Low-voltage flexible DC micro-grid fault positioning method based on improved Tiancattle swarm algorithm

Abstract

The invention discloses a low-voltage flexible DC micro-grid fault positioning method based on an improved Tiancattle swarm algorithm. According to the method, the fault point can be accurately positioned when the inter-electrode short-circuit fault or the single-electrode grounding fault occurs in the flexible direct-current power distribution network. The method is slightly influenced by excessive resistance, and a fault positioning problem is converted into a fault parameter identification problem. And in combination with the improved Tiancattle swarm algorithm, the accuracy of fault point positioning can be improved, errors are reduced, and the information distortion resistance is improved. The method is of great significance in improving the safety and reliability of the direct-current power distribution system.

Description

Technical Field

[0001] This invention belongs to the field of power system fault diagnosis, and particularly relates to a fault location method for low-voltage flexible DC microgrids based on an improved beetle swarm algorithm. Background Technology

[0002] Due to its advantages of low line loss and high power supply reliability, DC distribution systems have become a research hotspot. However, from the perspective of safety and reliability, it is essential to quickly and accurately determine the fault location when a fault occurs. Therefore, research on fault location technology for DC distribution lines is of great significance to the reliability of power systems. Currently, the main methods for fault location in DC power grids are the traveling wave method and the non-traveling wave method. The traveling wave method is relatively simple in principle, utilizing the reflection characteristics of traveling waves at discontinuous nodes to accurately locate faults. However, for DC distribution systems, the traveling wave method has high requirements for the selection of the time window, and the usable time window is short, which significantly reduces the accuracy of the fault distance calculation method. Therefore, the traveling wave method is generally not used for fault location in DC distribution networks. The non-traveling wave method mainly uses single-end or double-end electrical quantity data at the moment of fault occurrence for fault location. The first method uses single-end electrical quantities for fault location, but this method is easily affected by the control mode and transition resistance of the converter, resulting in low accuracy of fault location. Another method uses electrical quantities at both ends for fault location. Compared to single-sided electrical quantity fault location, two-end electrical quantity location can eliminate the influence of transition resistance and has higher location accuracy. However, because the first and second derivatives are obtained using the differential method, the accuracy is relatively poor, which has a significant impact on the location accuracy. Summary of the Invention

[0003] The purpose of this invention is to address the accuracy issues inherent in traditional methods. It targets two common short-circuit faults in DC distribution networks: unipolar grounding faults and bipolar short-circuit faults. A fitness function applicable to both models is constructed, transforming the fault location problem into a parameter identification problem. Parameter identification is achieved by combining the beetle swarm algorithm with an improved impedance method. This method offers significant advantages such as high accuracy and strong robustness. Research on this method is of great importance for improving the safety and reliability of DC distribution systems.

[0004] The technical solution adopted in this invention is: A fault location method for low-voltage flexible DC microgrids based on an improved beetle swarm algorithm, comprising the following steps: (1) Set the population size n; (2) Detecting voltage value V dc1 V dc2 V Lt1 V Lt2 ; (3) Repeatedly test multiple sets of data; (4) Randomly select n sets of data from the multiple sets of detected data for calculation, calculate the parameters of single-pole grounding fault and inter-pole short-circuit fault, and use the results as the initial population; (5) Set the maximum number of iterations for the population and perform iterations; (6) Fitness value calculation: The fitness of each particle is taken as the absolute value, and the minimum value is selected as the optimal value. (7) Determine whether the fitness value obtained in this iteration meets the accuracy requirement. If not, continue iterating until the maximum number of iterations is reached or the accuracy requirement is met. If it meets the requirement, stop iterating and output the optimal solution.

[0005] The number of populations in step (1) is set according to the requirements and is a positive integer multiple of 4; the number of repeated detection groups in (3) is determined by the number of populations.

[0006] In step (2), V dc1 This is the voltage value on the left side when the DC distribution network is short-circuited, V. dc2 This is the voltage value on the right side when the DC distribution network is short-circuited, V. Lt1 This is the voltage value of the current-limiting reactor at the left outlet when the DC distribution network is short-circuited, V. Lt2 This is the voltage value of the current-limiting reactor at the right outlet when the DC distribution network is short-circuited.

[0007] The formula for calculating fault parameters in step (4) Discretizing equations (1) and (2) yields XA p-g -V dc2 (k)+V dc1 (k)+V Lt1 (k)-V Lt2 (k)=0#(5) XA p-p -V dc2 (k)+V dc1 (k)+2V Lt1 (k)-2V Lt2 (k)=0#(6) Where X represents the parameters [R1, R2, L1, L2] when the system fails, k is the sampling constant, and A p-g Let A be the system matrix under a single-pole short-circuit fault. p-p The system matrix A is for a bipolar fault. The iterative method used in step (5) is a combination of the beetle whisker algorithm and the particle swarm optimization algorithm, as shown in the following formula: x ni =x n(i-1) +v ni #(14) Where n is the number of particles; i is the algebra number of particle iterations; 1 is the step size of each iteration; m is the dimension of each particle; pbest and gbest are the optimal particle value and the population optimal value that appear in each iteration, respectively; r1 and r2 are random numbers between 0 and 1.

[0008] To better balance global and local search capabilities, in the early stages of iteration, the inertia weight *w* should decrease at a faster rate, increasing the step size of particle updates, allowing the swarm to quickly find the feasible solution region. In the later stages of iteration, the inertia weight *w* should decrease at a slower rate, reducing the step size of particle updates, allowing the particle swarm to fine-tune its search within the feasible solution region until it finds the global optimum. Furthermore, in the early stages of iteration, the swarm should possess strong self-learning capabilities; as iterations continue, self-learning capabilities should weaken, while social learning capabilities should continuously increase. The modifications to the particle swarm optimization algorithm are as follows: w(t) = w min +α(w max -w min )#(17) Where T is the total number of iterations; t is the current iteration number; β is a control parameter used to control the convergence rate, β>1; w max and w min These are the upper and lower bounds of the inertia weight, respectively; and These are the upper and lower bounds of the cognitive coefficient, respectively. and These represent the upper and lower bounds of the social coefficient, respectively.

[0009] To avoid getting trapped in local optima, we use the following iterative location information method: xn，i =x n(i-1) (1-sin(h))+v n，i sin(h)#(20) Equation (20) incorporates a sine function factor, which can generate oscillations in the position information and prevent it from getting stuck in a local optimum for a long time. h is an undetermined parameter; if it is too large or too small, it will lead to slow convergence or non-convergence.

[0010] Step (6) The fitness function is: Where f(x) = XA p-g -V dc2 (k)+V dc1 (k)+V Lt1 (k)-V Lt2 (k), or f(x) = XA p-p -V dc2 (k)+V dc1 (k)+2V Lt1 (k)-2V Lt2 (k), let the right side of the equation be 0. N is the sampling point selected within a certain period after the fault occurs.

[0011] This invention discloses a fault location method for low-voltage flexible DC microgrids based on an improved longhorn beetle swarm algorithm, with the following technical advantages: 1. The algorithm combining the beetle whisker algorithm and the particle swarm algorithm has high fault location accuracy and strong resistance to excessive resistance.

[0012] 2: This invention combines the sampling values ​​from multiple sampling points, resulting in smaller errors and higher resistance to information distortion. Attached Figure Description

[0013] Figure 1 A flowchart of a fault location method for low-voltage flexible DC microgrids based on an improved longhorn beetle swarm algorithm; Figure 2 This is a simulation diagram of a six-terminal DC power distribution system. Figure 3 This is an error diagram of the fault location result under the condition of information distortion according to the present invention; Figure 4 The simulation graph shows the average error with and without information distortion. Detailed Implementation

[0014] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto: Figure 1 This is a flowchart of a fault location method for low-voltage flexible DC microgrids based on an improved beetle swarm algorithm.

[0015] A fault location method for low-voltage flexible DC microgrids based on an improved beetle swarm algorithm, comprising the following steps: (1) Set the population size according to the accuracy and precision required by the system. The larger the size, the higher the accuracy. In this method, it is set to 40.

[0016] (2) Detect the four voltage values ​​V of the simulation circuit. dc1 V dc2 V Lt1 V Lt2 .

[0017] (3) Repeat the test 200 times, with 4 data points in each group.

[0018] (4) Randomly select 40 sets of data from the detection data to calculate the parameters of single-pole grounding fault and inter-pole short-circuit fault. The results are used as the initial population. The calculation formula is as follows: XA p-g -V dc2 (k)+V dc1 (k)+V Lr1 (k)-V Lt2 (k)=0 XA p-p -V dc2 (k)+V dc1 (k)+2V Lt1 (k)-2V Lt2 (k)=0 The unknowns R1, R2, L1, L2 can be calculated from the formula, where X is the parameter [R1, R2, L1, L2] when the system fails, k is the sampling constant, and A p-g Let A be the system matrix under a single-pole short-circuit fault. p-p The system matrix A is for a bipolar fault. And from R1 = xr1 R² = xr² L1 = xl1 L2 = xl2 The location of the fault can be calculated. However, distortion may occur during the uploading of information such as voltage and current, causing significant errors in the calculated impedance value. Therefore, it is necessary to combine multiple data points and optimize the calculation using a beetle swarm algorithm.

[0019] (5) Perform iterations, setting the maximum number of iterations for the population to 50. The following iterative method is formed by combining the beetle whisker algorithm and the particle swarm optimization algorithm. x ni =x n(i-1) +v ni Where n is the number of particles; i is the algebra number of particle iterations; l is the step size of each iteration, which is a random value between 0 and 1; m is the dimension of each particle, which is set to 4 here; pbest and gbest are the optimal particle value and the optimal population value that appear in each iteration, respectively; r1 and r2 are random numbers between 0 and 1, which can improve the randomness of the iteration. The changes to the particle swarm optimization algorithm are as follows: w(t) = w min +α(w max -w min )#(15) Where T is the total number of iterations; t is the current iteration number; β is a control parameter used to control the convergence speed, β>1, and is set to 10 here; w max and w min These are the upper and lower bounds of the inertia weight, set to 1.2 and 0.9 respectively. and These are the upper and lower bounds of the cognitive coefficient, set here to 2.5 and 0.1 respectively; and These are the upper and lower bounds of the social coefficient, set here to 3.2 and 0.8 respectively; To avoid getting trapped in local optima, we use the following iterative location information method: x n， i = x n，(i-1) (1-sin(h))+v n，i sin(h)#(18).

[0020] (6) The fitness of each particle is taken as its absolute value, and the minimum value is selected as the optimal value. The fitness calculation function is as follows: Where f(x) = XA p-g -V dc2 (k)+V dc1 (k)+V Lt1 (k)-VLt2 (k), or f(x) = XA p-p -V dc2 (k)+V dc1 (k)+2V Lt1 (k)-2V Lt2 (k), let the right side of the equation be 0. N is the sampling point selected within a certain period after the fault occurs.

[0021] (7) Determine whether the fitness value obtained in this iteration meets the accuracy requirement. If not, continue iterating until the maximum number of iterations is reached or the accuracy requirement is met. If it meets the requirement, stop iterating and output the optimal solution.

[0022] Figure 2 This is a simulation diagram of a six-terminal DC power distribution system. The constructed six-terminal DC power distribution system, as shown in the diagram, employs a dual closed-loop control system: an inner loop current control and an outer loop voltage control. Energy storage elements act as the balancing node for the entire system, balancing the overall power. Photovoltaic elements are connected to the grid via a DC / DC system. The fault simulation section is marked with V. dc(a) and V dc(b) The simulation distance for the line between points is assumed to be the distance from point a to the short-circuit point. The DC bus voltage is set to 500V, the DC side capacitor of the converter is 20mF, the resistance per unit length of the line is r1 = 0.03Ω / km, the inductance per unit length of the line is l1 = 0.2865mH / km, the line length is 10km, and the sampling interval is 10μs.

[0023] Figure 3 This is an error diagram of the fault location result under the condition of information distortion according to the present invention; exist Figure 3 In the figure, the bar chart represents the fault distance measurement error for single-pole grounding faults and double-pole short-circuit faults under conditions of information distortion, while the line graph represents the average fault distance measurement error under conditions of no information distortion. As can be seen from the figures, the algorithm of this invention exhibits relatively small errors in fault distance measurement under both conditions of information distortion and no information distortion, with the maximum error not exceeding 2%, demonstrating extremely high accuracy.

[0024] Figure 4 The simulation graphs show the average error with and without information distortion. Given the algorithm's instability, each case was run ten times, and the average positioning error under all cases was as follows: Figure 4 As shown in the figure, the horizontal axis represents the fault type number. There are a total of 18 fault types, and the overall average error fluctuates around 1%, demonstrating the high robustness of the beetle swarm algorithm.

Claims

1. A fault location method for low-voltage flexible DC microgrids based on an improved beetle swarm algorithm, characterized in that... Includes the following steps: (1) Set the population size n; (2) Detecting voltage value V dc1 , dc2 , Lt1 , Lt2 ; (3) Repeatedly test multiple sets of data; (4) Randomly select n sets of data from the multiple sets of detected data for calculation, calculate the parameters of single-pole grounding fault and inter-pole short-circuit fault, and use the results as the initial population; (5) Set the maximum number of iterations for the population and perform iterations; (6) Fitness value calculation: The fitness of each particle is taken as the absolute value, and the minimum value is selected as the optimal value. (7) Determine whether the fitness value obtained in this iteration meets the accuracy requirement. If not, continue iterating until the maximum number of iterations is reached or the accuracy requirement is met. If it meets the requirement, stop iterating and output the optimal solution.

2. The fault location method for low-voltage flexible DC microgrids based on the improved beetle swarm algorithm according to claim 1, characterized in that: The number of populations in step (1) is set according to the requirements and is a positive integer multiple of 4; the number of repeated detection groups in (3) is determined by the number of populations.

3. The fault location method for low-voltage flexible DC microgrids based on the improved longhorn beetle swarm algorithm according to claim 1, characterized in that: In step (2), V dc1 This is the voltage value on the left side when the DC distribution network is short-circuited, V. dc2 This is the voltage value on the right side when the DC distribution network is short-circuited, V. Lt1 This is the voltage value of the current-limiting reactor at the left outlet when the DC distribution network is short-circuited, V. Lt2 This is the voltage value of the current-limiting reactor at the right outlet when the DC distribution network is short-circuited.

4. The fault location method for low-voltage flexible DC microgrids based on the improved beetle swarm algorithm according to claim 1, characterized in that: The formula for calculating fault parameters in step (4) Discretizing equations (1) and (2) yields X p-g -V dc2 (k)+V dc1 (k)+V Lt1 (k)-V Lt2 (k)=0#95) X p-p -V dc2 (k)+V dc1 (k)+2V Lt1 (k)-2V Lt2 (k)=0#(6) where X is the parameter [R1, R2, L1, L2] when the system fails, k is the sampling constant, A p-g Let A be the system matrix under a single-pole short-circuit fault. p-p Let A be the system matrix under bipolar fault conditions.

5. The fault location method for low-voltage flexible DC microgrids based on the improved beetle swarm algorithm according to claim 1, characterized in that: The iterative method used in step (5) is a combination of the beetle whisker algorithm and the particle swarm optimization algorithm, as shown in the following formula: x ni =x n(i-1) +v ni #(14) Where n is the number of particles; i is the generation of particle iteration; l is the step size of each iteration; m is the dimension of each particle; pbest and gbest are the optimal particle value and the swarm optimization value in each iteration, respectively; r1 and r2 are random numbers between 0 and 1. To better balance global and local search capabilities, in the early stages of iteration, the inertia weight w should have a faster descent rate, increasing the step size of particle updates, so that the swarm can quickly search for feasible solution regions. In the later stages of iteration, the inertia weight w should have a slower descent rate, decreasing the step size of particle updates, so that the particle swarm can fine-tune its search within the feasible solution region to find the global optimum. Furthermore, in the early stages of iteration, the swarm should have a relatively strong self-learning ability; as iteration continues, the self-learning ability should weaken, while the social learning ability should continuously increase. The modifications to the particle swarm optimization algorithm are as follows: w(t)=w min +α(w max -w min )#(17) Where T is the total number of iterations; t is the current iteration number; β is a control parameter used to control the convergence rate, β > 1; w max and w min These are the upper and lower bounds of the inertia weight, respectively; and These are the upper and lower bounds of the cognitive coefficient, respectively. and These represent the upper and lower bounds of the social coefficient, respectively. To avoid getting trapped in local optima, we use the following iterative location information method: x n.i =x n.(i-1) (1sin(h))+v n.i sin(h)#(20) Equation (20) incorporates a sine function factor, which can generate oscillations in the position information and prevent it from getting stuck in a local optimum for a long time. h is an undetermined parameter; if it is too large or too small, it will lead to slow convergence or non-convergence.

6. The fault location method for low-voltage flexible DC microgrids based on the improved beetle swarm algorithm according to claim 1, characterized in that: Step (6) The fitness function is: Where f(x) = X p-g -V dc2 (k)+V -c1 (k)+V Lt1 (k)-V Lt2 (k), or f(x) = XA p-p -V dc2 (k)+V dc1 (k)+2V Lt1 (k)-2V Lt3 (k), let the right side of the equation be 0. N is the sampling point selected within a certain period after the fault occurs.

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