Dispatching remote control switch state checking method and device based on causal state evolution

Abstract

The invention discloses a scheduling remote control switch state checking method and device based on causal state evolution, and belongs to the field of power systems, and the method comprises the steps: obtaining first power grid topological data and a first power grid power flow data set before a scheduling remote control switch carries out switching operation; obtaining a second power grid load flow data set according to simulation calculation, and obtaining a first power grid causal state evolution characteristic data set in combination with the first power grid load flow data set; obtaining a third power grid flow data set after switching operation, and obtaining a second power grid causal state evolution characteristic data set in combination with the first power grid flow data set; and sequentially checking each data in the first power grid causal state evolution characteristic data set by using the second power grid causal state evolution characteristic data set, and outputting a checking result. Therefore, by implementing the method and the device, the problem that the accuracy of state checking of the dispatching remote control switch is insufficient while high reliability is difficult to guarantee in the prior art can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, in particular to a dispatch remote control switch state checking method and device based on causal state evolution. BACKGROUND

[0002] In modern power systems, the operation of switching devices is the basis for realizing core tasks such as operation mode optimization, load transfer, and fault isolation.

[0003] In the prior art, the confirmation of switch state mainly relies on two ways: one is that when the traditional plant personnel operate on site, the mechanical position indicator is checked by visual inspection, the relevant interval power flow values and monitoring alarm information are checked, and manual confirmation is performed; the second is that in the integrated mode of dispatching and control, the dispatchers remotely control the operation, at this time the dispatchers cannot directly observe the physical state of the device, and instead rely on the information collected by various sensors installed in the operating mechanism, conducting arm and other parts to identify the state. Due to the different degrees of reliability problems of these sensor technologies, and the inability to comprehensively cover all types of switching devices, there is a risk of inaccurate switch state collection and identification. This leads to the fact that if the device state is incorrectly judged before and after the switch operation, it is easy to cause subsequent misoperation accidents, which can cause great harm to the power system. Therefore, there is an urgent need for a technical solution that can solve the problem of insufficient accuracy of dispatch remote control switch state checking while ensuring high reliability in the prior art. SUMMARY

[0004] The present application provides a dispatch remote control switch state checking method and device based on causal state evolution, which can solve the problem of insufficient accuracy of dispatch remote control switch state checking while ensuring high reliability in the prior art.

[0005] In a first aspect, the embodiments of the present application provide a dispatch remote control switch state checking method based on causal state evolution, comprising:

[0006] Obtaining first power grid topology data and a first power grid power flow data set before the dispatch remote control switch performs a switch operation;

[0007] Performing simulation calculation according to the first power grid topology data and the first power grid power flow data set to obtain a second power grid power flow data set, and filtering the second power grid power flow data set and the first power grid power flow data set to obtain a first power grid causal state evolution feature data set; wherein the first power grid topology data and the second power grid power flow data set both include current data of a plurality of nodes;

[0008] A third power flow data set after the switching operation of the remote control switch is obtained, and the third power grid topology data and the first power flow data set are filtered to obtain a second power grid causal state evolution feature data set; wherein, the third power grid topology data includes current data of several nodes;

[0009] The causal state evolution feature data of the first power grid causal state evolution feature data set are checked sequentially according to the second power grid causal state evolution feature data set. If all the causal state evolution feature data of the first power grid causal state evolution feature data set passes the check, the state check result of the dispatching remote control switch is output as correct; otherwise, the state check result of the dispatching remote control switch is output as incorrect.

[0010] This application's embodiments use grid topology and power flow data prior to remote-controlled switch operation as a benchmark. Combined with simulation calculations, it obtains the expected causal state evolution characteristics of the grid after the operation. Simultaneously, it acquires the actual grid state evolution characteristics after the operation, and then verifies the results by comparing the feature data of both. This allows for automatic verification of switch states based on the causal laws of grid state evolution. This method eliminates the need for manual on-site observation or single sensor information, covers all switching equipment, avoids the problems of insufficient reliability or incomplete coverage of traditional sensors, accurately analyzes switch opening and closing states, promptly detects states that do not conform to causal logic, and provides early warnings for risky and erroneous operations. Therefore, this application solves the problem in existing technologies that struggle to ensure high reliability while maintaining sufficient accuracy in remote-controlled switch state verification.

[0011] As a preferred example of the first aspect, the step of obtaining the first power grid causal state evolution characteristic data set based on the second power grid power flow data set and the first power grid power flow data set specifically includes:

[0012] Based on the second power flow data set and the first power flow data set, the absolute value of the current change rate of each node is calculated, and the absolute value of the current change rate of each node is matched with the current data of each node in the second power flow data set to obtain the fourth power flow data set.

[0013] The absolute value of the rate of change of current at each node in the fourth power grid power flow data set is compared with a first preset threshold in turn, and all current data that are greater than the first preset threshold are combined into the first power grid causal state evolution feature data set.

[0014] In this preferred example, by calculating the absolute value of the current change rate at each node and comparing it with a first preset threshold, node data significantly affected by switching operations are selected to form a first set of causal state evolution feature data for the power grid. This effectively focuses on the major power grid state changes caused by switching operations, eliminating weak interference caused by factors such as electromagnetic induction from nearby lines, making the extracted causal feature data more targeted and representative. This process, through the screening of preset thresholds, accurately captures key power grid state evolution information directly related to switching operations, providing a reliable and effective feature benchmark for subsequent switching state verification based on causal laws.

[0015] As a preferred example of the first aspect, the step of obtaining the second set of causal state evolution characteristic data of the power grid based on the third power grid topology data and the first power grid power flow data set specifically includes:

[0016] Based on the third power flow data set and the first power flow data set, the absolute value of the current change rate of each node is calculated, and the absolute value of the current change rate of each node is matched with the current data of each node in the third power flow data set to obtain the fifth power flow data set.

[0017] The absolute value of the rate of change of current at each node in the fifth power grid flow data set is compared with the second preset threshold in turn, and all current data that are greater than the second preset threshold are combined into the second power grid causal state evolution feature data set.

[0018] In this preferred example, by calculating the absolute value of the current change rate of each node after the switching operation and comparing it with a second preset threshold, the node data significantly affected by the operation in the actual power grid are selected to form a second set of power grid causal state evolution characteristic data. This effectively eliminates weak interference caused by factors such as electromagnetic induction from nearby lines, and accurately focuses on key state changes directly related to the switching operation in the actual power grid. This screening method based on preset thresholds makes the extracted actual state evolution characteristic data more targeted and representative, truly reflecting the actual evolution of the power grid after the switching operation, and laying a reliable foundation for subsequent comparison and verification with the causal state evolution characteristic data obtained from simulation.

[0019] As a preferred example of the first aspect, the step of verifying each power grid causal state evolution feature data in the first power grid causal state evolution feature data set according to the second power grid causal state evolution feature data set specifically involves:

[0020] The power grid causal state evolution feature data in the first power grid causal state evolution feature data set is traversed sequentially. Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data are obtained. If the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data satisfy the first preset condition and the second preset condition respectively, then it is determined that the verification of the currently traversed power grid causal state evolution feature data is passed.

[0021] In this preferred example, by sequentially traversing each data point in the first power grid causal state evolution feature data set and comparing it with the corresponding data in the second power grid causal state evolution feature data set, trend results and error results are obtained respectively, and it is determined whether preset conditions are met. This allows for rigorous verification of each key feature data point from two dimensions: consistency of change trend and reasonableness of change rate error. Simultaneously, through the constraint of dual conditions, unexpected state changes can be accurately identified, potential misoperations or state errors can be detected in a timely manner, thereby enhancing the early warning capability for risky operations and ensuring the safety and stability of power system dispatch operations.

[0022] As a preferred example of the first aspect, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set specifically includes:

[0023] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using a trend comparison formula, as shown below:

[0024]

[0025] Among them, W i V represents the current variation trend of the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t V represents the current value before the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t+1 W represents the current value after the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i Taking -1 indicates a decrease in current, Wi A value of 1 indicates an increase in current. Output represents the result of judging the current change trend. Output being 1 indicates that the current change trend is the same, and Output being 0 indicates that the current change trend is different.

[0026] In this preferred example, by employing a clear trend comparison formula to calculate the current change trend, a quantitative basis is provided for determining whether the change trends of corresponding data in the first and second power grid causal state evolution characteristic data are consistent. The formula clearly defines the representation of current decrease and increase, as well as the judgment results of similar and different trends, avoiding subjective bias and ambiguity in the trend comparison process and ensuring the objectivity and accuracy of trend judgment.

[0027] As a preferred example of the first aspect, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set specifically includes:

[0028] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using an error formula, as shown below:

[0029]

[0030] Where ε3 is the allowable error, E i E represents the error of the node corresponding to the causal state evolution feature data of the power grid currently being traversed. i Setting it to 1 indicates that E is within the allowable error range. i Setting it to 0 indicates that outside the allowable error, V i V represents the current data corresponding to the i-th power grid causal state evolution feature data in the first power grid causal state evolution feature data set. j This represents the current data corresponding to the i-th power grid causal state evolution feature data in the second power grid causal state evolution feature data set.

[0031] In this preferred example, by employing a clear error formula to calculate the error result corresponding to the currently traversed feature data, a quantitative basis is provided for judging whether the difference in current data of corresponding nodes in the first and second power grid causal state evolution feature data is reasonable. The formula clearly defines the allowable error range, as well as the judgment results for errors within and outside the allowable range, avoiding subjective arbitrariness in the error judgment process and ensuring the objectivity of the current data difference assessment.

[0032] Secondly, the present invention provides a scheduling remote control switch state verification device based on causal state evolution, comprising: a data acquisition module, a first processing module, a second processing module, and a verification module;

[0033] The data acquisition module is used to acquire the first set of power grid topology data and the first set of power grid power flow data before the remote control switch performs the switching operation.

[0034] The first processing module is used to perform simulation calculations of switching operations based on the first power grid topology data and the first power grid power flow data set to obtain a second power grid power flow data set, and to filter the second power grid power flow data set and the first power grid power flow data set to obtain a first power grid causal state evolution feature data set; wherein, both the first power grid topology data and the second power grid power flow data set include current data of several nodes;

[0035] The second processing module is used to acquire a third power flow data set after the scheduling remote control switch performs a switching operation, and to filter the third power grid topology data and the first power flow data set to obtain a second power grid causal state evolution feature data set; wherein, the third power grid topology data includes current data of several nodes;

[0036] The verification module is used to verify each power grid causal state evolution feature data in the first power grid causal state evolution feature data set in turn according to the second power grid causal state evolution feature data set. If each power grid causal state evolution feature data in the first power grid causal state evolution feature data set passes the verification, the state verification result of the dispatching remote control switch is output as correct; otherwise, the state verification result of the dispatching remote control switch is output as incorrect.

[0037] As a preferred example of the second aspect, the first processing module includes a first processing unit and a second processing unit;

[0038] The first processing unit is configured to calculate the absolute value of the current change rate of each node based on the second power grid power flow data set and the first power grid power flow data set, and match the absolute value of the current change rate of each node with the current data of each node in the second power grid power flow data set to obtain a fourth power grid power flow data set.

[0039] The second processing unit is used to sequentially compare the absolute value of the current change rate of each node in the fourth power grid power flow data set with a first preset threshold, and to form the first power grid causal state evolution feature data set by combining all current data that are greater than the first preset threshold.

[0040] As a preferred example of the second aspect, the second processing module includes a third processing unit and a fourth processing unit;

[0041] The third processing unit is used to calculate the absolute value of the current change rate of each node based on the third power grid power flow data set and the first power grid power flow data set, and to match the absolute value of the current change rate of each node with the current data of each node in the third power grid power flow data set to obtain the fifth power grid power flow data set.

[0042] The fourth processing unit is used to sequentially compare the absolute value of the current change rate of each node in the fifth power grid power flow data set with the second preset threshold, and to form the second power grid causal state evolution feature data set by combining all current data that are greater than the second preset threshold.

[0043] As a preferred example of the second aspect, the verification module includes a verification unit;

[0044] The verification unit is used to sequentially traverse each power grid causal state evolution feature data in the first power grid causal state evolution feature data set, and obtain the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set. If the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data satisfy the first preset condition and the second preset condition respectively, then it is determined that the verification of the currently traversed power grid causal state evolution feature data is passed.

[0045] As a preferred example of the second aspect, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution characteristic data based on the currently traversed power grid causal state evolution characteristic data and the corresponding power grid causal state evolution characteristic data in the second power grid causal state evolution characteristic data set specifically includes:

[0046] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using a trend comparison formula, as shown below:

[0047]

[0048] Among them, W i V represents the current variation trend of the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i tV represents the current value before the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t+1 W represents the current value after the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i Taking -1 indicates a decrease in current, W i A value of 1 indicates an increase in current. Output represents the result of judging the current change trend. Output being 1 indicates that the current change trend is the same, and Output being 0 indicates that the current change trend is different.

[0049] As a preferred example of the second aspect, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution characteristic data based on the currently traversed power grid causal state evolution characteristic data and the corresponding power grid causal state evolution characteristic data in the second power grid causal state evolution characteristic data set specifically includes:

[0050] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using an error formula, as shown below:

[0051]

[0052] Where ε3 is the allowable error, E i E represents the error of the node corresponding to the causal state evolution feature data of the power grid currently being traversed. i Setting it to 1 indicates that E is within the allowable error range. i Setting it to 0 indicates that outside the allowable error, V i V represents the current data corresponding to the i-th power grid causal state evolution feature data in the first power grid causal state evolution feature data set. j This represents the current data corresponding to the i-th power grid causal state evolution feature data in the second power grid causal state evolution feature data set.

[0053] In summary, this application's embodiments use the power grid topology and power flow data prior to the remote-controlled switch operation as a benchmark, combine this with simulation calculations to obtain the expected causal state evolution characteristics of the power grid after the operation, and simultaneously obtain the actual power grid state evolution characteristics after the operation. By comparing the feature data of both, automatic verification of the switch state can be achieved based on the causal laws of power grid state evolution. This method does not rely on manual on-site observation or single sensor information, can cover all switching equipment, avoids the problems of insufficient reliability or incomplete coverage of traditional sensors, can accurately analyze the switch opening and closing states, promptly detect states that do not conform to causal logic, and provide early warnings for risky and erroneous operations. Therefore, this application can solve the problem in the prior art of insufficient accuracy in remote-controlled switch state verification while ensuring high reliability. Attached Figure Description

[0054] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating an embodiment of a scheduling remote control switch state verification method based on causal state evolution provided by the present invention;

[0056] Figure 2 The electrical main wiring diagram of a local power grid is provided as an embodiment of a scheduling and remote control switch state verification method based on causal state evolution, which is provided by the present invention.

[0057] Figure 3 This is a module structure diagram of an embodiment of a scheduling remote control switch state verification device based on causal state evolution provided by the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0060] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0061] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0062] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0063] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple groups" refers to two or more (including two groups), and "multiple pieces" refers to two or more (including two pieces).

[0064] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0065] Example 1

[0066] See Figure 1 See Figure 1To address the problem of insufficient accuracy in scheduling and remote control switch state verification while ensuring high reliability in existing technologies, an embodiment of the present invention provides a scheduling and remote control switch state verification method based on causal state evolution, comprising:

[0067] S1. Obtain the first set of power grid topology data and the first set of power grid power flow data before the scheduling remote control switch performs the switching operation.

[0068] S2. Based on the first power grid topology data and the first power grid power flow data set, perform simulation calculations of switching operations to obtain a second power grid power flow data set. Then, filter the second power grid power flow data set and the first power grid power flow data set to obtain a first power grid causal state evolution characteristic data set. The first power grid topology data and the second power grid power flow data set each include current data of several nodes.

[0069] In some embodiments of this application, obtaining the first power grid causal state evolution feature data set based on the second power grid power flow data set and the first power grid power flow data set specifically involves:

[0070] Based on the second power flow data set and the first power flow data set, the absolute value of the current change rate of each node is calculated, and the absolute value of the current change rate of each node is matched with the current data of each node in the second power flow data set to obtain the fourth power flow data set.

[0071] The absolute value of the rate of change of current at each node in the fourth power grid power flow data set is compared with a first preset threshold in turn, and all current data that are greater than the first preset threshold are combined into the first power grid causal state evolution feature data set.

[0072] S3. Obtain the third power flow data set after the scheduling remote control switch performs the switching operation, and filter the third power grid topology data and the first power flow data set to obtain the second power grid causal state evolution feature data set; wherein, the third power grid topology data includes the current data of several nodes.

[0073] In some embodiments of this application, obtaining the second power grid causal state evolution characteristic data set based on the third power grid topology data and the first power grid power flow data set specifically involves:

[0074] Based on the third power flow data set and the first power flow data set, the absolute value of the current change rate of each node is calculated, and the absolute value of the current change rate of each node is matched with the current data of each node in the third power flow data set to obtain the fifth power flow data set.

[0075] The absolute value of the rate of change of current at each node in the fifth power grid flow data set is compared with the second preset threshold in turn, and all current data that are greater than the second preset threshold are combined into the second power grid causal state evolution feature data set.

[0076] S4. Based on the second power grid causal state evolution feature data set, the causal state evolution feature data of each power grid in the first power grid causal state evolution feature data set are checked in sequence. If all the causal state evolution feature data of each power grid in the first power grid causal state evolution feature data set are checked and passed, the state check result of the dispatching remote control switch is output as correct; otherwise, the state check result of the dispatching remote control switch is output as incorrect.

[0077] In some embodiments of this application, the step of sequentially verifying each power grid causal state evolution feature data in the first power grid causal state evolution feature data set according to the second power grid causal state evolution feature data set specifically involves:

[0078] The power grid causal state evolution feature data in the first power grid causal state evolution feature data set is traversed sequentially. Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data are obtained. If the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data satisfy the first preset condition and the second preset condition respectively, then it is determined that the verification of the currently traversed power grid causal state evolution feature data is passed.

[0079] In some embodiments of this application, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set specifically includes:

[0080] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using a trend comparison formula, as shown below:

[0081]

[0082] Among them, W i V represents the current variation trend of the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. it V represents the current value before the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t+1 W represents the current value after the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i Taking -1 indicates a decrease in current, W i A value of 1 indicates an increase in current. Output represents the result of judging the current change trend. Output being 1 indicates that the current change trend is the same, and Output being 0 indicates that the current change trend is different.

[0083] In some embodiments of this application, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set specifically includes:

[0084] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using an error formula, as shown below:

[0085]

[0086] Where ε3 is the allowable error, E i E represents the error of the node corresponding to the causal state evolution feature data of the power grid currently being traversed. i Setting it to 1 indicates that E is within the allowable error range. i Setting it to 0 indicates that outside the allowable error, V i V represents the current data corresponding to the i-th power grid causal state evolution feature data in the first power grid causal state evolution feature data set. j This represents the current data corresponding to the i-th power grid causal state evolution feature data in the second power grid causal state evolution feature data set.

[0087] For example, such as Figure 3 The diagram shown is the main electrical wiring diagram of the local power grid where the remote control switch is operated. Before operation, the operating mode of station 2 is as follows: line AB (station A to station B) and line BC (station B to station C) operate on bus #1, and main transformer T1 operates on bus #2; bus #1 and bus #2 operate in parallel. To fully explain the above steps, combined with... Figure 3 The following scheme will be used as an example for illustration:

[0088] The switch operation task is to switch the line AB to busbars, from busbar #1 to busbar #2. The specific operation includes: Step 1, "Close disconnect switch B1-2", and Step 2, "Open disconnect switch B1-1". The steps before and after Step 1, "Close disconnect switch B1-2", are analyzed as follows:

[0089] ① Obtain the actual power flow before the remote control switch operation and save it as a dataset RealBefore, as shown in the second column of Table 1;

[0090] ②Based on the RealBefore dataset, a power flow calculation program was used to simulate the power flow of the power grid after the operation of "closing the isolating switch B1-2", and the data was stored in the SimulateAfter dataset, as shown in the third column of Table 1.

[0091] ③ Compare and analyze the SimulateAfter dataset with the RealBefore dataset to obtain the absolute value and trend of the current change rate at each point, as shown in columns 4 and 5 of Table 1. These two columns are also stored in the SimulateAfter dataset.

[0092] Table 1. Simulation data analysis before and after the operation of "closing disconnector switch B1-2"

[0093]

[0094] ④ In the SimulateAfter dataset, feature data is first filtered. Considering the weak interference caused by electromagnetic induction from nearby lines, a filtering margin of 2% is set, and data from all locations with an absolute current change rate greater than 2% are extracted. These data are used as feature data of the causal state evolution of the power grid and stored as the SimulateAfterN dataset. Then, the datasets are sorted from largest to smallest by the absolute current change rate. Finally, the SimulateAfterN dataset is shown in Table 2.

[0095] Table 2. Power Grid Causal State Evolution Characteristics Data for the Operation of "Closed Disconnect Switch B1-2"

[0096] Branch name Current relative change rate % (absolute value) Current change trend Station B Breaker B3 41.50263377 Down Station B Breaker B4 40.83726088 Up

[0097] ⑤ Obtain the actual power flow after the remote control switch operation and save it as a dataset RealAfter, as shown in the third column of Table 3;

[0098] ⑥ Compare and analyze the RealAfter dataset with the RealBefore dataset to obtain the absolute value and trend of the current change rate at each point, as shown in columns 4 and 5 of Table 3. These two columns are also stored in the RealAfter dataset.

[0099] ⑦ In the RealAfter dataset, feature data is first filtered. Considering the weak interference caused by electromagnetic induction from nearby lines, a filtering margin of 2% is set, and data from all locations with an absolute current change rate greater than 2% are extracted. These data are used as feature data of the actual state evolution of the power grid and stored as the RealAfterM dataset. Then, the data are sorted from largest to smallest by the absolute current change rate. Finally, the RealAfterM dataset is shown in Table 4.

[0100] Table 3. Analysis of actual data before and after the operation of "closing disconnector switch B1-2"

[0101]

[0102] Table 4. Data on the actual state evolution of the power grid during the operation of "closing disconnector switch B1-2"

[0103] Branch name Current relative change rate % (absolute value) Current change trend Station B Breaker B3 42.00166343 Down Station B Breaker B4 39.86692542 Up

[0104] ⑧ In the SimulateAfterN dataset of causal state evolution features of the power grid, causal feature data are taken in descending order of the absolute value of the rate of change of current. Then, the actual feature data of the location is found in the RealAfterM dataset of actual state evolution features of the power grid. The data is shown in Table 5.

[0105] Table 5 Comparison of Feature Data

[0106]

[0107] ⑨ The current change trends of these two feature data must be consistent; otherwise, it is judged as not conforming to causal logic. As shown in Table 5, the trend comparison indicates that output = 1, and the current change trend is judged to conform to causal logic.

[0108] ⑩ Considering the environmental errors that exist between actual and simulated data, the allowable error value ε3% is set to 5%. Calculations show that the absolute errors of the current change rate of these two sets of characteristic data are 0.49903% and 0.97034%, respectively. Since this is within the allowable range of 5%, the output E = 1, indicating that it conforms to causal logic.

[0109] All feature data in the SimulateAfterN dataset of power grid causal state evolution features have been judged as qualified, and the output "the state verification after closing disconnecting switch B1-2 is correct" is output.

[0110] If, in step ⑤ above, the actual power flow dataset after the remote control switch operation is obtained as shown in column 3 of Table 6, and the subsequent steps yield the actual power grid state evolution feature dataset RealAft erM, as shown in columns 4 and 5 of Table 6, and the feature comparison dataset is shown in Table 7, the analysis shows that the current change trends of these two feature datasets are consistent, but the errors in the absolute values ​​of the current change rates of the two sets are 32.04879% and 32.65872%, respectively. These errors are outside the allowable range of 5%, indicating a violation of causal logic and a state verification error, and an operational risk warning is issued to the monitoring terminal.

[0111] Table 6. Analysis of Actual Data Before and After Operating "Closed Isolating Switch B1-2"

[0112]

[0113] Table 7 Comparison of Feature Data

[0114]

[0115] The status verification process after step 2, "disconnecting isolating switch B1-1", shall be performed according to ① to ② above. The characteristic data comparison is shown in Table 8. The current change trends of the two sets of characteristic data are consistent. The error of the absolute value of the current change rate is shown in column 6 of Table 8. It can be seen that both are within the allowable range of 5%, which is considered to conform to the causal logic and the state check is correct.

[0116] Table 8 Comparison of Simulation and Actual Characteristic Data After the Operation of "Disconnecting Isolating Switch B1-1"

[0117]

[0118] For example, the switching operation task is to switch line AB from operation to cold standby. The specific operation includes: step 1 "disconnect circuit breaker B1", step 2 "disconnect circuit breaker A1", step 3 "disconnect disconnect switch B1-3", step 4 "disconnect disconnect switch B1-1", step 5 "disconnect disconnect switch A1-3", and step 6 "disconnect disconnect switch A1-1".

[0119] The status verification process after step 1, "Disconnecting circuit breaker B1", shall be performed according to steps ① through ② in the above-mentioned operation task. The process was conducted with consideration of the weak interference caused by electromagnetic induction from nearby lines, and the increased load on other lines after a power outage. A screening margin of 10% was set, and data from all locations with an absolute current change rate greater than 10% were extracted. The characteristic data comparison is shown in Table 9. The current change trends of the characteristic data are consistent, and the errors in the absolute current change rate are shown in column 6 of Table 9, all within the allowable range of 5%. This indicates that the data conforms to causal logic, and the state verification is correct.

[0120] Table 9 Comparison of Simulation and Actual Characteristic Data After "Disconnecting Circuit Breaker B1" Operation

[0121]

[0122]

[0123] In steps 2 through 6 of this operation, the current in each branch does not change significantly compared to step 1, which is considered to conform to causal logic, and the state check is correct.

[0124] In summary, this application's embodiments use the power grid topology and power flow data prior to the remote-controlled switch operation as a benchmark, combine this with simulation calculations to obtain the expected causal state evolution characteristics of the power grid after the operation, and simultaneously obtain the actual power grid state evolution characteristics after the operation. By comparing the feature data of both, automatic verification of the switch state can be achieved based on the causal laws of power grid state evolution. This method does not rely on manual on-site observation or single sensor information, can cover all switching equipment, avoids the problems of insufficient reliability or incomplete coverage of traditional sensors, can accurately analyze the switch opening and closing states, promptly detect states that do not conform to causal logic, and provide early warnings for risky and erroneous operations. Therefore, this application can solve the problem in the prior art of insufficient accuracy in remote-controlled switch state verification while ensuring high reliability.

[0125] Example 2

[0126] like Figure 3 As shown, based on the above method embodiments, corresponding device embodiments are provided;

[0127] An embodiment of the present invention provides a scheduling remote control switch state verification device based on causal state evolution, including: a data acquisition module 31, a first processing module 32, a second processing module 33, and a verification module 34;

[0128] Data acquisition module 31 is used to acquire the first set of power grid topology data and the first set of power grid power flow data before the scheduling remote control switch performs the switching operation;

[0129] The first processing module 32 is used to perform simulation calculations of switching operations based on the first power grid topology data and the first power grid power flow data set to obtain a second power grid power flow data set, and to filter the second power grid power flow data set and the first power grid power flow data set to obtain a first power grid causal state evolution feature data set; wherein, the first power grid topology data and the second power grid power flow data set both include current data of several nodes;

[0130] The second processing module 33 is used to acquire a third power flow data set after the scheduling remote control switch performs a switching operation, and to filter the third power grid topology data and the first power flow data set to obtain a second power grid causal state evolution feature data set; wherein, the third power grid topology data includes current data of several nodes;

[0131] The verification module 34 is used to verify each power grid causal state evolution feature data in the first power grid causal state evolution feature data set in turn according to the second power grid causal state evolution feature data set. If each power grid causal state evolution feature data in the first power grid causal state evolution feature data set passes the verification, the state verification result of the dispatching remote control switch is output as correct; otherwise, the state verification result of the dispatching remote control switch is output as incorrect.

[0132] In some embodiments of this application, the first processing module includes a first processing unit and a second processing unit;

[0133] The first processing unit is configured to calculate the absolute value of the current change rate of each node based on the second power grid power flow data set and the first power grid power flow data set, and match the absolute value of the current change rate of each node with the current data of each node in the second power grid power flow data set to obtain a fourth power grid power flow data set.

[0134] The second processing unit is used to sequentially compare the absolute value of the current change rate of each node in the fourth power grid power flow data set with a first preset threshold, and to form the first power grid causal state evolution feature data set by combining all current data that are greater than the first preset threshold.

[0135] In some embodiments of this application, the second processing module includes a third processing unit and a fourth processing unit;

[0136] The third processing unit is used to calculate the absolute value of the current change rate of each node based on the third power grid power flow data set and the first power grid power flow data set, and to match the absolute value of the current change rate of each node with the current data of each node in the third power grid power flow data set to obtain the fifth power grid power flow data set.

[0137] The fourth processing unit is used to sequentially compare the absolute value of the current change rate of each node in the fifth power grid power flow data set with the second preset threshold, and to form the second power grid causal state evolution feature data set by combining all current data that are greater than the second preset threshold.

[0138] In some embodiments of this application, the verification module includes a verification unit;

[0139] The verification unit is used to sequentially traverse each power grid causal state evolution feature data in the first power grid causal state evolution feature data set, and obtain the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set. If the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data satisfy the first preset condition and the second preset condition respectively, then it is determined that the verification of the currently traversed power grid causal state evolution feature data is passed.

[0140] In some embodiments of this application, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set specifically includes:

[0141] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using a trend comparison formula, as shown below:

[0142]

[0143] Among them, W i V represents the current variation trend of the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t V represents the current value before the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t+1 W represents the current value after the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i Taking -1 indicates a decrease in current, W i A value of 1 indicates an increase in current. Output represents the result of judging the current change trend. Output being 1 indicates that the current change trend is the same, and Output being 0 indicates that the current change trend is different.

[0144] In some embodiments of this application, the step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set specifically includes:

[0145] Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using an error formula, as shown below:

[0146]

[0147] Where ε3 is the allowable error, E i E represents the error of the node corresponding to the causal state evolution feature data of the power grid currently being traversed. i Setting it to 1 indicates that E is within the allowable error range. i Setting it to 0 indicates that outside the allowable error, V i V represents the current data corresponding to the i-th power grid causal state evolution feature data in the first power grid causal state evolution feature data set. j This represents the current data corresponding to the i-th power grid causal state evolution feature data in the second power grid causal state evolution feature data set.

[0148] For more detailed steps and working principles of this embodiment, please refer to the relevant description in Embodiment 1, but not limited to these descriptions.

[0149] In summary, this application's embodiments use the power grid topology and power flow data prior to the remote-controlled switch operation as a benchmark, combine this with simulation calculations to obtain the expected causal state evolution characteristics of the power grid after the operation, and simultaneously obtain the actual power grid state evolution characteristics after the operation. By comparing the feature data of both, automatic verification of the switch state can be achieved based on the causal laws of power grid state evolution. This method does not rely on manual on-site observation or single sensor information, can cover all switching equipment, avoids the problems of insufficient reliability or incomplete coverage of traditional sensors, can accurately analyze the switch opening and closing states, promptly detect states that do not conform to causal logic, and provide early warnings for risky and erroneous operations. Therefore, this application can solve the problem in the prior art of insufficient accuracy in remote-controlled switch state verification while ensuring high reliability.

[0150] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the scheduling remote control switch state verification method based on causal state evolution provided by any of the above-described method embodiments of the present invention.

[0151] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0152] Example 3

[0153] Based on the above embodiments of the scheduling remote control switch state verification method based on causal state evolution, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the scheduling remote control switch state verification method based on causal state evolution of any embodiment of the present invention.

[0154] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0155] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0156] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0157] Example 4

[0158] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the scheduling remote control switch state verification method based on causal state evolution as described in any of the above-described method embodiments of the present invention.

[0159] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0160] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for verifying the state of a remote-controlled switch based on causal state evolution, characterized in that, include: Acquire the first set of power grid topology data and the first set of power grid power flow data before the scheduling remote control switch performs the switching operation; Simulation calculations of switching operations are performed based on the first power grid topology data and the first power grid power flow data set to obtain a second power grid power flow data set. The second power grid power flow data set and the first power grid power flow data set are then filtered to obtain a first power grid causal state evolution characteristic data set. The first power grid topology data and the second power grid power flow data set each include current data from several nodes. A third power flow data set after the switching operation of the remote control switch is obtained, and the third power grid topology data and the first power flow data set are filtered to obtain a second power grid causal state evolution feature data set; wherein, the third power grid topology data includes current data of several nodes; The causal state evolution feature data of the first power grid causal state evolution feature data set are checked sequentially according to the second power grid causal state evolution feature data set. If all the causal state evolution feature data of the first power grid causal state evolution feature data set passes the check, the state check result of the dispatching remote control switch is output as correct; otherwise, the state check result of the dispatching remote control switch is output as incorrect.

2. The scheduling and remote control switch state verification method based on causal state evolution as described in claim 1, characterized in that, The step of obtaining the first power grid causal state evolution characteristic data set based on the second power grid power flow data set and the first power grid power flow data set specifically involves: Based on the second power flow data set and the first power flow data set, the absolute value of the current change rate of each node is calculated, and the absolute value of the current change rate of each node is matched with the current data of each node in the second power flow data set to obtain the fourth power flow data set. The absolute value of the rate of change of current at each node in the fourth power grid power flow data set is compared with a first preset threshold in turn, and all current data that are greater than the first preset threshold are combined into the first power grid causal state evolution feature data set.

3. The scheduling and remote control switch state verification method based on causal state evolution as described in claim 1, characterized in that, The process of obtaining the second power grid causal state evolution characteristic data set based on the third power grid topology data and the first power grid power flow data set specifically involves: Based on the third power flow data set and the first power flow data set, the absolute value of the current change rate of each node is calculated, and the absolute value of the current change rate of each node is matched with the current data of each node in the third power flow data set to obtain the fifth power flow data set. The absolute value of the rate of change of current at each node in the fifth power grid flow data set is compared with the second preset threshold in turn, and all current data that are greater than the second preset threshold are combined into the second power grid causal state evolution feature data set.

4. The scheduling and remote control switch state verification method based on causal state evolution as described in claim 1, characterized in that, The step of verifying each power grid causal state evolution feature data in the first power grid causal state evolution feature data set according to the second power grid causal state evolution feature data set is as follows: The power grid causal state evolution feature data in the first power grid causal state evolution feature data set is traversed sequentially. Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data are obtained. If the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data satisfy the first preset condition and the second preset condition respectively, then it is determined that the verification of the currently traversed power grid causal state evolution feature data is passed.

5. The scheduling and remote control switch state verification method based on causal state evolution as described in claim 4, characterized in that, The step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set is as follows: Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using a trend comparison formula, as shown below: Among them, W i V represents the current variation trend of the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t V represents the current value before the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i t+1 W represents the current value after the switching operation corresponding to the i-th causal state evolution feature data in the first power grid causal state evolution feature data set. i Taking -1 indicates a decrease in current, W i A value of 1 indicates an increase in current. Output represents the result of judging the current change trend. Output being 1 indicates that the current change trend is the same, and Output being 0 indicates that the current change trend is different.

6. The scheduling and remote control switch state verification method based on causal state evolution as described in claim 4, characterized in that, The step of obtaining the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set is as follows: Based on the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set, the trend result corresponding to the currently traversed power grid causal state evolution feature data is calculated using an error formula, as shown below: Where ε3 is the allowable error, E i E represents the error of the node corresponding to the causal state evolution feature data of the power grid currently being traversed. i Setting it to 1 indicates that E is within the allowable error range. i Setting it to 0 indicates that outside the allowable error, V i V represents the current data corresponding to the i-th power grid causal state evolution feature data in the first power grid causal state evolution feature data set. j This represents the current data corresponding to the i-th power grid causal state evolution feature data in the second power grid causal state evolution feature data set.

7. A scheduling and remote control switch state verification device based on causal state evolution, characterized in that, include: The system comprises a data acquisition module, a first processing module, a second processing module, and a verification module. The data acquisition module is used to acquire the first set of power grid topology data and the first set of power grid power flow data before the remote control switch performs the switching operation. The first processing module is used to perform simulation calculations of switching operations based on the first power grid topology data and the first power grid power flow data set to obtain a second power grid power flow data set, and to filter the second power grid power flow data set and the first power grid power flow data set to obtain a first power grid causal state evolution feature data set; wherein, both the first power grid topology data and the second power grid power flow data set include current data of several nodes; The second processing module is used to acquire a third power flow data set after the scheduling remote control switch performs a switching operation, and to filter the third power grid topology data and the first power flow data set to obtain a second power grid causal state evolution feature data set; wherein, the third power grid topology data includes current data of several nodes; The verification module is used to verify each power grid causal state evolution feature data in the first power grid causal state evolution feature data set in turn according to the second power grid causal state evolution feature data set. If each power grid causal state evolution feature data in the first power grid causal state evolution feature data set passes the verification, the state verification result of the dispatching remote control switch is output as correct; otherwise, the state verification result of the dispatching remote control switch is output as incorrect.

8. The scheduling remote control switch state verification device based on causal state evolution as described in claim 7, characterized in that, The first processing module includes a first processing unit and a second processing unit; The first processing unit is configured to calculate the absolute value of the current change rate of each node based on the second power grid power flow data set and the first power grid power flow data set, and match the absolute value of the current change rate of each node with the current data of each node in the second power grid power flow data set to obtain a fourth power grid power flow data set. The second processing unit is used to sequentially compare the absolute value of the current change rate of each node in the fourth power grid power flow data set with a first preset threshold, and to form the first power grid causal state evolution feature data set by combining all current data that are greater than the first preset threshold.

9. The scheduling remote control switch state verification device based on causal state evolution as described in claim 7, characterized in that, The second processing module includes a third processing unit and a fourth processing unit; The third processing unit is used to calculate the absolute value of the current change rate of each node based on the third power grid power flow data set and the first power grid power flow data set, and to match the absolute value of the current change rate of each node with the current data of each node in the third power grid power flow data set to obtain the fifth power grid power flow data set. The fourth processing unit is used to sequentially compare the absolute value of the current change rate of each node in the fifth power grid flow data set with the second preset threshold, and to form the second power grid causal state evolution feature data set by combining all current data that are greater than the second preset threshold.

10. The scheduling remote control switch state verification device based on causal state evolution as described in claim 7, characterized in that, The verification module includes a verification unit; The verification unit is used to sequentially traverse each power grid causal state evolution feature data in the first power grid causal state evolution feature data set, and obtain the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data and the corresponding power grid causal state evolution feature data in the second power grid causal state evolution feature data set. If the trend result and error result corresponding to the currently traversed power grid causal state evolution feature data satisfy the first preset condition and the second preset condition respectively, then it is determined that the verification of the currently traversed power grid causal state evolution feature data is passed.

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