A cable partial discharge positioning method and device based on particle filtering and a medium

Abstract

The present application relates to a kind of cable partial discharge positioning method based on particle filtering, device and medium, belong to cable partial discharge positioning technical field, wherein method includes the following steps: obtaining the cable partial discharge signal data of multi-sensor acquisition and carrying out data fusion, obtain comprehensive feature vector;Comprehensive feature vector is used as the observation value of particle filtering algorithm, particle initialization is carried out using particle filtering algorithm, particle state is updated and resampling, constructs new particle set, according to the particle position and weight in new particle set carries out the cable partial discharge positioning of next time.Compared with prior art, the present application is through the complementary advantages of multiple detection methods and the application of data fusion algorithm, improves the precision and anti-interference ability of partial discharge positioning, applicable to cable monitoring and fault diagnosis in power system.

Description

Technical Field

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

[0002] Power cables are essential components of power transmission and distribution systems, and their operational status is directly related to the reliability and safety of the power system. Partial discharge (PD) in cables is a major precursor to cable insulation aging and failure. Promptly detecting and locating PD helps prevent further cable failures and reduce the occurrence of power outages.

[0003] Traditional partial discharge detection methods primarily include the following: Ultrasonic testing: Partial discharge generates ultrasonic signals, which can be captured by ultrasonic sensors. Ultrasonic testing has high sensitivity, but is susceptible to noise interference in complex environments, affecting positioning accuracy. High-frequency pulsed current (HFCT) testing: Partial discharge generates high-frequency current pulse signals, which can be detected by HFCT sensors. HFCT testing is suitable for online monitoring, but its detection range is limited, making it difficult to accurately locate the source of partial discharge. Although each of these methods has its advantages in partial discharge detection, a single method often has limitations when locating the source of partial discharge. For example, ultrasonic testing is easily affected by environmental noise, and HFCT has a limited detection range. Therefore, how to comprehensively leverage 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 a key and difficult issue in current research.

[0004] In recent years, the development of data fusion technology has provided a new approach to solving this problem. By fusing data from multiple detection methods, the strengths of each method can be fully utilized, while their weaknesses can be complemented, thereby improving positioning accuracy and anti-interference capabilities. However, research on how to accurately extract features from the fused data remains limited, resulting in limited 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:

[0007] A cable partial discharge location method based on particle filtering includes the following steps:

[0008] Obtain cable partial discharge signal data collected by multiple sensors and perform data fusion to obtain a comprehensive feature vector;

[0009] The comprehensive eigenvector 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.

[0010] The sensors include ultrasonic sensors and high-frequency pulse current sensors.

[0011] The ultrasonic sensors are evenly arranged along the surface of the cable, 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.

[0012] 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. 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.

[0013] The particle initialization includes the following steps:

[0014] 1) Determine the number of particles and distribute them evenly along the cable length to determine the initial particle positions;

[0015] ;

[0016] in, L is the cable length, N is the number of particles, For particles i The initial position of

[0017] 2) Give each particle the same initial weight:

[0018] ;

[0019] in, For particles i The initial weight of

[0020] 3) Calculate the characteristic values ​​of the particles

[0021] 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, and determine the position of the particle The positions of the nearest left and right sensors are denoted as and , based on the feature map table, determine the corresponding comprehensive feature vector and , calculated using linear interpolation The eigenvalues ​​of:

[0022] .

[0023] 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 measurement model, and normalize the weight, wherein,

[0024] The state transition model is expressed as:

[0025] ;

[0026] in, is zero-mean Gaussian noise;

[0027] Using observations and measurement models Update the particle weight:

[0028] ;

[0029] ;

[0030] in, for t Particles of Moment i The weight of for t The observed value at time, is the standard deviation of the measurement noise;

[0031] Normalize the updated particle weights to:

[0032] ;

[0033] in, The normalized particles i exist t +1 moment weight.

[0034] The resampling comprises the following steps:

[0035] Calculate the cumulative weight based on the normalized weight in the particle state update:

[0036] ;

[0037] in, For particles i The cumulative weight of

[0038] generate N A random number uniformly distributed in the interval [0,1] ;

[0039] Resample using the accumulated weights and generated random numbers to select a new set of particles: Initialize index i =1;

[0040] 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 the particles in the new particle set are assigned uniformly distributed weights:

[0041] .

[0042] The calculation method for locating the cable partial discharge at the next moment based on the particle positions and weights in the new particle set is:

[0043] ;

[0044] in, for t Estimated value of the PD position at time +1.

[0045] A cable partial discharge locating 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.

[0046] A storage medium stores a program thereon, and when the program is executed, the method described above is implemented.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) Multi-sensor fusion improves positioning accuracy:

[0049] This invention integrates ultrasonic testing and high-frequency pulsed current (HFCT) testing technologies to achieve multi-source data fusion. This approach leverages the strengths of each detection method, overcoming the limitations of a single one, significantly improving the accuracy of cable partial discharge (PD) location. It also enhances the system's reliability and anti-interference capabilities, providing more comprehensive and accurate data support for cable PD location.

[0050] (2) Introducing data fusion algorithm to optimize observation values:

[0051] This invention innovatively introduces a data fusion algorithm that performs a weighted average or feature concatenation of feature data extracted by different sensors to form a comprehensive feature vector, which serves as the observation value for the particle filter. This data fusion method fully utilizes the multidimensional information provided by various sensors, improving the accuracy and stability of the observations and ensuring efficient and accurate partial discharge location even in complex electromagnetic environments.

[0052] (3) Improved particle filtering and efficient resampling strategy:

[0053] The present invention achieves high-precision localization of partial discharge sources within a particle filter algorithm through steps such as initializing particle positions and weights, state prediction, measurement updates, and resampling. In particular, an improved resampling strategy, which generates a new particle set through a systematic resampling method, effectively addresses the problem of particle weight degradation. This strategy maintains the numerical stability and positioning accuracy of the particle filter algorithm, ensures the rationality of the particle weight distribution, and thus improves the reliability and robustness of the positioning results. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below with reference to 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.

[0056] Example 1

[0057] This embodiment provides a cable partial discharge location method based on particle filtering, such as Figure 1 As shown, the following steps are included:

[0058] S1, obtains the cable partial discharge signal data collected by multiple sensors and performs data fusion to obtain a comprehensive feature vector.

[0059] S11, Local Sensor Deployment

[0060] In this embodiment, the sensors used include ultrasonic sensors and high-frequency pulse current sensors. In order to ensure effective detection of cable partial discharge signals, the sensors need to be reasonably deployed at key locations on the cable.

[0061] 1) Ultrasonic sensor deployment plan: Ultrasonic sensors are primarily used to capture ultrasonic signals generated by partial discharge (PD). They are evenly distributed along the cable surface to ensure comprehensive coverage. Furthermore, during deployment, focus on cable joints, corners, and other areas prone to PD, placing at least one ultrasonic sensor in these areas.

[0062] 2) High-Frequency Pulse Current Sensor Deployment: High-Frequency Pulse Current Sensors are used to detect high-frequency current pulse signals and are primarily deployed at locations 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.

[0063] S12, data acquisition: using ultrasonic sensors and high-frequency pulse current sensors to collect cable partial discharge signal data.

[0064] S13, Data Processing and Fusion

[0065] 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 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 .

[0066] In this embodiment, data fusion adopts the method of feature splicing to obtain the comprehensive feature vector , at each time step t , the fused comprehensive feature vector F As the observation value of the particle filter algorithm .

[0067] 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.

[0068] S21, particle initialization

[0069] 1) Determine the number of particles and distribute them evenly along the cable length to determine the initial particle positions;

[0070] ;

[0071] in, L is the cable length, N is the number of particles, For particles i The initial position of .

[0072] 2) Give each particle the same initial weight:

[0073] ;

[0074] in, For particles i The initial weight of .

[0075] 3) Calculate the characteristic values ​​of the particles

[0076] At each time step t , calculate each particle i Location The corresponding comprehensive feature vector , the specific steps are as follows:

[0077] According to the position of each sensor and the comprehensive feature vector of the corresponding position Constructing feature maps ;

[0078] Determine the particle position The positions of the nearest left and right sensors are denoted as and , based on the feature map table, determine the corresponding comprehensive feature vector and ;

[0079] Calculated using linear interpolation The eigenvalues ​​of:

[0080] .

[0081] S22, particle state update: predict the next moment position of the particle based on the state transition model, update the particle weight using the observation value and measurement model, and normalize the weight, where,

[0082] The state transition model is expressed as:

[0083] ;

[0084] in, is zero-mean Gaussian noise.

[0085] Using observations and measurement models Update the particle weight:

[0086] ;

[0087] ;

[0088] in, for t Particles of Moment i The weight of for t The observed value at time, is the standard deviation of the measurement noise.

[0089] Normalize the updated particle weights:

[0090] ;

[0091] in, The normalized particles i exist t +1 moment weight.

[0092] S23, resampling:

[0093] S231, calculate the cumulative weight according to the normalized weight in the particle state update to form a cumulative weight array C :

[0094] ;

[0095] in, For particles i The cumulative weight of .

[0096] S232, generated N A random number uniformly distributed in the interval [0,1] ; In this embodiment, the following method is used to generate these random numbers:

[0097] ;

[0098] in, Represents a random number uniformly distributed in the interval [0,1].

[0099] S233, resample using the accumulated weights and generated random numbers to select a new set of particles:

[0100] Initialize index i =1;

[0101] 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 , make the next judgment;

[0102] After the traversal is completed, a new particle set is obtained, and the particles in the new particle set are assigned uniformly distributed weights:

[0103] .

[0104] S24, partial discharge position estimation: The cable partial discharge position at the next moment is determined based on the particle positions and weights in the new particle set:

[0105] ;

[0106] in, for t Estimated value of the PD position at time +1.

[0107] In summary, the present invention realizes cable partial discharge positioning by comprehensively utilizing ultrasonic detection and high-frequency pulse current (HFCT) detection technologies and based on the particle filtering method, which significantly improves the accuracy and reliability of cable partial discharge positioning.

[0108] Example 2

[0109] This embodiment provides a cable partial discharge locating device based on particle filtering, comprising a memory, a processor, and a program stored in the memory. When the processor executes the program, the method described in the above embodiment 1 is implemented.

[0110] In a preferred embodiment, the apparatus comprises:

[0111] Data acquisition module: acquires cable partial discharge signal data collected by multiple sensors and performs data fusion to obtain a comprehensive feature vector;

[0112] 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 based on the particle positions and weights in the new particle set.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described modules can refer to the corresponding process in the foregoing method embodiments, and will not be described here again.

[0114] Embodiment 3

[0115] The embodiment provides a storage medium, which has a program stored thereon, the program being executed to implement the method in the foregoing embodiment 1.

[0116] The storage medium includes permanent and non-permanent, removable and non-removable media, which can be implemented by any method or technology to store information. The 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition in the present application, the computer readable medium does not include transitory computer readable media, such as modulated data signals and carriers.

[0117] The preferred embodiments of the present application are described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations to the present application without creative work based on the concept of the present application. Therefore, any technical solutions obtained by logical analysis, reasoning or limited experiments based on the prior art according to the concept of the present application shall be within the protection scope 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 eigenvector 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 based on the particle positions and weights in the new particle set. The particle initialization includes the following steps: 1) Determine the number of particles and evenly distribute the particles along the cable length to determine the initial position of the particles; Where L is the cable length, N is the number of particles, is the initial position of particle i; 2) Give each particle the same initial weight: in, is the initial weight of particle i; 3) Calculate the characteristic value of the particle At each time step t, calculate the position of each particle i 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, and determine the position of the particle. The positions of the nearest left and right sensors are denoted by x s1 and x s2 , based on the feature map table, determine the corresponding comprehensive feature vector and Calculated using linear interpolation The eigenvalues ​​of:

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 surface of the cable, 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. 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. The cable partial discharge location method based on particle filtering according to claim 1, 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 measurement model, and normalize the weight, wherein, The state transition model is expressed as: in, is zero-mean Gaussian noise; Using the observed value z (t) and measurement model p(z (t) |z (t+1) )Update the particle weight: in, is the weight of particle i at time t, z (t) is the observed value at time t, σ z is the standard deviation of the measurement noise; Normalize the updated particle weights to: in, is the normalized weight of particle i at time t+1.

6. A cable partial discharge location method based on particle filtering according to claim 5, characterized in that: The resampling comprises the following steps: Calculate the cumulative weight based on the normalized weight in the particle state update: Among them, C i is the cumulative weight of particle i; Generate N random numbers {u1,u2,...,u N }; Use the accumulated weights and the generated random numbers to resample and select a new set of particles: initialize index i = 1; for the random number u j (j=1,2,...,N), judge u j Is it less than or equal to C? i , if u j ≤C i , then add the corresponding particle i to the new particle set; if u j >C i , then increase the value of i and make the next judgment; after the traversal is completed, a new particle set is obtained, and the particles in the new particle set are assigned uniformly distributed weights:

7. A cable partial discharge location method based on particle filtering according to claim 6, characterized in that: The calculation method for locating the cable partial discharge at the next moment based on the particle positions and weights in the new particle set is: in, is the estimated value of the partial discharge position at time t+1.

8. 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 7 is implemented.

9. 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 7 is implemented.

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