An intelligent positioning method and system for power supply line faults

Abstract

The present invention discloses an intelligent fault location method and system for a power supply line. The intelligent fault location method is based on the transient zero-sequence current algorithm and the binary bi-state particle swarm optimization (BBPSO) algorithm. The fault location system includes four parts: a signal detection module, a data transmission module, a monitoring module, and a fault location method. The signal detection module converts the collected zero-sequence current into a digital signal through an analog-to-digital converter and transmits it to a digital signal processor (DSP). Once a fault occurs in the line, the DSP transmits the fault signal to the serial port of the data transmission module through a serial communication interface. The data transmission module packs the data received by the serial communication interface through network transparent transmission and uploads it to the monitoring module wirelessly through the Internet of Things. The monitoring module can enter the monitoring interface through the status parameter real-time display module to monitor the line. If a fault is detected, it will alarm and locate the fault, and then display and send the location result to the relevant person in charge.

Description

Technical Field

[0001] The present invention relates to the field of power supply line fault location, and particularly to an intelligent fault location method and system for power supply lines. Background Art

[0002] Intelligent fault location of power supply lines is one of the key contents for realizing distribution automation. It mainly comprehensively analyzes the fault information reported by feeder terminal units to determine the fault section, providing conditions for power supply restoration after the fault.

[0003] Currently, power supply line fault location methods are mainly divided into two types: matrix algorithms and intelligent algorithms. However, in the current complex distribution network structure with many nodes, the computational amount of matrix algorithms has been greatly increased, unable to meet the requirements of distribution network fault location and unable to quickly and accurately obtain the fault point. Currently, the intelligent algorithms mainly used include genetic algorithms, ant colony algorithms, artificial neural networks, particle swarm algorithms, etc. Such algorithms have a certain degree of fault tolerance, but generally have the disadvantages of large population requirements, many dimensions of the solution space, many iteration times, or being easily trapped in local optima. Therefore, based on intelligent algorithms, it is of great significance to study an intelligent fault location method that first determines the suspected fault interval based on the zero-sequence current algorithm and then determines the fault section based on the binary state particle swarm optimization algorithm in terms of shortening the power outage time, narrowing the power outage range, reducing losses in life and production, improving power supply reliability, and improving the accuracy of fault location. Summary of the Invention

[0004] In order to solve the problems of power supply line fault location and real-time monitoring, the present invention discloses an intelligent fault location method and monitoring system for power supply lines through the following embodiments.

[0005] The first aspect of the present invention discloses an intelligent fault location method for power supply lines, including:

[0006] According to the zero-sequence current signals collected at each feeder measurement point, the direction of the transient zero-sequence current is extracted by using the integral closed-open difference operation (ICODO).

[0007] Based on the distribution law of the transient zero-sequence current and applying the shortest path theory in graph theory, the suspected fault area is determined to reduce the scale of the solution space for fault location.

[0008] According to the network structure of the suspected fault area, the expected measurement point state matrix is constructed through the expected measurement point state function. E * .

[0009] A new evaluation function is proposed for fault location to make it sensitive to distorted information.

[0010] Set the parameters of the Binary Bat Particle Swarm Optimization (BBPSO) algorithm according to the determined suspected fault area.

[0011] Use the BBPSO algorithm to search for the faulty feeder, and introduce an evolution factor to judge whether the algorithm falls into local convergence, so as to achieve accurate fault location.

[0012] The second aspect of the present invention discloses a power supply line fault location system, which is applied to a power supply line fault intelligent location method disclosed in the first aspect of the present invention. The system includes:

[0013] The power supply line fault location system consists of four parts: a signal detection module, a data transmission module, a monitoring module, and a fault location method.

[0014] The signal detection module is composed of current and voltage transformers, zero-sequence current transformers, a digital signal processor (DSP), an analog-to-digital converter (ADC), an RS232 serial communication interface, a reset chip, a clock chip, a data memory, a program memory, and a temperature sensor, etc.

[0015] The signal detection module converts the zero-sequence current collected at the switch into a digital signal through an analog-to-digital converter (ADC) and transmits it to the digital signal processor (DSP). Once a fault occurs in the line, the digital signal processor (DSP) transmits the fault signal to the serial port of the data transmission module through the serial communication interface.

[0016] The data transmission module is composed of a serial communication interface circuit, a digital signal processor, a wireless communication module, and a wireless antenna.

[0017] The data transmission module packs the data received by the serial communication interface through network transparent transmission and uploads it to the monitoring module wirelessly through the Internet of Things.

[0018] The monitoring module is composed of a real-time status parameter display module, an alarm rule setting module, an alarm signal processing module, and a data storage module.

[0019] The monitoring module can enter the monitoring interface through the real-time status parameter display module. In the monitoring interface, it can monitor the electrical quantities of the line and various devices, as well as control various devices. At the same time, it can also query historical data and send the historical data to other servers. If a fault is detected in the monitoring interface, the monitoring interface will give an alarm, and at the same time, perform fault location and display the location result on the interface.

[0020] Beneficial effects:

[0021] The present invention determines the suspected fault area of the distribution network according to the distribution law of transient zero-sequence current by applying the shortest path theory in graph theory, realizes the dimensionality reduction of the solution space of the intelligent algorithm, and at the same time proposes an expected measurement point state function and an evaluation function based on the forward zero-sequence current path for fault location, effectively improving the accuracy and efficiency of fault location. In addition, the binary particle swarm optimization algorithm is improved, a two-state particle model is proposed, and an evolution factor is added to avoid the defect that the algorithm is prone to local convergence when solving discrete problems, and more accurate fault location can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 FIG. is a schematic working flow diagram of an intelligent fault location method for a power supply line disclosed in an embodiment of the present invention.

[0023] Figure 2 FIG. is a schematic principle diagram of an intelligent fault location method for a power supply line disclosed in an embodiment of the present invention.

[0024] Figure 3 FIG. is a schematic structural principle diagram of a power supply line fault location system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] In order to solve the problem of intelligent fault location of power supply lines, the present invention discloses an intelligent fault location method and system for power supply lines based on zero-sequence current algorithm and two-state binary particle optimization algorithm (BBPSO) through the following embodiments.

[0026] The first embodiment of the present invention discloses an intelligent fault location method for a power supply line, as shown in Figure 1 shown, including:

[0027] Step S11: According to the zero-sequence current signals collected by each feeder measurement point, use the integral closed-open difference operation (ICODO) to extract the direction of the transient zero-sequence current;

[0028] Step S12: Based on the distribution law of the transient zero-sequence current and applying the shortest path theory in graph theory, determine the suspected fault area to reduce the scale of the solution space for fault location;

[0029] Step S13: Starting from the network structure of the suspected fault area, construct an expected measurement point state matrix through the expected measurement point state function E * ;

[0030] Step S14: Propose a new evaluation function for fault location to make it sensitive to distorted information;

[0031] Step S15: Set the BBPSO algorithm parameters;

[0032] Step S16: The BBPSO algorithm is used to search for the faulty feeder, and an evolution factor is introduced to judge whether the algorithm falls into local convergence, so as to achieve accurate fault location.

[0033] A method for intelligent fault location of a power supply line disclosed in an embodiment of the present invention has a schematic diagram as shown in Figure 2 shown.

[0034] Furthermore, for the closed-open difference operation of extracting zero-sequence current in step S1, since measurement errors often need to be considered for the interference brought to the actual output result in actual measurement, the present invention improves on the basis of CODO, and the specific implementation method of the integral closed-open difference operation (ICODO) is as follows:

[0035]

[0036] Equation F ICODO ( t ) represents t the output for identifying the transient zero-sequence current direction at time t r is the time window length for identifying the transient zero-sequence current direction after detecting the zero-sequence current signal at t r has a relatively small value.

[0037] Furthermore, according to the final output result of ICOD, the polarity criterion of the transient zero-sequence current of each feeder is:

[0038]

[0039] In the formula D set is the threshold for judging the polarity of the zero-sequence current, and its value is related to the scale, topological structure and feeder length of the distribution network, and generally takes 0.1 max | y CODO |.

[0040] Furthermore, the solution space dimension reduction method based on the zero-sequence current distribution law in step S2 is as follows:

[0041]

[0042] In the formula P i is the shortest path between the measurement point with the reported feeder fault information of 1 and the main node.

[0043] Furthermore, the state function of the expected measurement point in step S3 is as follows:

[0044]

[0045] In the formula F n { i, 1} means that if the i th feeder fails, the states of the measurement points from 1 to i are 1.

[0046] Furthermore, the formula for constructing the state matrix of the expected measurement points in step S3 is as follows:

[0047]

[0048] In the formula: j represents the total number of elements with a value of 1 in the solution space generated during one iteration of the algorithm, that is, the total number of faulty feeders; F n { i ,1}=1 means that according to F n { i ,1}, the element values at the corresponding positions in E * are set to 1. represents the total number of faulty feeders j , and using the formula E * ( F n { i ,1})=1 for j operations.

[0049] Furthermore, the evaluation function based on the positive sequence zero-sequence current path in step S4 is:

[0050]

[0051] In the formula: S pj represents the length of the zero-sequence current flow path from the measurement point p to the main node e j when the element value in the collected measurement point state matrix is V 1. When p takes the value of 1, at this time S pj is the length of the zero-sequence current flow path from the measurement point e j to the main node V 1; when p takes the value of 0, at this time S pj = 0. S aj* It represents the element value in the expected measurement point state matrix generated during the algorithm iteration process to be a When measuring e j to the main node V the length of the zero-sequence current flow path between 1. l aj It represents that during the algorithm iteration process, the distribution network feeder l j The state is a When, the value of the j bit of the feeder fault matrix.

[0052] Furthermore, in step S5, the binary bi-state particle swarm optimization algorithm (BBPSO) is adopted:

[0053] First, set the parameters according to the network topology of the suspected fault area: set the particle size N , the dimension of the search solution space D , the total number of operations of BBPSO T and related parameters such as the learning factor C 1 、C 2 , speed V of V max 、V min . The particle N can be expressed as:

[0054]

[0055] In the formula: l aj It represents that during the algorithm iteration process, the distribution network feeder l j The state is a When, the value of the j bit of the feeder fault matrix, the value is 1 or 0; when a The value is 0, indicating that the feeder l j has no fault, when a The value is 1, indicating that the feeder l j has a fault.

[0056] Secondly, initialize the predator particle swarm: obtain the initial individual extreme values of each particle i in the predator state P id , the initial individual extreme values P idThe minimum value in it is the initial global extreme value in the particle swarm, that is P gd 。 v i The initial value is generally between -4 and 4.

[0057] Then update the velocity and position of the particle swarm in the predation state. The update formula for the velocity of the particle in the predation state is:

[0058]

[0059] The update formula for the position of the particle in the predation state is:

[0060]

[0061]

[0062] In the formula: r id ( t + 1) is a random number in [0, 1]; to avoid sigmoid ( v id ( t + 1)) function saturation, it is generally assumed that the velocity v id is between [-4, 4].

[0063] Then calculate the P gd of the current particle swarm. During iteration, through the evaluation function, the obtained P id is compared with P gd . If P id is better than P gd , then P gd is updated, otherwise it is not updated; when the number of iterations is greater than or equal to 3, calculate the evolution factor f and judge it. If 0 < f < 1, select M exploration particles for exploration, and the remaining particles continue to prey and execute the next step. The particle swarm evolution factor f is defined as:

[0064]

[0065] In the formula: Fbest t is the overall optimal fitness value of BBPSO when performing t operations; is the smoothing coefficient to prevent the denominator from being zero.

[0066] Then, number the M exploration particles separated, and obtain x i through the determined evaluation function and use it as the initial individual value of the exploration particle swarm P kd , v k The initial value is generally between -4 and 4; calculate the P gd . During iteration, obtain the P id of the particles in the predation state and the P kd of the particles in the exploration state. P id and P kd of the two particle swarms are better than the previous global extreme value P gd , then replace and update P gd otherwise do not update; the update formulas for the velocity and position of the particles in the exploration state are respectively:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] In the formula: c 3 is a random function between (0, 1); u (0, 1) is the Gaussian distribution function; hP gd is the global optimum for the particles in the exploration state to search in the solution space; sign ( r ) is the sign function; r is a random number; T 1 is the maximum number of iterations required after entering the exploration state; t 1 is the current number of iterations after entering the exploration state.

[0073] Finally, when the entire particle swarm performs T operations through the algorithm, at this time, find P gdThe position of the particles is the actual operating state of each feeder section of the current network.

[0074] The second example of the present invention discloses an intelligent fault location system for a power supply line, and its schematic diagram is as Figure 3 shown, and is applied to an intelligent fault location method for a power supply line disclosed in the first embodiment of the present invention. The system includes:

[0075] A signal detection module 10, which mainly includes a power supply subsystem, a zero-sequence voltage measurement subsystem, and a signal acquisition, analysis and processing subsystem. Among them, the power supply subsystem is powered by the voltages of two phases among the three phases A, B, and C to the AC / DC power supply module connected in parallel; the zero-sequence current transformer subsystem is configured with 1 to 3 sets of outgoing line switch current measurement transformers according to the number of load distribution switches at the transformer output end, and each set of zero-sequence current measurement transformers for the outgoing line switch detects 4-way outgoing line currents in real time; the signal acquisition, analysis and processing system is composed of a temperature measurement circuit, an input signal conditioning circuit, a reset circuit, a digital signal processor (DSP), a power supply, a memory, a crystal oscillator, an analog-to-digital converter (ADC), and a serial communication interface circuit such as RS232.

[0076] A data transmission module 20, which is composed of an RS232 serial communication interface circuit, a digital signal processor based on DSP, a wireless communication module, and a wireless antenna.

[0077] A monitoring module 30, which is composed of a real-time display module for status parameters, an alarm rule setting module, an alarm information push mode alarm signal processing module, and a data storage module.

[0078] Furthermore, the signal detection module, based on the Modbus communication protocol, sends the real-time measurement values and the fault location results to the serial communication interface based on the data transmission module through serial communication interfaces such as RS232.

[0079] Furthermore, the data transmission module packs the data received by the serial communication interface through the network transparent transmission method, and uploads it to the remote monitoring platform wirelessly through the Internet of Things.

[0080] Furthermore, the monitoring module can monitor the electrical quantities of the line and each device, can also query historical data, and can send the historical data to other servers. If a fault is detected on the monitoring interface, an alarm signal is generated according to the preset alarm rules and alarm push methods, and at the same time, fault location is performed. Then, the fault location result is displayed on the fault interface, and the generated fault location result is sent to the relevant responsible personnel of the power supply line by text message or email for timely maintenance.

[0081] The present invention has been described in detail in conjunction with specific embodiments and exemplary examples. However, these descriptions should not be construed as limitations on the present invention. Those skilled in the art understand that without departing from the spirit and scope of the present invention, various equivalent substitutions, modifications or improvements can be made to the technical solutions of the present invention and their implementation manners, and these all fall within the scope of the present invention. The protection scope of the present invention shall be subject to the appended claims.

Claims

1. An intelligent positioning method for power supply line faults, characterized in that, Including: S11: According to the zero-sequence current signals collected at each feeder measurement point, use the integrated closed-open difference operation ICODO to extract the direction of the transient zero-sequence current; S12: Determine the suspected fault area according to the distribution law of the transient zero-sequence current and apply the shortest path theory in graph theory to reduce the scale of the solution space for fault location; S13: Starting from the network structure of the suspected fault area, construct the expected measurement point state matrix E* through the expected measurement point state function; S14: Propose a new evaluation function for fault location to make it sensitive to distorted information; S15: Set the parameters of the binary state binary particle swarm optimization algorithm BBPSO; S16: Use the binary state binary particle swarm optimization algorithm BBPSO to search for the faulty feeder, and introduce an evolution factor to judge whether the algorithm falls into local convergence to achieve accurate fault location; The integrated closed-open difference operation for extracting the transient zero-sequence current described in step S11 is improved based on CODO because measurement errors often need to be considered for the interference brought to the actual output result in actual measurement. The specific implementation method is as follows: Where F ICODO (t) represents the output for identifying the transient zero-sequence current direction at time t; t r is the time window length for identifying the transient zero-sequence current direction after detecting the zero-sequence current signal.

2. The intelligent location method for power supply line faults according to claim 1, wherein: The method for reducing the scale of the solution space for fault location according to the distribution law of the transient zero-sequence current in step S12 is as follows: P = P1 ∪ P2 ∪ … ∪ P i ; Where P i is the shortest path between the measurement point with the reported feeder fault information of 1 and the main node.

3. The intelligent location method for power supply line faults according to claim 1, characterized in that: The formula for constructing the state matrix of the expected measurement point in step S13 is as follows: Where: j represents the total number of elements with a value of 1 in the solution space generated during one iteration of the algorithm, that is, the total number of faulty feeders; F n {i,1}=1 means that according to F n the element values at the corresponding positions in E* are set to 1 according to the magnitudes of the element values in {i,1}, means that according to the total number of faulty feeders j, using the formula E*(F n {i,1}) = 1 for j operations.

4. The intelligent positioning method for power supply line faults according to claim 1, wherein: The evaluation function based on the positive zero-sequence current path in step S14 is as follows: Where: S pj represents the length of the path through which the zero-sequence current flows from the measurement point e j to the main node V1 when the element value in the collected measurement point status matrix is p; When the value of p is 1, S at this time pj is the length of the path through which the zero-sequence current flows between the measurement point e j and the main node V1; when the value of p is 0, S at this time pj = 0; S aj * represents the length of the path of the zero-sequence current flowing between the measurement point e and the main node V1 when the element value of the expected measurement point state matrix generated during the algorithm iteration process is a; j ​ l aj It represents the value of the j-th bit of the feeder fault matrix when the state of the distribution network feeder l j is a during the algorithm iteration process.

5. A power supply line fault monitoring system, characterized in that, Including: A power supply line fault monitoring system, which consists of a signal detection module, a data transmission module, a monitoring module, and an intelligent fault location method for power supply lines described in any one of claims 1-4; The signal detection module consists of current and voltage transformers, a zero-sequence current transformer, a digital signal processor, an analog-to-digital converter, an RS232 serial communication interface, a reset chip, a clock chip, a data memory, a program memory, and a temperature sensor; The data transmission module consists of a serial communication interface circuit, a digital signal processor, a wireless communication module, and a wireless antenna; The monitoring module consists of a real-time display module for status parameters, an alarm rule setting module, an alarm signal processing module, and a data storage module.

6. The system according to claim 5, characterized in that, Including: The signal detection module converts the current, voltage, and zero-sequence current collected at the switch into digital signals through an analog-to-digital converter and transmits them to the digital signal processor. Once a line fault occurs, the digital signal processor transmits the fault signal to the serial port of the data transmission module through the serial communication interface; The data transmission module packs the data received by the serial communication interface through network transparent transmission and uploads it to the monitoring module wirelessly through the Internet of Things; The monitoring module enters the monitoring interface through the real-time display module for status parameters. In the monitoring interface, it can monitor the electrical quantities of the line and various devices, as well as control various devices. At the same time, it can also query historical data and send the historical data to other servers; if a fault is monitored on the monitoring interface, the monitoring interface will give an alarm, and at the same time, perform fault location and display the located result on the interface.

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