A method, system, detection device and medium for locating faults of power distribution equipment

Abstract

The present invention relates to the field of fault location of power distribution equipment, and in particular to a fault location method, system, detection equipment and medium for power distribution equipment. Electrical quantity and non-electrical quantity data are collected through an intelligent sensing module and encrypted for transmission. The data are then preprocessed to achieve data synchronization and normalization. A method combining principal component analysis and empirical mode decomposition is used to extract fault feature vectors, and a fault location model with minimizing fault location error as an objective function and a power distribution system power flow equation as a constraint condition is constructed. The fault location is encoded as a particle in a particle swarm algorithm, and search and location are performed to quickly and accurately find the fault point. In addition, the present invention also includes a fault location system, which realizes full-process management from data collection and processing to fault processing. The method and system can accurately find the fault location, adapt to complex fault scenarios, improve the operation and maintenance efficiency and power supply reliability of the power distribution system, and have significant technical effects and practical application value.

Description

Technical Field

[0001] The present invention relates to the field of fault location of power distribution equipment, and in particular to a fault location method, system, detection equipment and medium for power distribution equipment. Background Art

[0002] In modern power distribution systems, the reliable operation of distribution equipment is crucial to ensuring the stability and continuity of power supply. However, distribution equipment failures occur from time to time, and accurately and quickly locating faults has become a key challenge to improving the operation and maintenance efficiency of distribution systems and power supply reliability.

[0003] In the prior art, traditional fault location methods mainly rely on a single type of information, such as current or voltage measurement data alone. This single information source method often cannot fully and accurately reflect the fault situation when facing complex distribution system faults. At the same time, it lacks comprehensive intelligent perception and fusion capabilities for electrical and non-electrical quantities. When faced with large-scale and complex distribution networks, there are problems such as high computational complexity, slow convergence speed, and easy to fall into local optimal solutions.

[0004] Therefore, it is necessary to propose a fault location method and system for power distribution equipment to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a fault location method, system, detection equipment and medium for power distribution equipment, so as to solve the problems that a single information source method often cannot fully and accurately reflect the fault situation when facing complex power distribution system faults, and has high computational complexity, slow convergence speed and is easy to fall into local optimal solutions when facing large-scale and complex power distribution networks.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for locating a fault of a power distribution device, the method comprising: Intelligent perception and data collection and transmission. Various sensors collect data according to the set sampling frequency. For electrical quantities with fast current and voltage changes, the sampling frequency is higher; for non-electrical quantities with relatively slow temperature changes, the sampling frequency is appropriately reduced. After data collection is completed, the data is encrypted and transmitted using an encryption algorithm. Preprocess the collected data, map them to a uniform value range using a normalization algorithm, and synchronize the data from different sensors in time using timestamp matching and interpolation algorithms. Fault feature extraction is performed by combining principal component analysis and empirical mode decomposition to extract the main characteristic components in the data and remove redundant information. The characteristic vector that can reflect the fault type and location is extracted by analyzing the energy distribution, frequency characteristics and specific modal components related to the fault of the intrinsic mode function. Construct a fault location model with the objective function of minimizing the fault location error and setting the power flow equation of the distribution system as a constraint to ensure that the fault location result meets the basic physical laws of the power system; Encode the locations where the fault may occur, use the codes of different fault locations as particles to form a particle swarm, determine the number of particles in the particle swarm, and randomly initialize the position of each particle; Based on the objective function established by the fault localization model, the particle matching value is calculated and the optimal particle is determined. For each particle, its current position is substituted into the objective function to calculate the matching value, the particle position is decoded into a specific fault hypothesis, and then the matching degree with the actual fault characteristics is calculated according to the objective function in the fault localization model.

[0007] Preferably, the sensor comprises: Current and voltage sensors: Current and voltage sensors monitor the three-phase current, voltage and the voltage at the high-voltage user boundary point in real time; Fuse position detection sensor monitors the opening and closing status of the fuse body in real time; Temperature sensor: The temperature sensor is installed near the fuse body and is used to monitor the temperature change of the fuse; Arc sensor: The arc sensor is used to detect the arc signal that may be generated when the fuse is activated.

[0008] Preferably, the method for constructing the objective function includes: Determine the influencing factors of the objective function, including the actual fusing state of the intelligent fuse, the deviation between the current and voltage measurement values ​​and the theoretical values, the relationship between temperature change and threshold value, the presence or absence of arc light signal, and the distance from the fault point to the monitoring point; The objective function is constructed by weighted summation.

[0009] Preferably, for the actual blown state of the smart fuse, if the fault location assumed by the particle is consistent with the actual blown smart fuse location, then the error term corresponding to this part in the objective function should be smaller; otherwise, if they are not consistent, the error is larger; define a function , if the smart fuse at the assumed position actually blows, then =0, if the fuse is not actually blown but is assumed to be blown or vice versa, then =1; For the deviation between the current and voltage measurement values ​​and the theoretical values, assuming that the current measurement value is The theoretical value is , is the deviation, for voltage V, These deviation terms are added together to obtain a function that reflects the impact of current and voltage deviation on fault location: ; Among them, i and j represent different measurement points; For the relationship between temperature change and threshold, if it is assumed that the fault location causes the temperature change to exceed the normal threshold range and is inconsistent with the actual temperature change, the error will increase. Assuming T is the actual temperature, is the normal threshold temperature, t is the expected temperature change based on the assumed fault location, when When the temperature is abnormal, define the function Indicates the error, ,otherwise ; For the presence or absence of arc signal, define the function If there is actually an arc and the assumed position is reasonable to produce an arc, or if there is actually no arc and the assumed position should not produce an arc, ,otherwise ; For the distance from the fault point to the monitoring point, assume that d is the distance from the fault point to the monitoring point, is the reference distance, Indicates the error, .

[0010] Preferably, the weight coefficient of each factor is determined. The weight coefficient can reflect the relative importance of each influencing factor in evaluating the quality of the fault hypothesis. Assuming that w1, w2, w3, w4, and w5 are the weight coefficients of f1, f2, f3, f4, and f5 respectively, the objective function can be the weighted sum of these factors. Let F be the objective function, and the objective function F is .

[0011] Preferably, before encoding the location where the fault may occur, the size of the particle swarm must be determined first, and an appropriate number of particles are selected according to the complexity of the problem and the computing resources. If there are N particles, the location where the fault may occur, such as different parts inside the smart fuse and the line section connected to the smart fuse, is encoded to form a particle position vector. If there are M possible fault locations in a distribution system, the position vector of a particle can be an M-dimensional vector, and the value of each dimension represents the possibility that the corresponding position is assumed to be a fault point. At the same time, the speed of each particle is randomly initialized, and the dimension of the speed vector is the same as that of the position vector. The speed value is randomly given within a set speed range. The direction and magnitude of the speed determine the movement of the particle in the solution space in the subsequent iterative calculation to search for the optimal solution. In the early stage of fault location, a larger initial speed can enable the particle to quickly cover a larger range of fault location assumptions. As the iteration proceeds, the speed is gradually adjusted to focus on the area closer to the fault location for searching, and finally accurately find the fault location.

[0012] Preferably, for each particle, its current position vector is substituted into the objective function to calculate the fitness value of the fault hypothesis. If the particle fault is at a certain intelligent fuse or a part of the line section connected to the intelligent fuse, the fitness value of the fault hypothesis is calculated by obtaining the actual current and voltage measurement values, temperature changes, arc signal conditions, and distance from the monitoring point corresponding to the corresponding position of the particle, and substituting them into the objective function to calculate, after calculating the fitness values ​​of all particles, these values ​​are compared and sorted to determine the global optimal particle in the current particle group and the optimal particle in the individual particle's own history. The global optimal particle represents the hypothesis closest to the actual fault position in the entire particle group, and the individual optimal particle represents the hypothesis closest to the actual fault position found by each particle itself during the search process; After each iteration is completed, check whether the convergence condition is met. The convergence condition is that the maximum number of iterations is reached or the fitness value change is less than the set threshold. If the convergence condition is met, stop the iteration, output the current global optimal particle position and the individual optimal particle of the particle, and the particle position is the specific location of the faulty device finally determined; if it is not satisfied, return to the step of calculating the particle fitness value, continue to the next iteration, continuously optimize the particle swarm, and find a better fault location hypothesis until the convergence condition is met.

[0013] A fault location system for power distribution equipment, the system comprising: An intelligent sensing module, including fuses, various types of sensors and data acquisition circuits, is used to collect electrical quantity and non-electrical quantity data of the intelligent fusing system and transmit them to the data processing module; Data processing module, which performs data fusion and preprocessing, fault feature extraction and selection, fault location model construction, and fault location result evaluation and decision making; The communication module is responsible for data transmission between the intelligent sensing module and the data processing module, and for transmitting the fault location results to the external system; Decision execution module: Executes corresponding fault isolation, repair or further detection operations according to the fault location result evaluation and decision instructions in the decision.

[0014] Preferably, a fault location detection device for power distribution equipment comprises a bus, a transceiver, a memory, a processor and storage, a computer program on the memory and executable on the processor, the transceiver, the memory and the processor are connected via the bus, and is characterized in that the computer program, when executed by the processor, implements the steps in the method as described in any one of claims 1 to 7.

[0015] Preferably, a computer-readable storage medium stores a computer program thereon, wherein the computer program, when executed by a processor, implements the steps in the method as claimed in any one of claims 1 to 7.

[0016] Technical effects and advantages of the present invention: 1. Construct a fault location model with minimizing the fault location error as the objective function and the power flow equation of the distribution system as the constraint condition to ensure that the results meet the basic physical laws of the power system and improve the accuracy of fault location; 2. Encode the location where the fault may occur and search it through the particle swarm algorithm to reasonably determine the particle swarm size and initialize the particle position and speed. In the early stage of fault location, a large range is quickly covered. With the iteration, the area closer to the fault location is gradually focused to accurately find the fault location. 3. Combining principal component analysis and empirical mode decomposition to achieve efficient data dimension reduction and information concentration, remove redundancy and noise interference, deeply mine potential fault information, enhance the distinguishability of fault characteristics, adapt to complex fault scenarios and changes, and improve positioning accuracy and timeliness; 4. The fault location system includes intelligent perception, data processing, communication and decision-making execution modules, which realizes the full process management from data collection to fault handling, and improves the operation and maintenance efficiency and power supply reliability of the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of a method for locating a fault of a power distribution device.

[0018] Figure 2 The present invention is a flow chart of the method for constructing a fault location model.

[0019] Figure 3 This is a flow chart of the location where the encoding failure occurs in the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1 The present invention provides Figure 1-3 A method for locating a fault of a power distribution device is shown, the method comprising: S1. Intelligent perception and data collection and transmission. Various sensors collect data according to the set sampling frequency. For electrical quantities that change quickly, such as current and voltage, the sampling frequency is higher; for non-electrical quantities that change relatively slowly, such as temperature, the sampling frequency is appropriately reduced; after data collection is completed, the data is encrypted and transmitted using an encryption algorithm; Sensors include: Current and voltage sensors: Current and voltage sensors monitor the three-phase current, voltage and the voltage at the high-voltage user boundary point in real time; Fuse position detection sensor monitors the opening and closing status of the fuse body in real time; Temperature sensor: The temperature sensor is installed near the fuse body and is used to monitor the temperature change of the fuse; Arc sensor: The arc sensor is used to detect the arc signal that may be generated when the fuse is actuated; S11. Intelligent perception and data collection and transmission. Various sensors collect data according to the set sampling frequency: For electrical quantities that change rapidly, such as current and voltage, a higher sampling frequency is set. In the power distribution system, the current and voltage may change instantly when a fault occurs. In the case of a short circuit fault, the current may reach a peak value within a few milliseconds. Therefore, the sampling frequency of current and voltage is set to once per millisecond or even higher (such as 2kHz or higher) to ensure that these rapidly changing signal details can be captured, providing an accurate data basis for subsequent fault analysis; For non-electrical quantities such as temperature that change relatively slowly, the sampling frequency is appropriately reduced. Temperature changes are usually relatively slow. Collecting data once per second can meet the needs of fault location, and it can also reduce the pressure of data storage and processing. The arc signal is an event-triggered signal. Under normal circumstances, no arc is generated. Once an arc signal is detected, its duration may be short. Therefore, the arc sensor adopts an event-triggered sampling method, that is, when an arc signal is detected, high-speed sampling is immediately performed (such as the sampling frequency is increased to above 10kHz) to accurately determine the type and severity of the fault; S12. Data encryption transmission: Select reliable wireless communication technology or high-speed wired communication network for data transmission. For wireless communication, establish a secure communication connection and adopt encrypted communication protocol to ensure the confidentiality and integrity of data during transmission. During data transmission, the encrypted data is packaged and transmitted according to a certain communication protocol format, and unpacked and decrypted according to the same protocol at the receiving end. At the same time, set a data verification mechanism, such as cyclic redundancy check, to verify the received data at the receiving end. If data errors or integrity damage are found, request retransmission in time to ensure data accuracy and reliability; In this step, for data collection, the advantages of different sampling frequencies for different physical quantities are emphasized, including improving data accuracy and optimizing resource utilization; for data transmission, the role of encryption algorithms, secure communication protocols and verification mechanisms in ensuring data security is highlighted, thereby ensuring data accuracy and reliability, and providing support for stable operation and fault handling of the distribution system.

[0022] S2. Preprocess the collected data, map the data to a unified value range using a normalization algorithm, and synchronize the data of different sensors in time through timestamp matching and interpolation algorithms; S21. Data preprocessing: preprocess the collected data, including data cleaning, data normalization, and data synchronization, to ensure the reliability and accuracy of the data; S22. Data cleaning: The collected current and voltage data are processed using a denoising algorithm based on wavelet transform. Wavelet denoising can make the current and voltage waveforms smoother, which is convenient for subsequent analysis. For temperature and arc data, the sliding average filter method is used for smoothing. The sliding average filter method replaces the current data point by calculating the average value of the data in a certain window, thereby eliminating abnormal fluctuations and spikes in the data, so that the temperature data can better reflect the actual temperature change trend of the fuse. S23. Use a normalization algorithm to map various data to a unified numerical range: by calculating the maximum and minimum values ​​of the current, and then converting each current value to the [-1, 1] interval according to the normalization formula, the electrical quantity data of different types and magnitudes are comparable in subsequent analysis and calculation, which is convenient for algorithm processing; for temperature data, normalize it to the [0, 1] interval, convert the actual measured temperature value into a relative proportional value, which is convenient for comprehensive analysis with other normalized data; for arc signal intensity, normalize it to the [0, 1] interval as well, so that it can be integrated and analyzed with other data on the same scale; S24. Synchronize the time of different sensor data through timestamp matching and interpolation algorithms. First, match the data from different sensors at the same time according to the timestamp of the data. However, due to the influence of factors such as sensor sampling frequency and transmission delay, some data may not be completely aligned in time. In this case, an interpolation algorithm (such as linear interpolation or spline interpolation) is used to supplement and correct the missing or mismatched data so that all data are consistent in the time dimension. In this step, to improve data quality, emphasis is placed on data cleaning to remove noise and abnormal fluctuations to make data more reliable and true; to enhance algorithm applicability and performance, the normalization algorithm is explained to make different data comparable, facilitate algorithm processing and improve computing efficiency; to ensure data time consistency, the timestamp matching and interpolation algorithms are explained to solve the time inconsistency problem, so that all data remain consistent in the time dimension, so as to accurately determine the fault type and location.

[0023] S3. Fault feature extraction is performed by combining principal component analysis and empirical mode decomposition to extract the main characteristic components in the data and remove redundant information; by analyzing the energy distribution, frequency characteristics and specific modal components related to the fault of the intrinsic mode function, the characteristic vector that can reflect the fault type and location is extracted; S31. Extract fault features by principal component analysis: principal component analysis includes data standardization, calculation of covariance matrix, calculation of eigenvalues ​​and eigenvectors, selection of principal components, and generation of new feature data sets. By extracting principal components through principal component analysis, high-dimensional data can be converted into low-dimensional data, while retaining the main information in the original data, removing noise and redundant information, and enhancing the separability of fault features. S32. Fault feature extraction by empirical mode decomposition: empirical mode decomposition includes signal decomposition and fault feature extraction. Empirical mode decomposition can better handle non-stationary and nonlinear signals, more accurately capture the local features and transient changes of fault signals, decompose the signal into multiple intrinsic mode functions, and extract features such as energy and frequency, which can enhance the expressiveness of fault features. S33. Analyze the intrinsic mode function: short circuit fault and overload fault may have obvious differences in the energy ratio and frequency distribution of each intrinsic mode function, which makes the fault characteristics more obvious and easy to distinguish. This enhanced fault characteristic helps to improve the accuracy of fault diagnosis and location, especially in a complex distribution system environment, where multiple fault types may exist at the same time or fault characteristics are intertwined. The features extracted by empirical mode decomposition can help to more clearly identify the fault type and location; In this step, fault features are extracted by combining principal component analysis and empirical mode decomposition to achieve efficient dimensionality reduction, reduce data volume and complexity, enhance the distinguishability of fault features, and adapt to changes in complex fault scenarios, thereby significantly improving the accuracy and timeliness of fault diagnosis and location, and more accurately determining the fault type and location.

[0024] S4. Construct a fault location model, with the objective function of minimizing the fault location error, and set the power flow equation of the distribution system as a constraint condition to ensure that the fault location result meets the basic physical laws of the power system; S41. Determine the influencing factors of the objective function: the influencing factors include the actual fusing state of the intelligent fuse, the deviation between the current and voltage measurement values ​​and the theoretical values, the relationship between the temperature change and the threshold value, the presence or absence of an arc signal, and the distance from the fault point to the monitoring point; S42, Constraints: When a certain location is a fault point, the power distribution and voltage level of the area calculated according to the power flow equation should be within a reasonable range. If the calculated result violates the power flow equation (such as the power injection or outflow of a node does not conform to the actual physical law, or the node voltage exceeds the normal operating range), it means that the fault hypothesis is unreasonable, and the corresponding particle fitness value will be reduced, thereby guiding the algorithm to eliminate these unreasonable fault hypotheses and make the final fault location result more accurate and reliable; S43. Minimize the fault location error: For the actual blown state of the smart fuse, if the fault location assumed by the particle is consistent with the actual blown smart fuse location, then the error term corresponding to this part in the objective function should be smaller; otherwise, if they are not consistent, the error is larger; define a function , if the smart fuse at the assumed position actually blows, then =0, if the fuse is not actually blown but is assumed to be blown or vice versa, then =1; For the deviation between the current and voltage measurement values ​​and the theoretical values, assuming that the current measurement value is The theoretical value is , is the deviation, for voltage V, These deviation terms are added together to obtain a function that reflects the impact of current and voltage deviation on fault location: ; Among them, i and j represent different measurement points; For the relationship between temperature change and threshold, if it is assumed that the fault location causes the temperature change to exceed the normal threshold range and is inconsistent with the actual temperature change, the error will increase. Assuming T is the actual temperature, is the normal threshold temperature, t is the expected temperature change based on the assumed fault location, when When the temperature is abnormal, define the function Indicates the error, ,otherwise ; For the presence or absence of arc signal, define the function If there is actually an arc and the assumed position is reasonable to produce an arc, or if there is actually no arc and the assumed position should not produce an arc, ,otherwise ; For the distance from the fault point to the monitoring point, assume that d is the distance from the fault point to the monitoring point, is the reference distance, Indicates the error, ; S44. Determine the weight coefficient of each factor and establish the objective function: The weight coefficient can reflect the relative importance of each influencing factor in evaluating the pros and cons of the fault hypothesis. Assume that w1, w2, w3, w4, and w5 are the weight coefficients of f1, f2, f3, f4, and f5 respectively. The objective function can be the weighted sum of these factors. Let F be the objective function. The objective function F is ; In this step, by comprehensively considering multiple influencing factors (such as the state of the smart fuse, current and voltage deviations, temperature changes, arc signals, and the distance from the fault point to the monitoring point) and minimizing the positioning errors caused by these factors, the accuracy of fault positioning can be significantly improved. The distribution system power flow equation is used as a constraint to ensure that the fault positioning result is consistent with the actual operation of the power system, avoiding subsequent operational problems caused by positioning errors.

[0025] S5, encoding the locations where the fault may occur, using the codes of different fault locations as particles to form a particle swarm, determining the number of particles in the particle swarm, and randomly initializing the position of each particle; S51. Determine the size of the particle swarm: select an appropriate number of particles according to the complexity of the problem and the computing resources. If there are N particles, encode the locations where the fault may occur, such as different parts inside the smart fuse and the line sections connected to the smart fuse, to form a particle position vector. If there are M possible fault locations in a distribution system, the position vector of a particle can be an M-dimensional vector, and the value of each dimension represents the possibility that the corresponding location is assumed to be a fault point. S52, encoding the location where the fault occurs: encoding different parts inside the smart fuse and the line sections connected to the smart fuse, randomly initializing the speed of each particle, the dimension of the speed vector is the same as the position vector, and the speed value is randomly given within a set speed range; In this step, a solid foundation is provided for the subsequent fault location by encoding the fault location, determining the size of the particle swarm, and randomly initializing the particle position and velocity. This method not only simplifies the problem and improves the search efficiency, but also significantly improves the accuracy and reliability of fault location by balancing global and local searches, optimizing resource utilization, and increasing search diversity.

[0026] S6. Based on the objective function established by the fault location model, the particle matching value is calculated and the optimal particle is determined. For each particle, its current position is substituted into the objective function to calculate the matching value, the particle position is decoded into a specific fault hypothesis, and then the matching degree with the actual fault feature is calculated according to the objective function in the fault location model; S61. Substitute the current position vector of the particle into the objective function to calculate the fitness value of the fault hypothesis: the particle fault is at a certain intelligent fuse or a part of the line section connected to the intelligent fuse. By obtaining the actual current and voltage measurement values, temperature changes, arc signal conditions, and distance from the monitoring point corresponding to the corresponding position of the particle, and substituting them into the objective function to calculate the fitness value of the fault hypothesis, after calculating the fitness values ​​of all particles, compare these values ​​and sort them, and determine the global optimal particle in the current particle group and the optimal particle in the history of the individual particle itself. The global optimal particle represents the hypothesis closest to the actual fault position in the entire particle group, and the individual optimal particle represents the hypothesis closest to the actual fault position found by each particle itself during the search process; S62, iterative search for fault location: after each iteration is completed, check whether the convergence condition is met. The convergence condition is that the maximum number of iterations is reached or the fitness value change is less than the set threshold. If the convergence condition is met, stop the iteration, output the current global optimal particle position and the individual optimal particle of the particle, and the particle position is the specific location of the faulty device finally determined; if not, return to the step of calculating the particle fitness value, continue to the next iteration, continuously optimize the particle group, and find a better fault location hypothesis until the convergence condition is met; in the early stage of fault location, a larger initial speed can enable the particles to quickly cover a larger range of fault location hypotheses. As the iteration proceeds, the speed is gradually adjusted, focusing on the area closer to the fault location for search, and finally accurately finding the fault location; In this step, by calculating the matching value of the particles and determining the optimal particle, the iterative search method is used to continuously approach the optimal solution, thereby achieving accurate and efficient positioning of the fault location, providing a strong guarantee for the safe and stable operation of the power system.

[0027] Embodiment 2 The present invention provides a fault location system for power distribution equipment, which specifically includes the following modules: An intelligent sensing module, including fuses, various types of sensors and data acquisition circuits, is used to collect electrical quantity and non-electrical quantity data of the intelligent fusing system and transmit them to the data processing module; Data processing module, which performs data fusion and preprocessing, fault feature extraction and selection, fault location model construction, and fault location result evaluation and decision making; The communication module is responsible for data transmission between the intelligent sensing module and the data processing module, and for transmitting the fault location results to the external system; Decision execution module: Executes corresponding fault isolation, repair or further detection operations according to the fault location result evaluation and decision instructions in the decision.

[0028] The various variations and specific embodiments of the fault location method for the power distribution equipment in the aforementioned embodiment 1 are also applicable to the fault location system for the power distribution equipment in this embodiment. Through the aforementioned detailed description of the fault location method for the power distribution equipment, technical personnel in this field can clearly know the implementation method of the fault location system for the power distribution equipment in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0029] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described in detail here.

[0030] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for locating a fault of a power distribution device, characterized in that: The method comprises: Intelligent perception and data collection and transmission. Various sensors collect data according to the set sampling frequency. For electrical quantities with fast current and voltage changes, the sampling frequency is higher; for non-electrical quantities with relatively slow temperature changes, the sampling frequency is appropriately reduced. After data collection is completed, the data is encrypted and transmitted using an encryption algorithm. Preprocess the collected data, map them to a uniform value range using a normalization algorithm, and synchronize the data from different sensors in time using timestamp matching and interpolation algorithms. Fault feature extraction is performed by combining principal component analysis and empirical mode decomposition to extract the main characteristic components in the data and remove redundant information. The characteristic vector that can reflect the fault type and location is extracted by analyzing the energy distribution, frequency characteristics and specific modal components related to the fault of the intrinsic mode function. Construct a fault location model with the objective function of minimizing the fault location error and setting the power flow equation of the distribution system as a constraint to ensure that the fault location result meets the basic physical laws of the power system; Encode the locations where the fault may occur, use the codes of different fault locations as particles to form a particle swarm, determine the number of particles in the particle swarm, and randomly initialize the position of each particle; Based on the objective function established by the fault localization model, the particle matching value is calculated and the optimal particle is determined. For each particle, its current position is substituted into the objective function to calculate the matching value, the particle position is decoded into a specific fault hypothesis, and then the matching degree with the actual fault characteristics is calculated according to the objective function in the fault localization model.

2. A method for locating a fault of a power distribution device according to claim 1, characterized in that: The sensor comprises: Current and voltage sensors: Current and voltage sensors monitor the three-phase current, voltage and the voltage at the high-voltage user boundary point in real time; Fuse position detection sensor monitors the opening and closing status of the fuse body in real time; Temperature sensor: The temperature sensor is installed near the fuse body and is used to monitor the temperature change of the fuse; Arc sensor: The arc sensor is used to detect the arc signal that may be generated when the fuse is activated.

3. A method for locating a fault of a power distribution device according to claim 1, characterized in that: The method for constructing the objective function comprises: Determine the influencing factors of the objective function, including the actual fusing state of the intelligent fuse, the deviation between the current and voltage measurement values ​​and the theoretical values, the relationship between temperature change and threshold value, the presence or absence of arc light signal, and the distance from the fault point to the monitoring point; The objective function is constructed by weighted summation.

4. A method for locating a fault of a power distribution device according to claim 3, characterized in that: For the actual blown state of the smart fuse, if the fault location assumed by the particle is consistent with the actual blown smart fuse location, then the error term corresponding to this part in the objective function should be smaller; otherwise, if they are not consistent, the error is larger. Define a function , if the smart fuse at the assumed position actually blows, then If the fuse is not actually blown but is assumed to be blown or vice versa, then ; For the deviation between the current and voltage measurement values ​​and the theoretical values, assuming that the current measurement value is The theoretical value is , is the deviation, for voltage V, These deviation terms are added together to obtain a function that reflects the impact of current and voltage deviation on fault location: ； Among them, i and j represent different measurement points; For the relationship between temperature change and threshold, if it is assumed that the fault location causes the temperature change to exceed the normal threshold range and is inconsistent with the actual temperature change, the error will increase. Assuming T is the actual temperature, is the normal threshold temperature, t is the expected temperature change based on the assumed fault location, when When the temperature is abnormal, define the function Indicates the error, ,otherwise ; For the presence or absence of arc signal, define the function If there is actually an arc and the assumed position is reasonable to produce an arc, or if there is actually no arc and the assumed position should not produce an arc, ,otherwise ; For the distance from the fault point to the monitoring point, assume that d is the distance from the fault point to the monitoring point, is the reference distance, Indicates the error, .

5. A method for locating a fault of a power distribution device according to any one of claims 3 to 4, characterized in that: Determine the weight coefficient of each factor. The weight coefficient can reflect the relative importance of each influencing factor in evaluating the quality of the fault hypothesis. Assume that w1, w2, w3, w4, and w5 are the weight coefficients of f1, f2, f3, f4, and f5 respectively. The objective function can be the weighted sum of these factors. Let F be the objective function. The objective function F is .

6. A method for locating a fault of a power distribution device according to claim 1, characterized in that: Before encoding the location where the fault may occur, the size of the particle swarm must be determined first. The appropriate number of particles is selected according to the complexity of the problem and the computing resources. If there are N particles, the locations where the fault may occur, such as different parts inside the smart fuse and the line sections connected to the smart fuse, are encoded to form the particle position vector. If there are M possible fault locations in a distribution system, the position vector of a particle can be an M-dimensional vector. The value of each dimension represents the possibility that the corresponding position is assumed to be the fault point. At the same time, the speed of each particle is randomly initialized. The dimension of the speed vector is the same as that of the position vector. The speed value is randomly given within the set speed range. The direction and magnitude of the speed determine the movement of the particle in the solution space in the subsequent iterative calculation to search for the optimal solution. In the early stage of fault location, a larger initial speed can enable the particle to quickly cover a larger range of fault location assumptions. As the iteration proceeds, the speed is gradually adjusted to focus on the area closer to the fault location for search, and finally accurately find the fault location.

7. A method for locating a fault of a power distribution device according to claim 6, characterized in that: For each particle, substitute its current position vector into the objective function to calculate the fitness value of the fault hypothesis. If the particle fault is at a certain intelligent fuse or a part of the line section connected to the intelligent fuse, the actual current and voltage measurement values, temperature changes, arc signal conditions and distance information from the monitoring point corresponding to the corresponding position of the particle are obtained and substituted into the objective function to calculate the fitness value of the fault hypothesis. After calculating the fitness values ​​of all particles, these values ​​are compared and sorted to determine the global optimal particle in the current particle group and the optimal particle in the individual particle's own history. The global optimal particle represents the hypothesis closest to the actual fault position in the entire particle group, and the individual optimal particle represents the hypothesis closest to the actual fault position found by each particle itself during the search process. After each iteration is completed, check whether the convergence condition is met. The convergence condition is that the maximum number of iterations is reached or the fitness value change is less than the set threshold. If the convergence condition is met, stop the iteration, output the current global optimal particle position and the individual optimal particle of the particle, and the particle position is the specific location of the faulty device finally determined; if it is not satisfied, return to the step of calculating the particle fitness value, continue to the next iteration, continuously optimize the particle swarm, and find a better fault location hypothesis until the convergence condition is met.

8. A fault location system for power distribution equipment, characterized in that: The system comprises: An intelligent sensing module, including fuses, various types of sensors and data acquisition circuits, is used to collect electrical quantity and non-electrical quantity data of the intelligent fusing system and transmit them to the data processing module; Data processing module, which performs data fusion and preprocessing, fault feature extraction and selection, fault location model construction, and fault location result evaluation and decision making; The communication module is responsible for data transmission between the intelligent sensing module and the data processing module, and for transmitting the fault location results to the external system; Decision execution module: Executes corresponding fault isolation, repair or further detection operations according to the fault location result evaluation and decision instructions in the decision.

9. A fault location detection device for power distribution equipment, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory and the processor are connected via the bus, characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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