Discharge detection method and system for high and low voltage power distribution cabinet

Abstract

The invention belongs to the technical field of discharge detection, and particularly relates to a discharge detection method and system for a high-low voltage power distribution cabinet, and the method comprises the steps: obtaining a measurement vector generated by a discharge source and a real-time temperature reading, and loading a preset reference sensing matrix; constructing a dynamic environment correction coefficient; adjusting the reference sensing matrix based on the dynamic environment correction coefficient to generate a dynamic sensing matrix matched with the current thermal field environment; and determining the position of the discharge source through a sparse reconstruction algorithm based on the dynamic sensing matrix and the measurement vector so as to realize discharge detection of the high and low voltage power distribution cabinet. According to the method, model mismatch caused by temperature change can be dynamically compensated by constructing the environment-adaptive dynamic sensing matrix, so that the accuracy and reliability of discharge fault positioning under complex working conditions are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of discharge detection technology. More specifically, this invention relates to a discharge detection method and system for high and low voltage distribution cabinets. Background Technology

[0002] As key hub equipment in the power system, the health of the internal insulation structure of high and low voltage switchgear is directly related to the safe and stable operation of the power grid. Partial discharge is a weak and localized breakdown phenomenon that occurs in insulating materials under the action of a strong electric field. The physical signals such as ultrasonic waves, ultra-high frequency electromagnetic waves and local temperature rise generated by it are important precursors to insulation degradation.

[0003] Early methods for effectively monitoring and locating partial discharges relied primarily on signal arrival time difference (OTD) or signal amplitude attenuation models. However, these methods often suffer from limited accuracy in environments like distribution cabinets, which are characterized by complex internal structures, strong electromagnetic interference, and widespread signal reflection and refraction. Currently, advanced signal processing theory based on compressed sensing (CS) has been introduced into this field. This method leverages the prior characteristic of the sparsity of the discharge source's spatial distribution. By solving a sparse optimization problem, it can accurately deduce the true location of the discharge source from limited sensor measurement data. The core of this method relies on a pre-constructed sensing matrix that precisely describes the response pattern of signals emitted from every possible location within the distribution cabinet as they propagate to the sensor array.

[0004] However, the core sensing matrix based on compressed sensing technology is usually generated in one go using physical simulation software based on an idealized 3D model of a power distribution cabinet with fixed geometric dimensions, at a certain standard reference temperature (e.g., 20°C). But in real industrial settings, the operating conditions of power distribution cabinets are complex and variable. Their internal temperature fluctuates unevenly due to factors such as changes in load current, ambient temperature differences, and ventilation conditions. This ultimately leads to a significant decrease in the accuracy of sparse reconstruction algorithms, resulting in large deviations or even errors in the positioning results. Summary of the Invention

[0005] To address the technical problem of inaccurate discharge positioning caused by the mismatch between the static characteristics of the sensing matrix and the dynamically changing temperature environment inside the distribution cabinet in the prior art, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a discharge detection method for high and low voltage switchgear, comprising: acquiring a measurement vector and a real-time temperature reading generated by a discharge source within the high and low voltage switchgear to be detected, and loading a preset reference sensing matrix; constructing a dynamic environment correction coefficient to characterize the influence of the current thermal environment on signal propagation based on the real-time temperature reading; adjusting the reference sensing matrix based on the dynamic environment correction coefficient to generate a dynamic sensing matrix that matches the current thermal environment; and determining the location of the discharge source using a sparse reconstruction algorithm based on the dynamic sensing matrix and the measurement vector, thereby realizing discharge detection of the high and low voltage switchgear.

[0007] This invention, by real-time monitoring of the dynamic thermal environment inside the power distribution cabinet and constructing a complete logical chain from environmental characteristics to physical influences to model correction, can generate a dynamically changing sensing matrix that can adapt to changes. This solves the technical problem of low positioning accuracy caused by the mismatch between the static sensing matrix and the actual working conditions in the prior art.

[0008] Preferably, the step of acquiring the measurement vector and real-time temperature reading generated by the discharge power source in the high- and low-voltage distribution cabinet to be detected includes: deploying a sensor array composed of multiple ultrasonic sensors and a temperature array composed of multiple temperature sensors in the high- and low-voltage distribution cabinet; when a discharge event is detected, the sensor array collects a response signal to form the measurement vector, and the temperature array synchronously collects the real-time temperature reading.

[0009] Preferably, the construction of dynamic environmental correction coefficients for characterizing the impact of the current thermal environment on signal propagation includes: extracting thermal field characteristic indicators based on the real-time temperature readings; modeling wavefront distortion indices for each propagation path from the potential discharge source to the sensor based on the thermal field characteristic indicators; and converting the wavefront distortion indices into the dynamic environmental correction coefficients through a nonlinear function.

[0010] By using a hierarchical and progressive modeling approach, the easily measurable physical quantity of temperature on a macroscopic scale can be gradually transformed into an accurate assessment of its impact on the propagation path of microscopic signals, thus ensuring the effectiveness of the correction.

[0011] Preferably, the thermal field characteristic indicators include the average internal temperature and the temperature gradient amplitude, which respectively satisfy the following relationship: ; ;in, The average internal temperature, The magnitude of the temperature gradient at temperature T. For the first Real-time readings from a temperature sensor. This represents the total number of temperature sensors. and These are the maximum and minimum values ​​among all temperature sensor readings. These are the characteristic dimensions of high and low voltage distribution cabinets.

[0012] Preferably, the wavefront distortion index satisfies the following relationship: ;in, This is the wavefront distortion index. For sensor indexing, Indexing of grid points for potential discharge sources. The reference temperature used when generating the reference sensing matrix. For the first From the first grid point to the... The straight-line distance between the sensors and These are the weighting coefficients used to balance the effects of global average temperature deviation and local path gradient.

[0013] Preferably, the dynamic environment correction coefficient satisfies the following relationship: ;in, For dynamic environment correction coefficients, For the i-th sensor at the i-th time Wavefront distortion index of each grid point This is a positive sensitivity parameter used to control the severity of correction.

[0014] Preferably, adjusting the reference sensing matrix based on the dynamic environment correction coefficient includes: combining the dynamic environment correction coefficients of all propagation paths into a correction matrix; and obtaining the dynamic sensing matrix by performing a Hadamard product operation on the correction matrix and the reference sensing matrix element by element.

[0015] By using the Hadamard product for matrix updates, computational complexity is low, it is easy to implement in hardware, and the real-time nature of the entire correction process is guaranteed, enabling the model to respond instantly to environmental changes.

[0016] Preferably, the sparse reconstruction algorithm is an orthogonal matching pursuit algorithm.

[0017] Preferably, the reference sensing matrix is ​​based on an idealized three-dimensional geometric model of a high- and low-voltage distribution cabinet, and is pre-calculated and generated by physical simulation software at a standard reference temperature. The reference sensing matrix describes the idealized signal propagation response from discretized grid points to each sensor.

[0018] Secondly, the present invention provides a discharge detection system for high and low voltage distribution cabinets, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned discharge detection method for high and low voltage distribution cabinets is implemented.

[0019] By adopting the above technical solution, a computer program for the discharge detection method of high and low voltage distribution cabinet is generated and stored in a memory so that it can be loaded and executed by a processor. Terminal equipment can then be made based on the memory and processor for convenient use.

[0020] This invention can dynamically compensate for signal propagation model mismatch caused by temperature changes, enabling the core sensing matrix to match the real physical environment in real time. This effectively solves the distortion problem of static models and makes fault source identification more accurate and reliable.

[0021] Furthermore, this eliminates the reliance of the discharge detection system on an idealized and constant operating environment. Regardless of the season or load conditions, the system can maintain high-precision positioning capabilities through adaptive correction. Attached Figure Description

[0022] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a flowchart illustrating a discharge detection method for a high- and low-voltage distribution cabinet according to the present invention; Figure 2 This is a comparison chart of discharge detection effects provided in an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] This invention discloses a discharge detection method for high and low voltage distribution cabinets, referring to... Figure 1 This includes steps S1-S4: S1. Obtain the measurement vector and real-time temperature reading generated by the discharge power source in the high and low voltage distribution cabinet to be tested, and load the preset reference sensing matrix.

[0026] In an optional embodiment, a unit may be deployed consisting of An array of ultrasonic sensors, exemplarily, Its location should be optimized through simulation to cover the entire cabinet space as much as possible, ensuring effective reception of discharge signals that may occur from various locations. Simultaneously, a [device name missing] should be deployed at different heights within the cabinet, near busbars, circuit breakers, and other major heat-generating components, as well as at ventilation openings. An array of high-precision digital temperature sensors, for example It is used to perceive the thermal field distribution inside the cabinet in real time and in multiple dimensions.

[0027] In this optional embodiment, during the system initialization phase, a pre-calculated reference sensing matrix, obtained using high-precision physical simulation software (such as COMSOL Multiphysics), can be loaded from local memory. The dimension of this matrix is... ,in This represents the total number of grid points after discretizing the internal space of the distribution cabinet. For example, if a cabinet space with a volume of 2 cubic meters is divided into voxels of 1 cubic centimeter, the total number of grid points is 2,000,000. Each element of the reference sensing matrix represents the signal amplitude response of a unit intensity discharge power source located at the i-th grid point on the j-th sensor at a standard reference temperature, for example, 20°C.

[0028] It is worth noting that the reference sensing matrix is ​​based on an idealized three-dimensional geometric model of the high and low voltage distribution cabinet. It is pre-calculated and generated by physical simulation software under standard reference temperature. The reference sensing matrix describes the idealized signal propagation response from the discretized grid points to each sensor.

[0029] Furthermore, when the front-end processing unit of the monitoring system, such as an FPGA-based signal acquisition card, detects a signal suspected of being a discharge event through energy detection or other methods, it immediately triggers the data acquisition process. The ultrasonic sensor array acquires... The path response signal, after amplification, filtering, and feature extraction, forms a... A dimensional measurement vector is simultaneously read in real time via a temperature sensor array. Temperature readings at each measuring point.

[0030] In this way, by simultaneously acquiring acoustic signals and thermal field data and loading the basic physical model, comprehensive and real-time input information is provided for subsequent adaptive correction and precise positioning calculations.

[0031] S2. Based on real-time temperature readings, construct dynamic environmental correction coefficients to characterize the impact of the current thermal environment on signal propagation.

[0032] In an optional embodiment, to comprehensively and concisely characterize the current thermal environment state within the cabinet, it can be obtained from... Two thermal field characteristic indicators were extracted from the temperature readings: average internal temperature and average internal temperature. and temperature gradient magnitude .in This reflects the overall thermodynamic state inside the cabinet, directly affecting the average propagation speed of sound waves; and This reflects the unevenness of the temperature distribution inside the cabinet. This unevenness causes sound waves to refract during propagation, causing their actual path to deviate from the ideal straight line.

[0033] Specifically, the average internal temperature and the magnitude of the temperature gradient satisfy the following relationship:

[0034]

[0035] in, For the first Real-time readings from a temperature sensor. This represents the total number of temperature sensors. and These are the maximum and minimum values ​​among all temperature sensor readings. Characteristic dimensions of high and low voltage distribution cabinets, such as the diagonal length of the cabinet, are used to normalize the gradient, thereby giving it a clear physical meaning.

[0036] For example, suppose that a deployment is made The temperature readings collected by the temperature sensors at a certain moment are as follows: ℃, ℃, ℃, ℃, If the characteristic dimension of the distribution cabinet is 2.5 meters, then the average internal temperature can be calculated to be 38.2℃, and the temperature gradient amplitude is 2.8℃ / meter.

[0037] Furthermore, based on thermal field characteristic indicators, wavefront distortion indices can be modeled for each propagation path from the potential discharge source to the sensor, and the wavefront distortion indices can be converted into dynamic environmental correction coefficients through nonlinear functions.

[0038] Specifically, for each potential discharge power grid point To the sensor The propagation path is used to construct a wavefront distortion index. This index is used to assess the degree to which the path deviates from the ideal state due to the influence of the current thermal environment. It integrates the global temperature deviation and the cumulative gradient effect along the path. The wavefront distortion index satisfies the following relationship:

[0039] in, The reference temperature used to generate the baseline sensing matrix is, for example, 20°C. For the first From the first grid point to the... The straight-line distance between the sensors and The weighting coefficients used to balance the effects of global average temperature deviation and local path gradient can be, for example, taken as 0.6 and 0.4 respectively, based on experience and simulation verification.

[0040] Furthermore, the wavefront distortion index is converted into a dynamic correction coefficient between 0 and 1 using a nonlinear function. When the distortion index is 0, the correction coefficient should be 1; as the distortion index increases, the correction coefficient should decrease smoothly. This scheme uses an exponential decay function to achieve this, and the dynamic environment correction coefficient satisfies the following relationship:

[0041] in, This is a positive sensitivity parameter used to control the severity of the correction; for example, it is set to 0.5.

[0042] For example, suppose we need to calculate from a certain grid point To the sensor The wavefront distortion index, the linear distance between them Rice, then Therefore, the dynamic correction coefficient can be calculated as follows: This means that due to the current thermal environment, from the grid points The signal response along the path to sensor i is only about 38.87% of that in the ideal case.

[0043] In this way, a precise correction factor can be generated for each signal propagation path in real time, providing a solid foundation for dynamically updating the sensing matrix.

[0044] S3. Adjust the reference sensing matrix based on the dynamic environment correction coefficient to generate a dynamic sensing matrix that matches the current thermal environment.

[0045] In an optional embodiment, the reference sensing matrix can be adjusted based on the obtained dynamic environment correction coefficients to generate a dynamic sensing matrix that matches the current thermal environment.

[0046] Specifically, the dynamic environment correction coefficients of all propagation paths are combined into a correction matrix C with the same dimension as the reference sensing matrix. Then, the final dynamic sensing matrix is ​​obtained by performing element-wise multiplication (Hadamard product) between the correction matrix and the reference sensing matrix. The dynamic sensing matrix satisfies the following relationship:

[0047] in, For dynamic sensing matrix, Given a reference sensing matrix, for any element in the matrix, we have:

[0048] The newly generated dynamic sensing matrix has its internal values ​​adaptively adjusted according to the real-time thermal field environment within the current power distribution cabinet, making it more efficient than the static matrix. It can more accurately describe the current physical characteristics because the Hadamard product has a very small computational cost, involving only a few steps. This multiplication operation allows for real-time adaptive adjustment.

[0049] In this way, through an efficient calculation method, the online adaptation of the complex physical model was completed, generating a dynamic sensing matrix that can accurately reflect the current working conditions.

[0050] S4. Based on the dynamic sensing matrix and measurement vector, the location of the discharge source is determined through a sparse reconstruction algorithm to achieve discharge detection of high and low voltage distribution cabinets.

[0051] In an optional embodiment, based on the acquired dynamic sensing matrix and measurement vector, the location of the discharge source can be determined using a sparse reconstruction algorithm, thereby achieving discharge detection of the high and low voltage distribution cabinet. The sparse reconstruction algorithm can be the Orthogonal Matching Pursuit (OMP) algorithm. OMP is a classic greedy sparse reconstruction algorithm whose core idea is to iteratively find the atom most relevant to the current signal residual, i.e. The column vectors are added to the support set, and then the optimal approximation of the signal is updated by the least squares method. Finally, the new residuals are calculated until the stopping condition is met.

[0052] Specifically, the main execution process of the OMP algorithm is as follows: Step 1: Initialization, using the real-time measured sensor signals as the initial signals to be interpreted; The second step is iterative optimization. In each iteration, the algorithm traverses all possible power source locations, that is... For each column, the position with the highest correlation to the current signal to be explained is found by calculating the inner product. This position is considered to be the most likely power source location. Step 3: Signal stripping. The signal component generated by the best matching position is subtracted from the signal to be interpreted to obtain a residual signal. Step 4: Loop. In the next iteration, the algorithm continues to repeat the above steps of finding the best matching position and subtracting from the new residual signal.

[0053] This process continues until a predetermined number of discharge sources are found, typically one or a few, or until the energy of the residual signal is negligible. Finally, the positions of the non-zero elements in the vector output by the algorithm represent the coordinates of the precisely located discharge source in three-dimensional space.

[0054] like Figure 2 The image shown is a comparison of the discharge detection effects provided by the embodiments of the present invention. It can be seen that in the three-dimensional space of the high and low voltage distribution cabinet, the static positioning position generated by the prior art deviates greatly from the actual discharge source position. This is because the sensing matrix of the prior art does not match the real environment, and the solution process of algorithms such as compressed sensing will collapse, with a high probability of converging to a ghost source position that is completely unrelated to the real position. However, the dynamic positioning position obtained in this embodiment is very close to the actual discharge source position, and the actual discharge source position is effectively detected.

[0055] Thus, by using a sensor matrix that has undergone environmental adaptive correction for sparse reconstruction, the adverse effects of model mismatch can be effectively overcome, thereby obtaining more accurate and reliable discharge fault location results than existing technologies.

[0056] This invention also discloses a discharge detection system for high and low voltage distribution cabinets, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a discharge detection method for high and low voltage distribution cabinets according to the present invention is implemented.

[0057] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0058] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise expressly and specifically defined.

[0059] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A discharge detection method for high and low voltage distribution cabinets, characterized in that, include: Acquire the measurement vector and real-time temperature reading generated by the discharge power source in the high and low voltage distribution cabinet to be tested, and load the preset reference sensing matrix; Based on the real-time temperature readings, a dynamic environmental correction coefficient is constructed to characterize the impact of the current thermal environment on signal propagation; The reference sensing matrix is ​​adjusted based on the dynamic environment correction coefficient to generate a dynamic sensing matrix that matches the current thermal environment. Based on the dynamic sensing matrix and the measurement vector, the location of the discharge source is determined by a sparse reconstruction algorithm to achieve discharge detection of high and low voltage distribution cabinets.

2. The discharge detection method for high and low voltage distribution cabinets according to claim 1, characterized in that, The acquisition of the measurement vector and real-time temperature reading generated by the discharge source in the high and low voltage distribution cabinet to be tested includes: A sensor array consisting of multiple ultrasonic sensors and a temperature array consisting of multiple temperature sensors are deployed inside the high and low voltage distribution cabinet. When a discharge event is detected, the sensor array acquires a response signal to form the measurement vector, and the temperature array synchronously acquires the real-time temperature reading.

3. The discharge detection method for high and low voltage distribution cabinets according to claim 1, characterized in that, The dynamic environmental correction coefficients constructed to characterize the impact of the current thermal environment on signal propagation include: Extract thermal field characteristic indicators based on the real-time temperature readings; Based on the aforementioned thermal field characteristic indicators, wavefront distortion indices are modeled for each propagation path from the potential discharge source to the sensor. The wavefront distortion index is converted into the dynamic environment correction coefficient through a nonlinear function.

4. The discharge detection method for high and low voltage distribution cabinets according to claim 3, characterized in that, The thermal field characteristic indicators include the average internal temperature and the temperature gradient amplitude, which respectively satisfy the following relationship: in, The average internal temperature, The magnitude of the temperature gradient at temperature T. For the first Real-time readings from a temperature sensor. This represents the total number of temperature sensors. and These are the maximum and minimum values ​​among all temperature sensor readings, respectively. These are the characteristic dimensions of high and low voltage distribution cabinets.

5. The discharge detection method for high and low voltage distribution cabinets according to claim 3, characterized in that, The wavefront distortion index satisfies the following relationship: in, This is the wavefront distortion index. For sensor indexing, Indexing of grid points for potential discharge sources. The reference temperature used when generating the reference sensing matrix. For the first From the first grid point to the... The straight-line distance between the sensors and These are the weighting coefficients used to balance the effects of global average temperature deviation and local path gradient.

6. The discharge detection method for high and low voltage distribution cabinets according to claim 3, characterized in that, The dynamic environment correction coefficients satisfy the following relationship: in, For dynamic environment correction coefficient, The i-th sensor is at the... Wavefront distortion index of each grid point This is a positive sensitivity parameter used to control the severity of correction.

7. The discharge detection method for high and low voltage distribution cabinets according to claim 1, characterized in that, The adjustment of the reference sensing matrix based on the dynamic environment correction coefficient includes: Combine the dynamic environmental correction coefficients of all propagation paths into a correction matrix; The dynamic sensing matrix is ​​obtained by performing a Hadamard product operation, which involves element-wise multiplication of the correction matrix and the reference sensing matrix.

8. The discharge detection method for high and low voltage distribution cabinets according to claim 1, characterized in that, The sparse reconstruction algorithm is an orthogonal matching pursuit algorithm.

9. The discharge detection method for high and low voltage distribution cabinets according to claim 1, characterized in that, The reference sensing matrix is ​​based on an idealized three-dimensional geometric model of a high- and low-voltage switchgear. It is pre-calculated and generated by physical simulation software at a standard reference temperature. The reference sensing matrix describes the idealized signal propagation response from discretized grid points to each sensor.

10. A discharge detection system for high and low voltage switchgear, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a discharge detection method for a high- and low-voltage distribution cabinet according to any one of claims 1-9.

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