Power system master station fault positioning method and device and computer program product

Abstract

The invention discloses a power system master station fault positioning method and device and a computer program product, and the method comprises the steps: S1, collecting node fault feature data of a master station control system, and carrying out the coding of the node fault feature data; s2, fusing a simulated annealing mechanism and a genetic algorithm, establishing a dynamic parameter adjustment mechanism based on adaptability evaluation according to the encoded node fault feature data, and generating an improved global search algorithm; s3, taking the minimization of the fault isolation area as a target function, constructing an optimization model containing network topology constraints, and carrying out optimal solution iterative calculation by adopting the improved global search algorithm; and S4, obtaining an optimal fault node clearing scheme, and performing isolation operation on the fault nodes. According to the invention, the precision and efficiency of power system master station fault positioning are effectively improved, the manual intervention intensity of operation and maintenance personnel is significantly reduced, and a core technical support is provided for rapid self-healing of a smart power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a method, device and computer program product for fault location of a power system master station. Background Art

[0002] With the deep integration of smart grids and high-proportion renewable energy, the grid-connected capacity of distributed energy has shown an exponential growth trend. In this context, as the core security defense line of a new type of power system, the dynamic response characteristics and multi-source collaboration capabilities of intelligent distributed protection technology are directly related to the transient stability and fault tolerance of the power grid. It is particularly worth noting that the master station fault location technology, as the decision-making center of the intelligent distributed protection system, its performance indicators directly affect the system self-healing time constant and the reliability of island operation, and has become a key technological breakthrough point in the field of smart grid research at home and abroad.

[0003] The current technological evolution presents a dual-track parallel feature: domestic research focuses on the intelligent analysis technology of fault characteristics, optimizes the self-healing control strategy through reverse analysis algorithms, establishes a fault detection system based on wide-area synchronous phasor measurement, and realizes the high-precision synchronous acquisition of fault characteristic quantities relying on an intelligent sensing network. At the same time, foreign scholars have made breakthroughs in the field of fault space location, innovatively combining deep belief networks (DBNs) with adaptive fuzzy inference systems to construct a fault location model with dynamic weight adjustment capabilities, achieving a relatively high location accuracy in the IEEE 39-bus test system. However, with the multi-dimensional heterogeneous characteristics of the topology structure of power electronic power systems, traditional methods have exposed three-dimensional technical bottlenecks: 1) There is information entropy loss in the fault feature extraction link, resulting in a low identification accuracy of small current grounding faults; 2) The fusion efficiency of multi-source heterogeneous data is low, and the location decision delay is large in typical scenarios; 3) The lack of topological adaptability in dynamic reconstruction scenarios. Summary of the Invention

[0004] The technical problem to be solved by the embodiments of the present invention is to provide a method, device and computer program product for fault location of a power system master station to improve the accuracy of master station fault location.

[0005] To solve the above technical problem, the present invention provides a method for fault location of a power system master station, including:

[0006] Step S1, collecting node fault feature data of the master station control system and encoding the node fault feature data;

[0007] Step S2, fusing the simulated annealing mechanism and the genetic algorithm, establishing a dynamic parameter adjustment mechanism based on adaptability evaluation according to the encoded node fault feature data, and generating an improved global search algorithm;

[0008] Step S3: Construct an optimization model with the goal of minimizing the fault isolation area, including network topology constraints, and perform iterative calculations for the optimal solution using the improved global search algorithm;

[0009] Step S4: Obtain the optimal fault node clearing scheme and perform isolation operations on the fault nodes.

[0010] Preferably, the node fault feature data includes the GOOSE signal feature vector of the node, the self-healing switch logic state matrix, the distributed FA setting parameter set, and the GOOSE interface topology information.

[0011] Preferably, the specific steps of Step S2 include:

[0012] Step S21: Perform binary encoding on N nodes in the master station control system;

[0013] Step S22: Construct a fitness function with the goal of clearing fault nodes and restoring system operation;

[0014] Step S23: Use the roulette wheel method to confirm the selection probability and number of times for each individual, form an initial population, and perform selection operations after calculating the fitness of the initial population to obtain an intermediate population;

[0015] Step S24: Pair the intermediate population in pairs. With a crossover probability P c Generate a random position n, and swap the numbers at the nth position of the genetic codes of the paired intermediate population;

[0016] Step S25: In the intermediate population, with a mutation probability P m Select the genetic code, and then generate a random integer Z to perform mutation operations at the Zth position of the selected genetic code;

[0017] Step S26: Iteratively execute Steps S22 - S25. When the maximum number of iterations is reached, generate an improved global search algorithm.

[0018] Preferably, the specific steps of Step S3 include:

[0019] Dynamically adjust the crossover probability P c and mutation probability P m of the genetic algorithm based on the fitness distribution characteristics, where when the individual fitness is lower than the population average, the crossover probability P c increases linearly with the degree of fitness deterioration, and the mutation probability P m increases exponentially with the degree of fitness deterioration;

[0020] Taking the fitness as the objective function and the successful removal of faulty nodes as the constraint condition, an improved global search algorithm is used to search for nodes in the system.

[0021] Preferably, the constraint condition satisfies:

[0022]

[0023] where x t represents whether node t is selected. If selected, x t is 0; if not selected, x t is 1; E is the system normal state identification quantity, and x t ·E is a logical operation, that is, the operation result is 1 when node t is not selected and the system state is normal.

[0024] Preferably, the calculation method of the crossover rate probability P c is as follows:

[0025]

[0026] where k1 is the value of P c set at the beginning of the algorithm; f is the fitness value with a smaller fitness in the contemporary genetic coding; f min is the value of the minimum fitness in the global genetic coding; f avg is the average value of the fitness values of all genetic codings.

[0027] Preferably, the calculation method of the mutation probability P m is as follows:

[0028]

[0029] where k2 is the value of P m set at the beginning of the algorithm.

[0030] The present invention also provides a main station fault location device for a power system, including:

[0031] A data acquisition and coding module, which is used to acquire node fault feature data of the main station control system and encode the node fault feature data;

[0032] An algorithm improvement module, which is used to fuse the simulated annealing mechanism and the genetic algorithm, establish a dynamic parameter adjustment mechanism based on adaptability evaluation according to the encoded node fault feature data, and generate an improved global search algorithm;

[0033] A search module, taking the minimization of the fault isolation area as the objective function, constructing an optimization model including network topology constraints, and performing iterative calculation of the optimal solution using the improved global search algorithm;

[0034] The isolation module obtains the optimal solution for clearing the faulty node and performs isolation operations on the faulty node.

[0035] The present invention also provides a power system master station fault location device, including:

[0036] One or more processors;

[0037] A memory;

[0038] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the power system master station fault location method described above.

[0039] The present invention also provides a computer program product, including computer instructions, and the computer instructions instruct a computer device to perform the operations corresponding to the method.

[0040] Implementing the present invention has the following beneficial effects: Through multi-dimensional technical improvements, the present invention effectively improves the accuracy and efficiency of power system master station fault location. First, based on the feature encoding system of multi-source data such as GOOSE signal characteristics and self-healing switch logic, the structured representation of fault information is realized, providing a high-dimensional feature space for subsequent algorithm processing; second, the genetic algorithm improvement strategy integrating the simulated annealing mechanism overcomes the defect that traditional algorithms are prone to fall into local optima by dynamically adjusting the crossover probability and mutation probability, and improves the convergence speed; third, the constructed node screening constraint conditions ensure the coordinated optimization of fault isolation operations and system topology integrity, greatly improving the fault location accuracy. While ensuring the transient stability of the power grid, the present invention shortens the typical fault handling duration, reduces the manual intervention intensity of maintenance personnel, and provides core technical support for the rapid self-healing of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a flowchart of a power system master station fault location method according to Embodiment 1 of the present invention.

[0043] Figure 2 It is a simple radial master station structure diagram of an example of an embodiment of the present invention.

[0044] Figure 3 is Figure 2The operation topology structure diagram of the shown simple radiation type master station.

[0045] Figure 4 It is a schematic diagram of the fitness comparison between the embodiment of the present invention and the genetic algorithm and the particle swarm algorithm. Specific implementation manners

[0046] The descriptions of the following embodiments refer to the accompanying drawings to exemplify specific embodiments in which the present invention can be implemented.

[0047] Please refer to Figure 1 As shown, Embodiment 1 of the present invention provides a method for fault location of a power system master station, including:

[0048] Step S1, collect the node fault feature data of the master station control system and encode the node fault feature data;

[0049] Step S2, fuse the simulated annealing mechanism and the genetic algorithm, establish a dynamic parameter adjustment mechanism based on adaptability evaluation according to the encoded node fault feature data, and generate an improved global search algorithm;

[0050] Step S3, taking the minimization of the fault isolation area as the objective function, construct an optimization model including network topology constraints, and use the improved global search algorithm to perform iterative calculation of the optimal solution;

[0051] Step S4, obtain the optimal fault clearing node scheme and perform isolation operation on the fault nodes.

[0052] Specifically, step S1 extracts the node fault feature data of the master station control system under intelligent distributed protection, which includes key information related to node faults such as the GOOSE signal, self-healing switch logic, distributed FA setting value, and GOOSE interface information of the node, and encodes the node fault feature data to form the initial parameters of the node fault information.

[0053] Step S2 uses simulated annealing operation to improve the traditional genetic algorithm and improve the convergence speed and stability of the traditional genetic algorithm, including the following steps:

[0054] Step S21, initialize the encoding: according to the initial parameters of the node fault information formed in step S1, perform binary encoding on N nodes in the master station control system.

[0055] Step S22, construct the fitness function: taking the clearing of fault nodes and the restoration of system operation as the optimization goal, construct the following fitness function:

[0056] maxF j

[0057] In the formula: F j is the system fitness.

[0058] Step S23, selection operation: In the embodiments of the present invention, the roulette method is used to confirm the probability and number of times each individual is selected, form an initial population, calculate the fitness of the initial population and then perform the selection operation, and finally leave the elite population. The population at this time is called the intermediate population.

[0059] Step S24, crossover operation: Pair the intermediate populations in pairs, and with a crossover probability P c Generate a random position n, and swap the numbers at the nth position of the genetic codes of the paired intermediate populations.

[0060] Step S25, mutation operation: Set the mutation probability P m , and select the genetic code in the intermediate population with a probability of P m . Then generate a random integer Z, and perform the mutation operation at the Zth position of the selected genetic code.

[0061] Step S26, the new population after selection, crossover, and mutation is called the first-generation population. At this time, enter the loop again through step S22. Set the maximum number of generations of iteration to D. When the maximum number of iteration generations is reached, end the algorithm.

[0062] Step S3 aims at clearing the faulty nodes as the objective function, and uses the improved genetic algorithm to search for the nodes in the system, including the following steps:

[0063] Step S31, add the Boltzmann strategy in the simulated annealing operation, and make the crossover probability P c and the mutation probability P m change with the fitness, so as to enhance the defect that the traditional genetic algorithm is prone to fall into local convergence.

[0064] Step S32, the calculation formulas for the crossover rate probability P c and the mutation probability P m are as follows:

[0065]

[0066]

[0067] In the formula, k1 and k2 are the values of P c , P m set at the beginning of the algorithm respectively; f is the fitness value with a smaller fitness in the contemporary genetic code; f min is the minimum fitness value in the global genetic code; f avg is the average value of the fitness values of all genetic codes.

[0068] Step S33: Using the fitness as the objective function and the successful removal of the fault node as the constraint condition, search for the nodes in the system using the improved genetic algorithm. The constraint conditions are as follows:

[0069]

[0070] In the formula: x t indicates whether node t is selected. If selected, x t is 0; if not selected, x t is 1; E is the system normal state identification quantity, and x t ·E is a logical operation, that is, the operation result is 1 when node t is not selected and the system state is normal.

[0071] Step S4: Obtain the optimal fault node removal scheme, isolate the fault node, and at the same time verify whether the system operates normally after isolating the fault node.

[0072] To verify the effectiveness of the embodiments of the present invention, a simple radial main station shown in Figure 2 is taken as an example for illustration. Figure 2 In it, S is the main station, L1 - L6 are feeder control sections. Each feeder control section is jointly controlled by a sectionalizing switch K and an FTU. t is the control node, L7 and L8 are tie lines, and a tie switch KL and an FTU are installed between the tie lines. For the convenience of analysis, the Figure 2 structural diagram is simplified again to Figure 3 the operating topological structure diagram, and at the same time assume that the tie switch KL is in the off state.

[0073] From Figure 3 it can be seen that section L5 is no longer responsible for the power transmission of node t5, and the power transmission is changed to be carried out by section L1 and tie line L7, and the adjacent relationship between section L4 and section L6 is released.

[0074] The constructed main station structure is subjected to fault location simulation through simulation software. Simulate a phase - to - phase short - circuit fault occurring in section L4. Use the method proposed by the present invention to initialize the encoding of each node. The encoding information includes node load information, section feeder FA information, GOOSE information of the FTU, etc., and start searching for the fault node. Finally, the fitness quantization reference values of each node are obtained as shown in Table 1 below:

[0075] Table 1 Fitness values of nodes

[0076]

[0077]

[0078] From the fitness of each node, it can be seen that the fitness of node t4 is the lowest, and the fault can be located at node t4.

[0079] The method proposed by the present invention is compared with traditional genetic algorithms and particle swarm algorithms, and the comparison results are as Figure 4 shown. It can be seen from Figure 4 that the method proposed by the present invention is superior to traditional genetic algorithms and particle swarm algorithms in terms of iteration speed, and has better accuracy and positioning speed, thus further verifying the accuracy and superiority of the present invention.

[0080] Corresponding to the power system master station fault location method described in the foregoing Embodiment 1 of the present invention, Embodiment 2 of the present invention further provides a power system master station fault location device, including:

[0081] A data acquisition and encoding module, configured to acquire node fault feature data of the master station control system and encode the node fault feature data;

[0082] An algorithm improvement module, configured to fuse the simulated annealing mechanism and the genetic algorithm, establish a dynamic parameter adjustment mechanism based on fitness evaluation according to the encoded node fault feature data, and generate an improved global search algorithm;

[0083] A search module, with minimizing the fault isolation area as the objective function, constructs an optimization model including network topology constraints, and uses the improved global search algorithm to perform iterative calculation of the optimal solution;

[0084] An isolation module, obtains the optimal fault node clearing scheme, and performs isolation operation on the fault nodes.

[0085] Corresponding to the power system master station fault location method described in the foregoing Embodiment 1 of the present invention, Embodiment 3 of the present invention further provides a power system master station fault location device, including:

[0086] One or more processors;

[0087] A memory;

[0088] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to execute the power system master station fault location method described in the foregoing Embodiment 1 of the present invention.

[0089] Corresponding to the power system master station fault location method described in the foregoing Embodiment 1 of the present invention, Embodiment 4 of the present invention further provides a computer program product, including computer instructions, and the computer instructions direct a computer device to perform operations corresponding to the power system master station fault location method described in the foregoing Embodiment 1 of the present invention.

[0090] Preferably, the processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor. The processor is the control center of the device and connects various parts of the device through various interfaces and circuits.

[0091] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant data, etc. In addition, the memory may be a high-speed random access memory, or may also be a non-volatile memory, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory may also be other volatile solid-state storage devices.

[0092] It should be noted that the above device may include but is not limited to a processor and a memory, which can be understood by those skilled in the art.

[0093] Regarding the working principle and process of the above embodiments, refer to the description of Embodiment 1 of the present invention above, and details will not be repeated here.

[0094] It can be seen from the above description that compared with the prior art, the beneficial effects of the present invention are as follows: The present invention effectively improves the accuracy and efficiency of fault location in the main station of the power system through multi-dimensional technical improvements. First, based on the feature encoding system of multi-source data such as GOOSE signal characteristics and self-healing switch logic, the structured representation of fault information is realized, providing a high-dimensional feature space for subsequent algorithm processing; second, the genetic algorithm improvement strategy integrating the simulated annealing mechanism overcomes the defect that the traditional algorithm is prone to falling into local optimum by dynamically adjusting the crossover probability and mutation probability, and improves the convergence speed; third, the constructed node screening constraint conditions ensure the coordinated optimization of fault isolation operations and system topology integrity, greatly improving the accuracy of fault location. While ensuring the transient stability of the power grid, the present invention shortens the typical fault handling time, reduces the manual intervention intensity of operation and maintenance personnel, and provides core technical support for the rapid self-healing of the smart grid.

[0095] The above-disclosed are only the preferred embodiments of the present invention. Of course, the scope of the rights of the present invention cannot be limited thereby. Therefore, equivalent changes made in accordance with the claims of the present invention still fall within the scope covered by the present invention.

Claims

1. A method for fault location of a power system master station, characterized in that including: Step S1: Collect the node fault feature data of the master station control system and encode the node fault feature data; Step S2: Integrate the simulated annealing mechanism and the genetic algorithm, establish a dynamic parameter adjustment mechanism based on adaptability evaluation according to the encoded node fault feature data, and generate an improved global search algorithm; Step S3: Take minimizing the fault isolation area as the objective function, construct an optimization model including network topology constraints, and perform iterative calculation of the optimal solution using the improved global search algorithm; Step S4: Obtain the optimal fault node clearing solution and perform isolation operation on the fault node.

2. The method according to claim 1, wherein The node fault feature data includes the GOOSE signal feature vector of the node, the self-healing switch logic state matrix, the distributed FA setting parameter set, and the GOOSE interface topology information.

3. The method according to claim 1, characterized in that, The specific steps of Step S2 include: Step S21: Perform binary encoding on N nodes in the master station control system; Step S22: Construct a fitness function with the objective of clearing the fault node and restoring system operation; Step S23: Use the roulette wheel method to confirm the selection probability and number of times of each individual, form an initial population, and perform a selection operation after calculating the fitness of the initial population to obtain an intermediate population; Step S24, pair the intermediate populations in pairs with a crossover probability P c Generate a random position n, and swap the numbers at the nth position of the genetic codes of the paired intermediate populations; Step S25, with a mutation probability P in the intermediate population m Select a genetic code, then generate a random integer Z, and perform a mutation operation at the position Z of the selected genetic code; Step S26: Iteratively execute Steps S22 - S25. When the maximum number of iterations is reached, generate an improved global search algorithm.

4. The method according to claim 1, characterized in that The specific steps of Step S3 include: Dynamically adjusting the crossover probability P of a genetic algorithm based on the fitness distribution characteristics c and the mutation probability P m , where when the individual fitness is lower than the population average, the crossover probability P c increases linearly with the degree of fitness deterioration, and the mutation probability P m increases exponentially with the degree of fitness deterioration; Taking fitness as the objective function and successful removal of the fault node as the constraint condition, use the improved global search algorithm to search for the nodes in the system.

5. The method according to claim 4, wherein The constraint conditions are satisfied: Among them, x t indicates whether node t is selected. If selected, x t is 0; if not selected, x t is 1; E is the system normal state identifier, x t ·E is a logical operation, that is, when node t is not selected and the system state is normal, the operation result is 1.

6. The method according to claim 4, characterized in that, Crossover rate probability P c is calculated as follows: Among them, k1 is the P set at the beginning of the algorithm c value; f is the fitness value with a relatively small fitness in the contemporary genetic coding; f min is the value of the minimum fitness in the global genetic coding; f avg is the average value of the fitness values of all genetic codings.

7. The method according to claim 6, wherein Mutation probability P m is calculated as follows: Among them, k2 is the P set at the beginning of the algorithm m numerical value.

8. A main station fault location device for a power system, characterized in that, including: A data acquisition and encoding module, configured to collect the node fault feature data of the master station control system and encode the node fault feature data; An algorithm improvement module, configured to integrate the simulated annealing mechanism and the genetic algorithm, establish a dynamic parameter adjustment mechanism based on adaptability evaluation according to the encoded node fault feature data, and generate an improved global search algorithm; A search module, taking minimizing the fault isolation area as the objective function, constructing an optimization model including network topology constraints, and performing iterative calculation of the optimal solution using the improved global search algorithm; An isolation module, obtaining the optimal fault node clearing solution and performing isolation operation on the fault node.

9. A main station fault location device for a power system, characterized in that, including: One or more processors; A memory; One or more applications, where the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the power system master station fault location method according to any one of claims 1 to 7.

10. A computer program product, characterized in that, including computer instructions, where the computer instructions direct the computer device to perform the operations corresponding to the method according to any one of claims 1 to 7.

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