Power grid abnormal state monitoring and early warning system

Abstract

The invention relates to the technical field of power grid monitoring, in particular to a power grid abnormal state monitoring and early warning system which comprises a historical reference establishment module, an operation deviation analysis module, an abnormal threshold adjustment module and an abnormal early warning module. According to the invention, through real-time data acquisition and historical data analysis, the monitoring and early warning efficiency of power grid operation is improved, and through collection of power grid operation records, extraction of data of differential time periods, evaluation and analysis of the average load and volatility of the data, and construction of a historical power grid operation reference, the operation trend and potential abnormity can be captured more accurately. The method reduces the uncertainty of judgment depending on experience, can recognize a deviation mode and an adjusted load detection threshold value by comparing current data with a historical reference, enables the abnormal early warning to be more accurate, avoids excessive false alarms and missed alarms, optimizes the evaluation of early warning levels and the implementation of early warning notification, guarantees the safety and stability of a power grid, and improves the safety and reliability of the power grid. And the economic efficiency and the reliability of power grid operation are also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a power grid abnormal state monitoring and early warning system. Background Art

[0002] The field of grid monitoring technology focuses on the continuous observation and analysis of power systems to ensure the stable operation and safety of the grid. This field covers real-time monitoring of grid status, anomaly identification, performance analysis and optimization. Technology applications include intelligent sensor deployment, data analysis algorithms and machine learning techniques to monitor changes in current, voltage, frequency and other key grid indicators. The technology enables grid operators to detect and warn of potential grid failures or performance degradation in a timely manner, and then take necessary maintenance or adjustment measures to improve the reliability and efficiency of the grid.

[0003] Among them, the abnormal state monitoring and early warning system of the power grid is a technical solution specifically used to identify and respond to potential problems in the power grid. The system collects power grid operation data in real time and uses analytical technology to detect abnormal conditions such as overload, voltage instability or equipment failure. Once an abnormality is detected, the system will automatically issue an early warning to help operation and maintenance personnel respond quickly, thereby avoiding or mitigating the impact of the accident. The main purpose of the system is to improve the operational safety of the power grid, optimize the response strategy, reduce power outages, and ensure the stability and reliability of power supply.

[0004] Traditional early warning systems rely on fixed thresholds and parameters, which cannot adapt to the dynamic changes in power grid operation, resulting in the inability to respond to rapid changes in power grid status in actual operation. This can easily lead to missed reports and false alarms, increasing system risks. When analyzing power grid operation data, traditional systems ignore the patterns and trends hidden in the data. Static analysis methods limit the effectiveness of early warning systems, and the response to potential problems is not sensitive enough or too slow, affecting the operating efficiency of the power grid and causing economic losses and safety accidents under abnormal circumstances. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a power grid abnormal state monitoring and early warning system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a power grid abnormal state monitoring and early warning system, the system comprising: The historical benchmark establishment module extracts the grid operation data of differentiated periods in previous years based on the grid operation records, evaluates the average operation load of differentiated periods, and analyzes the volatility of the load in differentiated periods to obtain the historical grid operation benchmark; The operation deviation analysis module analyzes the deviation between this year's power grid operation data and previous years' power grid operation data based on the historical power grid operation benchmark, evaluates the average operation load deviation, and analyzes the load volatility deviation to obtain the power grid operation deviation analysis result; The abnormal threshold adjustment module adjusts the grid load detection threshold of the current period based on the grid operation deviation analysis result and the historical grid operation benchmark, according to the average operation load, average operation load deviation, load volatility and load volatility deviation in the same period, combined with the preset benchmark abnormal load detection threshold, to obtain the abnormal threshold adjustment result; Based on the abnormal threshold adjustment result, the abnormal warning module compares the current power grid operating load with the adjusted load detection threshold, evaluates the abnormal index according to the deviation, and evaluates the warning level according to the deviation duration, implements corresponding abnormal warning notifications to the staff, and obtains the power grid abnormal warning result.

[0007] The present invention is improved in that the method for evaluating the average operating load of the differentiated time period is: Based on the grid operation records, the operation records of differentiated periods in previous years are extracted from the grid database. Each data point includes a timestamp and the corresponding grid load to obtain historical load data. Based on the historical load data, according to the timestamp of each data point, the formula is: ; Assign a timestamp recency weight to each data point to obtain the data weight evaluation result; in, is the attenuation coefficient, is the current year, Indicates The year in which the data points were collected, Indicates The timestamp recency weight of each data point, is the base of natural logarithms; Based on the data weight evaluation results, the formula is: ; Calculate the average operating load and obtain the average load evaluation result; in, Indicates The grid load of each data point, Indicates The timestamp recency weight of each data point, is the total number of data points, is the average operating load.

[0008] The present invention is improved in that the steps of obtaining the historical power grid operation benchmark are: Based on the average load evaluation results and data weight evaluation results, according to the load data of differentiated periods in previous years, the formula is: ; Calculate the load fluctuation index; in, For the The grid load of each data point, is the average operating load, For the The timestamp recency weight of each data point, is the load fluctuation index, is the total number of data points; Based on the load volatility index, the average operating load of the corresponding period is integrated to obtain a historical power grid operation benchmark.

[0009] The present invention is improved in that the method for evaluating the average operating load deviation is: Based on the historical power grid operation benchmark, average operation load data for multiple time periods this year are extracted, and average operation load data for corresponding time periods in previous years are extracted to obtain average load deviation correlation information; Based on the average load deviation association information, by formula: ; Calculate the average operating load deviation ratio and obtain the average load deviation analysis result; in, Indicates the current year The average operating load in a period of time, Indicates previous years The average operating load in a period of time, is the total number of time periods, is the average operating load deviation ratio.

[0010] The present invention is improved in that the steps of obtaining the power grid operation deviation analysis result are: Based on the historical power grid operation benchmark, extract the operating load volatility index of multiple time periods this year, and extract the operating load volatility index of corresponding time periods in previous years to obtain volatility deviation correlation information; Based on the volatility deviation correlation information, through the formula: ; Calculate the load fluctuation deviation ratio; in, is the total number of time periods, For the current year The load fluctuation index within a period of time, For previous years The load fluctuation index within a period of time, is the load fluctuation deviation ratio; Based on the load fluctuation deviation ratio, the average load deviation analysis result is integrated to obtain a power grid operation deviation analysis result.

[0011] The present invention is improved in that the step of obtaining the abnormal threshold adjustment result is: Based on the power grid operation deviation analysis results and the historical power grid operation benchmark, according to the current time period, the average operation load and the load volatility index of the same time period are extracted to obtain threshold-related data; Based on the threshold-related data, combined with the average operating load deviation ratio, the load volatility deviation ratio and the preset benchmark abnormal load detection threshold, the formula is: ; Calculate the adjusted abnormal threshold to obtain the abnormal threshold adjustment result; in, is the baseline abnormal load detection threshold, is the average operating load in the same period, is the average operating load deviation ratio, is the load fluctuation index in the same period, is the load fluctuation deviation ratio, is the adjustment factor, is the adjusted abnormal threshold.

[0012] The present invention is improved in that the method for evaluating the abnormality index is: Based on the abnormal threshold adjustment result, the current power grid operation load is collected in real time at a preset time interval to obtain real-time load data; Based on the real-time load data, the load detection threshold after adjustment is compared with the formula: ; Calculate the abnormal index and obtain the power grid abnormality assessment information; in, is the attenuation coefficient, is the actual grid operating load in the current period, is the adjusted abnormal threshold, is the abnormal index.

[0013] The present invention is improved in that the steps of obtaining the abnormal warning result of the power grid are: Based on the power grid abnormality assessment information, real-time monitoring of the power grid load is performed, and the duration of the power grid load exceeding the adjusted abnormal threshold is recorded to obtain early warning related data; Based on the warning related data, through the formula: ; Calculate warning level ; in, is the adjustment factor, is the abnormality index, is the abnormal duration, is the warning level; Based on the warning level, corresponding abnormal warning notifications are implemented to the staff to obtain abnormal power grid warning results.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through real-time data collection and historical data analysis, the monitoring and early warning efficiency of power grid operation is improved. By collecting power grid operation records, extracting data from differentiated time periods, evaluating and analyzing the average load and volatility of the data, and building a historical power grid operation benchmark, operation trends and potential anomalies can be captured more accurately, reducing the uncertainty of relying on experience-based judgment. By comparing current data with historical benchmarks, deviation patterns can be identified and load detection thresholds can be adjusted to make abnormal warnings more accurate, avoiding excessive false alarms and missed alarms, optimizing the evaluation of warning levels and the implementation of warning notifications, ensuring the safety and stability of the power grid, and improving the economic efficiency and reliability of power grid operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart for evaluating the average operating load for differentiated time periods for the present invention; Figure 3 A flow chart for obtaining historical grid operation benchmarks for the present invention; Figure 4 Flowchart for evaluating average operating load deviation for the present invention; Figure 5 A flow chart of obtaining the power grid operation deviation analysis result of the present invention; Figure 6 A flow chart of obtaining an abnormal threshold adjustment result of the present invention; Figure 7 A flow chart for evaluating anomaly index for the present invention; Figure 8 The present invention is a flow chart of obtaining power grid abnormality warning results. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: a power grid abnormal state monitoring and early warning system, the system comprising: The historical benchmark establishment module extracts the grid operation data of differentiated periods in previous years based on the grid operation records, evaluates the average operation load of differentiated periods, and analyzes the volatility of the load in differentiated periods to obtain the historical grid operation benchmark; The operation deviation analysis module analyzes the deviation between this year's grid operation data and previous years' grid operation data based on the historical grid operation benchmark, evaluates the average operation load deviation, and analyzes the load volatility deviation to obtain the grid operation deviation analysis results; The abnormal threshold adjustment module is based on the power grid operation deviation analysis results and the historical power grid operation benchmark, according to the average operating load in the same period, the average operating load deviation, the load volatility in the same period and the load volatility deviation, combined with the preset benchmark abnormal load detection threshold, to adjust the power grid load detection threshold in the current period to obtain the abnormal threshold adjustment result; Based on the abnormal threshold adjustment result, the abnormal warning module compares the current power grid operating load with the adjusted load detection threshold, evaluates the abnormal index according to the deviation, and evaluates the warning level according to the deviation duration, implements the corresponding abnormal warning notification to the staff, and obtains the abnormal power grid warning result.

[0019] The historical power grid operation benchmark includes the average load value and load volatility in differentiated time periods. The power grid operation deviation analysis results include the load deviation rate and the volatility deviation rate. The abnormal threshold adjustment results include the adjusted load abnormality threshold and volatility tolerance threshold. The power grid abnormal warning results include the abnormality level, warning level and corresponding notification content.

[0020] See also Figure 2, the method to evaluate the average operating load in differentiated time periods is: Based on the grid operation records, the operation records of differentiated periods in previous years are extracted from the grid database. Each data point includes a timestamp and the corresponding grid load to obtain historical load data. Based on historical load data, according to the timestamp of each data point, the formula: ; Assign a timestamp recency weight to each data point to obtain the data weight evaluation result; in, is the attenuation coefficient, is the current year, Indicates The year in which the data points were collected, Indicates The timestamp recency weight of each data point, is the base of natural logarithms; Based on the data weight evaluation results, the formula is: ; Calculate the average operating load and obtain the average load evaluation result; in, Indicates The grid load of each data point, Indicates The timestamp recency weight of each data point, is the total number of data points, is the average operating load.

[0021] formula: and ; Parameter details and how to obtain them Data collection year : Indicates The year in which each data point was collected. The year is obtained directly from the data recording system of the power grid and indicates the specific collection time of each data point; Grid load : Indicates The grid load at each data point is extracted from the grid monitoring system and records the grid load at a specific time period. Timestamp recency weight : Indicates The timestamp recency weight of each data point is used to emphasize the importance of data from more recent years. The calculation formula is ,in is the attenuation coefficient, is the current year. This calculation ensures that recent data have a greater impact on the assessment results; Attenuation coefficient : The decay rate of the timestamp recency weight is controlled and is pre-set based on the frequency of grid data updates and the importance of historical data changes. A value of indicates that the weight decays slowly, thus placing more emphasis on recent data; Current Year : Refers to the current year, which is used as a reference point when calculating the weights of all data points. This year directly affects the weight calculation of each data point.

[0022] Calculation example: Assuming the current year is 2024, the data points are as follows: , MW , MW, set the attenuation factor .

[0023] Calculate each weight: ; ; Calculate the load average: molecular: ; Denominator: ; Load Average: MW; The calculation example shows that by taking into account data from different years and assigning different time weights, the weighted average grid load is 126.2MW. This method more accurately reflects the operation of the grid in recent years, taking into account the impact of time, making the assessment more consistent with the actual load of the current grid.

[0024] See also Figure 3 , the steps to obtain the historical power grid operation benchmark are: Based on the average load assessment results and data weight assessment results, and according to the load data of differentiated periods in previous years, the formula is: ; Calculate the load fluctuation index; in, For the The grid load of each data point, is the average operating load, For the The timestamp recency weight of each data point, is the load fluctuation index, is the total number of data points; Based on the load volatility index, the average operating load in the corresponding period is integrated to obtain the historical power grid operation benchmark.

[0025] formula: ; Parameter details and how to obtain them: :Indicates the The grid load at each data point is obtained directly from the grid operation monitoring system, reflecting the power consumption at a specific point in time; : Average operating load, calculated using the previous method; : No. The timestamp recency weight of each data point is calculated using the previous method.

[0026] Calculation process: Set the current year , there are two data points, the data collection year and load are as follows: , MW , MW.

[0027] Assuming the attenuation coefficient , average operating load MW.

[0028] Calculate weights: , ; Calculate the square of the weight load difference: ; ; ; ; ; ; ; Calculating the Volatility Index : MW; The calculation process shows how to use time weighting and load data to calculate the grid load volatility. Volatility Index MW represents the degree of fluctuation of grid load relative to its average value, taking into account the timeliness of the data, making the evaluation results closer to actual operational needs.

[0029] See also Figure 4 , the method to evaluate the average operating load deviation is: Based on the historical grid operation benchmark, the average operation load data of multiple periods this year are extracted, and the average operation load data of the corresponding periods in previous years are extracted to obtain the average load deviation correlation information; Based on the average load deviation correlation information, the formula is: ; Calculate the average operating load deviation ratio and obtain the average load deviation analysis result; in, Indicates the current year The average operating load in a period of time, Indicates previous years The average operating load in a period of time, is the total number of time periods, is the average operating load deviation ratio.

[0030] formula: ; Parameter details and how to obtain them: :Current year The average operating load in a period of time is the average load in the target time period of the current year calculated by the power grid data monitoring system. The average value of each period is usually obtained based on the actual measured load data; :Indicates the previous years The average operating load in a period is extracted from the power grid historical operation record database and is calculated using the formula in the previous step; : Total number of time periods, indicating the number of time periods considered in the analysis, affects the calculation of the average deviation ratio, and each time period is an independent observation point.

[0031] Calculation example: The data of three periods are set for investigation. The average load data of the current year and the historical year are as follows: Period 1: Current year MW, historical year MW; Period 2: Current Year MW, historical year MW; Period 3: Current Year MW, historical year MW.

[0032] Calculate the deviation ratio for each period: Period 1: ; Period 2: ; Period 3: ; Calculate the mean deviation ratio : ; result It indicates that in the three periods considered, the average operating load of the current year has increased by an average of 22.7% compared with historical data. The indicator can help power grid operators understand the trend and scale of load changes and provide data support for adjusting operating strategies and equipment configuration.

[0033] See also Figure 5 , the steps for obtaining the results of power grid operation deviation analysis are: Based on the historical grid operation benchmark, the operating load volatility index of multiple periods this year is extracted, and the operating load volatility index of the corresponding periods in previous years is extracted to obtain the volatility deviation correlation information; Based on the volatility deviation correlation information, through the formula: ; Calculate the load fluctuation deviation ratio; in, is the total number of time periods, For the current year The load fluctuation index within a period of time, For previous years The load fluctuation index within a period of time, is the load fluctuation deviation ratio; Based on the load fluctuation deviation ratio, the average load deviation analysis results are integrated to obtain the power grid operation deviation analysis results.

[0034] formula: ; Parameter details and how to obtain them: :Current year The load volatility index within a period is obtained by calculating the standard deviation of the load data in the current annual target period; : Previous years The load fluctuation index within a period is extracted from the historical record database and calculated by the previous steps; : Total number of periods. This is the number of different time periods involved in the analysis and is used to determine the number of data points to consider during the calculations.

[0035] Calculation example: Set data for 3 time periods: Period 1: Current Year , historical year ; Period 2: Current Year , historical year ; Period 3: Current Year , historical year ; Calculate the deviation ratio for each period: Period 1: ; Period 2: ; Period 3: ; Calculate the mean deviation ratio : ; Calculation results , indicating that in the three time periods analyzed, the load volatility in the current year is on average about 11.4% higher than that in the historical years. The analysis can help understand the annual changes in grid load volatility and provide a basis for the formulation of grid management and optimization strategies.

[0036] See also Figure 6 , the steps to obtain the abnormal threshold adjustment result are: Based on the power grid operation deviation analysis results and the historical power grid operation benchmark, according to the current time period, the average operating load and the load volatility index of the same period are extracted to obtain the threshold correlation data; Based on the threshold correlation data, combined with the average operating load deviation ratio, load volatility deviation ratio and the preset benchmark abnormal load detection threshold, the formula is: ; Calculate the adjusted abnormal threshold to obtain the abnormal threshold adjustment result; in, is the baseline abnormal load detection threshold, is the average operating load in the same period, is the average operating load deviation ratio, is the load fluctuation index in the same period, is the load fluctuation deviation ratio, is the adjustment factor, is the adjusted abnormal threshold.

[0037] formula: ; Parameter details and how to obtain them: : The average operating load in the same period is the average operating load in the same period in the power grid data of previous years, which is calculated by the previous steps; : Average operating load deviation ratio, calculated by the previous steps; : The load fluctuation index of the same period is the load fluctuation of the same period in the power grid data of previous years, which is calculated by the previous steps; : Load fluctuation deviation ratio, calculated by the previous step; : The adjustment coefficient is a design parameter used to adjust the weight of the impact of volatility. This coefficient can be determined based on experience or an optimization algorithm to ensure that the adjusted threshold is both sensitive and does not overreact to small fluctuations; : Benchmark abnormal load detection threshold, which indicates the maximum load value that the power grid can accept and is set based on historical data and operating experience.

[0038] Calculation example: Set the historical data for the current period as follows: Historical average operating load ( ): 100MW, average operating load deviation ratio ( ): 0.05 (indicating that the current load is 5% higher than the historical average), historical load volatility ( ): 10MW, load fluctuation deviation ratio ( ): 0.10 (indicating that current volatility is 10% higher than historical volatility), adjustment factor ( ): 0.5, the baseline abnormal load detection threshold ( ):120MW.

[0039] Calculate the adjusted anomaly threshold : ; ; ; ; ; Calculation results MW, indicating that the new abnormal threshold is 110.5MW, which is lower than the original threshold of 120MW, reflecting a more accurate monitoring threshold adjusted based on current and historical load conditions. This result helps the power grid to more effectively identify and respond to possible abnormal load conditions.

[0040] See also Figure 7 , the method to evaluate the anomaly index is: Based on the abnormal threshold adjustment result, the current grid operation load is collected in real time at preset time intervals to obtain real-time load data; Based on the real-time load data, it is compared with the adjusted load detection threshold value, through the formula: ; Calculate the abnormal index and obtain the power grid abnormality assessment information; in, is the attenuation coefficient, is the actual grid operating load in the current period, is the adjusted abnormal threshold, is the abnormal index.

[0041] formula: ; Parameter details and how to obtain them: : The actual grid operating load in the current period. This data is obtained directly from the real-time monitoring system of the grid; : The adjusted abnormal threshold, calculated by the previous step; : The attenuation coefficient is a preset parameter used to adjust the sensitivity in the deviation calculation. It helps adjust the weight of the deviation impact to make the anomaly measurement more sensitive or robust, depending on the needs of grid operation and the results of historical data analysis.

[0042] Calculation example: The following parameters are set: actual operating load: 150MW, adjusted abnormal threshold: 140MW, attenuation coefficient: 10.

[0043] Calculate the difference ratio between the actual load and the threshold: ; Calculate the degree of abnormality: ; ; ; ; ; result , indicating that the grid operation faces a large anomaly or pressure. This calculation expresses the high responsiveness of anomaly detection when the actual load far exceeds the threshold.

[0044] See also Figure 8 , the steps to obtain the abnormal warning results of the power grid are: Based on the grid abnormality assessment information, the grid load is monitored in real time, the duration of the grid load exceeding the adjusted abnormal threshold is recorded, and the warning-related data is obtained; Based on the warning related data, through the formula: ; Calculate warning level ; in, is the adjustment factor, is the abnormality index, is the abnormal duration, is the warning level; Based on the warning level, corresponding abnormal warning notifications are implemented to the staff to obtain the abnormal warning results of the power grid.

[0045] formula: ; Parameter details and how to obtain them: : Anomaly index, calculated by the previous steps; : Abnormal duration, which is the data directly obtained from power grid monitoring, indicating the duration of the power grid load exceeding the adjusted abnormal threshold; :Adjustment coefficient, warning sensitivity adjustment coefficient, is a preset parameter used to adjust the impact of abnormality degree and duration on the warning level. The coefficient is determined based on historical data analysis and experience to ensure that the sensitivity of the warning level matches the actual operational needs.

[0046] Calculation example: Set the following parameters: Anomaly Index ( ): 1.5 (indicates that the actual load has increased significantly relative to the threshold), abnormal duration ( ): 30 minutes, warning sensitivity adjustment coefficient ( ):0.5.

[0047] Apply the warning level formula: ; ; ; ; Calculation results It indicates that the warning level is 2, which means that although there is a certain degree of abnormality, it does not reach the highest warning state.

[0048] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A power grid abnormal state monitoring and early warning system, characterized in that: The system comprises: The historical benchmark establishment module extracts the grid operation data of differentiated periods in previous years based on the grid operation records, evaluates the average operation load of differentiated periods, and analyzes the volatility of the load in differentiated periods to obtain the historical grid operation benchmark; The operation deviation analysis module analyzes the deviation between this year's power grid operation data and previous years' power grid operation data based on the historical power grid operation benchmark, evaluates the average operation load deviation, and analyzes the load volatility deviation to obtain the power grid operation deviation analysis result; The abnormal threshold adjustment module adjusts the grid load detection threshold of the current period based on the grid operation deviation analysis result and the historical grid operation benchmark, according to the average operation load, average operation load deviation, load volatility and load volatility deviation in the same period, combined with the preset benchmark abnormal load detection threshold, to obtain the abnormal threshold adjustment result; Based on the abnormal threshold adjustment result, the abnormal warning module compares the current power grid operating load with the adjusted load detection threshold, evaluates the abnormal index according to the deviation, and evaluates the warning level according to the deviation duration, implements corresponding abnormal warning notifications to the staff, and obtains the power grid abnormal warning result.

2. The power grid abnormal state monitoring and early warning system according to claim 1, characterized in that: The method for evaluating the average operating load of the differentiated time period is: Based on the grid operation records, the operation records of differentiated periods in previous years are extracted from the grid database. Each data point includes a timestamp and the corresponding grid load to obtain historical load data. Based on the historical load data, according to the timestamp of each data point, the formula is: ； Assign a timestamp recency weight to each data point to obtain the data weight evaluation result; in, is the attenuation coefficient, is the current year, Indicates The year in which the data points were collected, Indicates The timestamp recency weight of each data point, is the base of natural logarithms; Based on the data weight evaluation results, the formula is: ； Calculate the average operating load and obtain the average load evaluation result; in, Indicates The grid load of each data point, Indicates The timestamp recency weight of each data point, is the total number of data points, is the average operating load.

3. The power grid abnormal state monitoring and early warning system according to claim 2 is characterized in that: The steps for obtaining the historical power grid operation benchmark are: Based on the average load evaluation results and data weight evaluation results, according to the load data of differentiated periods in previous years, the formula is: ； Calculate the load fluctuation index; in, For the The grid load of each data point, is the average operating load, For the The timestamp recency weight of each data point, is the load fluctuation index, is the total number of data points; Based on the load volatility index, the average operating load of the corresponding time period is integrated to obtain a historical power grid operation benchmark.

4. The power grid abnormal state monitoring and early warning system according to claim 1, characterized in that: The method for evaluating the average operating load deviation is: Based on the historical power grid operation benchmark, average operation load data for multiple time periods this year are extracted, and average operation load data for corresponding time periods in previous years are extracted to obtain average load deviation correlation information; Based on the average load deviation association information, through the formula: ； Calculate the average operating load deviation ratio and obtain the average load deviation analysis result; in, Indicates the current year The average operating load in a period of time, Indicates previous years The average operating load in a period of time, is the total number of time periods, is the average operating load deviation ratio.

5. The power grid abnormal state monitoring and early warning system according to claim 4, characterized in that: The steps for obtaining the power grid operation deviation analysis result are as follows: Based on the historical power grid operation benchmark, extract the operating load volatility index of multiple time periods this year, and extract the operating load volatility index of corresponding time periods in previous years to obtain volatility deviation correlation information; Based on the volatility deviation correlation information, through the formula: ； Calculate the load fluctuation deviation ratio; in, is the total number of time periods, For the current year The load fluctuation index within a period of time, For previous years The load fluctuation index within a period of time, is the load fluctuation deviation ratio; Based on the load fluctuation deviation ratio, the average load deviation analysis result is integrated to obtain a power grid operation deviation analysis result.

6. The power grid abnormal state monitoring and early warning system according to claim 1, characterized in that: The steps for obtaining the abnormal threshold adjustment result are: Based on the power grid operation deviation analysis results and the historical power grid operation benchmark, according to the current time period, the average operation load and the load volatility index of the same time period are extracted to obtain threshold-related data; Based on the threshold-related data, combined with the average operating load deviation ratio, the load volatility deviation ratio and the preset benchmark abnormal load detection threshold, the formula is: ； Calculate the adjusted abnormal threshold to obtain the abnormal threshold adjustment result; in, is the baseline abnormal load detection threshold, is the average operating load in the same period, is the average operating load deviation ratio, is the load fluctuation index in the same period, is the load fluctuation deviation ratio, is the adjustment factor, is the adjusted abnormal threshold.

7. The power grid abnormal state monitoring and early warning system according to claim 1, characterized in that: The method for evaluating the abnormality index is: Based on the abnormal threshold adjustment result, the current power grid operation load is collected in real time at a preset time interval to obtain real-time load data; Based on the real-time load data, the load detection threshold after adjustment is compared with the formula: ； Calculate the abnormal index and obtain the power grid abnormality assessment information; in, is the attenuation coefficient, is the actual grid operating load in the current period, is the adjusted abnormal threshold, is the abnormal index.

8. The power grid abnormal state monitoring and early warning system according to claim 7, characterized in that: The steps for obtaining the power grid abnormality warning result are: Based on the power grid abnormality assessment information, real-time monitoring of the power grid load is performed, and the duration of the power grid load exceeding the adjusted abnormal threshold is recorded to obtain early warning related data; Based on the warning related data, through the formula: ； Calculate warning level ; in, is the adjustment factor, is the abnormality index, is the abnormal duration, is the warning level; Based on the warning level, corresponding abnormal warning notifications are implemented to the staff to obtain abnormal power grid warning results.

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