Cable partial discharge positioning method and device based on particle filtering, and medium

Abstract

The invention relates to a cable partial discharge positioning method and device based on particle filtering and a medium, and belongs to the technical field of cable partial discharge positioning, and the method comprises the following steps: obtaining cable partial discharge signal data collected by multiple sensors, and carrying out the data fusion, and obtaining a comprehensive feature vector; and taking the comprehensive feature vector as an observation value of a particle filtering algorithm, carrying out particle initialization, particle state updating and resampling by using the particle filtering algorithm, constructing a new particle set, and carrying out cable partial discharge positioning at the next moment according to particle positions and weights in the new particle set. Compared with the prior art, by means of advantage complementation of multiple detection methods and application of a data fusion algorithm, the partial discharge positioning precision and the anti-interference capacity are improved, and the method is suitable for cable monitoring and fault diagnosis in an electric power system.

Description

Technical Field

[0001] The present invention relates to the technical field of cable partial discharge positioning, and in particular to a cable partial discharge positioning method, device and medium based on particle filtering. Background Art

[0002] As an important component of power transmission and distribution systems, the operating status of power cables is directly related to the reliability and safety of power systems. The partial discharge (PD) phenomenon of cables is the main precursor of cable insulation aging and failure. Timely detection and location of partial discharge can help prevent further deterioration of cable faults and reduce the occurrence of power outages.

[0003] Traditional partial discharge detection methods mainly include the following: Ultrasonic detection: Partial discharge will generate ultrasonic signals, and ultrasonic sensors can capture these signals. Ultrasonic detection has high sensitivity, but it is easily interfered by noise in complex environments, affecting positioning accuracy. High-frequency pulse current (HFCT) detection: Partial discharge will generate high-frequency current pulse signals, and HFCT sensors can detect these signals. HFCT detection is suitable for online monitoring, but its detection range is limited and it is difficult to accurately locate the partial discharge source. Although the above methods have their own advantages in partial discharge detection, a single method often has certain limitations in locating the partial discharge source. For example, ultrasonic detection is easily affected by environmental noise, and the HFCT detection range is limited. Therefore, how to comprehensively utilize the advantages of multiple detection methods and combine them with other detection methods to improve the accuracy and reliability of cable partial discharge positioning has become the focus and difficulty of current research.

[0004] In recent years, the development of data fusion technology has provided a new idea for solving this problem. By fusing data from multiple detection methods, we can make full use of the advantages of different methods and complement their deficiencies, thereby improving positioning accuracy and anti-interference capabilities. However, there is currently a lack of research on how to accurately extract features from fused data after data fusion, resulting in low accuracy and reliability in PD positioning. Summary of the invention

[0005] The purpose of the present invention is to provide a cable partial discharge positioning method, device and medium based on particle filtering, which significantly improves the positioning accuracy and reliability of the partial discharge source by comprehensively utilizing ultrasonic detection and HFCT detection technology.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A cable partial discharge positioning method based on particle filtering comprises the following steps: Obtain cable partial discharge signal data collected by multiple sensors and perform data fusion to obtain a comprehensive feature vector; The comprehensive feature vector is used as the observation value of the particle filter algorithm. The particle filter algorithm is used to initialize particles, update particle states and resample particles to construct a new particle set. The cable partial discharge is located at the next moment according to the particle positions and weights in the new particle set.

[0007] The sensors include ultrasonic sensors and high-frequency pulse current sensors.

[0008] The ultrasonic sensors are evenly arranged along the cable surface, wherein at least one ultrasonic sensor is arranged at the cable joints, corners and other areas prone to partial discharge; the high-frequency pulse current sensor is arranged at each joint and grounding point of the cable.

[0009] The cable partial discharge signal data collected by the ultrasonic sensor and the high-frequency pulse current sensor are time synchronized, and their respective signal features are extracted. The extracted features are weighted fused or feature spliced ​​to obtain a comprehensive feature vector, wherein the signal features of the data collected by the ultrasonic sensor include the amplitude, frequency and arrival time of the ultrasonic signal, and the signal features of the data collected by the high-frequency pulse current sensor include the amplitude, rise time and pulse width of the waveform of the high-frequency current pulse signal.

[0010] The particle initialization comprises the following steps: 1) Determine the number of particles and distribute them evenly over the cable length to determine the initial position of the particles; ; in, L is the cable length, N is the number of particles, For particles i The initial position of 2) Give each particle the same initial weight: ; in, For particles i The initial weight of 3) Calculate the characteristic values ​​of particles At each time step t , calculate each particle i Location The corresponding comprehensive feature vector The specific steps are as follows: construct a feature mapping table based on the position of each sensor and the comprehensive feature vector of the corresponding position to determine the position of the particle The positions of the nearest left and right sensors are denoted as and , based on the feature mapping table, determine the corresponding comprehensive feature vector and , calculated using linear interpolation The characteristic values ​​of: .

[0011] The particle state update is specifically to predict the next moment position of the particle according to the state transition model, update the particle weight using the observation value and the measurement model, and normalize the weight, wherein, The state transition model is expressed as: ; in, is zero-mean Gaussian noise; Using Observations and measurement models Update the particle weight: ; ; in, for t Particles of time i The weight of for t The observed value at time, is the standard deviation of the measurement noise; The updated particle weights are normalized as follows: ; in, The normalized particle i exist t +1 moment weight.

[0012] The resampling comprises the following steps: Calculate the cumulative weight based on the normalized weight in the particle state update: ; in, For particles i The cumulative weight of generate N A random number uniformly distributed in the interval [0,1] ; Resample using the accumulated weights and generated random numbers to select a new set of particles: Initialize index i =1; For random numbers ( ),judge Is it less than or equal to ,like , then the corresponding particles i Add a new particle set; if , then increase i The value of is used for the next judgment; after the traversal is completed, a new particle set is obtained, and uniformly distributed weights are assigned to the particles in the new particle set: .

[0013] The calculation method for locating the cable partial discharge at the next moment according to the particle positions and weights in the new particle set is: ; in, for t Estimated value of the PD position at time +1.

[0014] A cable partial discharge positioning device based on particle filtering comprises a memory, a processor, and a program stored in the memory. When the processor executes the program, the method described above is implemented.

[0015] A storage medium stores a program, which implements the method described above when executed.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) Multi-sensor fusion improves positioning accuracy: The present invention realizes the fusion of multi-source data by comprehensively utilizing ultrasonic detection and high-frequency pulse current (HFCT) detection technology. This method gives full play to the advantages of each detection method, overcomes the limitations of a single detection method, and thus significantly improves the accuracy of cable partial discharge (PD) positioning. It enhances the reliability and anti-interference ability of the system, and provides more comprehensive and accurate data support for cable partial discharge positioning.

[0017] (2) Introducing data fusion algorithm to optimize observation values: The present invention innovatively introduces a data fusion algorithm to perform weighted averaging or feature splicing on the feature data extracted by different sensors to form a comprehensive feature vector as the observation value of the particle filter. Through this data fusion method, the multi-dimensional information provided by various sensors can be fully utilized, the accuracy and stability of the observation value can be improved, and the PD positioning can be performed efficiently and accurately in a complex electromagnetic environment.

[0018] (3) Improved particle filtering and efficient resampling strategy: In the particle filter algorithm, the present invention realizes high-precision localization of partial discharge sources by initializing particle positions and weights, state prediction, measurement update and resampling. In particular, the improved resampling strategy generates a new particle set through a system resampling method, which effectively solves the problem of particle weight degradation. This strategy can maintain the numerical stability and positioning accuracy of the particle filter algorithm, ensure the rationality of particle weight distribution, and thus improve the reliability and robustness of the positioning result. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0020] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0021] Example 1

[0022] This embodiment provides a cable partial discharge location method based on particle filtering. Figure 1 As shown, the following steps are included: S1, obtain the cable partial discharge signal data collected by multiple sensors and perform data fusion to obtain a comprehensive feature vector.

[0023] S11, Local Sensor Deployment In this embodiment, the sensors used include ultrasonic sensors and high-frequency pulse current sensors. In order to ensure effective detection of partial discharge signals of cables, the sensors need to be reasonably deployed at key positions of the cables.

[0024] 1) Deployment plan of ultrasonic sensors: Ultrasonic sensors are mainly used to capture ultrasonic signals generated by partial discharge and are evenly arranged along the cable surface to ensure full coverage. In addition, during deployment, focus on cable joints, corners and other areas prone to partial discharge, and arrange at least one ultrasonic sensor in these areas.

[0025] 2) Deployment plan of high-frequency pulse current sensors: High-frequency pulse current sensors are used to detect high-frequency current pulse signals, and are mainly deployed in areas prone to partial discharge, such as cable joints and grounding points. In this embodiment, an HFCT sensor is installed at each joint and grounding point.

[0026] S12, data acquisition: using ultrasonic sensors and high-frequency pulse current sensors to collect cable partial discharge signal data.

[0027] S13, Data Processing and Fusion The cable partial discharge signal data collected by the ultrasonic sensor and the high-frequency pulse current sensor are synchronized in time, and their respective signal features are extracted. The extracted features are weighted fused or feature spliced ​​to obtain a comprehensive feature vector. The signal features of the data collected by the ultrasonic sensor include the amplitude of the ultrasonic signal. ,frequency and arrival time The signal characteristics of the data collected by the high-frequency pulse current sensor include the amplitude of the waveform of the high-frequency current pulse signal , Rise time , Pulse Width .

[0028] In this embodiment, data fusion adopts the method of feature splicing to obtain a comprehensive feature vector , at each time step t , the fused comprehensive feature vector F As the observation value of the particle filter algorithm .

[0029] S2, taking the comprehensive feature vector as the observation value of the particle filter algorithm, using the particle filter algorithm to initialize particles, update particle states and resample, constructing a new particle set, and locating the cable partial discharge at the next moment according to the particle positions and weights in the new particle set.

[0030] S21, particle initialization 1) Determine the number of particles and distribute them evenly over the cable length to determine the initial position of the particles; ; in, L is the cable length, N is the number of particles, For particles i The initial position of .

[0031] 2) Give each particle the same initial weight: ; in, For particles i The initial weight of .

[0032] 3) Calculate the characteristic values ​​of particles At each time step t , calculate each particle i Location The corresponding comprehensive feature vector , the specific steps are as follows: Depending on the location of each sensor and the comprehensive feature vector of the corresponding position Constructing feature maps ; Determine the particle position The positions of the nearest left and right sensors are denoted as and , based on the feature mapping table, determine the corresponding comprehensive feature vector and ; Calculated using linear interpolation The characteristic values ​​of: .

[0033] S22, particle state update: predict the next moment position of the particle according to the state transition model, update the particle weight using the observation value and measurement model, and normalize the weight, where: The state transition model is expressed as: ; in, is zero-mean Gaussian noise.

[0034] Using Observations and measurement models Update the particle weight: ; ; in, for t Particles of time i The weight of for t The observed value at time, is the standard deviation of the measurement noise.

[0035] Normalize the updated particle weights: ; in, The normalized particle i exist t +1 moment weight.

[0036] S23, resampling: S231, calculate the cumulative weight according to the normalized weight in the particle state update to form a cumulative weight array C : ; in, For particles i The cumulative weight of .

[0037] S232, generated NA random number uniformly distributed in the interval [0,1] ; In this embodiment, the following method is used to generate these random numbers: ; in, Represents a random number uniformly distributed in the interval [0,1].

[0038] S233, resample using the accumulated weights and generated random numbers to select a new set of particles: Initialize index i =1; For random numbers ( ),judge Is it less than or equal to ,like , then the corresponding particles i Add a new particle set; if , then increase i The value of , makes the next judgment; After the traversal is completed, a new particle set is obtained, and uniformly distributed weights are assigned to the particles in the new particle set: .

[0039] S24, partial discharge position estimation: The cable partial discharge position at the next moment is located according to the particle positions and weights in the new particle set: ; in, for t Estimated value of the PD position at time +1.

[0040] In summary, the present invention realizes cable partial discharge positioning by comprehensively utilizing the technical means of ultrasonic detection and high-frequency pulse current (HFCT) detection based on the particle filtering method, which significantly improves the accuracy and reliability of cable partial discharge positioning.

[0041] Example 2

[0042] This embodiment provides a cable partial discharge locating device based on particle filtering, including a memory, a processor, and a program stored in the memory, and when the processor executes the program, the method described in the above embodiment 1 is implemented.

[0043] In a preferred embodiment, the device comprises: Data acquisition module: obtains cable partial discharge signal data collected by multiple sensors and performs data fusion to obtain a comprehensive feature vector; Positioning module: The comprehensive feature vector is used as the observation value of the particle filter algorithm, and the particle filter algorithm is used to initialize particles, update particle states and resample particles to construct a new particle set. The cable partial discharge positioning at the next moment is performed according to the particle positions and weights in the new particle set.

[0044] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0045] Example 3

[0046] This embodiment provides a storage medium on which a program is stored. When the program is executed, the method described in the above embodiment 1 is implemented.

[0047] Storage media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in the present invention, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0048] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning, or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A cable partial discharge location method based on particle filtering, characterized in that: The following steps are involved: Obtain cable partial discharge signal data collected by multiple sensors and perform data fusion to obtain a comprehensive feature vector; The comprehensive feature vector is used as the observation value of the particle filter algorithm. The particle filter algorithm is used to initialize particles, update particle states and resample particles to construct a new particle set. The cable partial discharge is located at the next moment according to the particle positions and weights in the new particle set.

2. A cable partial discharge location method based on particle filtering according to claim 1, characterized in that: The sensors include ultrasonic sensors and high-frequency pulse current sensors.

3. A cable partial discharge location method based on particle filtering according to claim 2, characterized in that: The ultrasonic sensors are evenly arranged along the cable surface, wherein at least one ultrasonic sensor is arranged at the cable joints, corners and other areas prone to partial discharge; the high-frequency pulse current sensor is arranged at each joint and grounding point of the cable.

4. A cable partial discharge location method based on particle filtering according to claim 2, characterized in that: The cable partial discharge signal data collected by the ultrasonic sensor and the high-frequency pulse current sensor are time synchronized, and their respective signal features are extracted. The extracted features are weighted fused or feature spliced ​​to obtain a comprehensive feature vector, wherein the signal features of the data collected by the ultrasonic sensor include the amplitude, frequency and arrival time of the ultrasonic signal, and the signal features of the data collected by the high-frequency pulse current sensor include the amplitude, rise time and pulse width of the waveform of the high-frequency current pulse signal.

5. A cable partial discharge location method based on particle filtering according to claim 1, characterized in that: The particle initialization comprises the following steps: 1) Determine the number of particles and distribute them evenly over the cable length to determine the initial position of the particles; ； in, L is the cable length, N is the number of particles, For particles i The initial position of 2) Give each particle the same initial weight: ； in, For particles i The initial weight of 3) Calculate the characteristic values ​​of particles At each time step t , calculate each particle i Location The corresponding comprehensive feature vector The specific steps are as follows: construct a feature mapping table based on the position of each sensor and the comprehensive feature vector of the corresponding position to determine the position of the particle The positions of the nearest left and right sensors are denoted as and , based on the feature mapping table, determine the corresponding comprehensive feature vector and , calculated using linear interpolation The characteristic values ​​of: 。 6. A cable partial discharge location method based on particle filtering according to claim 5, characterized in that: The particle state update is specifically to predict the next moment position of the particle according to the state transition model, update the particle weight using the observation value and the measurement model, and normalize the weight, wherein, The state transition model is expressed as: ； in, is zero-mean Gaussian noise; Using Observations and measurement models Update the particle weight: ； ； in, for t Particles of time i The weight of for t The observed value at time, is the standard deviation of the measurement noise; The updated particle weights are normalized as follows: ； in, The normalized particle i exist t +1 moment weight.

7. A cable partial discharge location method based on particle filtering according to claim 6, characterized in that: The resampling comprises the following steps: Calculate the cumulative weight based on the normalized weight in the particle state update: ； in, For particles i The cumulative weight of generate N A random number uniformly distributed in the interval [0,1] ; Resample using the accumulated weights and generated random numbers to select a new set of particles: Initialize index i =1; For random numbers ( ),judge Is it less than or equal to ,like , then the corresponding particles i Add a new particle set; if , then increase i The value of is used for the next judgment; after the traversal is completed, a new particle set is obtained, and uniformly distributed weights are assigned to the particles in the new particle set: 。 8. A cable partial discharge location method based on particle filtering according to claim 7, characterized in that: The calculation method for locating the cable partial discharge at the next moment according to the particle positions and weights in the new particle set is: ； in, for t Estimated value of the PD position at time +1.

9. A cable partial discharge positioning device based on particle filtering, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the method according to any one of claims 1 to 8 is implemented.

Images