Automatic monitoring method and system for power dispatching

Abstract

The invention provides an automatic monitoring method and system for power dispatching, and provides a series of matching schemes from details on the basis of providing a set of specific system architecture scheme of the automatic monitoring system for power dispatching, so that automatic power dispatching can be realized, and the power dispatching efficiency can be improved. The power dispatching process can be effectively monitored, the power grid fault performance and the power grid hidden danger degree can be gradually and progressively evaluated when the dispatching is judged to be normal, the intelligent degree in the aspect of automatic monitoring is continuously enhanced, and therefore the high-quality automatic power dispatching requirement can be met, and the power dispatching efficiency is improved. And safe and stable operation of a power grid is facilitated.

Description

Technical Field

[0001] The present application relates to the technical field of power management, and in particular to a method and system for automated monitoring of power dispatching. Background Art

[0002] In terms of power management, the implementation of automated power dispatching through the configuration of relevant power dispatching systems will help ensure the safe operation of the power grid, which is obviously of great significance.

[0003] However, the inventors of the present application have discovered that the existing automated power dispatching process focuses on data collection and command control, but in actual applications, it is difficult to effectively monitor the power dispatching process and accurately feedback the control efficiency status. When the degree of intelligence is low, the effect of automated power dispatching is poor, which makes it difficult to meet the needs of high-quality automated power dispatching and is not conducive to the safe and stable operation of the power grid. Summary of the invention

[0004] The present application provides a method and system for automated monitoring of electric power dispatching. In addition to providing a specific system architecture solution for an automated monitoring system for electric power dispatching, a series of supporting solutions are also provided in detail, thereby enabling automated electric power dispatching to be realized and effective monitoring of the electric power dispatching process to be performed. Furthermore, when judging that the dispatching is normal, a step-by-step progressive evaluation can be performed on the performance of power grid faults and the degree of hidden dangers in the power grid, thereby continuously enhancing the intelligence level of automated monitoring. In this way, the needs for high-quality automated electric power dispatching can be met, which is beneficial to the safe and stable operation of the power grid.

[0005] In the first aspect, the present application provides a method for monitoring power dispatching automation, which is applied to a power dispatching automation monitoring system. The power dispatching automation monitoring system includes a power data acquisition module, a power data processing module, a dispatching strategy analysis and output module, a power dispatching center, and a dispatching strategy execution monitoring module. The power dispatching automation monitoring method includes:

[0006] The power data collection module collects the first real-time operation data of the power grid object at the current time;

[0007] The power data processing module performs data preprocessing on the first real-time operation data, and extracts corresponding key data from the obtained second real-time operation data;

[0008] The dispatch strategy analysis output module performs risk assessment on power grid objects based on key data, and generates corresponding power dispatch strategies according to the risk assessment results;

[0009] The power dispatching center drives the automation control module to execute the power dispatching operations involved in the power dispatching strategy to prompt the power grid objects to operate according to the corresponding predetermined operating states;

[0010] The dispatch strategy execution monitoring module monitors the power dispatch process. When it is judged that there is a dispatch anomaly based on the monitoring results, a dispatch anomaly signal is sent to the power dispatch center, prompting the power dispatch center to issue a corresponding early warning. When it is judged that there is no dispatch anomaly based on the monitoring results, a dispatch qualification signal is sent to the power dispatch center.

[0011] In a second aspect, the present application provides a power dispatching automation monitoring system for executing the method provided in the first aspect of the present application.

[0012] In a third aspect, the present application provides a computer-readable storage medium, which stores a plurality of instructions, and the instructions are suitable for loading by a processor to execute the method provided in the first aspect of the present application.

[0013] From the above content, it can be concluded that the present application has the following beneficial effects:

[0014] Aiming at the goal of automated monitoring of electric power dispatching, this application not only provides a specific system architecture solution for an automated monitoring system for electric power dispatching, but also gives a series of supporting solutions in detail. This can not only realize automated power dispatching, but also effectively monitor the power dispatching process. When it is judged that the dispatching is normal, it can also gradually and progressively evaluate the performance of power grid faults and the degree of power grid hidden dangers, and continuously enhance the intelligence level of automated monitoring. In this way, it can meet the needs of high-quality automated power dispatching and benefit the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A schematic diagram of the system architecture of the electric power dispatching automation monitoring system of this application.

[0017] Figure 2 A schematic diagram of a flow chart of the electric power dispatching automation monitoring method of the present application;

[0018] Figure 3 A schematic diagram of a partial system architecture of the electric power dispatching automation monitoring system of this application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0020] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0021] The division of modules in this application is a logical division. There may be other division methods when it is implemented in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. In addition, the modules or submodules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present application.

[0022] First, the present application provides a method for monitoring power dispatching automation, which is applied to a power dispatching automation monitoring system, such as Figure 1A system architecture diagram of the electric power dispatching automation monitoring system of the present application is shown, and the electric power dispatching automation monitoring system includes several major modules: a power data acquisition module, a power data processing module, a dispatching strategy analysis and output module, a power dispatching center, and a dispatching strategy execution monitoring module. These modules can be configured in the form of independent hardware devices or modules abstracted from functional services. In other words, a hardware device may be equipped with one or more modules. This involves the adjustment of the types of equipment involved and the form of equipment deployment, which is related to how to deploy the electric power dispatching automation monitoring system provided by the present application in specific application scenarios in actual applications to better meet user needs.

[0023] On this basis, continue to refer to Figure 2 A flow chart of the electric power dispatching automation monitoring method of the present application is shown. The electric power dispatching automation monitoring method provided by the present application may specifically include the following steps S201 to S205:

[0024] Step S201, the power data collection module collects the first real-time operation data of the power grid object at the current time;

[0025] It can be understood that the power data acquisition module in the system architecture is responsible for real-time collection of one or more aspects of the operating data of the power grid objects, such as voltage, current, frequency, load, etc. The specific parameter types involved can obviously be adjusted according to actual conditions, and this application does not specifically limit this.

[0026] Among them, it can be understood that for the power data acquisition module, the data source corresponding to the data to be collected is the power grid object, which may be the entire power grid or a local part of the power grid. This involves the pre-set specific data to be collected and the data acquisition target.

[0027] Secondly, for the convenience of explanation, the real-time operation data collected by the power data collection module here is recorded as the first real-time operation data.

[0028] After collecting the data in real time, or after waiting for a certain waiting period or accumulating a certain amount of data, the power data acquisition module can send the collected first real-time operation data to the power data processing module according to the preset data transmission link / communication connection (the same applies to the communication between modules involved later) for further data processing.

[0029] Step S202, the power data processing module performs data preprocessing on the first real-time operation data, and extracts corresponding key data from the obtained second real-time operation data;

[0030] It can be understood that after obtaining the first real-time operating data from the power data acquisition module, the corresponding data analysis work cannot usually be carried out directly at this time. Before carrying out data analysis, data preprocessing is required to enhance data quality, which not only simplifies the data, but also helps to improve the processing efficiency and accuracy of subsequent data analysis work.

[0031] It can be understood that data preprocessing is a common operation in data processing work. In practical applications, relevant existing technical means can be directly adopted to achieve the data preprocessing goals. Of course, in some cases, it is possible to make further optimization and improvement on the basis of existing technical means or adopt self-developed novel technical means.

[0032] After the first real-time operation data has completed data preprocessing, the second real-time operation data can be obtained. At this time, key data extraction processing can be performed to extract key data required for subsequent data analysis work.

[0033] It can be understood that the key data extraction and processing here have once again simplified the data and also realized the conversion of the data format to obtain a data format that is more suitable for subsequent data analysis work. The specific data extraction and processing operations involved are obviously highly adapted to the subsequent data analysis work. It is necessary to fully consider the data usage requirements of the subsequent data analysis work in advance on the basis of the application scenarios involved in this application to perform corresponding adaptation work. This is the biggest difference from the previous data preprocessing. Data preprocessing usually follows the conventional and general data enhancement concept.

[0034] After completing the data preprocessing and key data extraction processing in sequence, the obtained key data can be sent to the scheduling strategy analysis output module.

[0035] Step S203, the dispatch strategy analysis output module performs risk assessment processing on the power grid object based on the key data, and generates a corresponding power dispatch strategy according to the risk assessment processing result;

[0036] It can be understood that the data analysis work mentioned above is the risk assessment processing carried out by the dispatching strategy analysis output module here. Its purpose is to fully / comprehensively consider the supply and demand balance, stability, economy and environmental protection of the power grid on the basis of the key data obtained earlier, and to evaluate and predict the risks that have been exposed to the power grid and the potential risks. This process can also be understood by big data analysis. After completing the risk assessment processing, the corresponding risk assessment processing can be output.

[0037] Among them, the risk assessment processing here can be implemented in practical applications through corresponding artificial intelligence (AI) technology, such as deep learning neural network models, so as to make full use of the powerful computing power of AI technology to achieve efficient and high-precision processing effects. Furthermore, for the AI ​​technology used here, the specific model type, model training scheme and loss function in the model training process, in practical applications, you can directly adopt the existing scheme, or make further optimization and improvement on the basis of the existing scheme or adopt a self-developed novel scheme. These are all possible. Considering that it is not the focus of this application, it will not be repeated here.

[0038] After completing the risk assessment process, further power dispatch strategy generation processing can be carried out based on the risk assessment results obtained from the processing, so as to provide a highly adaptive specific power dispatch strategy when it is determined / judged that a dispatch operation is required.

[0039] In this processing link, the specific content of the power dispatching strategy is obviously developed with the risk assessment results as input. Similarly, it can also be implemented through the AI ​​technology mentioned above.

[0040] In this way, after the power dispatching strategy adapted to the current situation is obtained through the power dispatching strategy generation process, it can be sent to the power dispatching center for further processing.

[0041] Among them, it should be noted that for normal situations, that is, situations where no scheduling operation is required, in specific operations, it is not necessary to trigger the power dispatching strategy generation process, or the processing result output by the power dispatching strategy generation process can be empty or no processing result needs to be generated, or the processing result output by the power dispatching strategy generation process and sent to the power dispatching center can indicate that no processing is required. Obviously, this is more flexible and can be adjusted according to the specific data connection relationship configured between the risk assessment process, the power dispatching strategy generation process and the power dispatching strategy sending process under actual circumstances.

[0042] Step S204, the power dispatching center drives the automation control module to execute the power dispatching operation involved in the power dispatching strategy to prompt the power grid objects to operate according to the corresponding predetermined operating states;

[0043] It is easy to understand that after obtaining the power dispatching strategy used to alleviate / solve the risks analyzed above, the power dispatching center can implement the power dispatching operations involved in the power dispatching strategy by sending specific operation instructions corresponding to the power dispatching strategy to the automation control module, which may specifically involve control adjustments in software, hardware, and software + hardware, so as to achieve all-round precise control and allow power grid objects to operate according to the corresponding predetermined operating states pre-set by the power grid or the power dispatching strategy, so as to optimize the operating state of the power grid and ensure the balance of power supply and demand and the safety and stability of the power grid.

[0044] Step S205, the dispatch strategy execution monitoring module monitors the power dispatch process. When it is determined based on the monitoring results that there is a dispatch anomaly, a dispatch anomaly signal is sent to the power dispatch center to prompt the power dispatch center to issue a corresponding warning. When it is determined based on the monitoring results that there is no dispatch anomaly, a dispatch qualification signal is sent to the power dispatch center.

[0045] During the process of power dispatching by the power dispatching center, the power dispatching center can notify / trigger the dispatching strategy execution monitoring module to monitor the power dispatching process. Of course, the dispatching strategy execution monitoring module can also autonomously / continuously monitor the power dispatching operations that may exist / occur in the power grid.

[0046] In this way, the power dispatching process can be monitored and corresponding monitoring results can be formed. At this time, further data analysis can be done based on the monitoring results to determine whether there are any scheduling abnormalities. It can be understood that the analysis of the scheduling abnormalities here involves data input and data processing complexity, which is relatively low compared to the previous risk assessment process. It is possible to focus on monitoring whether the status / data of several nodes are abnormal, and to do a deeper analysis. The data input involved may overlap with the first real-time operation data involved earlier (during the power dispatching process, the previous power data acquisition module will still perform real-time power data acquisition work to provide data for the subsequent modules), or it may be new data, and the corresponding data acquisition operation / module needs to be configured accordingly.

[0047] When it is determined that there is a dispatching abnormality, it is obvious that a dispatching abnormality signal can be generated and sent to the power dispatching center to prompt the power dispatching center to issue a corresponding warning on the system or to issue a warning to the corresponding management personnel, and further to suspend the power dispatching process or adjust the power dispatching strategy and other operations; on the contrary, when it is judged based on the monitoring results that there is no dispatching abnormality, a dispatching qualified signal can be generated and sent to the power dispatching center, so that the power dispatching center does not need to make corresponding responses such as warnings, and can continue to carry out power dispatching.

[0048] It can be understood that under the above-mentioned processing content, the corresponding automated power dispatching processing can be carried out based on the implementation and dynamic power grid conditions, so as to optimize the operating status of the power grid, ensure the balance of power supply and demand and the safety and stability of the power grid, and also monitor and analyze the dispatching process. When an abnormal dispatching signal is generated, corresponding optimization and improvement measures are taken as soon as possible to ensure the execution effect of automated power dispatching and further ensure the safe and stable operation of the power grid.

[0049] Next, we will further introduce the optimization solutions that may be adopted in practical applications of the power dispatching automation monitoring method provided in this application.

[0050] As an exemplary embodiment, for the previous step S202 or the power data processing module, data preprocessing may specifically include data integrity check, abnormal data identification and data denoising and smoothing. In addition, key data extraction processing specifically includes basic calculations, advanced analysis, fault diagnosis and power flow calculations.

[0051] Specifically, there are:

[0052] (1) Data screening

[0053] Data integrity check: Perform an integrity check on the received data to ensure that all data items have been received completely without omission or loss; check whether the format and length of the data conform to the preset standards and specifications to eliminate data corruption caused by transmission errors.

[0054] Abnormal data identification: By setting reasonable thresholds and ranges, it automatically identifies and marks data points that are beyond the normal range or illogical. For example, for voltage and current data, it checks whether they are within the safe operating range. For switch position data, it verifies whether its status is consistent with the actual operating status of the power grid.

[0055] Data denoising and smoothing: For data containing noise or large fluctuations, appropriate algorithms are used for denoising to improve the accuracy and reliability of the data. Through smoothing, random fluctuations in the data can be reduced, making the data curve smoother, which is conducive to subsequent analysis and processing.

[0056] (2) Extraction and processing of key data

[0057] Basic calculations: perform basic mathematical operations and statistical analysis based on the received data, such as sum, average, maximum, minimum, etc. These basic calculations help understand the overall operation status and trends of the power grid and provide basic data support for subsequent dispatching decisions;

[0058] Advanced analysis: Use advanced algorithms and technologies to conduct advanced analysis, such as load forecasting, fault diagnosis, and power flow calculation. Load forecasting can predict the power demand for a period of time in the future based on historical data and current trends, providing a basis for the formulation of power generation plans and transmission plans;

[0059] Fault diagnosis: By analyzing abnormal data and behavior patterns, quickly locate the fault point in the power grid and take appropriate measures to deal with it;

[0060] Power flow calculation: Calculate and analyze the distribution of current and voltage in the power grid to ensure the stable operation and power supply quality of the power grid.

[0061] It can be seen that the embodiments here are the data preprocessing and key data extraction processing involved in this application. Combined with the application scenario of this application, a specific supporting solution suitable for practical use is given, so as to achieve the goal of efficient, high-precision and simplified key data acquisition.

[0062] After obtaining these data, they can also be organized and archived based on the database. This corresponds to the need for recording grid data in actual grid management work so that they can be viewed, called up and analyzed at any time.

[0063] In addition, for the dispatching strategy execution monitoring module for monitoring the power dispatching process, as an embodiment of the practice, the generation and processing of the dispatching abnormality signal and the dispatching qualified signal by the dispatching strategy execution monitoring module may specifically include the following contents:

[0064] For each power dispatch operation corresponding operation instruction, the time when the automation control module receives the power dispatch operation corresponding operation instruction is set as the first time (i.e., T1), and the time when the automation control module completes the power dispatch operation corresponding operation instruction is set as the second time (i.e., T2). The time difference between the first time and the second time is calculated to obtain the strategy execution time (i.e., |T1-T2|). If the strategy execution time exceeds the strategy execution time threshold (i.e., T N ), then the current strategy execution time is recorded as the bad execution time;

[0065] Compare the number of different poor execution durations with the number of different strategy execution durations in the first monitoring duration window (first detection period) to obtain a strategy execution abnormal detection value QN, and respectively compare the different strategy execution durations in the first monitoring duration window with the corresponding strategy execution duration thresholds to obtain multiple strategy execution time values, and average the multiple strategy execution time values ​​to obtain a strategy execution time value QK;

[0066] Based on the strategy execution anomaly value QN and the strategy execution condition value QK, a scheduling control evaluation value QR is generated, and when the scheduling control evaluation value QR is greater than the preset scheduling control evaluation value QR N When the dispatch control evaluation value QR is less than the preset dispatch control evaluation value QR N When , a dispatch qualified signal is generated, where the dispatch control evaluation value QR is calculated according to the following formula:

[0067] QR=k1*QN+k2*QK,

[0068] Among them, k1 and k2 are different proportional coefficients, and both are greater than 0.

[0069] It can be understood that the monitoring results obtained above may directly include the operation instructions and the first time and the second time, or these data may be obtained through secondary processing based on the monitoring results.

[0070] There is a negative correlation between the dispatch control evaluation value QR and the power dispatch efficiency. That is, the larger the value of the dispatch control evaluation value QR, the slower the power dispatch efficiency for the power grid within the monitoring time window, and the higher the value of the dispatch control evaluation value QR, the lower the power dispatch efficiency for the power grid within the monitoring time window. N When the power dispatch risk exceeds the critical state / warning value, an early warning is required.

[0071] It can be understood that the embodiment here provides a very specific implementation plan for how the scheduling strategy execution monitoring module performs automated power scheduling monitoring, combining specific parameters and quantification methods, which has better practical significance and can further ensure the safe and stable operation of the power grid in details.

[0072] Next, continue to refer to Figure 3 A partial system architecture diagram of the power dispatching automation monitoring system of the present application is shown. Figure 3 It can be seen that the power dispatching automation monitoring system can also include a dispatching strategy execution monitoring module and a strategy accuracy analysis module corresponding to the power dispatching center.

[0073] In this case, as an exemplary embodiment, when it is determined based on the monitoring results that there is no abnormal dispatching situation, the dispatching strategy execution monitoring module may send a dispatching qualified signal to the strategy accuracy analysis module in addition to sending a dispatching qualified signal to the power dispatching center;

[0074] The strategy accuracy analysis module can analyze the performance of power grid faults. When it is judged based on the analysis results that there is a scheduling abnormality, a strategy accuracy abnormality signal is sent to the power dispatching center, prompting the power dispatching center to issue a corresponding early warning. When it is judged based on the analysis results that there is no scheduling abnormality, a strategy accuracy normal signal is sent to the power dispatching center.

[0075] It can be understood that the configuration of the strategy accuracy analysis module is aimed at introducing another layer of monitoring mechanism, continuing to expand / strengthen the monitoring function of the power dispatching automation monitoring system of this application, and is different from the basic dispatching abnormality monitoring targeted by the dispatching strategy execution monitoring module. It specifically targets the power grid fault performance, so as to capture the power grid fault performance that the dispatching strategy execution monitoring module fails to identify / detect, and promote timely system optimization and upgrading as needed, to ensure the accuracy of the subsequently generated power dispatching strategy, reduce the frequency of power grid failures, and further improve the safety and stability of power grid operation.

[0076] Specifically, for the analysis and processing of the strategy accuracy analysis module on the performance of power grid faults, as an exemplary embodiment, the generation and processing of the strategy accuracy error signal and the strategy accuracy normal signal by the strategy accuracy analysis module may specifically include the following contents:

[0077] Assume that different types of faults occurring in the power grid object within the second monitoring time window are different target objects (which can be recorded as target object i, where i is a natural number greater than 1), the number of times each target object is generated is the generated frequency detection value, and each target object is respectively provided with a corresponding preset weight value, which is greater than 0 (the greater the adverse effect of the corresponding fault, the greater the preset weight value), for each target object, the corresponding generated frequency detection value is multiplied by the corresponding preset weight value to obtain the target detection value, and the different target detection values ​​are summed to obtain the power grid performance value WF;

[0078] Assume that the second monitoring time window includes several detection time periods, the time period without grid fault is recorded as the grid excellent meter time period, the time period with grid fault is recorded as the grid poor meter time period, the number of different grid poor meter time periods is recorded as the grid abnormal condition value WR, the number of grid poor meter time periods between two adjacent grid excellent meter time periods is recorded as the poor meter duration value, the different poor meter duration values ​​are averaged to obtain the poor meter duration value WS, the number of different poor meter duration values ​​exceeding the preset poor meter duration threshold is recorded as the grid poor holding value WL, based on the grid abnormal condition value WR, the poor meter duration value WS and the grid poor holding value WL, a grid operation and inspection value WX is generated (the larger the value, the more unstable the grid operation during the detection period), wherein the grid operation and inspection value WX is calculated by the following formula:

[0079]

[0080] Among them, fu1, fu2 and fu3 are different proportional coefficients, and all are greater than 0;

[0081] The power grid control analysis value NG is generated based on the power grid performance value WF and the power grid operation and inspection value WX, and when the power grid control analysis value NG exceeds the preset power grid control analysis threshold NG NWhen the power grid control analysis value NG does not exceed the preset power grid control analysis threshold NG, a strategy accuracy error signal is generated. N When , a strategy accuracy normal signal is generated, where the power grid control analysis value NG is calculated according to the following formula:

[0082] NG=g1*WF+g2*WX,

[0083] Among them, g1 and g2 are different proportional coefficients, and both are greater than 0.

[0084] It can be understood that there is a negative correlation between the grid control analysis value NG and the accuracy of the power dispatching strategy. That is, the larger the value of the grid control analysis value NG, the greater the probability that the accuracy of the generated power dispatching strategy is abnormal, and the probability that the accuracy exceeds the preset grid control analysis threshold NG is higher. N When the accuracy risk of the power dispatch strategy exceeds the critical state / warning value, an early warning is required.

[0085] It can be understood that the embodiment here provides a very specific implementation solution for how the strategy accuracy analysis module analyzes the performance of power grid faults in the process of automated power grid dispatching, combining specific parameters and quantification methods. It has better practical significance and can further ensure the safe and stable operation of the power grid in details.

[0086] In addition, from Figure 3 It can also be seen that the electric power dispatching automation monitoring system can also include a power grid hidden danger classification module corresponding to the dispatching strategy execution monitoring module and the electric power dispatching center.

[0087] In this case, as an exemplary embodiment, when it is determined based on the analysis result that there is no abnormal dispatching situation, the strategy accuracy analysis module can send a normal strategy accuracy signal to the power dispatching center and also send a normal strategy accuracy signal to the power grid hidden danger classification module;

[0088] The power grid hidden danger classification module classifies the degree of hidden dangers in power grid management. When it is judged that there is a high hidden danger degree based on the classification results, a high power grid hidden danger signal is sent to the power dispatching center, prompting the power dispatching center to issue a corresponding early warning. When it is judged that there is a low hidden danger degree based on the classification results, a low power grid hidden danger signal is sent to the power dispatching center.

[0089] It can be understood that the purpose of configuring the power grid hidden danger classification module is to introduce a new layer of monitoring mechanism on the basis of introducing another layer of monitoring mechanism by configuring the strategy accuracy analysis module, and continue to expand / strengthen the monitoring function of the power dispatching automation monitoring system of this application. It is different from the basic dispatching abnormality monitoring targeted by the dispatching strategy execution monitoring module and the power grid fault performance monitoring targeted by the power grid hidden danger classification module. It specifically targets the degree of power grid management hidden dangers, so as to capture the power grid management hidden dangers that the previous modules failed to identify / detect, and promote timely and reasonable allocation of resources related to power grid hidden dangers such as old and damaged equipment and power grid management personnel, so as to further improve the safety and stability of power grid operation.

[0090] Specifically, as an exemplary embodiment, the generation and processing of the power grid high hidden danger signal and the power grid low hidden danger signal by the power grid hidden danger classification module may specifically include the following contents:

[0091] The coverage of the power grid is recorded as the power grid coverage value TX, the number of different power equipment to be managed by the power grid is recorded as the power equipment supervision value TY, and the number of old and damaged equipment among the power equipment to be managed by different power grids is obtained through equipment detection and analysis and recorded as the old and damaged equipment supervision value TM;

[0092] The number of different power grid managers is recorded as the manager inspection value, and the corresponding manager inspection values ​​of different power grid managers are averaged to obtain the manager analysis value HX, and the management table inspection value HY of different power grid managers is obtained through personnel detection analysis;

[0093] Based on the grid coverage value TX, the power equipment supervision value TY, the old and damaged equipment supervision value TM, the manager analysis value HX and the manager inspection value HY, a grid hidden danger analysis value YL is generated, and when the grid hidden danger analysis value YL exceeds the preset grid hidden danger analysis threshold YL N When the power grid hidden danger analysis value YL does not exceed the preset power grid hidden danger analysis threshold YL N When , a low potential danger signal of the power grid is generated, wherein the potential danger analysis value YL of the power grid is calculated according to the following formula:

[0094]

[0095] Among them, sp1, sp2, sp3, sp4 and sp5 are different proportional coefficients, and all are greater than 0. The larger the value of the power grid hidden danger analysis value YL is, the greater the degree of hidden danger in power grid management is, and the more it is necessary to strengthen power grid supervision.

[0096] It can be understood that the embodiment here, similar to the previous embodiment, is how the power grid hidden danger classification module performs power grid hidden danger classification in the process of automated power grid dispatching. On the basis of considering the two major influencing factors of old and damaged equipment and power grid management personnel, it provides a very specific implementation plan in combination with specific parameters and quantitative methods. It has better practical significance to further ensure the safe and stable operation of the power grid in details.

[0097] It is also easy to understand that the equipment detection and analysis involves specific analysis strategies on how to analyze old and damaged equipment, and the personnel detection and analysis involves specific analysis strategies on the grid management experience / grid management capabilities of grid management personnel.

[0098] In this regard, as an exemplary embodiment, on the one hand, the device detection and analysis may specifically include the following processing contents:

[0099] For the power equipment that needs to be managed in the current power grid, the interval between the current date and the production date of the power equipment that needs to be managed in the power grid is recorded as the production interval value LS. In all maintenance moments for the power equipment that needs to be managed in the historical stage, the interval between two adjacent groups of maintenance moments is recorded as the maintenance interval value. All maintenance interval values ​​are averaged to obtain the maintenance interval value LP. The number of maintenance interval values ​​that exceed the preset maintenance interval threshold is recorded as the maintenance interval outlier value LK.

[0100] Based on the production interval value LS, the maintenance interval value LP and the maintenance interval difference value LK, the power equipment inspection value LW is generated. If the power equipment inspection value LW exceeds the preset power equipment inspection threshold LW N , then the power equipment that needs to be managed by the power grid is recorded as old and damaged equipment, where the power equipment inspection value is calculated by the following formula:

[0101]

[0102] Among them, ey1, ey2 and ey3 are different proportional coefficients, ey3>ey2>ey1>0, the larger the value of the power equipment inspection value LW, the more serious the operation aging condition of the corresponding power equipment is, until it exceeds the preset power equipment inspection threshold LW N And it is identified as old and damaged equipment.

[0103] On the other hand, personnel detection analysis can specifically include the following processing contents:

[0104] The length of service of the power grid management personnel is recorded as the power management time value NT, and the number of errors made by the power grid management personnel in the historical stage when performing power management is recorded as the power management error value NL. Based on the power management time value NT and the power management error value NL, the management table check value NY is generated, where the management table check value NY is calculated by the following formula:

[0105] NY=ry1*NT / (ry2*NL+1),

[0106] Among them, ry1 and ry2 are different proportional coefficients, ry2>ry1>0, the larger the value of the management table inspection value NY, the richer the power management experience of the corresponding manager and the stronger the power management ability.

[0107] It can be understood that the embodiment here, similar to the previous embodiment, provides a very specific implementation plan for how the power grid hidden danger classification module performs equipment detection and analysis and personnel detection and analysis in the process of automated power grid dispatching, combining specific parameters and quantification methods. It has better practical significance and can further ensure the safe and stable operation of the power grid in details.

[0108] Finally, for the above solution contents (including the solution contents of each exemplary embodiment), overall, for the goal of automated monitoring of power dispatching, this application not only provides a set of specific system architecture solutions for automated monitoring systems for power dispatching, but also provides a series of supporting solutions in details, thereby not only realizing automated power dispatching, but also effectively monitoring the power dispatching process, and when judging that the dispatching is normal, it can also gradually and progressively evaluate the performance of power grid faults and the degree of power grid hidden dangers, and continuously enhance the intelligence level of automated monitoring. In this way, it can meet the needs of high-quality automated power dispatching and facilitate the safe and stable operation of the power grid.

[0109] The above is an introduction to the electric power dispatching automation monitoring method provided by the present application. In order to facilitate better implementation of the electric power dispatching automation monitoring method provided by the present application, the present application also provides an electric power dispatching automation monitoring system from the perspective of the hardware system. The electric power dispatching automation monitoring system includes an electric power data acquisition module, an electric power data processing module, a dispatching strategy analysis and output module, an electric power dispatching center and a dispatching strategy execution monitoring module. The electric power dispatching automation monitoring system is used to execute the electric power dispatching automation monitoring method.

[0110] In brief, there are:

[0111] The power data collection module collects the first real-time operation data of the power grid object at the current time;

[0112] The power data processing module performs data preprocessing on the first real-time operation data, and extracts corresponding key data from the obtained second real-time operation data;

[0113] The dispatch strategy analysis output module performs risk assessment on power grid objects based on key data, and generates corresponding power dispatch strategies according to the risk assessment results;

[0114] The power dispatching center drives the automation control module to execute the power dispatching operations involved in the power dispatching strategy to prompt the power grid objects to operate according to the corresponding predetermined operating states;

[0115] The dispatch strategy execution monitoring module monitors the power dispatch process. When it is judged that there is a dispatch anomaly based on the monitoring results, a dispatch anomaly signal is sent to the power dispatch center, prompting the power dispatch center to issue a corresponding early warning. When it is judged that there is no dispatch anomaly based on the monitoring results, a dispatch qualification signal is sent to the power dispatch center.

[0116] As an exemplary embodiment, data preprocessing includes data integrity check, abnormal data identification and data denoising and smoothing, and key data extraction processing includes basic calculation, advanced analysis, fault diagnosis and power flow calculation.

[0117] As another exemplary embodiment, the generation and processing of the scheduling abnormality signal and the scheduling qualified signal by the scheduling strategy execution monitoring module includes the following contents:

[0118] For each operation instruction corresponding to the power dispatching operation, the time when the automation control module receives the operation instruction corresponding to the power dispatching operation is set as the first time, and the time when the automation control module completes the operation instruction corresponding to the power dispatching operation is set as the second time, the time difference between the first time and the second time is calculated and the strategy execution duration is obtained. If the strategy execution duration exceeds the strategy execution duration threshold, the current strategy execution duration is recorded as the poor execution duration;

[0119] Compare the number of different poor execution durations in the first monitoring duration window with the number of different strategy execution durations to obtain a strategy execution abnormal detection value QN, and respectively compare the different strategy execution durations in the first monitoring duration window with the corresponding strategy execution duration thresholds to obtain multiple strategy execution time values, and average the multiple strategy execution time values ​​to obtain a strategy execution time value QK;

[0120] Based on the strategy execution anomaly value QN and the strategy execution condition value QK, a scheduling control evaluation value QR is generated, and when the scheduling control evaluation value QR is greater than the preset scheduling control evaluation value QR N When the dispatch control evaluation value QR is less than the preset dispatch control evaluation value QR N When , a dispatch qualified signal is generated, where the dispatch control evaluation value QR is calculated according to the following formula:

[0121] QR=k1*QN+k2*QK,

[0122] Among them, k1 and k2 are different proportional coefficients, and both are greater than 0.

[0123] As another exemplary embodiment, the power dispatching automation monitoring system further includes a strategy accuracy analysis module, and when it is determined based on the monitoring result that there is no abnormal dispatching situation, the dispatching strategy execution monitoring module further sends a dispatching qualified signal to the strategy accuracy analysis module;

[0124] The strategy accuracy analysis module analyzes the performance of power grid faults. When it is judged based on the analysis results that there is a scheduling abnormality, a strategy accuracy abnormality signal is sent to the power dispatching center, prompting the power dispatching center to issue a corresponding early warning. When it is judged based on the analysis results that there is no scheduling abnormality, a strategy accuracy normal signal is sent to the power dispatching center.

[0125] As another exemplary embodiment, the generation and processing of the strategy accuracy analysis module for the strategy accuracy difference signal and the strategy accuracy normal signal includes the following contents:

[0126] Assume that different types of faults occurring in the power grid object within the second monitoring time window are different target objects, the number of times each target object is generated is the generated frequency detection value, each target object is respectively provided with a corresponding preset weight value, the preset weight value is greater than 0, for each target object, the corresponding generated frequency detection value is multiplied by the corresponding preset weight value to obtain the target detection value, and the different target detection values ​​are summed to obtain the power grid performance value WF;

[0127] Assume that the second monitoring time window includes several detection time periods, the time period without grid fault is recorded as the grid excellent meter time period, the time period with grid fault is recorded as the grid poor meter time period, the number of different grid poor meter time periods is recorded as the grid abnormal condition value WR, the number of grid poor meter time periods between two adjacent grid excellent meter time periods is recorded as the poor meter duration value, the different poor meter duration values ​​are averaged to obtain the poor meter duration value WS, the number of different poor meter duration values ​​exceeding the preset poor meter duration threshold is recorded as the grid poor holding value WL, based on the grid abnormal condition value WR, the poor meter duration value WS and the grid poor holding value WL, the grid operation and inspection value WX is generated, wherein the grid operation and inspection value WX is calculated by the following formula:

[0128]

[0129] Among them, fu1, fu2 and fu3 are different proportional coefficients, and all are greater than 0;

[0130] The power grid control analysis value NG is generated based on the power grid performance value WF and the power grid operation and inspection value WX, and when the power grid control analysis value NG exceeds the preset power grid control analysis threshold NG N When the power grid control analysis value NG does not exceed the preset power grid control analysis threshold NG, a strategy accuracy error signal is generated. N When , a strategy accuracy normal signal is generated, where the power grid control analysis value NG is calculated according to the following formula:

[0131] NG=g1*WF+g2*WX,

[0132] Among them, g1 and g2 are different proportional coefficients, and both are greater than 0.

[0133] As another exemplary embodiment, the power dispatching automation monitoring system further includes a power grid hidden danger classification module, and when it is determined based on the analysis result that there is no abnormal dispatching situation, the strategy accuracy analysis module further sends a strategy accuracy normal signal to the power grid hidden danger classification module;

[0134] The power grid hidden danger classification module classifies the degree of hidden dangers in power grid management. When it is judged that there is a high hidden danger degree based on the classification results, a high power grid hidden danger signal is sent to the power dispatching center, prompting the power dispatching center to issue a corresponding early warning. When it is judged that there is a low hidden danger degree based on the classification results, a low power grid hidden danger signal is sent to the power dispatching center.

[0135] As another exemplary embodiment, the generation and processing of the power grid high hidden danger signal and the power grid low hidden danger signal by the power grid hidden danger classification module includes the following contents:

[0136] The coverage of the power grid is recorded as the power grid coverage value TX, the number of different power equipment to be managed by the power grid is recorded as the power equipment supervision value TY, and the number of old and damaged equipment among the power equipment to be managed by different power grids is obtained through equipment detection and analysis and recorded as the old and damaged equipment supervision value TM;

[0137] The number of different power grid managers is recorded as the manager inspection value, and the corresponding manager inspection values ​​of different power grid managers are averaged to obtain the manager analysis value HX, and the management table inspection value HY of different power grid managers is obtained through personnel detection analysis;

[0138] Based on the grid coverage value TX, the power equipment supervision value TY, the old and damaged equipment supervision value TM, the manager analysis value HX and the manager inspection value HY, a grid hidden danger analysis value YL is generated, and when the grid hidden danger analysis value YL exceeds the preset grid hidden danger analysis threshold YL N When the power grid hidden danger analysis value YL does not exceed the preset power grid hidden danger analysis threshold YL N When , a low potential danger signal of the power grid is generated, wherein the potential danger analysis value YL of the power grid is calculated according to the following formula:

[0139]

[0140] Among them, sp1, sp2, sp3, sp4 and sp5 are different proportional coefficients, and all are greater than 0.

[0141] As another exemplary embodiment, the device detection analysis includes the following processing contents:

[0142] For the power equipment that needs to be managed in the current power grid, the interval between the current date and the production date of the power equipment that needs to be managed in the power grid is recorded as the production interval value LS. In all maintenance moments for the power equipment that needs to be managed in the historical stage, the interval between two adjacent groups of maintenance moments is recorded as the maintenance interval value. All maintenance interval values ​​are averaged to obtain the maintenance interval value LP. The number of maintenance interval values ​​that exceed the preset maintenance interval threshold is recorded as the maintenance interval outlier value LK.

[0143] Based on the production interval value LS, the maintenance interval value LP and the maintenance interval difference value LK, the power equipment inspection value LW is generated. If the power equipment inspection value LW exceeds the preset power equipment inspection threshold LW N , then the power equipment that needs to be managed by the power grid is recorded as old and damaged equipment, where the power equipment inspection value is calculated by the following formula:

[0144]

[0145] Among them, ey1, ey2 and ey3 are different proportional coefficients, ey3>ey2>ey1>0;

[0146] Personnel detection analysis includes the following processing:

[0147] The length of service of the power grid management personnel is recorded as the power management time value NT, and the number of errors made by the power grid management personnel in the historical stage when performing power management is recorded as the power management error value NL. Based on the power management time value NT and the power management error value NL, the management table check value NY is generated, where the management table check value NY is calculated by the following formula:

[0148] NY=ry1*NT / (ry2*NL+1),

[0149] Among them, ry1 and ry2 are different proportional coefficients, ry2>ry1>0.

[0150] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the power dispatching automation monitoring system described above can refer to Figure 2 The description of the electric power dispatching automation monitoring method in the corresponding embodiment will not be repeated here in detail.

[0151] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0152] To this end, the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the present application as follows: Figure 2 The steps of the power dispatching automation monitoring method in the corresponding embodiment, the specific operation can refer to the following Figure 2 The description of the electric power dispatching automation monitoring method in the corresponding embodiment will not be repeated here.

[0153] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0154] Due to the instructions stored in the computer-readable storage medium, the present application can be executed. Figure 2 The steps of the power dispatching automation monitoring method in the corresponding embodiment, therefore, the present application can be implemented as follows Figure 2 The beneficial effects that can be achieved by the electric power dispatching automation monitoring method in the corresponding embodiment are detailed in the previous description and will not be repeated here.

[0155] The above is a detailed introduction to the electric power dispatching automation monitoring method, system and computer-readable storage medium provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for monitoring power dispatch automation, characterized in that: The power dispatching automation monitoring method is applied to the power dispatching automation monitoring system, which includes a power data acquisition module, a power data processing module, a dispatching strategy analysis and output module, a power dispatching center and a dispatching strategy execution monitoring module. The power dispatching automation monitoring method includes: The power data collection module collects the first real-time operation data of the power grid object at the current time; The power data processing module performs data preprocessing on the first real-time operation data, and extracts corresponding key data from the obtained second real-time operation data; The dispatching strategy analysis output module performs risk assessment processing on the power grid object based on the key data, and generates a corresponding power dispatching strategy according to the risk assessment processing result; The power dispatching center drives the automation control module to execute the power dispatching operation involved in the power dispatching strategy to prompt the power grid object to operate according to the corresponding predetermined operating state; The dispatch strategy execution monitoring module monitors the power dispatch process. When it is determined based on the monitoring results that there is a dispatch anomaly, a dispatch anomaly signal is sent to the power dispatch center, prompting the power dispatch center to issue a corresponding warning. When it is determined based on the monitoring results that there is no dispatch anomaly, a dispatch qualification signal is sent to the power dispatch center.

2. The method according to claim 1, characterized in that The data preprocessing includes data integrity check, abnormal data identification and data denoising and smoothing, and the key data extraction processing includes basic calculation, advanced analysis, fault diagnosis and power flow calculation.

3. The method according to claim 1, characterized in that The generation and processing of the scheduling abnormality signal and the scheduling qualified signal by the scheduling strategy execution monitoring module includes the following contents: For each operation instruction corresponding to the power dispatching operation, the time when the automation control module receives the operation instruction corresponding to the power dispatching operation is set as the first time, and the time when the automation control module completes the operation instruction corresponding to the power dispatching operation is set as the second time, the time difference between the first time and the second time is calculated to obtain the strategy execution duration, and if the strategy execution duration exceeds the strategy execution duration threshold, the current strategy execution duration is recorded as the poor execution duration; Compare the number of different poor execution durations in the first monitoring duration window with the number of different strategy execution durations to obtain a strategy execution abnormal detection value QN, and respectively compare the different strategy execution durations in the first monitoring duration window with the corresponding strategy execution duration thresholds to obtain multiple strategy execution time values, and average the multiple strategy execution time values ​​to obtain a strategy execution time value QK; Based on the strategy execution anomaly detection value QN and the strategy execution condition value QK, a scheduling control evaluation value QR is generated, and when the scheduling control evaluation value QR is greater than the preset scheduling control evaluation value QR N When the scheduling control evaluation value QR is less than the preset scheduling control evaluation value QR N When the dispatch qualification signal is generated, the dispatch control evaluation value QR is calculated according to the following formula: QR=k1*QN+k2*QK, Among them, k1 and k2 are different proportional coefficients, and both are greater than 0.

4. The method according to claim 1, characterized in that: The electric power dispatching automation monitoring system further comprises a strategy accuracy analysis module. When it is determined based on the monitoring result that there is no abnormal dispatching situation, the dispatching strategy execution monitoring module further sends the dispatching qualified signal to the strategy accuracy analysis module. The strategy accuracy analysis module analyzes the performance of power grid faults. When it is determined based on the analysis results that there is a scheduling abnormality, a strategy accuracy abnormality signal is sent to the power dispatching center, prompting the power dispatching center to issue a corresponding warning. When it is determined based on the analysis results that there is no scheduling abnormality, a strategy accuracy normal signal is sent to the power dispatching center.

5. The method according to claim 4, characterized in that The generation and processing of the strategy accuracy analysis module for the strategy accuracy difference signal and the strategy accuracy normal signal includes the following contents: Assume that different types of faults occurring in the power grid object within the second monitoring time window are different target objects, the number of times each target object is generated is a generated frequency detection value, each target object is respectively provided with a corresponding preset weight value, and the preset weight value is greater than 0. For each target object, the corresponding generated frequency detection value is multiplied by the corresponding preset weight value to obtain a target detection value, and the different target detection values ​​are summed to obtain a power grid performance value WF; Assume that the second monitoring time window includes several detection time periods, the time period without grid fault is recorded as the grid excellent meter time period, the time period with grid fault is recorded as the grid poor meter time period, the number of different grid poor meter time periods is recorded as the grid abnormal condition value WR, the number of the grid poor meter time periods between two adjacent grid excellent meter time periods is recorded as the poor meter duration value, the different poor meter duration values ​​are averaged to obtain the poor meter duration value WS, the number of different poor meter duration values ​​exceeding the preset poor meter duration threshold is recorded as the grid poor holding value WL, based on the grid abnormal condition value WR, the poor meter duration value WS and the grid poor holding value WL, a grid operation and inspection value WX is generated, wherein the grid operation and inspection value WX is calculated by the following formula: Among them, fu1, fu2 and fu3 are different proportional coefficients, and all are greater than 0; A power grid control analysis value NG is generated based on the power grid performance value WF and the power grid operation and inspection value WX, and when the power grid control analysis value NG exceeds a preset power grid control analysis threshold NG N When the power grid control analysis value NG does not exceed the preset power grid control analysis threshold NG, the strategy accuracy difference signal is generated. N When , the strategy accuracy normal signal is generated, wherein the power grid control analysis value NG is calculated according to the following formula: NG=g1*WF+g2*WX, Among them, g1 and g2 are different proportional coefficients, and both are greater than 0.

6. The method according to claim 4, characterized in that The electric power dispatching automation monitoring system further includes a power grid hidden danger classification module. When it is determined based on the analysis result that there is no abnormal dispatching situation, the strategy accuracy analysis module further sends the strategy accuracy normal signal to the power grid hidden danger classification module; The power grid hidden danger grading module grades the degree of hidden dangers in power grid management. When it is judged based on the grading result that a high hidden danger degree exists, a high power grid hidden danger signal is sent to the power dispatching center, prompting the power dispatching center to issue a corresponding early warning. When it is judged based on the grading result that a low hidden danger degree exists, a low power grid hidden danger signal is sent to the power dispatching center.

7. The method according to claim 6, characterized in that The generation and processing of the power grid high hidden danger signal and the power grid low hidden danger signal by the power grid hidden danger classification module includes the following contents: The coverage of the power grid is recorded as the power grid coverage value TX, the number of different power equipment to be managed required by the power grid is recorded as the power equipment supervision value TY, and the number of old and damaged equipment among the power equipment to be managed required by the different power grids is obtained through equipment detection and analysis and recorded as the old and damaged equipment supervision value TM; The number of different power grid managers is recorded as the manager inspection value, and the corresponding manager inspection values ​​of different power grid managers are averaged to obtain the manager analysis value HX, and the management table inspection values ​​HY of different power grid managers are obtained through personnel detection and analysis; Based on the grid coverage value TX, the power equipment supervision value TY, the old and damaged equipment supervision value TM, the manager analysis value HX and the manager inspection value HY, a grid hidden danger analysis value YL is generated, and when the grid hidden danger analysis value YL exceeds the preset grid hidden danger analysis threshold YL N When the power grid high hidden danger signal is generated, when the power grid hidden danger analysis value YL does not exceed the preset power grid hidden danger analysis threshold YL N When , the power grid low hidden danger signal is generated, wherein the power grid hidden danger analysis value YL is calculated according to the following formula: Among them, sp1, sp2, sp3, sp4 and sp5 are different proportional coefficients, and all are greater than 0.

8. The method according to claim 7, characterized in that The device detection and analysis includes the following processing contents: For the current power equipment to be managed by the power grid, the interval between the current date and the production date of the power equipment to be managed by the power grid is recorded as the production interval value LS. In the historical stage, for all maintenance moments of the power equipment to be managed by the power grid, the interval between two adjacent groups of maintenance moments is recorded as the maintenance interval value. The maintenance interval value LP is obtained by calculating the mean of all the maintenance interval values. The number of the maintenance interval values ​​exceeding the preset maintenance interval threshold is recorded as the maintenance interval outlier value LK. Based on the production interval value LS, the maintenance interval value LP and the maintenance interval difference value LK, a power equipment condition value LW is generated. If the power equipment condition value LW exceeds a preset power equipment condition threshold value LW N , then the power equipment that needs to be managed by the power grid is recorded as the old and damaged equipment, wherein the power equipment inspection value is calculated by the following formula: Among them, ey1, ey2 and ey3 are different proportional coefficients, ey3>ey2>ey1>0; The personnel detection analysis includes the following processing contents: The length of service of the power grid manager is recorded as the power management time value NT, and the number of errors made by the power grid manager in the historical stage when performing power management is recorded as the power management abnormality value NL. Based on the power management time value NT and the power management abnormality value NL, the management table check value NY is generated, wherein the management table check value NY is calculated by the following formula: NY=ry1*NT / (ry2*NL+1), Among them, ry1 and ry2 are different proportional coefficients, ry2>ry1>0.

9. An automatic detection system for power dispatching, characterized in that: Used to perform the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the method according to any one of claims 1 to 8.

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