3 As shown in the figure, based on the equipment failure probability index, the key monitoring points of the power grid are determined, the monitoring point data is transmitted to the power grid dispatching center in real time, and the key monitoring data is analyzed for failures. The specific steps to obtain the failure time prediction record are as follows:

[0076] S201: Based on the equipment failure probability index, identify key monitoring points in the power grid where potential failures may occur, mark the monitoring points, and adjust the monitoring mechanism according to the failure risk level of the monitoring points to obtain a directory of key monitoring points;

[0077] Identify the monitoring points where faults occur in the power grid, extract the risk level of each monitoring point from the fault probability index, classify the monitoring points according to the set fault probability threshold, mark the monitoring points with higher risk levels as key monitoring points, and then analyze the operating status and fault history of each key monitoring point, store the data in a dedicated database for subsequent calls, and assign a unique identification code to each monitoring point to track its operating information. Adjust the monitoring mechanism for monitoring points of different risk levels, including increasing the frequency of data collection, strengthening the capture of abnormal data and real-time feedback capabilities, and generating a directory of key monitoring points in the power grid.

[0078] S202: Based on the key monitoring point directory, set the time interval and priority for transmitting data to the power grid dispatching center, optimize the transmission efficiency of the data stream, verify the real-time and accuracy of the data, and obtain the data stream status analysis results;

[0079] A data transmission time interval is set for each monitoring point in the directory. The time interval is set according to the risk level of the monitoring point. The data transmission frequency of high-risk points is set to a short interval, and that of medium and low-risk points is set to a longer interval. The priority is graded according to the importance of the data and is divided into three levels: high, medium, and low. An independent transmission channel is allocated for high-priority data. When verifying the real-time nature of the data, the delay in the transmission process is monitored and the transmission accuracy is recorded. By optimizing the transmission mechanism, the efficient operation of the data flow is ensured to obtain the data flow status analysis results.

[0080] S203: Based on the data stream state analysis result, perform fault analysis on key monitoring data, adjust fault identification sensitivity, and optimize the accuracy of fault diagnosis to obtain a fault time prediction record;

[0081] Filter key data, classify abnormal data and arrange them in chronological order, analyze data change trends to determine fault points, adjust the sensitivity of fault identification, set differentiated sensitivity standards according to the risk level of the monitoring point, use archived historical fault data to verify the accuracy of the current fault analysis, and adjust the diagnostic model parameters of the monitoring point according to the analysis results, record the predicted fault time point and related status, and obtain the fault moment prediction record.

[0082] like Figure 4 As shown, based on the fault time prediction record, the steps of comparing with the power equipment maintenance cycle data, identifying the key power equipment that needs maintenance, and adjusting the maintenance time and resource allocation to obtain the optimized maintenance schedule are as follows:

[0083] S301: Based on the fault time prediction record, compare the maintenance cycle data of the power equipment, identify the maintenance requirement window and the equipment maintenance frequency difference, mark the equipment with high maintenance priority, and obtain a maintenance requirement list;

[0084] Compare the maintenance cycle data of power equipment, according to the formula:

[0085]

[0086] Calculate the maintenance cycle variance index , where represents the current maintenance cycle of the i-th device, represents the maintenance period of the i-th device based on the fault prediction, M represents the total number of devices, It is a weight factor determined based on the equipment type and frequency of use. For example, a key equipment that is used frequently may have a higher weight.

[0087] Considering the situation of three devices, the weight factors, actual maintenance cycles, and predicted maintenance cycles of the devices are as follows:

[0088] Device 1: , Month, months;

[0089] Device 2: , Month, months;

[0090] Device 3: , Month, months;

[0091] Calculate the numerator:

[0092]

[0093]

[0094]

[0095] The denominator is the sum of the weights:

[0096]

[0097] Maintenance cycle difference index calculation results:

[0098]

[0099] The results show that considering the weighted average of equipment importance and forecast deviation, the average difference between the actual and forecasted maintenance period is 2 months, reflecting the need to adjust the forecast or re-evaluate the equipment maintenance strategy in actual operation to more accurately meet the actual maintenance needs.

[0100] S302: Based on the maintenance requirement list, adjust the maintenance time, reallocate available technical resources and manpower, balance maintenance tasks and resource supply, and merge schedule and resource allocation data to obtain an optimized maintenance schedule;

[0101] Collect the availability of technical resources and manpower, divide resources into three categories: technical tools, manpower and time, calculate the total available amount for each type of resource, analyze the resource allocation required for each maintenance task in the maintenance requirements list, compare the resource allocation with the available resources, allocate sufficient resources to high-priority tasks according to task priority, and readjust the schedule of low-priority tasks to free up resources, integrate the allocation data and schedule of all tasks, and generate an optimized maintenance schedule.

[0102] like Figure 5 As shown, based on the optimized maintenance schedule, remote maintenance tasks are formulated, execution commands are sent, maintenance task instructions are remotely verified, the correctness and completeness of the maintenance tasks are determined, and the steps for obtaining maintenance task verification information are as follows:

[0103] S401: Based on the optimized maintenance schedule, a process and time schedule for remote maintenance tasks are formulated, key execution commands are sent to the maintenance team, and a maintenance operation plan is obtained;

[0104] Disassemble the time arrangement and resource allocation of each maintenance task in the schedule, arrange the tasks according to their priority, extract the execution steps of high-priority tasks, and formulate specific operation procedures for each task, assign team members and technical resources to perform the tasks, and generate instruction sets according to task requirements, including initialization instructions, operating status detection instructions and recovery instructions for maintenance equipment, arrange the instructions in sequence according to the execution steps, set the execution time and feedback deadline of the instructions, send the instructions to the relevant maintenance team through the communication platform, record the time when the instructions were sent and the receiving status, and generate a maintenance operation plan with time arrangements and task details.

[0105] S402: Based on the maintenance operation plan, remote verification is performed to check the accuracy of the maintenance instructions and the compliance of the task parameters, adjust the settings that do not meet the requirements, and determine the correctness and completeness of the maintenance task to obtain maintenance task verification information;

[0106] Check the received maintenance instructions to confirm that the content of the instructions is consistent with the tasks specified in the plan, extract the task parameters and compare them with the planned parameters item by item, including the equipment model, task start time, execution steps, etc., check whether the parameters meet the standard requirements, adjust the parameters that do not meet the requirements, and synchronize the adjustment results to the maintenance operation plan, then simulate the execution of key maintenance steps to test the accuracy of the instructions, record the feedback data and results of the simulated execution, verify the correctness and completeness of the maintenance tasks through data comparison, and generate verification information for the maintenance tasks.

[0107] like Figure 6 As shown in the figure, based on the maintenance task verification information, the power measurement and control instrument is used to collect the real-time load data of the power grid, calculate the difference between the current real-time load and the historical predicted load, and analyze the load difference value to predict the future short-term load change. The specific steps for obtaining an overview of the short-term load change are as follows:

[0108] S501: Based on the maintenance task verification information, the power grid is monitored in real time through the power measurement and control instrument, current load data is collected, the real-time data stream is updated synchronously, and time series analysis is performed on the data to obtain real-time load monitoring records;

[0109] Connect to the data acquisition interface of the power measurement and control instrument, extract real-time load data and synchronize and calibrate the data timestamp, integrate it with the existing data stream records, and then perform time series analysis on the integrated real-time data stream to extract key load parameters at each time point, including load peak, average and change rate, divide the data into segments according to the set time interval, analyze the fluctuation trend of each data segment, record abnormal points and load change rate, and generate real-time load monitoring records.

[0110] S502: Based on the real-time load monitoring records, data comparison and analysis are performed to calculate the deviation from the historical load, identify key load fluctuation points, and mark key changes to obtain load fluctuation analysis results;

[0111] Carry out data comparison analysis according to the formula:

[0112]

[0113] Calculate load deviation , where Representative The real-time load value at the moment, Representative The historical load value at the time, Represents the total number of measurement points in the time window, It is The weight factor of a moment indicates the load criticality at that moment. is a compensation term, based on environmental variables or other factors, used to adjust the Load difference at each moment;

[0114] In the power system, the weight factor for peak hours (such as 7 to 9 p.m.) It may be higher, so it is determined based on real-time data analysis. The weight factor is sorted by load change sensitivity through historical data statistics and normalized so that the sum is 1. For example, the weights of 7pm, 8pm, and 9pm are 0.3, 0.4, and 0.3 respectively;

[0115] Real-time and historical loads and ,Acquired through the real-time monitoring device and historical data records of the power system, in a 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;

[0116] Compensation Adjustments based on environmental factors or unexpected events, such as temperature changes or the impact of emergencies, can be made by comparing with the same period in history to identify abnormal deviations and set compensation values. For example, if the load increases abnormally at 7pm due to a special event, the compensation item may be set to +50kW;

[0117] Considering the load data and parameters at three time points, the calculation is performed:

[0118] ;

[0119] Weight Factor ;

[0120] Real-time load kW;

[0121] Historical Load kW;

[0122] Compensation kW;

[0123] The numerator of the calculation formula is:

[0124]

[0125]

[0126]

[0127] The denominator is the sum of the weights:

[0128]

[0129] Load deviation The calculation result is:

[0130]

[0131] The results show that the overall deviation between the actual load considering the weight factor and environmental compensation and the historical load is 53kW. The results reflect the load changes after considering key periods and abnormal events, and provide an important reference for system adjustment and energy allocation.

[0132] S503: Based on the load fluctuation analysis result, evaluate the short-term data change trend, and integrate the short-term forecast data to obtain an overview of short-term load changes;

[0133] Extract the time series data of key load change points, calculate the short-term load change trend according to the set time window, analyze the increase or decrease and change rate of the trend, integrate the trend data with recent historical records, predict the load change value in the short term, and then summarize and integrate the predicted data, output the change trend, predicted load value and corresponding time interval into a unified record file to generate an overview of short-term load changes.

[0134] like Figure 7 As shown in the figure, based on the overview of short-term load changes, the current operation status of the power grid is analyzed, and the load distribution parameters of the power grid are remotely adjusted through the power measurement and control instrument to ensure the efficiency and stability of the power grid operation. The specific steps for obtaining the adjusted power grid operation status are:

[0135] S601: Based on the overview of short-term load changes, analyze the current operation status of the power grid, compare key operation parameters through real-time data, identify potential performance problems, and adjust parameters to obtain the operation status evaluation results;

[0136] Extract key operating parameters from short-term load change records, compare the parameters with real-time collected operating data item by item, identify parameter items with large deviations, analyze the change trend of the deviation data and locate performance problems, and then adjust related parameters, including resetting the load distribution ratio, correcting the frequency offset value and adjusting the operating voltage range. At the same time, record the key data and feedback information during the adjustment process to obtain the evaluation results of the power grid operation status.

[0137] S602: Based on the operation status evaluation result, the load distribution parameters are remotely adjusted through the power measurement and control instrument, the load distribution logic is reconfigured, the power grid resource distribution is optimized, and the resource optimization configuration is obtained;

[0138] Extract the load distribution plan from the evaluation results, compare each load distribution task in the plan with the current configuration, identify the parameter range and distribution logic that need to be adjusted, use the remote control interface to modify the parameters one by one, reallocate the power distribution value of each load point, and verify the distribution logic to ensure that the total load is consistent with the power supply capacity of the power grid, record the change history of all adjustment parameters and update the system configuration file to generate the optimized resource configuration results.

[0139] S603: Based on the resource optimization configuration, perform grid load adjustment, verify the adjusted grid operation efficiency and stability, and determine whether the predetermined target is achieved, thereby obtaining the adjusted grid operation status;

[0140] The new load distribution configuration is loaded through the power measurement and control instrument, the adjusted load operating parameters are monitored in real time, data is collected and changes before and after the adjustment are recorded, and the operating efficiency and stability are evaluated, including calculation of the operating loss rate and power fluctuation rate, and analysis of whether the adjusted power grid operating indicators are within the predetermined range. If it is found that the target is not reached, the configuration is re-optimized and the adjustment process is repeated, all verification data and adjustment steps are recorded, and the adjusted power grid operating status is output.

[0141] like Figure 8 As shown, an operating system based on a power measurement and control instrument includes:

[0142] The data acquisition and analysis module is based on the power measurement and control instrument, remotely collects real-time operation data of the power grid, screens and removes abnormal data, integrates historical operation data, analyzes data collected from sensors, and obtains equipment failure probability indicators;

[0143] The fault monitoring and identification module determines the key monitoring points of the power grid based on the equipment failure probability index, transmits the monitoring point data to the power grid dispatching center in real time, and performs fault analysis to obtain the prediction record of the fault time;

[0144] The maintenance planning module compares the fault time prediction records with the power equipment maintenance cycle data, identifies the key power equipment that needs maintenance, and adjusts the maintenance time and resource allocation to obtain an optimized maintenance schedule;

[0145] The remote maintenance execution module formulates remote maintenance tasks based on the optimized maintenance schedule, sends execution commands, remotely verifies maintenance task instructions, determines the correctness and completeness of maintenance tasks, and obtains maintenance task verification information;

[0146] The grid load optimization module collects real-time grid load data based on maintenance task verification information, calculates the difference with historical predicted loads, analyzes the load difference value, predicts future short-term load changes, and evaluates the current operating status of the grid. It remotely adjusts the grid load distribution parameters to obtain the adjusted grid operating status.

[0147] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0148] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0149] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0150] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0154] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0156] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An operation method based on a power measurement and control instrument, characterized in that: The method comprises: Based on the power measurement and control instrument, remotely collect real-time operation data of the power grid, filter and eliminate abnormal data, integrate historical operation data, analyze the data collected from sensors, and obtain equipment failure probability indicators; Based on the equipment failure probability index, determine the key monitoring points of the power grid, transmit the monitoring point data to the power grid dispatching center in real time, and perform fault analysis on the key monitoring data to obtain the prediction record of the fault time; Based on the fault time prediction record, the record is compared with the power equipment maintenance cycle data to identify the key power equipment that needs maintenance, and the maintenance time and resource allocation are adjusted to obtain an optimized maintenance schedule; Based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, the maintenance task instruction is remotely verified, the correctness and completeness of the maintenance task is determined, and maintenance task verification information is obtained; Based on the maintenance task verification information, the real-time load data of the power grid is collected by using the power measurement and control instrument, the difference between the current real-time load and the historical predicted load is calculated, and the load difference value is analyzed to predict the future short-term load change and obtain an overview of the short-term load change; Based on the short-term load change overview, the current operation status of the power grid is analyzed, and the load distribution parameters of the power grid are remotely adjusted through the power measurement and control instrument to obtain the adjusted power grid operation status.

2. The operation method based on the power measurement and control instrument according to claim 1 is characterized in that: The equipment failure probability index includes the potential of failure, the scope of failure impact, and the warning level. The failure time prediction record includes the prediction accuracy, the equipment affected by the failure, and the prediction basis. The optimized maintenance schedule includes the key maintenance date, resource allocation details, and the maintenance task priority. The maintenance task verification information includes the verification result, task compliance, and error detection information. The short-term load change overview includes the prediction error range, key influencing factors, and the load adjustment range. The adjusted power grid operation status includes the operation efficiency index, the stability level, and the adjustment feedback result.

3. The operation method based on the power measurement and control instrument according to claim 1 is characterized in that: Based on the power measurement and control instrument, remotely collect the real-time operation data of the power grid, filter and eliminate abnormal data, integrate historical operation data, analyze the data collected from sensors, and obtain the equipment failure probability index in the following steps: Based on the power measurement and control instrument, the real-time operation data of the power grid is collected remotely, the collected data is screened, noise and abnormal points are eliminated, and the historical operation data is merged to obtain the real-time power grid data set; Based on the real-time power grid data set, classify the data, mark the device status corresponding to the data, and extract key parameters of device operation to obtain device status information; Based on the equipment status information, the abnormal operation frequency of the equipment is calculated, and combined with the historical failure data of the equipment, the potential of future failures of the equipment is evaluated to obtain the equipment failure probability index.

4. The operation method based on the power measurement and control instrument according to claim 1 is characterized in that: Based on the equipment failure probability index, the key monitoring points of the power grid are determined, the monitoring point data is transmitted to the power grid dispatching center in real time, and the key monitoring data is analyzed for failures to obtain the prediction record of the failure time. Specifically, the steps are as follows: Based on the equipment failure probability index, identify key monitoring points in the power grid where potential failures may occur, mark the monitoring points, and adjust the monitoring mechanism according to the failure risk level of the monitoring points to obtain a directory of key monitoring points; Based on the key monitoring point directory, set the time interval and priority of data transmission to the power grid dispatching center, optimize the transmission efficiency of the data stream, verify the real-time and accuracy of the data, and obtain the data stream status analysis results; Based on the data stream state analysis results, fault analysis is performed on key monitoring data, the fault identification sensitivity is adjusted, and the accuracy of fault diagnosis is optimized to obtain a fault time prediction record.

5. The operation method based on the power measurement and control instrument according to claim 1 is characterized in that: Based on the fault time prediction record, the steps of comparing with the power equipment maintenance cycle data, identifying the key power equipment that needs maintenance, and adjusting the maintenance time and resource allocation to obtain the optimized maintenance schedule are as follows: Based on the fault time prediction record, the maintenance cycle data of the power equipment is compared to identify the maintenance requirement window and the equipment maintenance frequency difference, and the equipment with high maintenance priority is marked to obtain a maintenance requirement list; Based on the maintenance requirement list, the maintenance time is adjusted, the available technical resources and manpower are reallocated, the maintenance tasks and resource supply are balanced, and the schedule and resource allocation data are merged to obtain an optimized maintenance schedule.

6. The operation method based on the power measurement and control instrument according to claim 5 is characterized in that: Compare the maintenance cycle data of the power equipment, according to the formula: Calculate the maintenance cycle variance index , where represents the current maintenance cycle of the i-th device, represents the maintenance period of the i-th device based on the fault prediction, M represents the total number of devices, It is a weight factor determined based on device type and frequency of use.

7. The operation method based on the power measurement and control instrument according to claim 6 is characterized in that: Based on the optimized maintenance schedule, a remote maintenance task is formulated, an execution command is sent, the maintenance task instruction is remotely verified, the correctness and completeness of the maintenance task is determined, and the steps of obtaining the maintenance task verification information are specifically as follows: Based on the optimized maintenance schedule, a process and time schedule for remote maintenance tasks are formulated, key execution commands are sent to the maintenance team, and a maintenance operation plan is obtained; Based on the maintenance operation plan, remote verification is performed to check the accuracy of maintenance instructions and the compliance of task parameters, adjust settings that do not meet the requirements, and determine the correctness and completeness of maintenance tasks to obtain maintenance task verification information.

8. The operation method based on the power measurement and control instrument according to claim 1 is characterized in that: Based on the maintenance task verification information, the power measurement and control instrument is used to collect real-time load data of the power grid, the difference between the current real-time load and the historical predicted load is calculated, and the load difference value is analyzed to predict the future short-term load change. The steps of obtaining an overview of the short-term load change are as follows: Based on the maintenance task verification information, the power grid is monitored in real time through the power measurement and control instrument, current load data is collected, the real-time data stream is updated synchronously, and the data is analyzed in time series to obtain real-time load monitoring records; Based on the real-time load monitoring records, data comparison and analysis are performed to calculate deviations from historical loads, identify key load fluctuation points, and mark key changes to obtain load fluctuation analysis results; Based on the load fluctuation analysis results, the short-term data change trend is evaluated, and the short-term forecast data is integrated to obtain an overview of short-term load changes.

9. The operation method based on the power measurement and control instrument according to claim 8, characterized in that: The data comparison analysis is performed according to the formula: Calculate load deviation , where Representative The real-time load value at the moment, Representative The historical load value at the time, Represents the total number of measurement points in the time window, It is The weight factor of a moment indicates the load criticality at that moment. It is a compensation item.

10. An operating system based on a power measurement and control instrument, the operating system based on a power measurement and control instrument is used to implement the operating method based on a power measurement and control instrument as claimed in any one of claims 1 to 9, characterized in that: The system comprises: The data acquisition and analysis module is based on the power measurement and control instrument, remotely collects real-time operation data of the power grid, screens and removes abnormal data, integrates historical operation data, analyzes data collected from sensors, and obtains equipment failure probability indicators; The fault monitoring and identification module determines the key monitoring points of the power grid based on the equipment failure probability index, transmits the monitoring point data to the power grid dispatching center in real time, and performs fault analysis to obtain the prediction record of the fault time; The maintenance planning module compares the fault time prediction record with the power equipment maintenance cycle data, identifies the key power equipment that needs maintenance, and adjusts the maintenance time and 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 the maintenance task instruction, determines the correctness and completeness of the maintenance task, and obtains maintenance task verification information; The power grid load optimization module collects real-time power grid load data based on the maintenance task verification information, calculates the difference with the historical predicted load, analyzes the load difference value, predicts future short-term load changes, and evaluates the current operating status of the power grid, remotely adjusts the power grid load distribution parameters, and obtains the adjusted power grid operating status.

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