A power grid fault diagnosis method and system based on differential evolution logic operation

Abstract

This invention belongs to the field of power grid fault diagnosis technology and discloses a power grid fault diagnosis method and system based on differential evolutionary logic operations. The method includes: collecting the action information of protection devices and circuit breakers in the power system; filtering out a set of candidate elements that have experienced faults based on preset association rules between faulty elements and protection / circuit breaker actions; establishing a 0-1 integer programming model based on the determined protection action information, circuit breaker action information, and candidate element set; and solving the 0-1 integer programming model using an improved differential evolutionary algorithm based on logic operations to determine the fault state of the candidate elements. This invention directly encodes individuals using binary, eliminating the need for floating-point to binary conversion. It also constructs a binary mutation operator and an improved crossover operator based on logic operations and employs adaptive parameter adjustment to balance the population diversity and search efficiency of the algorithm.

Description

Technical Field

[0001] This invention belongs to the field of power grid fault diagnosis technology, specifically relating to a power grid fault diagnosis method and system based on differential evolutionary logic operation. Background Technology

[0002] As one of the most important infrastructures in modern society, the safe and stable operation of the power system is directly related to national development and people's livelihood. With the continuous expansion of the power grid and the increasing complexity of its topology, the rapid and accurate diagnosis of faulty sections has become a key issue in ensuring power supply reliability. Fault section diagnosis technology analyzes the operating signals of circuit breakers and protective relays, combined with alarm information from the monitoring system, to achieve precise location of faulty sections. It plays a crucial role in shortening power outage time, preventing the escalation of accidents, and maintaining stable system operation. However, in actual operation, when multiple faults occur in the system, fault diagnosis faces severe challenges. Especially in complex power grid environments, control centers often need to process massive amounts of alarm information, which undoubtedly greatly increases the difficulty of fault location. Therefore, researching more intelligent and efficient fault section diagnosis methods and improving the automation level of fault diagnosis is of great significance for enhancing the safety and reliability of power grid operation. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a power grid fault diagnosis method and system based on differential evolutionary logic operations. It directly encodes individuals using binary, eliminating the need for floating-point to binary conversion. It also constructs a binary mutation operator and an improved crossover operator based on logic operations, and adopts adaptive parameter adjustment to balance the population diversity and search efficiency of the algorithm.

[0004] To achieve the above objectives, the present invention provides the following solution: A power grid fault diagnosis method based on differential evolutionary logic operations, the method comprising: Collect action information of protection devices and circuit breakers in the power system, and filter out a set of candidate components that have failed based on preset association rules between faulty components and protection and circuit breaker actions. Based on the determined protection action information, circuit breaker action information, and candidate component set, a 0-1 integer programming model is established. An improved differential evolution algorithm based on logical operations is used to solve the 0-1 integer programming model, thereby enabling the determination of the fault state of candidate components.

[0005] Preferably, the method for establishing a 0-1 integer programming model based on determined protection action information, circuit breaker action information, and a set of candidate components includes: Based on the determined protection action information, circuit breaker action information and candidate component set, the input variables and constraint boundary conditions of the model are defined. The input variables are represented by 0-1 variables to represent the fault state of the candidate components: 1 represents fault and 0 represents normal. Based on input variables and constraint boundaries, an objective function is constructed with the core objective of minimizing the difference between the actual observed protection-circuit breaker action information and the theoretically expected action behavior. At the same time, combined with the logical rules of power system fault diagnosis, protection action range constraints and circuit breaker action response constraints are established to complete the construction of a complete 0-1 integer programming model.

[0006] Preferably, the objective function, with the core objective of "minimizing the difference between the actual observed protection circuit breaker action information and the theoretically expected action behavior," is as follows: ; ; In the formula, and Representing the first The actual action state and expected action state of a protection. , and These represent primary protection, near-backup protection, and far-backup protection, respectively. and Indicates the first The actual and expected states of each circuit breaker , , and These are the weighting factors for each element. Representing the The operating state of each component can be represented in binary.

[0007] Preferred, improved differential evolution algorithms based on logical operations include: Improved mutation operator: ; In the formula, the parameter , , From a set Randomly selected distinct values ​​that are not equal to the current index Integers; This indicates the current iteration number of the algorithm. Represents a mutated individual. These are three individuals with different random numbers in the population. , , and These are the logical operators OR, AND, XOR, and XNOR, respectively. Improved crossover operator and adaptive crossover rate: ; ; In the formula, Representing the The optimal individual of the generation The value at the bit position, This represents the value after the crossover. Indicates the current iteration number, parameter This represents the crossover probability value randomly generated within the interval (0, 1); For the first The variant individuals at position. For from set The random integer index selected from the list.

[0008] The present invention also provides a power grid fault diagnosis system based on differential evolutionary logic operation. The system is used to implement the aforementioned method and includes: a data acquisition and screening module, a model building module, and a fault determination module. The acquisition and filtering module is used to acquire the action information of protection devices and circuit breakers in the power system, and to filter out a set of candidate components that have failed based on preset association rules between faulty components and protection and circuit breaker actions. The model building module is used to establish a 0-1 integer programming model based on the determined protection action information, circuit breaker action information and candidate component set. The fault determination module is used to solve the 0-1 integer programming model using an improved differential evolution algorithm based on logical operations, so as to determine the fault status of candidate components.

[0009] Preferably, the process of establishing a 0-1 integer programming model based on determined protection action information, circuit breaker action information, and a set of candidate components includes: Based on the determined protection action information, circuit breaker action information and candidate component set, the input variables and constraint boundary conditions of the model are defined. The input variables are represented by 0-1 variables to represent the fault state of the candidate components: 1 represents fault and 0 represents normal. Based on input variables and constraint boundaries, an objective function is constructed with the core objective of minimizing the difference between the actual observed protection-circuit breaker action information and the theoretically expected action behavior. At the same time, combined with the logical rules of power system fault diagnosis, protection action range constraints and circuit breaker action response constraints are established to complete the construction of a complete 0-1 integer programming model.

[0010] Preferably, the objective function, with the core objective of "minimizing the difference between the actual observed protection circuit breaker action information and the theoretically expected action behavior," is as follows: ; ; In the formula, and Representing the first The actual action state and expected action state of a protection. , and These represent primary protection, near-backup protection, and far-backup protection, respectively. and Indicates the first The actual and expected states of each circuit breaker , , and These are the weighting factors for each element. Representing the The operating state of each component can be represented in binary.

[0011] Preferred, improved differential evolution algorithms based on logical operations include: Improved mutation operator: ; In the formula, the parameter , , From a set Randomly selected distinct values ​​that are not equal to the current index Integers; This indicates the current iteration number of the algorithm. Represents a mutated individual. These are three individuals with different random numbers in the population. , , and These are the logical operators OR, AND, XOR, and XNOR, respectively. Improved crossover operator and adaptive crossover rate: ; ; In the formula, Representing the The optimal individual of the generation The value at the bit position, This represents the value after the crossover. Indicates the current iteration number, parameter This represents the crossover probability value randomly generated within the interval (0, 1); For the first The variant individuals at position. For from set The random integer index selected from the list.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention innovatively employs a binary encoding scheme and introduces dedicated logical operators to achieve efficient mutation operations. Simultaneously, an adaptive crossover operator is designed, and the optimal individual is mutated, effectively improving search accuracy and global optimization capability. Furthermore, dual transformation techniques are used to enhance population diversity and prevent the algorithm from getting trapped in local optima. Moreover, the method of this invention is computationally simple, convenient to apply, and has high engineering practical value. Attached Figure Description

[0013] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the differential evolution method for solving power grid faults based on logical operations according to an embodiment of the present invention; Figure 2 This is a schematic diagram showing the structure of the power grid and the configuration details of related protection devices according to an embodiment of the present invention; Figure 3 This is a convergence curve of the fitness function in an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Example 1 This invention provides a power grid fault diagnosis method based on differential evolutionary logic operations. Specifically, it relates to a power grid fault diagnosis method that integrates logic operations to improve the differential evolutionary algorithm. This method is particularly suitable for scenarios where efficient and accurate fault location is achieved through binary encoding optimization, logic-operational operator design, and adaptive parameter adjustment. It covers the entire technical system from fault action information acquisition, diagnostic model construction, improved algorithm solution, and performance evaluation, aiming to improve the accuracy, efficiency, and algorithm stability of power grid fault diagnosis. The method includes: Collect action information of protection devices and circuit breakers in the power system, and filter out a set of candidate components that have failed based on preset association rules between faulty components and protection and circuit breaker actions. Based on the determined protection action information, circuit breaker action information, and candidate component set, a 0-1 integer programming model is established. An improved differential evolution algorithm based on logical operations is used to solve the 0-1 integer programming model, thereby enabling the determination of the fault state of candidate components.

[0018] like Figure 1 The specific implementation process is shown below: S1. Fault Action Information Acquisition and Candidate Component Determination: Collect action information of protection devices and circuit breakers in the power system. Based on the preset association rules between fault components and protection / circuit breaker actions, filter out a set of candidate components that may cause faults and clarify the core input parameters required for diagnosis.

[0019] S2, the basic parameter settings for the 0-1 integer programming model are based on the protection action information, circuit breaker action information and candidate component set determined by S1 as core parameters. The input variables, constraint conditions and boundaries of the model are defined. The input variables use 0-1 variables to represent the fault state of the candidate components (1 represents fault, 0 represents normal).

[0020] S3. Construction of Objective Function and Constraints: Based on S2, an objective function is constructed with the core objective of "minimizing the difference between the actual observed protection-circuit breaker action information and the theoretically expected action behavior". At the same time, combined with the logical rules of power system fault diagnosis, a series of constraints such as protection action range constraints and circuit breaker action response constraints are established to complete the construction of a complete 0-1 integer programming model.

[0021] S4. Improved differential evolution algorithm is used to solve the 0-1 integer programming model constructed in S3. By using an improved differential evolution algorithm based on logical operations, the fault status of candidate components can be accurately determined.

[0022] S5. Multi-dimensional evaluation of algorithm performance: After the solution is completed, the accuracy, solution efficiency, average function evaluation times, fitness function mean, standard deviation and iteration curve are selected as core evaluation indicators to conduct a comprehensive performance evaluation of the improved differential evolution algorithm based on logical operation, and to verify the effectiveness and superiority of the algorithm in fault diagnosis.

[0023] In this embodiment, the fault action information acquisition and candidate component determination described in step S1 are characterized as follows: Accurately collect protection device and circuit breaker action information in the power system. The protection device action information includes detailed data on the activation status of different levels of protection, such as main protection and backup protection. The circuit breaker action information covers key information such as circuit breaker fault trip signals, ensuring the integrity of the collected data. Based on the power system topology, component fault mechanisms, and protection configuration principles, a pre-set association rule library is established between faulty components and protection and circuit breaker actions. This rule library clarifies the type of protection device to be activated, the action logic, and the opening and closing operation logic of the circuit breaker to be executed when different types of components (such as lines, transformers, and busbars) fail, thereby determining the fault area.

[0024] In this embodiment, the objective function and constraints described in step S3 are constructed as follows: The core objective function is to minimize the difference between the observed actions of protection and circuit breakers and their theoretically expected actions. It identifies the component fault state that best matches the actual fault scenario by quantifying the deviation between actual and theoretical actions. Based on 0-1 variables, including candidate component fault states and consistency variables of protection and circuit breaker actions, the differences are transformed into quantifiable mathematical expressions: if the actual action of a protection or circuit breaker is consistent with the theoretical action of the corresponding candidate component at fault, the deviation is 0; if there is inconsistency, such as theoretically expected action but actual non-action, or theoretically not expected action but actual action, the deviation is 1. The objective function minimizes the total deviation of all protection and circuit breaker actions to ensure that the solution best matches the characteristics of the actual fault actions. Based on the core logic rules of power system fault diagnosis, two types of key constraints are constructed: Protection action range constraints and Circuit breaker action response constraints. The first constraint clarifies the preset protection range of each type of protection device based on the power system topology and protection configuration principles. This constraint ensures that the protection device will only trigger its action when a candidate component within the protection range fails, excluding the impact of component failures outside the protection range. The second constraint considers the linkage between the circuit breaker and the protection device. This constraint limits the circuit breaker to only performing opening and closing actions when its associated protection device issues an action command, while excluding unreasonable action logic other than equipment anomalies such as circuit breaker failure to operate or maloperation. The specific objective function is as follows: In the formula, Representing the The operating state of each component can be represented in binary (normal = 0, fault = 1). Its objective function can be expressed as: In the formula, and Representing the first The actual action state and expected action state of a protection. , and These represent primary protection, near-backup protection, and far-backup protection, respectively. and Indicates the first The actual and expected states of each circuit breaker , , and These are the weighting factors for each element, with values ​​of 0.9, 0.8, 0.7, and 0.95 respectively.

[0025] Based on the relevant principles of relay protection systems, when a fault occurs in a certain line section, the main protection device should prioritize tripping to disconnect the faulty section; conversely, if the line section is in normal operating condition, the main protection should not be triggered. Therefore, the expected operating state of the main protection is completely consistent with the operating state of the corresponding line section, which can be specifically stated as follows: In the formula, Representing the The operating state of each component can be represented in binary.

[0026] When a fault exists in the corresponding section and the main protection fails to operate, the near-backup protection needs to be activated to isolate the faulty section; conversely, if the corresponding section is operating normally, the near-backup protection does not need to operate. Based on this, the expected operating state of the near-backup protection can be summarized as follows: Symbols in the formula Logic AND operation.

[0027] When a fault occurs on a section of the line, and neither the main protection nor the near-backup protection can successfully isolate the fault, the remote backup protection must be activated to disconnect the faulty section, which will expand the power outage area. The operating status of the remote backup protection can be described as follows: In the formula, the symbol and These represent logical AND and logical OR operations, respectively. The OR operation is defined as follows: if there is at least one non-zero element in the summation term, the result is 1; otherwise, it is 0. Representatives received Protected components Indicates by arrive Circuit breakers on the power transmission lines it passes through, Representing the The operating state of each component can be represented in binary. Indicates the first The actual state of each circuit breaker.

[0028] Regarding circuit breakers, all main protection, near-backup protection, and far-backup protection configured within their body are capable of tripping them. Based on this characteristic, their ideal operating state can be described as follows: In the formula, the symbol This represents the logical AND operation. Where, This indicates a OR operation. This indicates that the circuit breaker can be enabled. Protection of movement, and Representing the first The actual action state and expected action state of a protection.

[0029] In this embodiment, the improved differential evolution based on logical operations described in step S4 is characterized as follows: Differential Evolution (DE) is renowned for its fast convergence and strong robustness in continuous-valued search spaces, making it particularly suitable for complex multi-peak functions and high-dimensional optimization problems. However, its floating-point encoding method struggles to directly handle discrete problems and carries the risk of insufficient convergence leading to local optima. Therefore, this invention proposes a power grid fault diagnosis method based on differential evolution logic operations.

[0030] (1) Population binary initialization During the initialization phase, IABODE uses MATLAB functions to generate random integers to initialize the population, as follows: (2) Logical mutation operator To address the characteristics of 0-1 integer programming problems, LOADE uses logical operations to directly generate mutation vectors, thereby avoiding reliance on knowledge ratios. F This improvement eliminates the dependency of mutation operations on continuous parameters in the traditional DE algorithm, enabling the algorithm to perform efficient mutation operations directly in binary space. The specific implementation is as follows: ; In the formula, the parameter , , From a set Randomly selected distinct values ​​that are not equal to the current index Integers; This indicates the current iteration number of the algorithm. Represents a mutated individual. These are three individuals with different random numbers in the population. , , and These are the logical operators OR, AND, XOR, and XNOR, respectively. The update strategy in the formula employs binary logic operations, directly handling 0-1 variables without floating-point encoding conversion. This strategy generates mutation vectors through OR, AND, XOR, and XNOR operations, completing individual updates entirely within the binary space. This logic-based update mechanism is particularly suitable for power grid fault diagnosis problems because it effectively handles the binary characteristics of protection actions and circuit breaker states, significantly improving computational efficiency while maintaining the algorithm's search capabilities.

[0031] (3) Improve cross operations The remarkable effectiveness of the GSK algorithm largely relies on its utilization of optimal subpopulations, which play a crucial guiding role in the algorithm's execution. Therefore, this invention proposes a novel method: incorporating the currently found optimal individual into the crossover operation. This approach aims to allow this optimal individual to play a leading role in the population's evolution, guiding the entire population towards a more efficient search direction. In the traditional implementation of the DE algorithm, the scaling factor... and cross rate Static parameter settings are typically used or determined through trial and error. This approach has two main limitations: first, parameter tuning requires significant computational resources; second, static parameter settings are difficult to adapt to the varying needs of the algorithm at different evolutionary stages. Crossover rate As a key parameter controlling population updates, its value directly affects the algorithm's search behavior: a larger value indicates a more stable or stable population. A higher value allows offspring to inherit more information from the mutation vector, enhancing global exploration capabilities by increasing population diversity; while a lower value... A higher value encourages offspring to retain more characteristics of their parents, thus accelerating the algorithm's local convergence. Therefore, a higher value is used in the early stages of the algorithm. This is beneficial, as it helps the algorithm search the solution space more broadly and reduces the risk of getting trapped in local optima. In the later stages of the algorithm, appropriately reducing... This helps the algorithm perform a more refined search near known excellent solutions, thereby accelerating the convergence process. The specific implementation is as follows: In the formula, Representing the The optimal individual of the generation The value at the bit position, This represents the value after the crossover. Indicates the current iteration number, parameter This represents the crossover probability value randomly generated within the interval (0, 1); For the first The variant individuals at position. For from set The random integer index selected from the list.

[0032] (4) Select operation Differential evolution algorithm employs a greedy selection mechanism based on fitness competition, following the evolutionary principle of "survival of the fittest." This operation compares target individuals... With test individuals The fitness values ​​are used to select the better individuals as members of the next generation of the population. This is implemented as follows: In the formula, For the next generation of individuals, This represents the fitness function.

[0033] (5) Dual transformation In optimization algorithms employing binary iterative approaches, insufficient population diversity can lead to the search process getting stuck in local optima. To address this issue, a diversity enhancement strategy based on dual transformation of elite individuals is introduced: in each iteration, a certain proportion of high-fitness individuals are selected and subjected to a logical negation operation, thereby effectively expanding the search space.

[0034] In the formula, For the first Generation, first The first individual Individual gene values, This represents the dual transformation rate. Considering that the primary function of this operation is to enhance population diversity, the parameter... and The value should not be set too high. In the simulation experiment of this invention, this invention selected... and All values ​​are 0.3. This setting helps to maintain population diversity while avoiding excessive interference with the population's evolutionary process.

[0035] The pseudocode for the LOBDE algorithm is shown in Table 1 below.

[0036] Table 1 Compared to the DE algorithm, LOBDE demonstrates highly competitive performance in terms of solution quality, extraction accuracy, robustness, and stability. The specific steps are as follows: 1) Generate the initial population. Calculate the mutation and crossover operators based on the number of generations. Initialize the individuals within each population by randomly generating a number within a given range for each individual. A certain number of initial individuals constitute the initial population.

[0037] 2) The fitness function is calculated based on minimizing the difference between the actual observed information and the theoretical expected behavior. New individuals are generated using the mutation operator, and then crossover is performed using the crossover operator. The next generation of individuals is selected based on the smaller fitness, and the best individual is retained. Then, the dual operator is used to perform dual transformation on the selected individuals.

[0038] 3) Determine if the population meets the maximum number of iterations. If it does, stop iterating after 500 function evaluations. If it does not meet the requirement, return to step 2) and start over.

[0039] In order to verify the actual performance of the LOBDE algorithm in fault section diagnosis, this invention applies the LOBDE algorithm to a real fault case in the Jilin power grid of China. Figure 2 The structure of the power grid and the configuration details of the relevant protection devices are presented. When a fault occurs on bus B2, the main protection device B2m fails to start normally for certain reasons. In this situation, the backup protection devices PRs, L4Ss, L3Rs, and L1Rs function, successfully tripping circuit breakers 3104, 1504, and 8204. Simultaneously, a fault also occurs on bus B7, and its main protection device B7m starts correctly, causing circuit breakers 4811, 4803, and 4802 to trip, but circuit breaker 4801 fails to operate normally. Subsequently, the backup protection device L10Ss starts, tripping circuit breaker 3107. Furthermore, due to the influence of the fault current, protection device L11Sm malfunctions, causing circuit breaker 3106 to trip incorrectly.

[0040] The power grid fault diagnosis method based on differential evolutionary logic operation of this invention is compared with the original differential evolutionary algorithm. The results are shown in Table 2, where NA represents a diagnosis accuracy of less than 100%, and the iteration curve is shown in Table 2. Figure 3 As shown.

[0041] Table 2 LOBDE achieved correct diagnostic results in all tests, demonstrating its superior performance and robustness across various fault scenarios. In contrast, the DE algorithm is significantly inferior to LOBDE in all aspects.

[0042] Figure 3 A comparison of the convergence performance of the logic-based differential evolution algorithm and the original differential evolution algorithm is conducted using 500 data points as a reference. The convergence curves show that the logic-based differential evolution algorithm proposed in this invention converges faster than the original differential evolution algorithm and achieves a better result, while the original differential evolution algorithm struggles to iterate to the optimal solution. Furthermore, the results in Table 2 demonstrate that the logic-based differential evolution algorithm in this invention improves the diagnostic success rate. Moreover, for fault diagnosis, the logic-based differential evolution algorithm converges faster during the solution process and finds a better solution set.

[0043] In this embodiment, the multi-dimensional evaluation of algorithm performance in step S5 specifically includes: A series of performance metrics were used to evaluate the algorithm's performance from multiple dimensions. These metrics specifically include success rate, solution quality stability (characterized by standard deviation), convergence efficiency (reflected by the number of average function evaluations (ANFEs)), and diagnostic time.

[0044] Its accuracy is the number of correct diagnoses after 100 iterations, the diagnostic efficiency is the total time spent on 100 diagnoses, the average function evaluation count is the average number of evaluations required to reach the optimal point after 100 diagnoses, the fitness function mean is the average fitness function of 100 diagnoses, the standard deviation is the standard deviation of the average fitness function of 100 diagnoses, and the iteration curve is the graph of the average fitness function of 100 diagnoses.

[0045] In summary, the differential evolution algorithm based on logical operations has excellent performance in power grid fault diagnosis and can provide guidance for rapid fault identification in practical engineering.

[0046] Example 2 The present invention also provides a power grid fault diagnosis system based on differential evolutionary logic operation. The system is used to implement the method described in Embodiment 1. The system includes: a data acquisition and screening module, a model building module, and a fault determination module. The data acquisition and filtering module is used to collect the action information of protection devices and circuit breakers in the power system. Based on the preset association rules between faulty components and protection and circuit breaker actions, it filters out a set of candidate components that have failed. The model building module is used to establish a 0-1 integer programming model based on the determined protection action information, circuit breaker action information, and candidate component set. The fault determination module is used to solve the 0-1 integer programming model using an improved differential evolution algorithm based on logical operations, and to determine the fault status of candidate components.

[0047] In this embodiment, the process of establishing a 0-1 integer programming model based on the determined protection action information, circuit breaker action information, and candidate component set includes: Based on the determined protection action information, circuit breaker action information and candidate component set, the input variables and constraint boundary conditions of the model are defined. The input variables are represented by 0-1 variables to represent the fault state of the candidate components: 1 represents fault and 0 represents normal. Based on input variables and constraint boundaries, an objective function is constructed with the core objective of minimizing the difference between the actual observed protection-circuit breaker action information and the theoretically expected action behavior. At the same time, combined with the logical rules of power system fault diagnosis, protection action range constraints and circuit breaker action response constraints are established to complete the construction of a complete 0-1 integer programming model.

[0048] In this embodiment, the objective function, with the core objective of "minimizing the difference between the actual observed protection circuit breaker action information and the theoretically expected action behavior," is as follows: ; ; In the formula, and Representing the first The actual action state and expected action state of a protection. , and These represent primary protection, near-backup protection, and far-backup protection, respectively. and Indicates the first The actual and expected states of each circuit breaker , , and These are the weighting factors for each element. Representing the The operating state of each component can be represented in binary.

[0049] In this embodiment, the improved differential evolution algorithm based on logical operations includes: Improved mutation operator: ; In the formula, the parameter , , From a set Randomly selected distinct values ​​that are not equal to the current index Integers; This indicates the current iteration number of the algorithm. Represents a mutated individual. These are three individuals with different random numbers in the population. , , and These are the logical operators OR, AND, XOR, and XNOR, respectively. Improved crossover operator and adaptive crossover rate: ; ; In the formula, Representing the The optimal individual of the generation The value at the bit position, This represents the value after the crossover. Indicates the current iteration number, parameter This represents the crossover probability value randomly generated within the interval (0, 1); For the first The variant individuals at position. For from set The random integer index selected from the list.

[0050] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A power grid fault diagnosis method based on differential evolutionary logic operations, characterized in that, The method includes: Collect action information of protection devices and circuit breakers in the power system, and filter out a set of candidate components that have failed based on preset association rules between faulty components and protection and circuit breaker actions. Based on the determined protection action information, circuit breaker action information, and candidate component set, a 0-1 integer programming model is established. An improved differential evolution algorithm based on logical operations is used to solve the 0-1 integer programming model, thereby enabling the determination of the fault state of candidate components.

2. The method according to claim 1, characterized in that, Methods for establishing 0-1 integer programming models based on determined protection action information, circuit breaker action information, and candidate component sets include: Based on the determined protection action information, circuit breaker action information and candidate component set, the input variables and constraint boundary conditions of the model are defined. The input variables are represented by 0-1 variables to represent the fault state of the candidate components: 1 represents fault and 0 represents normal. Based on input variables and boundary constraints, an objective function is constructed with the core objective of minimizing the difference between the actual observed protection-circuit breaker action information and the theoretically expected action behavior. At the same time, combined with the logical rules of power system fault diagnosis, protection action range constraints and circuit breaker action response constraints are established to complete the construction of a complete 0-1 integer programming model.

3. The method according to claim 2, characterized in that, The objective function, with the core objective of "minimizing the difference between the actual observed protection circuit breaker action information and the theoretically expected action behavior", is as follows: ； ； In the formula, and Representing the first The actual action state and expected action state of a protection. , and These represent primary protection, near-backup protection, and far-backup protection, respectively. and Indicates the first The actual and expected states of each circuit breaker , , and These are the weighting factors for each element. Representing the The operating state of each component can be represented in binary.

4. The method according to claim 1, characterized in that, Improved differential evolution algorithms based on logical operations include: Improved mutation operator: ； In the formula, the parameter , , From a set Randomly selected distinct values ​​that are not equal to the current index Integers; This indicates the current iteration number of the algorithm. Represents a mutated individual. These are three individuals with different random numbers in the population. , , and These are the logical operators OR, AND, XOR, and XNOR, respectively. Improved crossover operator and adaptive crossover rate: ； ； In the formula, Representing the The optimal individual of the generation The value at the bit position, This represents the value after the crossover. Indicates the current iteration number, parameter This represents the crossover probability value randomly generated within the interval (0, 1); For the first The variant individuals at position. For from set The random integer index selected from the list.

5. A power grid fault diagnosis system based on differential evolutionary logic operations, the system being used to implement the method described in any one of claims 1-4, characterized in that, The system includes: a data acquisition and filtering module, a model building module, and a fault determination module; The acquisition and filtering module is used to acquire the action information of protection devices and circuit breakers in the power system, and to filter out a set of candidate components that have failed based on preset association rules between faulty components and protection and circuit breaker actions. The model building module is used to establish a 0-1 integer programming model based on the determined protection action information, circuit breaker action information and candidate component set. The fault determination module is used to solve the 0-1 integer programming model using an improved differential evolution algorithm based on logical operations, so as to determine the fault status of candidate components.

6. The system according to claim 5, characterized in that, The process of establishing a 0-1 integer programming model based on determined protection action information, circuit breaker action information, and candidate component set includes: Based on the determined protection action information, circuit breaker action information and candidate component set, the input variables and constraint boundary conditions of the model are defined. The input variables are represented by 0-1 variables to represent the fault state of the candidate components: 1 represents fault and 0 represents normal. Based on input variables and boundary constraints, an objective function is constructed with the core objective of minimizing the difference between the actual observed protection-circuit breaker action information and the theoretically expected action behavior. At the same time, combined with the logical rules of power system fault diagnosis, protection action range constraints and circuit breaker action response constraints are established to complete the construction of a complete 0-1 integer programming model.

7. The system according to claim 6, characterized in that, The objective function, with the core objective of "minimizing the difference between the actual observed protection circuit breaker action information and the theoretically expected action behavior", is as follows: ； ； In the formula, and Representing the first The actual action state and expected action state of a protection. , and These represent primary protection, near-backup protection, and far-backup protection, respectively. and Indicates the first The actual and expected states of each circuit breaker , , and These are the weighting factors for each element. Representing the The operating state of each component can be represented in binary.

8. The system according to claim 5, characterized in that, Improved differential evolution algorithms based on logical operations include: Improved mutation operator: ； In the formula, the parameter , , From a set Randomly selected distinct values ​​that are not equal to the current index Integers; This indicates the current iteration number of the algorithm. Represents a mutated individual. These are three individuals with different random numbers in the population. , , and These are the logical operators OR, AND, XOR, and XNOR, respectively. Improved crossover operator and adaptive crossover rate: ； ； In the formula, Representing the The optimal individual of the generation The value at the bit position, This represents the value after the crossover. Indicates the current iteration number, parameter This represents the crossover probability value randomly generated within the interval (0, 1); For the first The variant individuals at position. For from set The random integer index selected from the list.

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