A method for locating faults in distribution networks

Abstract

The present invention discloses a method for locating faults in a distribution network, comprising the following steps: S1. Encoding the status of feeder segments within a monitored grid area to form a position sequence; S2. After the number of iterations of a swarm particle algorithm reaches a maximum number of iterations, converting the fitness value of the particle swarm at each position into an initial increment of pheromone, and assigning the initial pheromone to each ant in the ant colony; S3. Assigning the position of each particle in the particle swarm to each ant in the ant colony, finding the optimal position based on the ant colony algorithm, and using the status of each segment at the optimal position as the current status of each feeder segment within the monitored grid area. The present invention utilizes a particle swarm algorithm to increase convergence speed in the initial stage, utilizing it for coarse search, and then utilizes an ant colony algorithm for fine search in the later stage to achieve more accurate and rapid fault location in the distribution network. This more accurate and rapid fault location is of great significance for restoring power to the distribution network.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and more specifically, relates to a method for locating a distribution network fault. Background Art

[0002] The distribution network is the most crucial link in delivering electrical energy to consumers. However, due to its vast distribution, complex topology, and vast number of devices, it is the most complex and prone to accidents. Therefore, accurately locating faults, and promptly isolating and restoring them is crucial to improving power supply reliability and minimizing losses from power outages.

[0003] Most existing feeder faults are located based on swarm particle algorithms. However, this method has a relatively fast positioning speed but a low positioning accuracy. Summary of the Invention

[0004] The present invention provides a distribution network fault location method, aiming to improve the above-mentioned problem.

[0005] The present invention is implemented in this way: a distribution network fault location method, the method specifically comprising the following steps:

[0006] S1. Encode the feeder segment status within the monitored power grid area to form a position sequence;

[0007] S2. After the number of iterations of the swarm particle algorithm reaches the maximum number of iterations, the fitness value of the particle group at each position is converted into the initial increment of pheromone, and the initial pheromone is assigned to each ant in the ant colony;

[0008] S3. Assign the position of each particle in the particle swarm to each ant in the ant colony, find the optimal position based on the ant colony algorithm, and use the status of each section at the optimal position as the current status of each feeder section in the monitoring grid area.

[0009] Furthermore, the fitness calculation formula of the particle at each position is as follows:

[0010]

[0011] Where N is the total number of feeder segments in the monitored grid area, I j Indicates the fault current information stored by the jth switch FTU. 1 indicates that the switch has experienced a fault current, and 0 indicates no fault current. represents the expected state of the jth switch FTU, S B (j) represents the status of the jth switch FTU, and its value is 1 or 0, indicating equipment failure and normal, respectively. w is the fault diagnosis weight coefficient, which ranges from [0, 1].

[0012] Further, The method for determining the expected state of the j-th switch FTU is as follows:

[0013] Among them, S B (j) to S B (j+m) is the downstream node of the jth switch FTU. If there is a node that experiences a fault current, then The value is 1, in S B (j) to S B (j+m) do not experience fault current, then The value is 0.

[0014] Furthermore, the fitness value of the particle at each position Fit Converted into the incremental value of the initial pheromone. The calculation formula of the incremental value of the initial pheromone is as follows:

[0015]

[0016] Wherein, k is a constant greater than 0, and 0<a<1.

[0017] In the initial stage of the present invention, the particle swarm algorithm is used to improve the convergence speed and perform a coarse search. In the later stage, the ant colony algorithm is used to perform a fine search to achieve more accurate and rapid fault location in the distribution network. The more accurate and rapid fault location is of great significance for the restoration of power supply in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a distribution network fault location method provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the topological structure of a distribution network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The specific implementation methods of the present invention will be further explained in detail below by describing the embodiments with reference to the accompanying drawings, so as to help those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention.

[0021] Figure 1 This is a flow chart of a method for locating a distribution network fault provided by an embodiment of the present invention. The method specifically includes the following steps:

[0022] S1. Encode the feeder segment status within the monitored power grid area to form a position sequence;

[0023] In the embodiment of the present invention, the feeder terminal equipment FTU in the distribution network is regarded as a node. The feeder section status between two nodes includes: fault and non-fault, with a value of 0 indicating no fault and a value of 1 indicating a fault. Assuming that there are 20 feeder sections in the current detection grid area, the possible states of these 20 feeder sections are encoded and combined to form a position sequence. Each position point in the position sequence is a combination of the 20 feeder section states.

[0024] S2. After the number of iterations of the swarm particle algorithm reaches the maximum number of iterations, the fitness value of the particle group at each position is converted into the initial increment of pheromone, and the initial pheromone is assigned to each ant in the ant colony;

[0025] In the embodiment of the present invention, the swarm particle algorithm is specifically as follows:

[0026] S11. Determine the particle swarm parameters, including the particle swarm size M and the maximum number of iterations t max ;

[0027] S12, calculating the fitness value of each particle at the current position according to the fitness function;

[0028] In the embodiment of the present invention, the fitness function F it The specific expression is as follows:

[0029]

[0030] Where N is the total number of feeder segments in the monitored grid area, I j Indicates the fault current information stored by the jth switch FTU. 1 indicates that the switch has experienced a fault current, and 0 indicates no fault current. represents the expected state of the jth switch FTU, S B (j) represents the status of the jth switch FTU, and its value is 1 or 0, indicating equipment failure and normal, respectively. w is the fault diagnosis weight coefficient, which ranges from [0, 1].

[0031] Among them, S B (j) to S B (j+m) is the downstream node of the jth switch FTU. After only one node experiences the fault current, its value is 1. The value is 1, only in S B (j) to S B If (j+m) does not experience fault current and both values ​​are 0, then The value is 0.

[0032] The expected state determination method of the jth switch FTU is combined with Figure 2 For explanation, S represents power supply, k1~k14 Represents nodes, s1~s 14 Represents the feeder section and the desired state of each switch The values ​​of are as follows:

[0033]

[0034] against For example, its downstream nodes are s4, s5 and s6. Only when there is no fault current at nodes s4, s5 and s6, The value of is 0. When there is a fault current in one of nodes s4, s5 and s6, The value of is 1.

[0035] S13. Update individual optimal value p best , the individual optimal value p of each particle best Compared with the current fitness value of each particle, if the fitness value of the particle is greater than the individual optimal value p best , then the fitness value is taken as the individual optimal value p of the particle best , and save it, otherwise, the optimal value p of the particle best remain unchanged;

[0036] S14. Update the global optimal value g best , the global optimal value g best Compared with the current maximum global fitness value, if the current maximum global fitness value is greater than the global optimal value g best , then the fitness value is taken as the global optimal value g best , and save it, otherwise, keep the current global optimal value g best constant;

[0037] S15. Set the number of iterations t = t + 1, and check whether the number of iterations t reaches the maximum number of iterations t. max If the test result is negative, the fitness value of the particle group at each position is converted into an increment of the initial pheromone and assigned to the initial pheromone of each ant in the ant colony. If the test result is negative, step S12 is executed.

[0038] In the embodiment of the present invention, the number of ants in the ant colony is the same as the number of particles in the particle swarm. The particle position in the particle swarm is assigned to each ant in the corresponding ant colony. At the same time, the fitness value of the particle at the position is converted into the increment of the initial pheromone, and the initial pheromone τ of each ant is initialized. c , then the pheromone τ of each ant s =τ c +τ p ;

[0039] The particles are placed at various positions based on the following formulaXi The fitness value on Fit Converted into the incremental value of the initial pheromone, the calculation formula is as follows:

[0040]

[0041] Among them, k is a constant greater than 0, 0<a<1, and the larger the fitness value, the more pheromones are left at the corresponding position.

[0042] S3. Assign the position of each particle in the particle swarm to each ant in the ant colony, find the optimal position based on the ant colony algorithm, and use the status of each section at the optimal position as the current status of each feeder section in the monitoring grid area.

[0043] In the early stage, the convergence speed of the particle swarm algorithm is improved and used for coarse search. In the later stage, the ant colony algorithm is used for fine search to achieve more accurate and rapid fault location in the distribution network. Fault location will be more accurate and rapid, which is of great significance for the restoration of power supply in the distribution network.

[0044] The present invention has been described exemplarily. Obviously, the specific implementation of the present invention is not limited to the above-mentioned method. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the scope of protection of the present invention.

Claims

1. A distribution network fault location method, characterized in that: The method specifically comprises the following steps: S1. Encode the feeder segment status within the monitored power grid area to form a position sequence; S2. After the number of iterations of the swarm particle algorithm reaches the maximum number of iterations, the fitness value of the particle group at each position is converted into the increment of the initial pheromone and assigned to the initial pheromone of each ant in the ant colony; S3. Assign the position of each particle in the particle swarm to each ant in the ant colony, find the optimal position based on the ant colony algorithm, and use the status of each section at the optimal position as the current status of each feeder section in the monitoring grid area; The fitness value F of the particle at each position it Converted into the incremental value of the initial pheromone. The calculation formula of the incremental value of the initial pheromone is as follows: Wherein, k is a constant greater than 0, and 0<a<1.

2. The distribution network fault location method according to claim 1, characterized in that: The fitness calculation formula of particles at each position is as follows: Where N is the total number of feeder segments in the monitored grid area, I j Indicates the fault current information stored by the jth switch FTU. 1 indicates that the switch has experienced a fault current, and 0 indicates no fault current. represents the expected state of the jth switch FTU, S B (j) represents the status of the jth switch FTU, and its value is 1 or 0, indicating equipment failure and normal, respectively. w is the fault diagnosis weight coefficient, which ranges from [0, 1].

3. The distribution network fault location method according to claim 2, wherein: I j * The method for determining the expected state of the j-th switch FTU is as follows: Among them, S B (j) to S B (j+m) is the downstream node of the jth switch FTU. If there is a node that experiences a fault current, then The value is 1, in S B (j) to S B (j+m) do not experience fault current, then The value is 0.

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