Fault recovery method and fault recovery device of power system and electronic equipment

Abstract

The invention provides a fault recovery method and device of a power system and electronic equipment. The method comprises the steps that fault features in a current power system are acquired, the current fault features are input into a fault model, a current fault type is obtained, the fault model is obtained through training of multiple sets of historical data, each set of historical data comprises historical fault features and a corresponding historical fault type, and the historical fault features and the corresponding historical fault type are compared; the fault features are abnormal change data of the power system; a corresponding fault recovery model is determined according to the current fault type, the fault recovery model comprises a target function and constraint conditions, the fault recovery model is solved by adopting an adaptive quantum particle swarm algorithm to obtain an optimal fault position, and a fault recovery scheme is determined based on the fault type and the optimal fault position. According to the method, the problem of low accuracy of a fault detection method in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of power system faults, and in particular, to a power system fault recovery method, a computer-readable storage medium, a fault recovery device, and an electronic device. Background Art

[0002] As an important part of the power system, the distribution network has the main function of transmitting high-voltage electricity to users at all levels through substations and distribution lines to ensure the power supply of users. However, the distribution network often faces various faults during operation, which not only cause power outages, but also affect the normal production and life of users. The existing automatic fault detection methods based on sensor networks and smart meters have insufficient accuracy in abnormal state recognition in the face of complex power grid environments.

[0003] Therefore, a method for detecting faults with high accuracy is urgently needed. Summary of the invention

[0004] The main purpose of the present application is to provide a fault recovery method for an electric power system, a computer-readable storage medium, a fault recovery device and an electronic device, so as to at least solve the problem of low accuracy of the fault detection method in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a fault recovery method for an electric power system is provided, comprising obtaining fault characteristics in a current electric power system, and inputting the current fault characteristics into a fault model to obtain a current fault type, wherein the fault model is obtained by training with multiple groups of historical data, each group of the historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the electric power system; determining a corresponding fault recovery model according to the current fault type, solving the fault recovery model using an adaptive quantum particle swarm algorithm to obtain an optimal fault location, the optimal fault location being the global optimal location of the particles, the fault recovery model including an objective function and constraints, determining a fault recovery plan based on the fault type and the optimal fault location, and executing the corresponding fault recovery plan.

[0006] Optionally, operating data of the current power system is acquired; the operating data is analyzed to obtain abnormal change data; time domain analysis and / or frequency domain analysis is performed on the abnormal change data to obtain the fault characteristics in the current power system.

[0007] Optionally, in the fault recovery model, a quantum particle swarm is randomly generated; the state of each particle of the quantum particle swarm is initialized to obtain the initial position and initial velocity of the particle, and the initial position is randomly distributed in the solution space, represented by Xi (0) = X min +rand()×(X max -X min ), where X min and X max Respectively represent the lower bound and upper bound of the solution space, rand() is a function for generating a random number between [0,1], and the initial velocity is a random number or zero; according to the objective function in the fault recovery model, calculate the first fitness value of the particle; use the first fitness value to update the individual historical optimal positions of all the particles; analyze the individual historical optimal positions of all the particles to obtain the individual historical optimal position of the optimal particle, the optimal particle is the particle corresponding to the individual historical optimal position, and the individual historical optimal position of the optimal particle is the global optimal position; based on the initial velocity, the initial position and quantum behavior of the particle, obtain the current position of the particle; according to the individual historical optimal position of the particle and the current position of the particle, update the global optimal position of the particle; according to the updated a global optimal position, calculating a second fitness value of the global optimal position; comparing the first fitness value and the second fitness value to determine a change trend of the fitness value of the particle; judging whether the parameters of the adaptive quantum particle swarm algorithm meet a predetermined condition according to the change trend of the fitness value of the particle, wherein the predetermined condition is at least that a convergence curve of a function value of the objective function in the adaptive quantum particle swarm algorithm converges to a solution corresponding to the optimal fault position, and the parameters include a convergence factor, an inertia weight parameter, and a learning factor; if the parameters of the adaptive quantum particle swarm algorithm do not meet the predetermined condition, dynamically adjusting the parameters by using an adaptive strategy until the adaptive quantum particle swarm algorithm meets a termination condition and outputs the optimal fault position, wherein the termination condition is one of the number of iterations reaching a threshold and the second fitness value being less than or equal to a fitness value threshold.

[0008] Optionally, when the first fitness value of the current position of the particle is better than the fitness of the individual historical optimal position, the individual historical optimal position is updated to be the current position of the particle.

[0009] Optionally, when one of the fitness value of the current position of the particle and the fitness value of the individual historical optimal position is better than the fitness value of the global optimal position, the global optimal position is updated to the optimal position of the two, and the optimal position of the two is the position with the smallest fitness value between the current position of the particle and the individual historical optimal position.

[0010] Optionally, when the fault type is a short circuit fault, the fault recovery plan is determined to be fault isolation, and according to the optimal fault location, the fault recovery plan of fault isolation is executed; when the fault type is an open circuit fault, the fault recovery plan is determined to be power supply recovery, and according to the optimal fault location, the fault recovery plan of power supply recovery is executed; when the fault type is a ground fault, the fault recovery plan is determined to be load transfer, and according to the optimal fault location, the fault recovery plan of load transfer is executed.

[0011] Optionally, the objective function is Among them, r ij represents the line resistance from node i to node j of the power system, x ij represents the decision variable, c k represents the unit load cost of node k in the power system, y k Represents the operating status index of the node k of the power system after network reconstruction; the constraints are voltage stability constraint, power balance constraint and equipment operation restriction, the voltage stability constraint is expressed as the voltage of any node is maintained between the maximum voltage and the minimum voltage, the power balance constraint is that the total power input is the same as the total power output, and the equipment operation restriction is expressed as the actual current between any two nodes is less than the maximum current allowed between the two nodes.

[0012] According to another aspect of the present application, there is provided a fault recovery device for an electric power system, comprising:

[0013] An acquisition unit, which acquires fault features in the current power system, and inputs the current fault features into a fault model to obtain a current fault type, wherein the fault model is obtained by training with multiple groups of historical data, each group of historical data includes historical fault features and corresponding historical fault types, and the fault features are abnormal change data of the power system;

[0014] The execution unit determines a corresponding fault recovery model according to the current fault type, uses an adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain an optimal fault position, where the optimal fault position is a global optimal position of particles. The fault recovery model includes an objective function and constraints, determines a fault recovery plan based on the fault type and the optimal fault position, and executes the corresponding fault recovery plan.

[0015] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein the program executes any one of the above-described methods.

[0016] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any of the methods described above.

[0017] Applying the technical solution of the present application, first, obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type; secondly, determine the corresponding fault recovery model according to the current fault type, use the adaptive quantum particle swarm algorithm to solve the fault recovery model, obtain the optimal fault location, determine the fault recovery plan based on the fault type and the optimal fault location, and execute the corresponding fault recovery plan. Compared with the automatic fault detection method based on sensor networks and smart meters in the prior art, which has insufficient accuracy in abnormal state recognition in the face of complex power grid environments, the solution of the present application uses the adaptive quantum particle swarm algorithm to provide faster global search capabilities by considering the probability distribution of quantum bits, and adaptively adjusts the search parameters to adapt to the dynamic changes of complex networks, and obtains the optimal location of the fault by searching the global optimal position of particles, thereby improving the accuracy of fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a mobile terminal for executing a fault recovery method for a power system provided in an embodiment of the present application is shown;

[0020] Figure 2 A schematic diagram of a flow chart of a fault recovery method for a power system provided according to an embodiment of the present application is shown;

[0021] Figure 3 A comparison diagram showing the response time of a power system fault recovery method provided according to an embodiment of the present application and different algorithms to different fault types is shown;

[0022] Figure 4 A comparison diagram showing the detection accuracy of different fault types for a power system fault recovery method provided according to an embodiment of the present application and different algorithms;

[0023] Figure 5A comparison diagram of the positioning errors of different fault types for a power system fault recovery method provided according to an embodiment of the present application and different algorithms is shown;

[0024] Figure 6 A comparison diagram of the location time of different fault types for a power system fault recovery method provided according to an embodiment of the present application and different algorithms is shown;

[0025] Figure 7 A comparison diagram of the comprehensive performance of a power system fault recovery method provided according to an embodiment of the present application and different algorithms is shown;

[0026] Figure 8 A flowchart of an implementation method of a power system fault recovery method provided according to an embodiment of the present application is shown;

[0027] Fig. 9 A structural block diagram of a fault recovery device for an electric power system provided according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] As introduced in the background technology, the intelligent algorithm in the prior art has the problem of low fault detection accuracy. To solve the above problem, an embodiment of the present application provides a fault recovery method for a power system.

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a power system fault recovery method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0034] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] In this embodiment, a fault recovery method for an electric power system running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 2 FIG. 1 is a flow chart of a method for recovering from a fault in a power system according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0037] Step S201, obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, wherein the fault model is trained using multiple groups of historical data, each group of historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the power system.

[0038] Specifically, a fault model refers to a model used to describe the possible faults and causes of a system or device. By establishing a fault model, we can better understand the problems that the system may face, so that we can take corresponding preventive and repair measures to improve the reliability and stability of the system.

[0039] Specifically, abnormal change data of the power system refers to data that has sudden or unexpected changes compared to the normal operating state. These abnormal changes may include sudden increase or decrease in power load, voltage fluctuation, frequency abnormality, equipment failure, etc.

[0040] Step S202, determine the corresponding fault recovery model according to the current fault type, use the adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain the optimal fault position, the optimal fault position is the global optimal position of the particle, the fault recovery model includes an objective function and constraints, determine the fault recovery plan based on the fault type and the optimal fault position, and execute the corresponding fault recovery plan.

[0041] Specifically, in the adaptive quantum particle swarm algorithm, the position and speed of the particles will be dynamically adjusted according to the current search situation and the characteristics of the problem to more effectively explore the search space and find the optimal solution. At the same time, the quantum state of the quantum particles will also be dynamically adjusted according to the characteristics of the problem to improve the efficiency of the search and the speed of convergence.

[0042] In the above embodiment, first, the fault characteristics in the current power system are obtained, and the current fault characteristics are input into the fault model to obtain the current fault type; secondly, the corresponding fault recovery model is determined according to the current fault type, and the adaptive quantum particle swarm algorithm is used to solve the fault recovery model to obtain the optimal fault location, and the fault recovery plan is determined based on the fault type and the optimal fault location, and the corresponding fault recovery plan is executed. Compared with the automatic fault detection method based on sensor networks and smart meters in the prior art, which is faced with complex power grid environments and has insufficient accuracy in abnormal state recognition, the solution of the present application uses an adaptive quantum particle swarm algorithm to provide a faster global search capability by considering the probability distribution of quantum bits, and adaptively adjusts the search parameters to adapt to the dynamic changes of complex networks, and obtains the optimal position of the fault by searching the global optimal position of particles, thereby improving the accuracy of fault detection.

[0043] In one embodiment of the present application, the fault characteristics in the current power system are obtained, and the current fault characteristics are input into the fault model to obtain the current fault type, including: obtaining the operation data of the current power system; analyzing the operation data to obtain abnormal change data; performing time domain analysis and / or frequency domain analysis on the abnormal change data to obtain the fault characteristics in the current power system. In this method, by collecting various operation data of the current power system, analyzing these operation data, comparing historical data and real-time data, finding abnormal change data, performing time domain and frequency domain analysis on the abnormal change data, and according to the results obtained by the time domain and frequency domain analysis, the possible fault characteristics in the current power system can be obtained, wherein the fault types include short circuit fault, open circuit fault and ground fault, the fault characteristics of the short circuit fault type are current surge, voltage drop and high temperature, the fault characteristics of the open circuit fault type are current interruption, voltage imbalance and line reactive power change, and the fault characteristics of the ground fault type are ground current, voltage drop and fault current path change.

[0044] In order to further improve the accuracy of fault location in fault detection after the fault type is detected, in one embodiment of the present application, an adaptive quantum particle swarm algorithm is used to solve the above fault recovery model to obtain the optimal fault location, including: randomly generating a quantum particle swarm in the above fault recovery model; initializing the state of each particle of the above quantum particle swarm to obtain the initial position and initial velocity of the particle, and the above initial position is randomly distributed in the solution space, which can be expressed as X i (0) = X min +rand()×(X max -X min ), where X min and X maxRespectively represent the lower bound and upper bound of the solution space, rand() is a function for generating a random number between [0,1], and the initial velocity is a random number or zero; according to the objective function in the fault recovery model, calculate the first fitness value of the particle; update the individual historical optimal positions of all the particles using the first fitness value; analyze the individual historical optimal positions of all the particles to obtain the individual historical optimal position of the optimal particle, the optimal particle is the particle corresponding to the individual historical optimal position, and the individual historical optimal position of the optimal particle is the global optimal position; based on the initial velocity, initial position and quantum behavior of the particle, obtain the current position of the particle; according to the individual historical optimal position of the particle and the current position of the particle, update the global optimal position of the particle; according to the updated global The method comprises the steps of: determining an optimal position of the particle, calculating a second fitness value of the global optimal position; comparing the first fitness value and the second fitness value to determine a change trend of the fitness value of the particle; determining whether the parameters of the adaptive quantum particle swarm algorithm meet predetermined conditions according to the change trend of the fitness value of the particle, wherein the predetermined condition is at least that a convergence curve of a function value of the objective function in the adaptive quantum particle swarm algorithm converges to a solution corresponding to the optimal fault position, and the parameters include a convergence factor, an inertia weight parameter, and a learning factor; in the case that the parameters of the adaptive quantum particle swarm algorithm do not meet the predetermined conditions, dynamically adjusting the parameters by using an adaptive strategy until the adaptive quantum particle swarm algorithm meets a termination condition and outputs the optimal fault position, wherein the termination condition is that the number of iterations reaches an iteration number threshold and the second fitness value is less than or equal to a fitness value threshold.

[0045] Specifically, before the algorithm starts, the position and velocity of each particle in the particle swarm are initialized, and for each particle, the fitness of its current position is evaluated using a predefined fitness function. The fitness function is designed based on the requirements of fault detection and location and is used to measure the quality of the solution. After that, the individual historical optimal position P is updated. best and the global optimal position G best If the current particle's fitness is better than its historical optimal fitness, update its P best ; If the fitness of the current particle is better than the global optimal fitness, update G bestDuring the iteration process, the current position of the particle is updated according to quantum behavior and calculated based on the concept of quantum bit probability cloud. At the same time, the algorithm parameters are dynamically adjusted according to the number of iterations or the change in the fitness of the particle. Finally, the algorithm is checked to see whether the maximum number of iterations has been reached or the fitness has reached a certain preset threshold. If the conditions are met, the algorithm stops and outputs the current global position as the fault position. This allows the fault to be located quickly and accurately, and the optimal fault position is obtained, further improving the accuracy and response time of fault detection.

[0046] like Figure 3 and Figure 4 As shown in the figure, the A-QPSO algorithm is compared with other algorithms by setting the parameters of the A-QPSO algorithm to the following values: the particle swarm size is 50, the maximum number of iterations is 200, the quantum behavior parameter is [0.5, 1.0], the initial value of the convergence factor is 1.0, and the final value of the convergence factor is 0.1. The response time and detection accuracy of the A-QPSO, PSO and GA under different fault types are compared. The results show that the A-QPSO has obvious advantages in response time and detection accuracy. Specifically, in the scenarios of short circuit fault, line break fault and ground fault, the response time of A-QPSO is 0.8 seconds, 0.9 seconds and 0.85 seconds respectively, which is significantly better than 1.2 seconds, 1.3 seconds and 1.25 seconds of PSO and 1.5 seconds, 1.6 seconds and 1.55 seconds of GA; in terms of detection accuracy, A-QPSO is 98%, 97% and 96% respectively, which is much higher than 90%, 89% and 88% of PSO and 85%, 84% and 83% of GA. These results show that A-QPSO has superior performance in fault detection and can detect fault location and type more quickly and accurately. This advantage is mainly attributed to the adaptive parameter adjustment mechanism of A-QPSO and the application of quantum particle swarm algorithm, which enables it to maintain the best performance in different search stages, improve the global search ability and convergence speed of the algorithm, and maintain efficient global search ability and adaptability in a diverse power grid environment. In summary, the superior performance of A-QPSO in fault detection makes it an efficient and reliable fault detection algorithm, which significantly improves the fault management capability and power supply reliability of the distribution network.

[0047] In another embodiment, the individual historical optimal positions of all the particles are updated using the first fitness value, including: when the first fitness value of the current position of the particle is better than the fitness of the individual historical optimal position, updating the individual historical optimal position to the current position of the particle. In this method, the particle continuously updates the individual historical optimal position according to the previous position and fitness value, so that the optimal solution can be found more quickly in subsequent searches. By continuously updating the individual historical optimal position, the adaptive quantum particle swarm algorithm can search the solution space more effectively and find a better solution, so that the individual historical optimal position of the particle can be better located.

[0048] In order to better locate the optimal fault position and further optimize the accuracy and positioning time of fault location in fault detection, in one embodiment of the present application, the global optimal position of the particle is updated according to the individual historical optimal position of the particle and the current position of the particle, including: when one of the fitness value of the current position of the particle and the fitness value of the individual historical optimal position is better than the fitness value of the global optimal position, the global optimal position is updated to the optimal position of the two, and the optimal position of the two is the position with the smallest fitness value between the current position of the particle and the individual historical optimal position. In this method, the particle group is prevented from falling into the local optimal solution and being unable to jump out during the search process. By updating the position of the particle, the particle searches along the direction of the global optimal position, thereby further improving the accuracy of fault position detection.

[0049] like Figure 5 and Figure 6 As shown in the figure, by setting the parameters of the adaptive quantum particle swarm algorithm (A-QPSO) to the following values: particle swarm size is 50, maximum number of iterations is 200, quantum behavior parameter is [0.5, 1.0], initial value of convergence factor is 1.0, and final value of convergence factor is 0.1, the positioning accuracy and positioning time of A-QPSO, PSO and GA under different fault types are compared. The results show that A-QPSO also has significant advantages in positioning accuracy and positioning time. In the scenarios of short circuit fault, line break fault and grounding fault, the positioning errors of A-QPSO are 0.5 meters, 0.6 meters and 0.55 meters, respectively, which are significantly lower than the corresponding positioning errors of PSO and GA; in terms of positioning time, the positioning time of A-QPSO is 1.0 seconds, 1.2 seconds and 1.1 seconds, respectively, which is also significantly better than PSO's 1.8 seconds, 2.0 seconds and 1.9 seconds, and GA's 2.5 seconds, 2.6 seconds and 2.55 seconds. Therefore, Figure 5 and Figure 6 It also shows that A-QPSO has superior performance in fault location and can locate the fault point more quickly and accurately.

[0050] Specifically, dynamically adjusting the algorithm parameters includes: adjusting the above-mentioned convergence factor to obtain the change rate parameter of the particle position, adjusting the above-mentioned convergence factor according to the preset search behavior and the complexity of the fault problem; adjusting the above-mentioned inertia weight parameter to obtain the above-mentioned particle position, and the above-mentioned particle position is expressed as Among them, u is a random number, w is the inertia weight parameter mentioned above, and the convergence factor β controls the balance between exploration and utilization of the algorithm. Its dynamic adjustment is expressed as Among them, βmin and βmax are the minimum and maximum values ​​of the convergence factor, respectively, to ensure that the search process gradually transitions from extensive exploration to precise utilization; t represents the current number of iterations; λ is an adjustment rate parameter that controls the rate of convergence factor adjustment, and the setting of this value depends on the expected search behavior and the complexity of the problem; the above learning factor is adjusted to obtain the moving direction of the above particle, and the above learning factor includes the first learning factor and the second learning factor. The above first learning factor controls the moving direction of the above particle to the above individual historical optimal position, and the above second learning factor controls the moving direction of the above particle to the above global optimal position. The above learning factor is expressed as Among them, c start are the parameters generated in the initial stage of the above learning factor search, c end The method adjusts the algorithm parameters to make the fault location more accurate, thereby further improving the accuracy of fault detection.

[0051] In another embodiment, a fault recovery plan is determined based on the above-mentioned fault type and the above-mentioned optimal fault location, and the corresponding above-mentioned fault recovery plan is executed, including: when the above-mentioned fault type is a short-circuit fault, the above-mentioned fault recovery plan is determined to be fault isolation, and according to the above-mentioned optimal fault location, the above-mentioned fault recovery plan of the fault isolation is executed; when the above-mentioned fault type is an open-circuit fault, the above-mentioned fault recovery plan is determined to be power supply recovery, and according to the above-mentioned optimal fault location, the above-mentioned fault recovery plan of power supply recovery is executed; when the above-mentioned fault type is a ground fault, the above-mentioned fault recovery plan is determined to be load transfer, and according to the above-mentioned optimal fault location, the above-mentioned fault recovery plan of load transfer is executed. The method executes the corresponding fault recovery plan for each fault type, thereby recovering the fault.

[0052] In order to further perform fault recovery, in one embodiment of the present application, the above objective function is: Among them, r ij represents the line resistance from node i to node j in the above power system, x ij represents the decision variable, c k represents the unit load cost of node k in the above power system, yk It represents the operating status index of the above-mentioned node k of the above-mentioned power system after network reconstruction; the above-mentioned constraints are voltage stability constraint, power balance constraint and equipment operation restriction, the above-mentioned voltage stability constraint is expressed as the voltage of any node is maintained between the maximum voltage and the minimum voltage, the above-mentioned power balance constraint is that the total power input is the same as the total power output, and the above-mentioned equipment operation restriction is expressed as the actual current between any two of the above-mentioned nodes is less than the maximum current allowed between the two nodes.

[0053] Specifically, the nodes i and j in the objective function are two different nodes in the power system. k is the operating status indicator of node k after network reconstruction, where network reconstruction refers to dynamically adjusting the parameters and structure of the particle swarm algorithm according to the characteristics and situation of the current problem to optimize the performance and convergence speed of the algorithm. The operating status after network reconstruction depends on the specific adjustment strategy and adjustment results, which generally include: faster convergence speed, improved convergence accuracy and enhanced stability.

[0054] Specifically, the constraints for distribution network fault recovery include: voltage stability constraints, power balance constraints, and equipment operation restrictions. The voltage stability constraint is represented by V min ≤V i ≤V max , the power balance constraint is expressed as Among them, P load，j is the load size of node j, and the equipment operation limit is I ij ≤I max,ij .

[0055] like Figure 7 As shown in the figure, the comprehensive performance of different algorithms is compared by setting the parameters of the adaptive quantum particle swarm algorithm (A-QPSO) to the following values: particle swarm size is 50, maximum number of iterations is 200, quantum behavior parameter is [0.5, 1.0], initial value of convergence factor is 1.0, and final value of convergence factor is 0.1. The comprehensive performance of A-QPSO in fault management and power grid reconstruction is significantly better than that of PSO, GA and traditional methods. In terms of response time, detection accuracy, positioning accuracy, positioning time and power supply restoration time, the scores of A-QPSO are 0.9, 0.98, 0.95, 0.92 and 0.91 respectively, which are higher than 0.7, 0.9, 0.85, 0.75 and 0.78 of PSO, 0.6, 0.85, 0.8, 0.7 and 0.68 of GA, and 0.5, 0.75, 0.7, 0.65 and 0.6 of traditional methods. This shows the significant advantages of A-QPSO in comprehensive performance, which can detect and locate faults more accurately in a shorter time and restore power supply more quickly.

[0056] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the fault recovery method of the power system of the present application will be described in detail below in combination with specific embodiments.

[0057] This embodiment relates to a specific method for restoring a fault in a power system. Figure 8 As shown, the following steps are included:

[0058] Step S1: Analyze the abnormal change data of the power system to obtain the fault characteristics of the current power system;

[0059] Step S2: input the above fault characteristics into the fault model to obtain the current fault type;

[0060] Step S3: Determine the corresponding fault recovery model according to the current fault type;

[0061] Step S4: In the above fault recovery model, a quantum particle group is randomly generated, and the position and velocity of each particle are initialized to obtain the initial position and initial velocity of the particle;

[0062] Step S5: Calculate the first fitness value of the particle according to the objective function in the fault recovery model, and use the first fitness value to update the individual historical optimal positions of all particles;

[0063] Step S6: Analyzing the individual historical optimal positions of all the above particles to obtain the above individual historical optimal positions of the optimal particle;

[0064] Step S7: based on the initial velocity of the particle, the initial position and the quantum behavior, obtaining the current position of the particle;

[0065] Step S8: updating the global optimal position of the particle according to the individual historical optimal position of the particle and the current position of the particle, and calculating the second fitness value of the global optimal position of the particle;

[0066] Step S9: comparing the first fitness value and the second fitness value, thereby analyzing the change trend of the fitness value of the particle, and judging whether the algorithm parameters are appropriate according to the change of the fitness value of the particle;

[0067] Step S10: When the algorithm parameters are inappropriate, the algorithm parameters are dynamically adjusted using an adaptive strategy until the adaptive quantum particle swarm algorithm meets the termination condition and the optimal fault location is output;

[0068] Step S11: determining a fault recovery plan based on the fault type and the optimal fault location, and executing the corresponding fault recovery plan.

[0069] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0070] The embodiment of the present application also provides a fault recovery device for an electric power system. It should be noted that the fault recovery device for an electric power system in the embodiment of the present application can be used to execute the fault recovery method for an electric power system provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0071] The following is an introduction to the fault recovery device of the power system provided in the embodiment of the present application.

[0072] Fig. 9 Schematic diagram of a fault recovery device for a power system according to an embodiment of the present application. Fig. 9 As shown, the device comprises:

[0073] The acquisition unit 10 is used to acquire the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, wherein the fault model is trained using multiple groups of historical data, each group of the historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the power system.

[0074] Specifically, a fault model refers to a model used to describe the possible faults and causes of a system or device. By establishing a fault model, we can better understand the problems that the system may face, so that we can take corresponding preventive and repair measures to improve the reliability and stability of the system.

[0075] Specifically, abnormal change data of the power system refers to data that has sudden or unexpected changes compared to the normal operating state. These abnormal changes may include sudden increase or decrease in power load, voltage fluctuation, frequency abnormality, equipment failure, etc.

[0076] The execution unit 20 is used to determine the corresponding fault recovery model according to the current fault type, and use an adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain an optimal fault position, which is the global optimal position of the particle. The fault recovery model includes an objective function and constraints, and determines a fault recovery plan based on the fault type and the optimal fault position, and executes the corresponding fault recovery plan.

[0077] Specifically, in the adaptive quantum particle swarm algorithm, the position and speed of the particles will be dynamically adjusted according to the current search situation and the characteristics of the problem to more effectively explore the search space and find the optimal solution. At the same time, the quantum state of the quantum particles will also be dynamically adjusted according to the characteristics of the problem to improve the efficiency of the search and the speed of convergence.

[0078] In the above embodiment, first, the fault characteristics in the current power system are obtained, and the current fault characteristics are input into the fault model to obtain the current fault type; secondly, the corresponding fault recovery model is determined according to the current fault type, and the adaptive quantum particle swarm algorithm is used to solve the fault recovery model to obtain the optimal fault location, and the fault recovery plan is determined based on the fault type and the optimal fault location, and the corresponding fault recovery plan is executed. Compared with the automatic fault detection method based on sensor networks and smart meters in the prior art, which is faced with complex power grid environments and has insufficient accuracy in abnormal state recognition, the solution of the present application uses an adaptive quantum particle swarm algorithm to provide a faster global search capability by considering the probability distribution of quantum bits, and adaptively adjusts the search parameters to adapt to the dynamic changes of complex networks, and obtains the optimal position of the fault by searching the global optimal position of particles, thereby improving the accuracy of fault detection.

[0079] In one embodiment of the present application, the acquisition unit includes a first acquisition module, a first analysis module and a second analysis module, wherein the first acquisition module is used to acquire the operation data of the current power system; the first analysis module is used to analyze the above operation data to obtain abnormal change data; the second analysis module is used to perform time domain analysis and / or frequency domain analysis on the above abnormal change data to obtain the above fault characteristics in the current power system. In this scheme, by collecting various operation data of the current power system, analyzing these operation data, comparing historical data and real-time data, finding abnormal change data, performing time domain and frequency domain analysis on the abnormal change data, and according to the results obtained by the time domain and frequency domain analysis, the possible fault characteristics in the current power system can be obtained, wherein the fault types include short circuit fault, open circuit fault and ground fault, the fault characteristics of the short circuit fault type are current surge, voltage drop and high temperature, the fault characteristics of the open circuit fault type are current interruption, voltage imbalance and line reactive power change, and the fault characteristics of the ground fault type are ground current, voltage drop and fault current path change.

[0080] In order to further improve the accuracy of fault location in fault detection after the fault type is detected, in one embodiment of the present application, the execution unit includes a generation module, an initial module, a first calculation module, a first update module, a second acquisition module, a second calculation module, a second update module, a third calculation module, a first judgment module, a second judgment module and an adjustment module, wherein the generation module is used to randomly generate a quantum particle group in the above-mentioned fault recovery model; the initial module is used to initialize the state of each particle of the above-mentioned quantum particle group to obtain the initial position and initial velocity of the particle, and the above-mentioned initial position is randomly distributed in the solution space, which can be expressed as X i (0) = X min +rand()×(X max -X min ), where X min and X maxRespectively represent the lower bound and upper bound of the solution space, rand() is a function for generating a random number between [0,1], and the initial velocity is a random number or zero; the first calculation module is used to calculate the first fitness value of the particle according to the objective function in the fault recovery model; the first update module is used to update the individual historical optimal positions of all the particles using the first fitness value; the second acquisition module is used to analyze the individual historical optimal positions of all the particles to obtain the individual historical optimal position of the optimal particle, the optimal particle is the particle corresponding to the individual historical optimal position, and the individual historical optimal position of the optimal particle is the global optimal position; the second calculation module is used to obtain the current position of the particle based on the initial velocity, initial position and quantum behavior of the particle; the second update module is used to update the global optimal position of the particle according to the individual historical optimal position of the particle and the current position of the particle; the third calculation module Used to calculate the second fitness value of the global optimal position of the above-mentioned particle according to the updated global optimal position; the first judgment module is used to compare the above-mentioned first fitness value and the above-mentioned second fitness value, so as to judge the changing trend of the fitness value of the above-mentioned particle; the second judgment module is used to judge whether the parameters of the above-mentioned adaptive quantum particle swarm algorithm meet the predetermined conditions according to the changing trend of the above-mentioned fitness value of the above-mentioned particle, and the above-mentioned predetermined conditions are at least that the convergence curve of the function value of the above-mentioned objective function in the above-mentioned adaptive quantum particle swarm algorithm converges to the solution corresponding to the above-mentioned optimal fault position, and the above-mentioned parameters include convergence factor, inertia weight parameter and learning factor; the adjustment module is used to dynamically adjust the above-mentioned parameters by using an adaptive strategy when the parameters of the above-mentioned adaptive quantum particle swarm algorithm do not meet the predetermined conditions, until the above-mentioned adaptive quantum particle swarm algorithm meets the termination condition and outputs the above-mentioned optimal fault position, and the above-mentioned termination condition is one of the number of iterations reaching a threshold and the above-mentioned second fitness value being less than or equal to the fitness value threshold.

[0081] Specifically, before the algorithm starts, the position and velocity of each particle in the particle swarm are initialized, and for each particle, the fitness of its current position is evaluated using a predefined fitness function. The fitness function is designed based on the requirements of fault detection and location and is used to measure the quality of the solution. After that, the individual historical optimal position P is updated. best and the global optimal position G best If the current particle's fitness is better than its historical optimal fitness, update its P best ; If the fitness of the current particle is better than the global optimal fitness, update G bestDuring the iteration process, the current position of the particle is updated according to quantum behavior and calculated based on the concept of quantum bit probability cloud. At the same time, the algorithm parameters are dynamically adjusted according to the number of iterations or the change in the fitness of the particle. Finally, the algorithm is checked to see whether the maximum number of iterations has been reached or the fitness has reached a certain preset threshold. If the conditions are met, the algorithm stops and outputs the current global position as the fault position. This allows the fault to be located quickly and accurately, and the optimal fault position is obtained, further improving the accuracy and response time of fault detection.

[0082] In another embodiment, the first update module includes a first update submodule, wherein the first update submodule is used to update the individual historical optimal position to the current position of the particle when the first fitness value of the current position of the particle is better than the fitness of the individual historical optimal position. In this scheme, the particle continuously updates the individual historical optimal position according to the previous position and fitness value, so that the optimal solution can be found faster in subsequent searches. By continuously updating the individual historical optimal position, the adaptive quantum particle swarm algorithm can search the solution space more effectively and find a better solution, so that the individual historical optimal position of the particle can be better located.

[0083] In order to better locate the optimal fault position and further optimize the accuracy and positioning time of fault location in fault detection, in one embodiment of the present application, the second update module includes a second update sub-module, wherein the second update sub-module is used to update the above-mentioned global optimal position to the optimal position of the two when one of the fitness value of the current position of the above-mentioned particle and the fitness value of the above-mentioned individual historical optimal position is better than the fitness value of the above-mentioned global optimal position. The above-mentioned optimal position of the two is the position with the best fitness value between the current position of the above-mentioned particle and the above-mentioned individual historical optimal position. In this scheme, the particle group is prevented from falling into the local optimal solution and being unable to jump out during the search process. By updating the position of the particle, the particle searches along the direction of the global optimal position, thereby further improving the accuracy of fault position detection.

[0084] Specifically, dynamically adjusting the algorithm parameters includes: adjusting the above-mentioned convergence factor to obtain the change rate parameter of the particle position, adjusting the above-mentioned convergence factor according to the preset search behavior and the complexity of the fault problem; adjusting the above-mentioned inertia weight parameter to obtain the above-mentioned particle position, and the above-mentioned particle position is expressed as Wherein, u is a random number, and w is the inertia weight parameter. The learning factor is adjusted to obtain the moving direction of the particle. The learning factor includes a first learning factor and a second learning factor. The first learning factor controls the moving direction of the particle toward the individual historical optimal position, and the second learning factor controls the moving direction of the particle toward the global optimal position. The learning factor is expressed as Among them, c start are the parameters generated in the initial stage of the above learning factor search, c end The method adjusts the algorithm parameters to make the fault location more accurate, thereby further improving the accuracy of fault detection.

[0085] In another embodiment, the execution unit also includes a first execution module, a second execution module and a third execution module, wherein the first execution module is used to determine that the fault recovery plan is fault isolation when the fault type is a short circuit fault, and execute the fault recovery plan of the fault isolation according to the optimal fault position; the second execution module is used to determine that the fault recovery plan is power supply recovery when the fault type is an open circuit fault, and execute the fault recovery plan of power supply recovery according to the optimal fault position; the third execution module is used to determine that the fault recovery plan is load transfer when the fault type is a ground fault, and execute the fault recovery plan of load transfer according to the optimal fault position, and the plan executes the corresponding fault recovery plan for each fault type to recover the fault.

[0086] In order to further perform fault recovery, in one embodiment of the present application, the above objective function is: Among them, r ij represents the line resistance from node i to node j in the above power system, x ij represents the decision variable, c k represents the unit load cost of node k in the above power system, y k It represents the operating status index of the above-mentioned node k of the above-mentioned power system after network reconstruction; the above-mentioned constraints are voltage stability constraint, power balance constraint and equipment operation restriction, the above-mentioned voltage stability constraint is expressed as the voltage of any node is maintained between the maximum voltage and the minimum voltage, the above-mentioned power balance constraint is that the total power input is the same as the total power output, and the above-mentioned equipment operation restriction is expressed as the actual current between any two of the above-mentioned nodes is less than the maximum current allowed between the two nodes.

[0087] Specifically, the nodes i and j in the objective function are two different nodes in the power system. k is the operating status indicator of node k after network reconstruction, where network reconstruction refers to dynamically adjusting the parameters and structure of the particle swarm algorithm according to the characteristics and situation of the current problem to optimize the performance and convergence speed of the algorithm. The operating status after network reconstruction depends on the specific adjustment strategy and adjustment results, which generally include: faster convergence speed, improved convergence accuracy and enhanced stability.

[0088] Specifically, the constraints for distribution network fault recovery include: voltage stability constraints, power balance constraints, and equipment operation restrictions. The voltage stability constraint is represented by V min ≤V i ≤V max , the power balance constraint is expressed as The equipment is limited to I ij ≤I max,ij .

[0089] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0090] The above-mentioned fault recovery device for the power system includes a processor and a memory. The above-mentioned acquisition unit, execution unit, etc. are stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0091] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the problem of low accuracy of the fault detection method can be solved by adjusting kernel parameters.

[0092] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0093] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the power system fault recovery method, the method comprising:

[0094] Step S201, obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, wherein the fault model is trained using multiple groups of historical data, each group of historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the power system.

[0095] Step S202, determine the corresponding fault recovery model according to the current fault type, use the adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain the optimal fault position, the optimal fault position is the global optimal position of the particle, the fault recovery model includes an objective function and constraints, determine the fault recovery plan based on the fault type and the optimal fault position, and execute the corresponding fault recovery plan.

[0096] An embodiment of the present invention provides a processor, the processor is used to run a program, wherein when the program is run, the fault recovery method of the power system is executed, the method comprising:

[0097] Step S201, obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, wherein the fault model is trained using multiple groups of historical data, each group of historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the power system.

[0098] Step S202, determine the corresponding fault recovery model according to the current fault type, use the adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain the optimal fault position, the optimal fault position is the global optimal position of the particle, the fault recovery model includes an objective function and constraints, determine the fault recovery plan based on the fault type and the optimal fault position, and execute the corresponding fault recovery plan.

[0099] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0100] Step S201, obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, wherein the fault model is trained using multiple groups of historical data, each group of historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the power system.

[0101] Step S202, determine the corresponding fault recovery model according to the current fault type, use the adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain the optimal fault position, the optimal fault position is the global optimal position of the particle, the fault recovery model includes an objective function and constraints, determine the fault recovery plan based on the fault type and the optimal fault position, and execute the corresponding fault recovery plan.

[0102] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0103] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0104] Step S201, obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, wherein the fault model is trained using multiple groups of historical data, each group of historical data includes historical fault characteristics and corresponding historical fault types, and the fault characteristics are abnormal change data of the power system.

[0105] Step S202, determine the corresponding fault recovery model according to the current fault type, use the adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain the optimal fault position, the optimal fault position is the global optimal position of the particle, the fault recovery model includes an objective function and constraints, determine the fault recovery plan based on the fault type and the optimal fault position, and execute the corresponding fault recovery plan.

[0106] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0107] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0108] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0111] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0112] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0113] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes and other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0114] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0115] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0116] In the above embodiment, first, the fault characteristics in the current power system are obtained, and the current fault characteristics are input into the fault model to obtain the current fault type; secondly, the corresponding fault recovery model is determined according to the current fault type, and the adaptive quantum particle swarm algorithm is used to solve the fault recovery model to obtain the optimal fault location, and the fault recovery plan is determined based on the fault type and the optimal fault location, and the corresponding fault recovery plan is executed. Compared with the automatic fault detection method based on sensor networks and smart meters in the prior art, which is faced with complex power grid environments and has insufficient accuracy in abnormal state recognition, the solution of the present application uses an adaptive quantum particle swarm algorithm to provide a faster global search capability by considering the probability distribution of quantum bits, and adaptively adjusts the search parameters to adapt to the dynamic changes of complex networks, and obtains the optimal position of the fault by searching the global optimal position of particles, thereby improving the accuracy of fault detection.

[0117] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for recovering from a fault in an electric power system, characterized in that: include: Obtaining fault features in the current power system, and inputting the current fault features into a fault model to obtain a current fault type, wherein the fault model is trained using multiple groups of historical data, each group of historical data includes historical fault features and corresponding historical fault types, and the fault features are abnormal change data of the power system; A corresponding fault recovery model is determined according to the current fault type, and an adaptive quantum particle swarm algorithm is used to solve the fault recovery model to obtain an optimal fault position, which is a global optimal position of particles. The fault recovery model includes an objective function and constraints. A fault recovery plan is determined based on the fault type and the optimal fault position, and the corresponding fault recovery plan is executed.

2. The fault recovery method of the power system according to claim 1, characterized in that: Obtain the fault characteristics in the current power system, and input the current fault characteristics into the fault model to obtain the current fault type, including: Obtain the current operation data of the power system; Analyzing the operation data to obtain the abnormal change data; Perform time domain analysis and / or frequency domain analysis on the abnormal change data to obtain the fault characteristics in the current power system.

3. The fault recovery method of the power system according to claim 1, characterized in that: The fault recovery model is solved by using an adaptive quantum particle swarm algorithm to obtain the optimal fault location, including: In the fault recovery model, a quantum particle group is randomly generated; Initialize the state of each particle of the quantum particle group to obtain the initial position and initial velocity of the particle. The initial position is randomly distributed in the solution space and is represented by X i (0) = X min +rand()×(X max -X min ), where X min and X max denote the lower bound and the upper bound of the solution space respectively, rand() is a function for generating a random number between [0,1], and the initial velocity is a random number or zero; Calculating a first fitness value of the particle according to the objective function in the fault recovery model; Using the first fitness value, updating the individual historical optimal positions of all the particles; Analyze the individual historical optimal positions of all the particles to obtain the individual historical optimal position of the optimal particle, the optimal particle is the particle corresponding to the individual historical optimal position, and the individual historical optimal position of the optimal particle is the global optimal position; Based on the initial velocity, the initial position and quantum behavior of the particle, obtaining a current position of the particle; updating the global optimal position of the particle according to the individual historical optimal position of the particle and the current position of the particle; Calculating a second fitness value of the global optimal position according to the updated global optimal position; Comparing the first fitness value with the second fitness value, thereby determining a change trend of the fitness value of the particle; According to the change trend of the fitness value of the particle, judging whether the parameters of the adaptive quantum particle swarm algorithm meet predetermined conditions, wherein the predetermined condition is at least that the convergence curve of the function value of the objective function in the adaptive quantum particle swarm algorithm converges to the solution corresponding to the optimal fault position, and the parameters include a convergence factor, an inertia weight parameter and a learning factor; When the parameters of the adaptive quantum particle swarm algorithm do not meet the predetermined conditions, the parameters are dynamically adjusted by using an adaptive strategy until the adaptive quantum particle swarm algorithm meets the termination condition and the optimal fault location is output, and the termination condition is one of the number of iterations reaching the iteration number threshold and the second fitness value being less than or equal to the fitness value threshold.

4. The fault recovery method of the power system according to claim 3, characterized in that: Using the first fitness value, updating the individual historical optimal positions of all the particles, including: When the first fitness value of the current position of the particle is less than or equal to the fitness value of the individual historical optimal position, the individual historical optimal position is updated to be the current position of the particle.

5. The fault recovery method of the power system according to claim 3, characterized in that: Updating the global optimal position of the particle according to the individual historical optimal position of the particle and the current position of the particle, comprising: When one of the fitness value of the current position of the particle and the fitness value of the individual historical optimal position is better than the fitness value of the global optimal position, the global optimal position is updated to be the optimal position of the two, and the optimal position of the two is the position with the smallest fitness value between the current position of the particle and the individual historical optimal position.

6. The fault recovery method of the power system according to claim 1, characterized in that: Determining a fault recovery plan based on the fault type and the optimal fault location, and executing the corresponding fault recovery plan, including: In the case where the fault type is a short circuit fault, determining that the fault recovery scheme is fault isolation, and executing the fault recovery scheme of the fault isolation according to the optimal fault location; In the case where the fault type is a circuit breaker fault, determining that the fault recovery scheme is power supply recovery, and executing the fault recovery scheme of power supply recovery according to the optimal fault location; In the case where the fault type is a ground fault, the fault recovery scheme is determined to be load transfer, and the fault recovery scheme of the load transfer is executed according to the optimal fault location.

7. The fault recovery method of the power system according to any one of claims 1 to 6, characterized in that: The objective function is Among them, r ij represents the line resistance from node i to node j of the power system, x ij represents the decision variable, c k represents the unit load cost of node k in the power system, y k An operating status indicator representing the node k of the power system after network reconstruction; The constraints are voltage stability constraints, power balance constraints and equipment operation limitations. The voltage stability constraint is expressed as the voltage of any node being maintained between the maximum voltage and the minimum voltage. The power balance constraint is that the total power input is the same as the total power output. The equipment operation limitation is expressed as the actual current between any two of the nodes is less than the maximum current allowed between the two nodes.

8. A fault recovery device for an electric power system, characterized in that: include: An acquisition unit, which acquires fault features in the current power system, and inputs the current fault features into a fault model to obtain a current fault type, wherein the fault model is obtained by training with multiple groups of historical data, each group of historical data includes historical fault features and corresponding historical fault types, and the fault features are abnormal change data of the power system; The execution unit determines a corresponding fault recovery model according to the current fault type, uses an adaptive quantum particle swarm algorithm to solve the fault recovery model to obtain an optimal fault position, where the optimal fault position is a global optimal position of particles. The fault recovery model includes an objective function and constraints, determines a fault recovery plan based on the fault type and the optimal fault position, and executes the corresponding fault recovery plan.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the fault recovery method for the power system according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the fault recovery method of the power system described in any one of claims 1 to 7.

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