Cross-level collaborative power grid dispatching operation anti-error method

Abstract

The invention discloses a cross-level collaborative power grid dispatching operation anti-misoperation method, which relates to the technical field of dispatching operation anti-misoperation, and comprises the following steps: determining dispatching operation characteristics of a to-be-analyzed power grid dispatching operation; judging whether the scheduling operation characteristic meets a preset first condition or not to obtain a first judgment result; if the first judgment result meets the first condition, judging whether the scheduling operation characteristic meets a preset second condition or not to obtain a second judgment result; if the second judgment result meets a second condition, outputting a first scanning result; if the first judgment result does not meet the first condition, outputting a second scanning result; and the second scanning result also updates the operation execution time delay according to the historical data, and through automation of the power grid dispatching process, the problems that the support capability of an existing automatic and intelligent system is limited, dispatching tasks cannot be effectively processed, and the misoperation risk is increased are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of preventing errors in dispatching operations, and more specifically, to a cross-level collaborative method for preventing errors in power grid dispatching operations. Background Art

[0002] The existing power grid dispatching operations cover all aspects from power generation, transmission to distribution, and are mainly carried out through real-time monitoring, command dispatching and equipment management through automation systems. Dispatching operations include starting and stopping generator sets, load distribution, line control, voltage regulation, etc., to ensure that the power grid can operate safely and stably under different load conditions. At the same time, the dispatcher also needs to adjust the power grid configuration based on real-time data to avoid equipment overload or power grid failure. In order to improve the operating efficiency and safety of the power grid, power grid dispatching has also incorporated intelligent technologies, such as optimizing dispatching plans through machine learning and big data analysis to improve the accuracy and scientificity of decision-making.

[0003] Error prevention refers to the use of a series of technical means and measures to ensure that any potential errors in the power grid dispatching process can be discovered and corrected in a timely manner to prevent operational errors from causing abnormal operation or failure of the power grid. The core goal of error prevention is to ensure the compliance of dispatching instructions, the accuracy of equipment status and the reliability of the dispatching process.

[0004] Grid dispatching operations are key to ensuring stable, safe and efficient operation of the grid system. With the growth of electricity demand and the expansion of the grid scale, the complexity of grid dispatching operations continues to increase. Dispatchers need to deal with various emergencies and complex decisions, and errors are prone to occur during operations.

[0005] For example, the invention patent publication No. CN117239768B discloses a method, system, device and medium for controlling voltage of an AC / DC hybrid distribution network, including the following steps: It includes constructing the objective function and constraints of the upper, middle and lower level voltage coordination control model of the AC / DC hybrid distribution network, obtaining the optimal solution by solving the upper, middle and lower level voltage coordination control model, and controlling the voltage of the AC / DC hybrid distribution network according to the optimal solution; the upper level decides the exchange power between the region and the AC / DC hybrid distribution network, and between regions; the middle level decides the exchange power between each feeder within the region; the lower level decides the output of distributed power sources, energy storage and loads in each substation on the feeder; giving full play to the regulation autonomy of distributed resources between different voltage levels, improving the robustness of voltage control, and ultimately maximizing the on-site consumption of new energy and promoting cross-level collaborative interaction of adjustable resources.

[0006] For example, the invention patent with announcement number: CN113572172B announces a power grid dispatching management system and management control method, including a power grid energy system, a substation operation monitoring module, a main station self-checking module, and an energy dispatching module. The power grid energy system supplies power to local areas, the substation operation monitoring module monitors the energy substations that supply power in real time, the main station self-checking module is used to detect the remaining power after the energy main station delivers power, and the energy dispatching module is used to deliver energy to the energy substation. The present invention monitors the power consumption of the energy substation in real time by setting up a substation operation monitoring module, and immediately determines the situation of the energy substation in the event of energy shortage. When the energy substation is in a period of excess power usage, the energy dispatching module selects the energy substation for energy dispatching, thereby ensuring the continuity of energy use, controlling the dispatching and use of the energy substation, and facilitating the management and coordination of each energy station.

[0007] The above disclosed technical solutions have at least the following technical problems: The existing power grid dispatch error prevention technology mainly relies on manual intervention and traditional single error prevention mechanism, which cannot effectively cope with complex dispatch scenarios with multiple levels, cross-targets and multiple devices. When dealing with complex and unexpected situations, dispatchers are prone to make wrong decisions due to insufficient information or excessive pressure, especially in emergencies, lacking efficient decision support and clear response strategies. In addition, the existing automation and intelligent systems have limited support capabilities and cannot effectively handle all dispatch tasks, resulting in the need for a large amount of manual intervention, which increases the risk of misoperation.

[0008] In the process of preventing errors in power grid dispatch, the length of the operation execution delay has an important impact on the stability of the system. Although a shorter delay can quickly recover, it may ignore potential problems due to hasty operations, increasing risks; while a longer delay can ensure more accurate repairs, but it may cause the fault to last too long, affecting system stability.

[0009] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0010] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a cross-level collaborative method for preventing errors in power grid dispatching operations, which, through the automation of the power grid dispatching process, solves the problem that the existing automation and intelligent systems have limited support capabilities and are unable to effectively handle dispatching tasks, resulting in the need for a large amount of manual intervention and increased risk of misoperation.

[0011] To achieve the above object, the present invention provides the following technical solutions: A method for preventing errors in cross-level collaborative power grid dispatching operations, comprising the following steps: determining dispatching operation characteristics of a power grid dispatching operation to be analyzed; judging whether the dispatching operation characteristics meet a preset first condition to obtain a first judgment result, wherein the first condition includes an erroneous operation condition; if the first judgment result meets the first condition, judging whether the dispatching operation characteristics meet a preset second condition to obtain a second judgment result, wherein the second condition includes a cross-level collaborative operation condition; if the second judgment result meets the second condition, outputting a first scanning result, wherein the first scanning result indicates that the power grid dispatching operation is correct; if the first judgment result does not meet the first condition, outputting a second scanning result, wherein the second scanning result indicates that the operation is terminated; the second scanning result also updates the operation execution delay according to historical data.

[0012] In a preferred embodiment, the determination of the dispatching operation characteristics of the power grid dispatching operation to be analyzed is specifically based on event-driven dispatching feature extraction; the dispatching operation characteristics include event characteristics, trigger characteristics and risk characteristics; based on the intelligent sensors in the power grid, real-time monitoring of sudden events in the operation of the power grid is carried out, and the event characteristics of each type of sudden event are recorded to form a real-time event data set; based on the convolutional neural network, feature extraction is performed on the real-time event data set to capture nonlinearity and time series correlation to obtain dynamic event triggering characteristics, and the triggering characteristics reflect the changes and impacts of the power grid state when the event is triggered; based on the real-time state of the power grid when the real-time event occurs, combined with historical event data, the time series of the neural network is used to predict the dispatching operation risks caused by the event to form risk characteristics.

[0013] In a preferred embodiment, the first scanning result also includes cross-level collaborative regulation of scheduling errors to resolve scheduling errors at other levels.

[0014] In a preferred embodiment, the error operation condition is specifically as follows: defining scheduling rules, each rule is judged according to different operation characteristics, and the scheduling rules include single rules and overall rules; the single rule judges whether the error operation condition is met based on the decision tree; the overall rule judges whether the error operation condition is met based on the improved DTW.

[0015] In a preferred embodiment, the cross-level collaborative operation conditions include collaborative operation conditions and hierarchical repair conditions, and the collaborative operation conditions are specifically: obtaining the total delay of the scheduling operation, and judging the scheduling stage of the scheduling operation by accumulating the total delay; obtaining the maximum preset time of each stage, and then calculating the remaining adjustable time based on the executed delay; obtaining the time required for cross-level collaborative operation, and judging whether repair can be performed by comparing the remaining adjustable time with the time required for cross-level collaborative operation. If the remaining adjustable time is greater than or equal to the time required for cross-level collaborative operation, it is considered that repair can be performed.

[0016] In a preferred embodiment, the hierarchical repair condition is specifically as follows: if the time required for cross-level collaborative operation exceeds the remaining adjustable time and does not meet the feasibility conditions of cross-level collaborative operation, it is determined whether to perform error repair through the hierarchical control mechanism; if the total repair delay is less than or equal to the remaining adjustable time, it means that the hierarchical control can solve the scheduling error; when the remaining time is insufficient to support repair or cross-level collaborative operation, the operation must be terminated.

[0017] In a preferred embodiment, if the first judgment result does not meet the first condition, the first scanning result is also output; the first scanning result also includes cross-level collaborative regulation of scheduling errors to resolve scheduling errors at other levels.

[0018] In a preferred embodiment, the operation termination also includes: when it is determined that the first condition is not met, the operation termination avoids the spread of system errors by dynamically adjusting the scope of the current operation, rather than terminating immediately; the operation termination process includes a multi-path recovery mechanism, specifically: when it is determined that there is a potential risk in the system, first trigger the preset recovery strategy, give priority to enabling the backup system and spare equipment, adjust the preset power grid parameters for risk isolation, and wait until the operation is completely restored to normal before completely terminating.

[0019] In a preferred embodiment, the updating of the operation execution delay is specifically as follows: the updating of the operation execution delay is implemented based on an improved PID control mechanism; the judgment result of each scheduling operation is obtained, including whether the operation is terminated and whether the error condition is met, and feedback parameters of the operation are obtained by analysis, and a feedback model is established to capture the relationship with the operation execution delay to obtain a target delay, wherein the feedback parameters include termination frequency, operation risk, and operation efficiency; the difference between the current operation delay and the target delay is defined as the error of the PID control mechanism; and the operation execution delay is dynamically adjusted using the PID control mechanism.

[0020] In a preferred embodiment, the single rule checks a single scheduling condition; the overall rule determines whether it meets the error operation condition based on the improved DTW, specifically: converting each scheduling operation feature into a scheduling operation feature time series; extracting the corresponding overall rule feature time series for the overall rule; performing layered processing on the scheduling operation feature time series and the overall rule feature time series respectively, extracting feature time series and overall rule features at different time scales; applying the DTW algorithm for matching at different scales to minimize the DTW difference between the scheduling operation feature time series and the overall rule feature time series, obtaining the most similar overall rule, and comparing the DTW distance corresponding to the most similar overall rule with a preset dynamic similarity threshold to determine whether it meets the error operation condition.

[0021] In a preferred embodiment, the specific method for obtaining the feedback model is: the feedback model is a specific regression model; the corresponding regression method is selected according to the relationship between the feedback parameters to establish the regression model, specifically: when the relationship between the operation delay and the termination frequency, operation risk, and operation efficiency is a linear relationship, a linear regression model is constructed as the feedback model; when the relationship between the operation delay and the termination frequency, operation risk, and operation efficiency is nonlinear, a polynomial regression model is constructed based on a support vector machine as the feedback model; the feedback model is trained according to the feedback parameters and the corresponding operation delay as training data, with the feedback parameters as input and the target delay as output.

[0022] In a preferred embodiment, the method for obtaining the dynamic similarity threshold is: obtaining a state evaluation factor according to different states of the power grid and the complexity of the dispatching operation; the state evaluation factor includes a load state evaluation factor, an equipment health state evaluation factor, an external environmental factor evaluation factor and a dispatching operation complexity evaluation factor; the dynamic similarity threshold is calculated by weighted summation of the state evaluation factors; in the overall rule matching process, the state evaluation factor is pre-set and has been determined before the judgment.

[0023] The technical effects and advantages of the cross-level collaborative power grid dispatching operation error prevention method of the present invention are as follows: 1. The present invention uses intelligent sensors to monitor emergencies in power grid operation in real time, uses convolutional neural networks to extract event features, and combines historical data to predict dispatch operation risks. The method determines whether the dispatch operation meets the error operation conditions, including load overload, abnormal equipment temperature, etc., and uses a multi-scale dynamic time warping (DTW) method to improve the matching accuracy and flexibly adjust the judgment criteria to adapt to different power grid states and the complexity of dispatch operations. In addition, the method also introduces cross-level collaborative operations and hierarchical repair mechanisms, and determines whether to perform repairs or terminate operations based on the remaining adjustable time, providing an efficient error repair solution to ensure the stability and reliability of power grid dispatch.

[0024] 2. The present invention significantly improves the response speed and stability of the system by dynamically updating the operation execution delay based on an improved PID control mechanism. The method establishes a feedback model by real-time monitoring of the judgment results of the dispatching operation (such as whether the operation is terminated, whether the error conditions are met, etc.), and combines the feedback parameters of the operation (such as termination frequency, operation risk and operation efficiency) to accurately capture the relationship between the operation delay and these parameters. Using the proportional, integral and differential parts of the PID control mechanism, the system can adjust the operation delay according to the real-time error, avoiding system instability caused by excessive or too small delays. This dynamic adjustment mechanism ensures that the system can adapt to the changing operating environment, thereby ensuring the smooth operation and efficient response of the power grid dispatch. In addition, by selecting an appropriate regression method to construct a feedback model, the adaptability and accuracy of the system are further enhanced, and the appropriate control method can be selected according to different operation delay characteristics, achieving more accurate and intelligent operation execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The present invention is a flowchart of a cross-level collaborative power grid dispatching operation error prevention method. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Embodiment 1, Figure 1 The present invention provides a cross-level collaborative power grid dispatching operation error prevention method, comprising the following steps: S1, determining the dispatching operation characteristics of the power grid dispatching operation to be analyzed; The determining of the dispatching operation characteristics of the power grid dispatching operation to be analyzed is specifically based on event-driven dispatching feature extraction; The scheduling operation characteristics include event characteristics, trigger characteristics and risk characteristics; Based on the intelligent sensors in the power grid, the sudden events in the power grid operation are monitored in real time, and the event characteristics of each type of sudden event are recorded to form a real-time event data set; Based on convolutional neural networks, feature extraction is performed on real-time event data sets to capture nonlinearity and time series correlations, and dynamic event triggering features are obtained. The triggering features reflect the changes and impacts of the power grid state when the event is triggered. Based on the immediate status of the power grid when a real-time event occurs, combined with historical event data, a neural network-based time series is used to predict the dispatching operation risks caused by the event and form risk characteristics.

[0028] S2, determining whether the scheduling operation characteristic meets a preset first condition, and obtaining a first determination result, wherein the first condition includes an erroneous operation condition; The erroneous operation conditions are specifically: Define dispatching rules, which are derived from the operation specifications of the power grid, the operation manual of the equipment, and historical experience. Each rule is determined based on different operating characteristics. Dispatching rules include single rules and overall rules. For example, the load exceeds the rated capacity of the equipment: when a dispatching operation causes the load of a certain equipment (such as a transformer or line) to exceed its rated capacity, it is considered an incorrect operation.

[0029] Abnormal equipment temperature: If the scheduling operation causes the temperature of certain equipment (such as transformers and cables) to exceed the safety threshold, it is judged as an incorrect operation.

[0030] Violation of dispatch priority: If the dispatch operation violates the predetermined load priority, resulting in the load that should not be powered off being disconnected, it is also an incorrect operation.

[0031] Current / voltage exceeds the safe range: If the scheduling instruction causes the system's current or voltage to be abnormal (such as overvoltage, overcurrent, etc.), it will be judged as an incorrect operation.

[0032] A single rule checks a single scheduling condition; The overall rule is a comprehensive judgment based on multi-dimensional analysis and multiple error operation conditions. The overall rule is used to judge the similarities between different scheduling operations and to discover potential risks and errors in the operation process. The overall rule can consider the interactivity, timing and combination of constraints in scheduling, and judge whether the scheduling operation is safe based on these comprehensive factors. A single rule determines whether the error operation condition is met based on a decision tree; For example, input parameters: The dispatching information of the current grid dispatching operation (such as load demand, current and voltage, temperature, equipment status, etc.); Real-time data of each power grid device (such as equipment temperature, load, frequency, etc.); The equipment's operating specifications and constraints (e.g., equipment rated capacity, temperature range, operating priority, etc.); Rule matching: Rule 1: If the load > the rated capacity of the equipment, output "wrong operation"; Rule 2: If the device temperature > the maximum operating temperature of the device, output "error operation"; Rule 3: If the scheduling operation violates the operation priority rule, output "wrong operation".

[0033] The overall rule is based on the improved DTW to determine whether the error operation conditions are met, specifically: Convert each scheduling operation feature into a scheduling operation feature time series; Extracting corresponding overall rule feature time series for the overall rule; The characteristic time series of scheduling operation and the characteristic time series of overall rule are processed in layers respectively, and the characteristic time series and overall rule features of different time scales are extracted; The DTW algorithm is applied at different scales for matching to minimize the DTW difference between the scheduling operation feature time series and the overall rule feature time series, obtain the most similar overall rule, and compare the DTW distance corresponding to the most similar overall rule with the preset dynamic similarity threshold to determine whether it meets the error operation condition.

[0034] The method for obtaining the dynamic similarity threshold is: The state assessment factors are obtained according to the different states of the power grid and the complexity of the dispatch operation, including: Load state: The load level of the power grid (such as high load, low load) will directly affect the flexibility of the dispatch operation. Under high load conditions, the dispatch of the power grid may be more tense, requiring a looser matching threshold to avoid overly strict erroneous judgments; while under low load, the power grid is more stable and may require stricter judgment criteria.

[0035] Equipment health status: The health status of key equipment in the power grid (such as transformers and lines) has an important impact on the stability of dispatch operations. When the equipment fails or is under maintenance, the similarity threshold should be stricter to reduce the occurrence of erroneous operations.

[0036] External environmental factors: such as weather and seasonal changes, can also affect the load and dispatching operations of the power grid. For example, abnormal weather may cause equipment to overload or load changes to be more drastic. In this case, the threshold can be moderately relaxed to tolerate a certain degree of uncertainty.

[0037] Scheduling operation complexity: Dynamically adjust the similarity threshold based on the complexity of the scheduling operation (such as multiple operations being performed simultaneously). Complex scheduling operations may require a higher fault tolerance, so the similarity threshold can be increased accordingly.

[0038] The weighted sum of the state evaluation factors is used to calculate a dynamically adjusted similarity threshold; the dynamically adjusted similarity threshold will determine the matching degree of DTW and avoid misjudgment of erroneous operations under extreme conditions.

[0039] For example, the calculation of the similarity threshold, assuming that at a certain moment, the operating status of the power grid is as follows: The power grid is in a high-load state, and the load impact factor T1=1.2 (the threshold is increased by 20%).

[0040] The equipment is in good health, and the equipment impact factor T2=1.0 (no change).

[0041] The current weather conditions are normal, and the environmental impact factor T3=1.0.

[0042] The scheduling operation is relatively complex, with the operation complexity factor T4=1.2 (the threshold is increased by 20%).

[0043] According to the preset state evaluation factor, the dynamic similarity threshold can be calculated:

[0044] It should be noted that the data point of each feature represents the value at a certain point in time. For example, the time series of load may include the change of load within an operation cycle. The time series of equipment status may include the change of equipment from normal status to fault status.

[0045] It should be noted that in the overall rule matching process, the state evaluation factors are pre-set and have been determined before the judgment. They are set according to the real-time state of the power grid (such as load, equipment health, external environment, etc.) to reflect the risk tolerance of the dispatch operation. These factors will play a decisive role in the dynamic adjustment of the similarity threshold in different situations, thereby ensuring that under certain conditions, the system can flexibly respond to erroneous operations and maintain dispatch safety.

[0046] It should be noted that a single rule: each rule corresponds to a specific condition, and as long as one condition is not met, it is considered an error. For example, when the load exceeds the rated capacity, the dispatch operation is considered to be an error.

[0047] Holistic rules: Considering the combination of multiple conditions, the operation must meet specific constraints in multiple dimensions. Only when the comprehensive conditions are met, the scheduling operation is considered correct. Holistic rules usually include a collection of multiple rules and mutual constraints between conditions.

[0048] The improved dynamic time warping (DTW) method applicable to multiple scales can more effectively capture the changes and correlations of power grid dispatching operation characteristics at different time scales. By comparing time series at multiple scales, it can identify potential complex patterns and long-term and short-term dependencies, and improve matching accuracy and fault tolerance. Compared with the traditional single-scale DTW, the improved multi-scale method can provide more sensitive and accurate error judgment when dealing with dispatching operations with strong nonlinearity and time series correlation, reduce the possibility of false alarms and missed alarms, and thus improve the reliability and safety of power grid dispatching.

[0049] The introduction of dynamic similarity thresholds can flexibly adjust the judgment criteria according to the real-time status and characteristic changes of power grid dispatching operations, making the overall rule matching more accurate and adaptable. Compared with static thresholds, dynamic thresholds can automatically adapt to fluctuations and abnormal conditions in different operating scenarios, avoiding misjudgments or missed judgments caused by overly strict or loose standards. It can adjust the tolerance of similarity in real time according to historical data, current operating characteristics and system status, thereby effectively improving the error detection accuracy and stability in the power grid dispatching process.

[0050] S3, if the first judgment result satisfies the first condition, determining whether the scheduling operation characteristic satisfies a preset second condition, and obtaining a second judgment result, wherein the second condition includes a cross-level collaborative operation condition; The cross-level collaborative operation conditions are specifically: The cross-level collaborative operation conditions include collaborative operation conditions and hierarchical repair conditions; The total delay of the scheduling operation is obtained, and the scheduling stage of the scheduling operation is determined by accumulating the total delay. The specific formula for stage identification is as follows:

[0051] is the total delay of the scheduling operation, is the delay of the i-th stage, i=1, 2, ..., n, n is the total number of stages of the scheduled operation. If the currently executed delay is the sum of the delay before the k-th time, the current operation is in the k-th stage; Get the maximum preset time for each stage, and then calculate the remaining adjustable time based on the executed delay, specifically:

[0052] For the remaining adjustable time, The maximum preset delay for each stage, is the executed delay; Get the time required for cross-level collaborative operations, and determine whether repair can be performed by comparing the remaining adjustable time with the time required for cross-level collaborative operations. If the remaining adjustable time is greater than or equal to the time required for cross-level collaborative operations, the operation is considered feasible. Specifically:

[0053] In the formula, The time required for cross-level coordination; If the time required for cross-level collaborative operations exceeds the remaining adjustable time and does not meet the feasibility conditions for cross-level collaborative operations, it is determined whether to perform error repair through the hierarchical control mechanism. The sum of the repair time for multiple levels is:

[0054] In the formula, To fix the total delay, is the number of layers, The time required for repair at each level. If the total repair delay is less than or equal to the remaining adjustable time, it means that hierarchical control can solve the scheduling error; When the remaining time is insufficient to support repair or cross-level collaborative operations, the operation must be terminated. The specific method for determining whether the operation needs to be terminated is as follows: if the remaining time is less than the minimum repair delay threshold or the total repair delay exceeds the remaining time, the operation needs to be terminated. The decision model for judging whether the scheduling operation characteristics meet the preset second condition is as follows: .

[0055] The cross-level collaborative operation condition provides a flexible and efficient solution for the repair of scheduling errors by comprehensively judging whether collaborative operation is feasible and whether hierarchical repair is feasible. First, the conditional judgment of collaborative operation ensures that when the remaining scheduling time is sufficient, the problem can be solved by coordinating the scheduling forces of the remaining levels, thereby avoiding the termination of the operation. If collaborative operation is not feasible, it is judged whether the error can be alleviated or repaired through hierarchical repair, relying on the collaboration between the levels. The advantage of this method is that it makes full use of multi-level scheduling resources and repair strategies, making the error repair process more intelligent and dynamic, and avoiding premature termination of scheduling, improving the stability and reliability of the system. In addition, by dynamically judging the feasibility of different repair paths, it can better adapt to the changing delays and resource constraints in actual scheduling operations.

[0056] It should be noted that the process of judging cross-level collaborative operations first determines whether there is enough time for cross-level regulation by evaluating the latency of the current scheduling operation and the stage of the operation. In this process, when judging whether cross-level collaborative operations are feasible, factors such as the feasibility of collaborative operations, the availability of resource allocation, and whether specific time constraints are met are considered. As an existing technology, layered repair has been widely used. Its core concept is to rely on coordination and repair between levels to ensure that even when an operation at a certain level fails, the remaining levels can intervene in time and take corresponding repair measures. Therefore, it will not be repeated. In this scheme, the judgment of cross-level collaborative operation conditions mainly depends on the real-time status and dynamic changes of scheduling execution, so as to flexibly determine whether errors can be repaired or mitigated through cross-level operations.

[0057] S4, if the second judgment result satisfies the second condition, outputting the first scanning result, wherein the first scanning result indicates that the grid dispatching operation is correct; If the first judgment result does not meet the first condition, the first scanning result is also output.

[0058] The first scanning result also includes cross-level coordinated regulation of scheduling errors to resolve scheduling errors at other levels.

[0059] It should be noted that cross-level coordinated control not only involves judging whether the error can be resolved by the remaining levels, but also needs to consider real-time data feedback, information flow between levels, and decision support. When making judgments, real-time monitoring mechanisms and dynamic adjustments are crucial because they can promptly correct dispatch operations based on changes in the current state of the power grid. In addition, the effectiveness of cross-level coordinated operation plans also needs to be evaluated through timeliness and efficiency to ensure rapid response and effectively reduce system errors, avoiding wider impacts due to delays.

[0060] S5, if the first judgment result does not meet the first condition, output a second scanning result, and the second scanning result indicates that the operation is terminated; The operation termination also includes, when it is determined that the first condition is not satisfied, the operation termination avoids the spread of system errors by dynamically adjusting the scope of the current operation, rather than terminating immediately.

[0061] For example: by gradually reducing the amplitude or scope of the scheduling operation, first terminate the operation locally, then observe whether there are further risks, and then decide whether to terminate the operation completely.

[0062] The operation termination also includes including a multipath recovery mechanism during the operation termination process; Specifically, when it is determined that there is a potential risk in the system, the preset recovery strategy is triggered first, the backup system and spare equipment are enabled first, and the preset power grid parameters are adjusted to isolate the risk until the operation is fully restored to normal and then completely terminated.

[0063] By introducing dynamic adjustment and multi-path recovery mechanisms, a more flexible response strategy is provided during the operation termination process, avoiding the "one-size-fits-all" approach of traditional operation termination. Specifically, when it is judged that the first condition is not met, the scope of the dispatch operation is first adjusted dynamically to gradually reduce the operation amplitude or scope, thereby effectively avoiding the spread of system errors and reducing unnecessary risks. By terminating local operations and observing further risks, it is possible to more accurately assess whether the operation needs to be completely terminated, thereby improving the accuracy of the operation and the stability of the system. At the same time, the added multi-path recovery mechanism can prioritize risk isolation through preset recovery strategies, such as enabling backup systems, spare equipment, or adjusting power grid parameters, when there are potential risks in the system, to ensure that the system will perform further operation terminations after returning to normal. This innovative measure improves the flexibility, fault tolerance and stability of the system, effectively prevents large-scale power grid failures caused by emergencies, and improves the safety and reliability of power grid dispatch operations.

[0064] This implementation uses smart sensors to monitor emergencies in power grid operation in real time, uses convolutional neural networks to extract event features, and combines historical data to predict dispatch operation risks. This method uses a multi-scale dynamic time warping (DTW) method to improve matching accuracy by judging whether the dispatch operation meets the error operation conditions, including load overload, abnormal equipment temperature, etc., and flexibly adjusts the judgment criteria to adapt to different power grid states and the complexity of dispatch operations. In addition, the method also introduces cross-level collaborative operations and hierarchical repair mechanisms, and judges whether to perform repairs or terminate operations based on the remaining adjustable time, providing an efficient error repair solution to ensure the stability and reliability of power grid dispatch.

[0065] Embodiment 2, S6, the second scanning result also updates the operation execution delay according to the historical data.

[0066] The updating of the operation execution delay is specifically as follows: The update of operation execution delay is realized based on the improved PID control mechanism; Obtain the judgment result of each scheduling operation, including whether the operation is terminated and whether the error condition is met, and analyze the feedback parameters of the operation, establish a feedback model to capture the relationship with the operation execution delay, and obtain the target delay. The feedback parameters include termination frequency, operation risk, and operation efficiency; Define the difference between the current operation delay and the target delay as the error of the PID control mechanism; Use PID control mechanism to dynamically adjust operation execution delay; The controller adjustment formula is as follows: In the formula, is the adjustment amount for the operation execution delay, The proportionality coefficient with the error, is the error at the previous moment, is the integral coefficient for accumulating past errors, is the error function, is the differential coefficient of the PID controller.

[0067] It should be noted that a PID controller usually consists of three parts: Proportional part (P): directly proportional to the error, representing the impact of the operation error on the execution delay, such as .

[0068] Integral part (I): accumulates the effect of past errors to ensure that the error deviation continues to be effective, such as the application .

[0069] Differential part (D): predict the trend of future errors and reduce system overshoot, such as the application .

[0070] For example, if a scheduling operation frequently triggers errors or terminates, the system will assume that the operation requires more time to evaluate and respond to potential risks, and therefore appropriately increase the execution delay; conversely, if the scheduling operation terminates less frequently, the system will appropriately reduce the delay and improve execution efficiency.

[0071] It is important to note that the termination frequency is the frequency with which operations were terminated over a period of time (for example, the number of terminations per hour or per day).

[0072] Operational risk: Based on historical error types and risk assessments during the scheduling process (e.g., equipment failure, improper operation).

[0073] Operational efficiency: How efficiently the system completes scheduled operations (e.g., time required, degree of completion).

[0074] The specific method for obtaining the feedback model is: The feedback model is a specific regression model; According to the relationship between the feedback parameters, the corresponding regression method is selected to establish the regression model, specifically: When the relationship between operation delay and termination frequency, operation risk, and operation efficiency is linear, a linear regression model is constructed as a feedback model; When the relationship between operation delay and termination frequency, operation risk, and operation efficiency is nonlinear, a polynomial regression model is constructed based on support vector machine as a feedback model; The feedback model is trained based on the feedback parameters and the corresponding operation delay as training data, with the feedback parameters as input and the target delay as output.

[0075] Updating the operation delay through the PID control mechanism can effectively improve the response speed and stability of the system. PID control can accurately control the real-time changes of the system by adjusting the three parameters of proportion (P), integral (I) and differential (D) in real time. This mechanism can automatically adjust the operation delay to avoid system instability caused by excessive or insufficient delay, thereby ensuring the smooth operation and response accuracy of the system.

[0076] The improved PID control mechanism further optimizes the performance of traditional PID control. By introducing adaptive adjustment strategies or improved parameter update algorithms, the control system can more flexibly respond to environmental changes or fluctuations in operating conditions. Compared with the traditional PID control mechanism, the improved PID control mechanism can provide higher accuracy and faster response speed in a wider range of application scenarios, and reduce the risk of over-adjustment or system oscillation. This mechanism is particularly suitable for complex systems with high requirements for real-time performance and stability, and can effectively improve overall performance and reliability.

[0077] By selecting the appropriate regression method to construct the feedback model based on the relationship between the operation delay and other key parameters (such as termination frequency, operation risk, and operation efficiency), the accuracy and adaptability of the model can be effectively improved. When these relationships are linear, the use of a linear regression model can simplify the calculation and improve the calculation efficiency; when the relationship is nonlinear, the polynomial regression model built based on the support vector machine can better capture complex nonlinear relationships, thereby improving the prediction accuracy. In addition, using feedback parameters as input and target delay as output for training allows the feedback model to be dynamically adjusted in actual operation, thereby optimizing the system's operation delay and efficiency and ensuring more accurate scheduling operations.

[0078] This embodiment significantly improves the response speed and stability of the system by dynamically updating the operation execution delay based on the improved PID control mechanism. The method establishes a feedback model by real-time monitoring of the judgment results of the dispatching operation (such as whether the operation is terminated, whether the error conditions are met, etc.), and combines the feedback parameters of the operation (such as termination frequency, operation risk and operation efficiency) to accurately capture the relationship between the operation delay and these parameters. Using the proportional, integral and differential parts of the PID control mechanism, the system can adjust the operation delay according to the real-time error, avoiding system instability caused by excessive or too small delays. This dynamic adjustment mechanism ensures that the system can adapt to the changing operating environment, thereby ensuring the smooth operation and efficient response of the power grid dispatch. In addition, by selecting an appropriate regression method to construct a feedback model, the adaptability and accuracy of the system are further enhanced, and the appropriate control method can be selected according to different operation delay characteristics, achieving more accurate and intelligent operation execution.

[0079] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0080] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0081] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0082] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0083] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0084] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cross-level collaborative power grid dispatching operation error prevention method, characterized in that: The steps include: Extracting dispatching operation characteristics of the power grid dispatching operation to be analyzed; Preset judgment conditions, compare the scheduling operation characteristics with the judgment conditions, obtain a judgment result, and output a scanning result according to the judgment result, wherein the scanning result includes a judgment on the delay update of the operation execution; The preset judgment conditions include a first condition for judging an error and a second condition for judging error repair; the first condition includes judging the overall rule based on a dynamic similarity threshold and DTW improved by hierarchical features.

2. The cross-level collaborative power grid dispatching operation error prevention method according to claim 1 is characterized in that: Preset judgment conditions, compare the scheduling operation characteristics with the judgment conditions, and obtain judgment results, including: The preset judgment condition includes a first condition and a second condition, the first condition includes an erroneous operation condition, and the second condition includes a cross-level collaborative operation condition; Comparing the scheduling operation characteristic with the first condition to obtain a first judgment result; When the first judgment result meets the first condition, the scheduling operation characteristics are compared with the second condition to obtain a second judgment result.

3. The cross-level collaborative power grid dispatching operation error prevention method according to claim 2 is characterized in that: The output scanning result includes a first scanning result and a second scanning result, If the second judgment result satisfies the second condition, the first scanning result is output, and the first scanning result indicates that the power grid dispatching operation is correct; If the first judgment result does not meet the first condition, a second scanning result is output, and the second scanning result indicates that the operation is terminated.

4. The cross-level collaborative power grid dispatching operation error prevention method according to claim 3 is characterized in that: The first scanning result also includes cross-level coordinated regulation of scheduling errors, and the second scanning result also updates the operation execution delay based on historical data.

5. The cross-level coordinated power grid dispatching operation error prevention method according to claim 4 is characterized in that: The specific judgment process of the erroneous operation condition is as follows: The scheduling rules are defined, each rule is judged according to different operation characteristics, and the scheduling rules include single rules and overall rules; the single rules are judged based on the decision tree, and the overall rules are judged based on the improved DTW.

6. The cross-level coordinated power grid dispatching operation error prevention method according to claim 4 is characterized in that: The cross-level collaborative operation conditions include collaborative operation conditions, specifically: Obtain the total delay of the scheduling operation, and determine the scheduling stage of the scheduling operation by accumulating the total delay; Get the maximum preset time for each stage, and calculate the remaining adjustable time based on the executed delay; Obtain the time required for cross-level collaborative operations, and determine whether the collaborative operation conditions are met by comparing the remaining adjustable time and the time required for cross-level collaborative operations.

7. The cross-level coordinated power grid dispatching operation error prevention method according to claim 6 is characterized in that: The cross-level collaborative operation conditions also include layered repair conditions, specifically: If the time required for cross-level collaborative operations exceeds the remaining adjustable time, it is determined whether to perform error repair through the hierarchical control mechanism; Obtain the total repair delay and compare it with the remaining adjustable time to determine whether the layered repair conditions are met; When the collaborative operation conditions and layered repair conditions are not met, the operation is terminated.

8. The cross-level coordinated power grid dispatching operation error prevention method according to claim 7 is characterized in that: The operation termination also includes a dynamic adjustment mechanism and a multipath recovery mechanism: The dynamic adjustment mechanism is specifically: When it is determined that the first condition is not satisfied, the operation is terminated by dynamically adjusting the scope of the current operation; The multipath recovery mechanism is specifically: When the second scan result is output, the preset recovery strategy is triggered first, the backup system and spare equipment are enabled first, and the preset power grid parameters are adjusted to isolate risks until the operation is fully restored to normal and then completely terminated.

9. The cross-level coordinated power grid dispatching operation error prevention method according to claim 4, characterized in that: The updating of the operation execution delay is implemented based on an improved PID control mechanism, specifically: Obtain the judgment result of each scheduling operation, analyze and obtain the feedback parameters of the operation, establish a feedback model to capture the relationship between the operation execution delay, and obtain the target delay. The judgment result includes whether the operation is terminated and whether the error condition is met. The feedback parameters include termination frequency, operation risk, and operation efficiency. The difference between the current operation delay and the target delay is defined as the error of the PID control mechanism, and the operation execution delay is dynamically adjusted based on the PID control mechanism.

10. The cross-level coordinated power grid dispatching operation error prevention method according to claim 5, characterized in that: The overall rule is based on the improved DTW, specifically: Convert each scheduling operation feature into a scheduling operation feature time series, and extract the overall rule feature time series; The characteristic time series of scheduling operation and the characteristic time series of overall rule are processed in layers respectively, and the characteristic time series and overall rule features of different time scales are extracted; Based on the DTW algorithm, the overall rule matching is performed to minimize the DTW difference between the scheduling operation feature time series and the overall rule feature time series, and the most similar overall rule is obtained by traversing; The DTW distance corresponding to the most similar overall rule is compared with a preset dynamic similarity threshold to determine whether the first condition is met.

11. The cross-level coordinated power grid dispatching operation error prevention method according to claim 9, characterized in that: The establishment of the feedback model captures the relationship between the operation execution delay and obtains the target delay, specifically including: Determine the linear relationship between operation delay and feedback parameters; If it is a linear relationship, a linear regression model is constructed as a feedback model; If it is a nonlinear relationship, a polynomial regression model is constructed based on the support vector machine as the feedback model; The feedback model is trained based on the feedback parameters and the corresponding operation delay as training data, with the feedback parameters as input and the target delay as output.

12. The cross-level coordinated power grid dispatching operation error prevention method according to claim 10, characterized in that: The method for obtaining the dynamic similarity threshold is: Acquire state assessment factors according to different states of the power grid and the complexity of the dispatching operation, wherein the state assessment factors include a load state assessment factor, an equipment health state assessment factor, an external environment factor assessment factor, and a dispatching operation complexity assessment factor; The dynamic similarity threshold is calculated by weighted summation of the state evaluation factors.

13. The cross-level coordinated power grid dispatching operation error prevention method according to claim 6, characterized in that: The specific judgment formula of the scheduling stage is as follows: In the formula, is the total delay of the scheduling operation, is the delay of the i-th stage, i=1, 2, …, n, n is the total number of stages of the scheduled operation. If the currently executed delay is the sum of the delay before the k-th time, the current operation is in the k-th stage.

14. The cross-level coordinated power grid dispatching operation error prevention method according to claim 7, characterized in that: The remaining adjustable time is specifically: For the remaining adjustable time, The maximum preset delay for each stage, is the executed delay; The total repair delay is specifically: In the formula, To fix the total delay, is the number of layers, The time required for repair at each level. If the total repair delay is less than or equal to the remaining adjustable time, it means that hierarchical control can solve the scheduling error; The decision model for judging whether the scheduling operation characteristics meet the preset second condition is as follows: 。 15. The cross-level coordinated power grid dispatching operation error prevention method according to claim 9, characterized in that: The PID control mechanism is specifically: In the formula, is the adjustment amount for the operation execution delay, The proportionality coefficient with the error, is the error at the previous moment, is the integral coefficient for accumulating past errors, is the error function, is the differential coefficient of the PID controller.

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