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

Abstract

The application 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, which comprises the following 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 a 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-low voltage power distribution cabinet. Through construction of an environment-adaptive dynamic sensing matrix, the application can dynamically compensate for model mismatch caused by temperature change, thereby significantly improving the accuracy and reliability of discharge fault positioning under complex working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of discharge detection. More particularly, the present application relates to a discharge detection method and system for high-low voltage power distribution cabinet. BACKGROUND

[0002] As a key hub device in the power system, the health status of the internal insulation structure of the high-low voltage power distribution cabinet is directly related to the safe and stable operation of the power grid. Partial discharge is a weak and local breakdown phenomenon of insulation materials under the action of strong electric field. The physical signals such as ultrasonic waves, very high frequency electromagnetic waves and local temperature rise generated by partial discharge are important precursors of insulation deterioration.

[0003] In order to effectively monitor and locate the partial discharge, the early methods mainly rely on the signal arrival time difference or signal amplitude attenuation model. However, the positioning accuracy of these methods is often limited in the environment of complex internal structure, strong electromagnetic interference and common signal reflection and refraction of the power distribution cabinet. At present, the advanced signal processing theory based on compressive sensing (CS) is introduced into this field. This method utilizes the prior feature of sparse distribution of discharge source in space, and through solving a sparse optimization problem, the real discharge source position can be accurately inverted from the limited sensor measurement data. The core of this method depends on a pre-constructed sensing matrix, which accurately describes the response mode of the signal from each possible position in the power distribution cabinet to the sensor array.

[0004] However, the core sensing matrix based on compressive sensing technology is usually based on an idealized and fixed-geometry three-dimensional model of the power distribution cabinet. At a certain standard reference temperature (such as 20℃), it is generated by physical simulation software once. However, in the real industrial field, the operating conditions of the power distribution cabinet are complex and changeable. The internal temperature will fluctuate unevenly due to factors such as load current change, environmental temperature difference, ventilation condition, etc., which eventually leads to a serious decline in the accuracy of the sparse reconstruction algorithm and a large deviation or even error in the positioning result. SUMMARY

[0005] In order to solve the technical problem that the static characteristics of the sensing matrix in the prior art do not match the dynamic change of the temperature environment inside the power distribution cabinet, resulting in inaccurate discharge positioning, the present application provides solutions in the following aspects.

[0006] In a first aspect, the present application provides a discharge detection method for high-low voltage power distribution cabinet, comprising: obtaining a measurement vector generated by a discharge source in a high-low voltage power distribution cabinet to be detected and a real-time temperature reading, and loading a preset reference sensing matrix; constructing a dynamic environment correction coefficient for characterizing the influence of the current thermal field environment on signal propagation according to the real-time temperature reading; 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; determining the position of the discharge source through a sparse reconstruction algorithm based on the dynamic sensing matrix and the measurement vector to realize the discharge detection of the high-low voltage power distribution cabinet.

[0007] The present application can generate a dynamic sensing matrix that can adapt to changes by monitoring the dynamic thermal field environment inside the power distribution cabinet in real time and constructing a complete logical chain from environmental characteristics to physical influence to model correction, solving the technical problem of low positioning accuracy caused by the mismatch between the static sensing matrix and the actual working condition in the prior art.

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

[0009] Preferably, the dynamic environment correction coefficient for characterizing the influence of the current thermal field environment on signal propagation is constructed by extracting a thermal field characteristic index according to the real-time temperature reading; modeling a wavefront distortion index for each propagation path from a potential discharge source to a sensor based on the thermal field characteristic index; and converting the wavefront distortion index into the dynamic environment correction coefficient through a nonlinear function.

[0010] Through the hierarchical progressive modeling method, the macroscopically easy-to-measure temperature physical quantity can be converted into an accurate evaluation of the influence on the micro signal propagation path level by level, ensuring the effectiveness of the correction.

[0011] Preferably, the thermal field characteristic index includes an average internal temperature and a temperature gradient amplitude, which satisfy the following relationships respectively: ; ; wherein, is the average internal temperature, is the temperature gradient amplitude of the temperature T, is the real-time reading of the i-th temperature sensor, is the total number of temperature sensors, and and respectively the maximum and minimum of all temperature sensor readings, are the characteristic dimensions of the high-low voltage power distribution cabinet.

[0012] Preferably, the wavefront distortion index satisfies the relationship: ; wherein, is the wavefront distortion index, is the sensor index, is the grid point index of the potential discharge source, is the reference temperature used when generating the reference sensor matrix, is the linear distance from the th grid point to the th sensor, and are weight coefficients used to balance the global average temperature deviation and the local path gradient influence.

[0013] Preferably, the dynamic environmental correction coefficient satisfies the relationship: ; wherein, is the dynamic environmental correction coefficient, is the wavefront distortion index of the i th sensor at the th grid point, is a positive sensitivity parameter used to control the correction severity.

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

[0015] By using Hadamard product for matrix updating, the calculation complexity is low, easy to implement in hardware, and ensures the real-time of the entire correction process, so that the model can respond to environmental changes instantaneously.

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

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

[0018] In a second aspect, the present application provides a discharge detection system for a high-low voltage power distribution cabinet, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned discharge detection method for a high-low voltage power distribution cabinet is realized.

[0019] By adopting the technical scheme, the discharge detection method of the high-low voltage power distribution cabinet is generated into a computer program and stored in a memory to be loaded and executed by a processor, so that a terminal device is manufactured according to the memory and the processor, and use is facilitated.

[0020] The application can dynamically compensate for signal propagation model mismatch caused by temperature change, so that the sensing matrix of the core can match the real physical environment in real time, effectively solves the distortion problem of the static model, and makes the fault source identification more accurate and reliable.

[0021] Further, the discharge detection system no longer depends on an ideal and constant operating environment, and the system can maintain high-precision positioning capability through adaptive correction under different seasons or different load conditions. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present application will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which several embodiments of the present application are shown by way of example, and wherein like reference numerals refer to like elements throughout. In the drawings:

[0023] Figure 1 is a flow chart schematically showing a discharge detection method of a high-low voltage power distribution cabinet in the present application;

[0024] Figure 2 is a discharge detection effect comparison chart provided by the embodiment of the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0026] The specific embodiments of the present application will be described in detail below with reference to the drawings.

[0027] The embodiment of the present application discloses a discharge detection method of a high-low voltage power distribution cabinet, referring to Figure 1 , comprising steps S1-S4:

[0028] S1, obtaining a measurement vector generated by a discharge source in a high-low voltage power distribution cabinet to be detected and a real-time temperature reading, and loading a preset reference sensing matrix.

[0029] In an optional embodiment, a discharge detection system can be deployed by 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] S2. Based on real-time temperature readings, construct dynamic environmental correction coefficients to characterize the impact of the current thermal environment on signal propagation.

[0035] 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 are extracted from the temperature readings: average internal temperature and temperature gradient amplitude . Among them reflects the overall thermodynamic state of the cabinet, directly affecting the average propagation speed of sound waves; while reflects the degree of unevenness of temperature distribution in the cabinet, which will cause refraction of sound waves during propagation, making their actual path deviate from the ideal straight line.

[0036] Specifically, the average internal temperature and the temperature gradient amplitude satisfy the following relationships respectively:

[0037]

[0038]

[0039] where, is the real-time reading of the i-th temperature sensor, is the total number of temperature sensors, and are the maximum and minimum values of all temperature sensor readings respectively, is the characteristic size of the high-low voltage power distribution cabinet, for example, the diagonal length of the cabinet body, used to normalize the gradient, so that it has a clear physical meaning. For example, assuming that

[0040] temperature sensors are deployed, the temperature readings collected at a certain moment are: ℃, ℃, ℃, ℃, ℃, and the characteristic size of the power distribution cabinet is 2.5 meters, then the average internal temperature can be calculated as 38.2℃, and the temperature gradient amplitude is 2.8℃ / m. Further, based on the thermal field characteristic indicators, the wavefront distortion index can be modeled for each propagation path from the potential discharge source to the sensor, and the wavefront distortion index is converted to the dynamic environment correction coefficient through a nonlinear function.

[0041] Specifically, for each propagation path from the potential discharge source grid point

[0042] to the sensor , a wavefront distortion index is constructed to evaluate the degree of deviation of the path from the ideal state due to the influence of the current thermal environment. The index integrates the global temperature deviation and the gradient accumulation effect along the path, and the wavefront distortion index satisfies the following relationship:

[0043] ​

[0044] wherein, is the reference temperature used for generating the reference sensing matrix, exemplary, 20℃, is the linear distance from the jth grid point to the ith sensor, is the linear distance from the jth grid point to the ith sensor, and are the weight coefficients used for balancing the global average temperature deviation and the local path gradient effect, exemplary, 0.6 and 0.4 respectively according to experience and simulation verification.

[0045] Further, the wavefront distortion index is converted into a dynamic correction coefficient between 0 and 1 by a nonlinear function When the distortion index is 0, the correction coefficient should be 1; when the distortion index increases, the correction coefficient should decrease smoothly, in this scheme, an exponential decay function is used to achieve it, the dynamic environmental correction coefficient satisfies the relationship:

[0046]

[0047] wherein, is a positive sensitivity parameter used for controlling the correction intensity, used for controlling the correction intensity, exemplary, 0.5.

[0048] Exemplary, assuming that the wavefront distortion index from a certain grid point to the sensor needs to be calculated, the linear distance between them is meters, then ; therefore the dynamic correction coefficient can be calculated as , which means that due to the current thermal environmental influence, the signal response on this path from the grid point to the sensor i is only about 38.87% of the ideal case.

[0049] In this way, an accurate correction factor can be generated for each signal propagation path in real time, providing a solid foundation for dynamically updating the sensing matrix.

[0050] S3, adjusting the reference sensing matrix based on the dynamic environmental correction coefficient to generate a dynamic sensing matrix matched with the current thermal field environment.

[0051] In an optional embodiment, the reference sensing matrix can be adjusted according to the obtained dynamic environmental correction coefficient, thereby generating a dynamic sensing matrix matched with the current thermal field environment.

[0052] ​Specifically, the dynamic environment correction coefficient sets of all propagation paths are combined into a correction matrix C with the same dimension as the reference sensing matrix, and then the final dynamic sensing matrix is obtained by element-wise multiplication of the correction matrix and the reference sensing matrix, that is, Hadamard product operation. The dynamic sensing matrix satisfies the relationship:

[0053]

[0054] Wherein, is the dynamic sensing matrix, is the reference sensing matrix, for any element in the matrix, there is:

[0055]

[0056] The newly generated dynamic sensing matrix has its internal values self-adaptively adjusted according to the real-time thermal field environment in the current power distribution cabinet, which is more accurate than the static can more accurately describe the current physical characteristics. Since the calculation amount of Hadamard product is extremely small, only times of multiplication operation is involved, so the self-adaptive adjustment process can be completed in real time.

[0057] In this way, through an efficient calculation method, online adaptation of the complex physical model is completed, and a dynamic sensing matrix that can accurately reflect the current working condition is generated.

[0058] S4, based on the dynamic sensing matrix and the measurement vector, the position of the discharge source is determined through a sparse reconstruction algorithm to realize discharge detection of the high-low voltage power distribution cabinet.

[0059] In an optional embodiment, according to the obtained dynamic sensing matrix and measurement vector, the position of the discharge source can be determined through a sparse reconstruction algorithm, thereby realizing discharge detection of the high-low voltage power distribution cabinet. The sparse reconstruction algorithm can select an orthogonal matching pursuit algorithm (Orthogonal Matching Pursuit, OMP). The OMP is a classical greedy sparse reconstruction algorithm, and its core idea is to iteratively find the atom that is most related to the current signal residual, that is, the column vector, and add it to the support set, then update the optimal approximation of the signal by least squares method, and finally calculate the new residual until the stop condition is met.

[0060] Specifically, the main execution process of the OMP algorithm is as follows:

[0061] Step 1: initialization, the real-time measured sensor signal is taken as the initial signal to be explained;

[0062] Step 2: iterative optimization, in each iteration, the algorithm will traverse all possible discharge source positions, that is, for each column of the matrix, find the position with the highest correlation to the current signal to be explained by computing the inner product, this position is considered as the most likely discharge source position at present;

[0063] Third step: signal stripping, the signal component generated by the best matching position found is subtracted from the signal to be explained, to obtain a residual error signal;

[0064] Fourth step: loop, the algorithm continues to repeat the above steps of finding the best matching position and subtracting in the next iteration for the new residual error signal.

[0065] This process will continue until a preset number of discharge sources are found, for example, usually one or a few, or the energy of the residual error signal is small enough to be ignored. Finally, the position of the non-zero element in the vector output by the algorithm is the coordinate of the discharge source in the three-dimensional space.

[0066] As Figure 2 shown in the discharge detection effect comparison diagram provided by the embodiment of the application, it can be seen that in the three-dimensional space of the high-low voltage power distribution cabinet, the static positioning position generated by the prior art has a very large deviation from the real discharge source position, because the sensing matrix of the prior art does not conform to the real environment, and the solving process of the compressed sensing algorithm will collapse, and there is a great probability of converging to a ghost (Ghost Source) position completely irrelevant to the real position; and the dynamic positioning position obtained in the embodiment is very close to the real discharge source position, effectively detecting the real discharge source position.

[0067] In this way, by using the sensing matrix that is self-adaptively corrected to perform sparse reconstruction, the adverse effects caused by model mismatch can be effectively overcome, so that more accurate and reliable discharge fault positioning results than the prior art can be obtained.

[0068] The embodiment of the application also discloses a discharge detection system for a high-low voltage power distribution cabinet, comprising a processor and a memory, and the memory stores computer program instructions, which realize the discharge detection method for a high-low voltage power distribution cabinet according to the application when executed by the processor.

[0069] The above system also comprises a communication bus and a communication interface and other components familiar to those skilled in the art, the settings and functions of which are known in the art, and thus will not be described here.

[0070] In the description of the present specification, the meaning of "a plurality of", "several" is at least two, for example, two, three or more, etc., unless otherwise explicitly and specifically limited.

[0071] While the specification has illustrated and described various embodiments of the application, it will be clear to those of ordinary skill in the art that various changes, modifications, and substitutions can be made thereto without departing from the spirit and scope of the application. It is understood that in the process of practicing the application, various alternatives, modifications, and equivalents can be employed.

Claims

1. A discharge detection method of a high-low voltage power distribution cabinet, characterized in that, The method comprises: acquiring a measurement vector generated by a discharge source in a high-low voltage power distribution cabinet to be detected and a real-time temperature reading, and loading a preset reference sensing matrix; constructing a dynamic environment correction coefficient for characterizing an influence of a current thermal field environment on signal propagation based on the real-time temperature reading, comprising: extracting a thermal field characteristic index based on the real-time temperature reading; modeling a wavefront distortion index for each propagation path from a potential discharge source to a sensor based on the thermal field characteristic index; and converting the wavefront distortion index into the dynamic environment correction coefficient through a nonlinear function; The thermal field characteristic indicators include average internal temperature (Tav) ) and temperature gradient amplitude (Gav) ). the wavefront distortion index satisfies a relationship: wherein, is the wavefront distortion index, is the sensor index, is the grid point index of the potential discharge source, is the reference temperature used when generating the reference sensor matrix, is the straight-line distance from the th grid point to the th sensor, and are the weight coefficients used to balance the global average temperature deviation and the local path gradient influence; 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; determining a position of the discharge source based on the dynamic sensing matrix and the measurement vector through a sparse reconstruction algorithm to realize discharge detection of the high-low voltage power distribution cabinet.

2. The discharge detection method of a high-low voltage power distribution cabinet according to claim 1, characterized in that, The method comprises: deploying a sensor array composed of multiple ultrasonic sensors and a temperature array composed of multiple temperature sensors inside the high-low voltage power distribution cabinet; when a discharge event is captured, a response signal is collected by the sensor array to form the measurement vector, and the real-time temperature reading is synchronously collected by the temperature array.

3. The discharge detection method of a high-low voltage power distribution cabinet according to claim 1, characterized in that, the average internal temperature and the temperature gradient amplitude of the thermal field characteristic index satisfy relationships: wherein, Tavg is the average internal temperature, Tgrad is the temperature gradient magnitude of temperature T, Tn is the real-time reading of the n th temperature sensor, N is the total number of temperature sensors, and Tmax and Tmin are the maximum and minimum values among all temperature sensor readings, respectively, L is the characteristic dimension of the high-low voltage switchgear.

4. The discharge detection method of a high-low voltage power distribution cabinet according to claim 1, characterized in that, the dynamic environment correction coefficient satisfies a relationship: wherein, is a dynamic environment correction coefficient, is the wavefront distortion index of the i-th sensor at the j-th grid point, the wavefront distortion index of the i-th sensor at the j-th grid point, is a positive sensitivity parameter for controlling the correction severity.

5. The discharge detection method of a high-low voltage power distribution cabinet according to claim 1, characterized in that, The adjustment of the reference sensing matrix based on the dynamic environment correction coefficient comprises: combining the dynamic environment correction coefficients of all propagation paths into a correction matrix; obtaining the dynamic sensing matrix by performing a Hadamard product operation of element-by-element multiplication of the correction matrix and the reference sensing matrix.

6. The discharge detection method of a high-low voltage power distribution cabinet according to claim 1, characterized in that, The sparse reconstruction algorithm is an orthogonal matching pursuit algorithm.

7. The discharge detection method of a high-low voltage power distribution cabinet according to claim 1, characterized in that, The reference sensing matrix is generated by pre-computing based on an idealized three-dimensional geometric model of the high-low voltage power distribution cabinet at a standard reference temperature through a physical simulation software, and the reference sensing matrix describes idealized signal propagation responses from discretized grid points to each sensor.

8. A discharge detection system for high-low voltage power distribution cabinet, characterized in that, The method comprises: a processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement a discharge detection method for a high-low voltage power distribution cabinet according to any one of claims 1-7.

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