Partial discharge detection method and device based on NV color center and electronic equipment

Abstract

The invention provides a partial discharge detection method and device based on an NV color center and electronic equipment, and relates to the technical field of partial discharge detection. The method comprises the following steps: acquiring detection signals of a plurality of NV color electrocardio field sensors arranged on target equipment; based on the PRPD technology, according to detection signals of all the NV color electrocardio field sensors, a discharge mode recognition result is determined; based on the TDOA technology, the positioning calculation result of the discharge source is determined according to detection signals of all the NV color electrocardio field sensors; and fusing the discharge mode identification result and the positioning calculation result of the discharge source to obtain a partial discharge detection result. The NV color electrocardio field sensor is adopted to sense the transient magnetic field generated by partial discharge, the interference of space electromagnetic noise is small, the extracted partial discharge signal is accurate, and the PRPD technology and the TDOA technology are fused, so that discharge type identification and discharge source positioning can be accurately realized.

Description

Technical Field

[0001] This invention relates to the field of partial discharge detection technology, and in particular to a partial discharge detection method, apparatus and electronic device based on NV color centers. Background Technology

[0002] Partial discharge (PD) refers to the weak breakdown or ionization phenomenon (such as bubble breakdown, surface creep, conductor tip corona) that occurs in local areas due to uneven electric field distribution inside or on the surface of high-voltage equipment insulation.

[0003] Insulation degradation in high-voltage electrical equipment often begins with partial discharge. Although the initial energy is weak, it can continuously erode the insulation material, eventually leading to serious faults such as equipment breakdown and explosion. Traditional regular overhauls make it difficult to detect potential problems in advance, which can easily cause grid outages and significant losses. Therefore, conducting partial discharge detection on equipment is a key requirement for ensuring grid safety and achieving precise operation and maintenance.

[0004] In existing technologies, pulse sensors (such as high-frequency current transformers) are typically used to collect pulse signals generated by partial discharge in equipment, and software algorithms are used to calculate parameters such as discharge quantity and frequency. However, in substation environments with high voltage and strong electromagnetic interference, these sensors are susceptible to spatial electromagnetic noise interference, resulting in a low signal-to-noise ratio and a high risk of misjudgment or missed detection. Furthermore, the spatial resolution is low (millimeter level), making it difficult to locate minute discharges or surface discharges. Summary of the Invention

[0005] This invention provides a partial discharge detection method, apparatus, and electronic device based on NV color centers to solve the problems of low resolution and susceptibility to spatial electromagnetic noise interference in the existing partial discharge detection based on electrical pulse sensors.

[0006] In a first aspect, embodiments of the present invention provide a partial discharge detection method based on NV color centers, comprising: Acquire detection signals from multiple NV color electrocardiogram field sensors deployed on the target device; Based on PRPD technology, the discharge mode identification result is determined according to the detection signals of each NV color electrocardiogram field sensor; Based on TDOA technology, the location calculation result of the discharge source is determined according to the detection signals of each NV color electrocardiogram field sensor; The results of discharge mode recognition and the location calculation of the discharge source are fused to obtain the partial discharge detection results.

[0007] Secondly, embodiments of the present invention provide a partial discharge detection device based on NV color centers, comprising: The signal acquisition module is used to acquire the detection signals of multiple NV color electrocardiogram field sensors arranged on the target device; The first detection module is used to determine the discharge mode identification result based on the detection signals of each NV color electrocardiogram sensor using PRPD technology. The second detection module is used to determine the location calculation result of the discharge source based on the detection signals of each NV color electrocardiogram field sensor using TDOA technology. The fusion module is used to fuse the discharge mode recognition results and the location calculation results of the discharge source to obtain the partial discharge detection results.

[0008] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the partial discharge detection method based on NV color centers as described in the first aspect or any possible implementation of the first aspect.

[0009] This invention provides a method, apparatus, and electronic device for partial discharge detection based on NV color centers. The method includes: acquiring detection signals from multiple NV color center electric field sensors arranged on a target device; determining discharge mode identification results based on the detection signals of each NV color center electric field sensor using PRPD technology; determining the location calculation results of the discharge source based on the detection signals of each NV color center electric field sensor using TDOA technology; and fusing the discharge mode identification results and the location calculation results of the discharge source to obtain the partial discharge detection result. This invention uses NV color center electric field sensors to sense the transient magnetic field generated by partial discharge, which is less susceptible to spatial electromagnetic noise interference, resulting in accurate extracted partial discharge signals. Furthermore, by fusing PRPD and TDOA technologies, it can accurately identify the discharge type and locate the discharge source. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the implementation of the partial discharge detection method based on NV color centers provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the partial discharge detection device based on NV color centers provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0012] Figure 1 This is a flowchart illustrating the implementation of a partial discharge detection method based on NV color centers, provided in an embodiment of the present invention. (Refer to...) Figure 1The partial discharge detection method based on NV color centers includes: S101: Acquire detection signals from multiple NV color electrocardiogram field sensors arranged on the target device; The core sensing unit of the NV color center electric field sensor in this application is based on a diamond NV color center chip (e.g., 532nm laser excitation + 637nm fluorescence collection). This chip operates through a laser-pumped fluorescence readout (Optically Detected Magnetic Resonance, ODMR) mechanism, converting the transient magnetic / electric field changes to be measured into optical signals of fluorescence intensity or microwave resonant frequency shift. The NV color center electric field sensor may also include an integrated low-noise amplifier, a filter (which can be set according to the target frequency band, such as UHF), and optical components (laser, photodetector, microwave antenna).

[0013] For example, when a target device (such as a GIS, transformer, or switchgear) experiences partial discharge, it generates a weak electric field that changes instantaneously. The electron spin energy level of the NV color center will undergo "Zeeman splitting" as the external electric field changes. Through laser excitation and microwave modulation, the change in electric field can be converted into a detectable fluorescence intensity signal. The electric field pulse generated by the discharge will cause characteristic fluctuations in fluorescence intensity. By capturing these fluctuations, the sensor can achieve "non-contact, high-fidelity" acquisition of the partial discharge signal.

[0014] Compared to traditional electrical pulse sensors (such as high-frequency current transformers), NV color center sensors do not require electrical connection to equipment, are unaffected by strong electromagnetic noise (such as switching operations and radio interference), and can stably acquire weak signals at the microvolt level. Furthermore, they are unaffected by antenna bandwidth, directly measuring extremely low-frequency and ultra-high-frequency electromagnetic pulses generated by discharges, avoiding signal distortion caused by antenna bandwidth and resonance limitations.

[0015] To further improve the positioning accuracy of the discharge source and the reliability of signal acquisition, the NV color ECG field sensor can be designed in an array, with multiple NV color ECG field sensors forming a sensor array.

[0016] In one possible implementation, the number of NV color electrocardiogram (ECG) field sensors can be at least four, and each NV color ECG field sensor can form a triangular or quadrilateral array.

[0017] This application sets up at least four NV color electrocardiogram field sensors to improve positioning stability and error tolerance through data redundancy.

[0018] For example, in the subsequent positioning calculation process, if the target device needs to achieve three-dimensional spatial (x / y / z axis) positioning of the discharge source, three sensors can only establish three sets of equations, which poses a risk of "non-unique solution"; four sensors can construct an overdetermined set of equations, and the measurement error of a single sensor (such as signal propagation delay deviation) can be eliminated by algorithms such as the least squares method, ensuring the uniqueness and accuracy of the positioning coordinates.

[0019] For example, local electromagnetic shielding may exist in the substation environment, which may reduce the signal-to-noise ratio of individual sensors. Four sensors can form "redundant monitoring" - even if the signal of one sensor is interfered with, the remaining three can still complete the positioning calculation normally, avoiding detection interruption due to the failure of a single sensor.

[0020] Furthermore, at least four sensors are arranged at key locations around the target device (e.g., insulators and busbars of GIS) to form a three-dimensional spatial array.

[0021] In one possible implementation, the number of NV color electrocardiogram (ECG) field sensors can be at least four, and each NV color ECG field sensor can form a triangular or quadrilateral array.

[0022] A spatial sampling network is formed by triangular or quadrilateral arrays. The spacing between each sensor can be optimized based on the electromagnetic wave propagation model and the size of the target device (e.g., 1-2 meters for GIS). The high Q value (Q>100) of the microstrip antenna ensures that the signal strength received by each sensor is consistent, reducing positioning errors caused by differences in signal attenuation in the algorithm layer.

[0023] It should be noted that this application also includes a multi-channel high-speed, high-resolution ADC acquisition card, used to convert the analog signals output by each NV color electrocardiogram sensor into digital signals; then, spin resonance analysis is used to extract the phase (corresponding to AC voltage period), amplitude (fluorescence intensity), and number of discharge pulses. The phase resolution reaches 3.6° (corresponding to a 360° / 100 interval, matching the PRPD format), the amplitude is normalized to the 0-1 range, and a two-dimensional scatter plot is generated, converting the original fluorescence signal of the hardware layer into a physically meaningful detection signal.

[0024] The core logic of TDOA (Time Difference of Arrival) localization in subsequent processing steps is to "calculate the discharge source coordinates by combining the time difference of the same discharge pulse received by different sensors with the sensor array position." If multiple channels are not synchronized, the "pulse arrival time" recorded by each channel will include "sampling time deviation" (rather than the actual signal propagation time difference). PRPD (Phase-Resolved Partial Discharge) pattern recognition requires statistical analysis based on "the amplitude, phase, and time interval of each discharge pulse" (e.g., drawing PRPD maps). If multiple channels are not synchronized, the "phase records" of the same discharge event in different channels will deviate, resulting in a chaotic statistical pulse phase distribution that cannot match typical discharge patterns, thus making it impossible to accurately determine the discharge type.

[0025] Based on this, to ensure that the sampling actions of all channels are strictly aligned on the time axis, a GPS / BeiDou precision clock source is also provided to generate a high-precision pulses per second (PPS) signal, providing a unified time reference for the acquisition card and ensuring synchronization accuracy at the nanosecond (ns) level. The acquisition card outputs a multi-channel synchronous digital signal stream with high-precision timestamps, thereby controlling the sensor time error to within 1 ns.

[0026] Meanwhile, through the timing control module, multiple sensors are synchronously triggered using microwave gating signals (2-3GHz) and laser pulses (532nm) to ensure that the signals collected by each sensor are strictly aligned in time, ensuring that the TDOA time difference measurement accuracy reaches the nanosecond level, providing a basis for TDOA time difference measurement (if the synchronization error is >1ns, it will lead to a positioning error >30cm).

[0027] S102: Based on PRPD technology, the discharge mode identification result is determined according to the detection signals of each NV color electrocardiogram field sensor; PRPD (Phase-Resolved Partial Discharge) is the core of identifying discharge types. Its principle is based on the difference in the correlation characteristics between the discharge pulse and the voltage phase to distinguish the discharge modes.

[0028] In one possible implementation, S102 may include: S1021: For any detection signal, construct a PRPD map based on the detection signal; extract features from the PRPD map to obtain the PRPD feature vector corresponding to the detection signal; Different types of partial discharges show significant differences in PRPD patterns: for example, the pulses of corona discharge are mostly concentrated near the voltage peak, with small amplitude and scattered distribution; while the pulses of internal bubble discharge are concentrated at the voltage rising and falling edges, with large amplitude and dense distribution. Therefore, this application first constructs a PRPD map for subsequent feature extraction and pattern recognition.

[0029] In one possible implementation, S1021 may include: 1. Filter and reduce noise on the detection signal to obtain the preprocessed detection signal; Since the detection signal contains partial discharge pulses, equipment electromagnetic interference, environmental noise, etc., the detection signal is first filtered and denoised to improve the signal-to-noise ratio.

[0030] For example, high-frequency electromagnetic noise and low-frequency drift are eliminated by bandpass filtering; at the same time, wavelet denoising or deep learning denoising algorithms are used to further suppress the remaining random noise and ensure the accuracy of subsequent pulse extraction.

[0031] 2. Extract the effective partial discharge pulse from the preprocessed detection signal; A constant false alarm rate (CFAR) or adaptive threshold algorithm can be used to detect and extract effective partial discharge pulse signals from continuous background noise.

[0032] 3. For any partial discharge pulse, using the power frequency voltage reference signal as a reference, determine the phase angle and peak amplitude of the partial discharge pulse; For each valid discharge pulse, the power frequency voltage reference signal of the power grid is synchronously acquired (obtained from the power grid or recovered from sensor data), and the phase angle at the time of pulse occurrence is determined with the voltage zero crossing point as the phase reference; at the same time, the peak amplitude of the pulse is calculated as an indicator for quantifying the discharge intensity.

[0033] 4. The phase angle and peak amplitude of each partial discharge pulse form the PRPD spectrum corresponding to the detection signal.

[0034] Using the voltage phase angle as the abscissa and the discharge pulse peak amplitude as the ordinate, a scatter plot of the phase angle and peak amplitude data of all effective pulses is generated, ultimately forming a PRPD map containing two-dimensional information of "phase distribution and amplitude range" or a PRPD map containing three-dimensional information of "phase distribution, amplitude range, and discharge frequency". Different types of partial discharges (such as corona discharge, surface discharge, and internal discharge) will exhibit unique distribution patterns in the map (e.g., corona discharge is mostly concentrated near the voltage peak, while internal discharge is mostly symmetrically distributed in the positive and negative half-cycles).

[0035] Based on this, features are extracted from the PRPD map to obtain the PRPD feature vectors corresponding to each detection signal, which are then used for subsequent pattern recognition.

[0036] For example, the maximum value of the phase distribution, the maximum value of the amplitude distribution, skewness, kurtosis, number of peak values, and cross-correlation coefficients are used to form the PRPD feature vector.

[0037] S1022: Obtain the target feature vector based on the PRPD feature vectors corresponding to each detection signal; Since each NV color ECG field sensor generates an independent PRPD map and obtains an independent feature vector, feature fusion can be used to eliminate the limitations of a single sensor and form a more comprehensive target feature vector; alternatively, the optimal PRPD feature vector can be selected as the target feature vector.

[0038] S1023: Input the target feature vector into the classification model to obtain the discharge pattern recognition result.

[0039] After the target feature vector is input into the classification model, the model's learning and reasoning capabilities output discharge pattern recognition results. For example, the discharge pattern recognition results include discharge type and confidence level.

[0040] The classification model can use support vector machine (SVM), random forest, or convolutional neural network (CNN) (directly using PRPD map as image input) to identify discharge types such as corona discharge, surface discharge, internal discharge, and floating potential discharge.

[0041] This application effectively integrates the detection information from multiple NV color center sensors, overcomes the limitations of a single sensor's field of view, significantly improves the accuracy and reliability of discharge mode recognition, and lays the foundation for the comprehensive judgment of subsequent partial discharge detection results.

[0042] S103: Based on TDOA technology, the location calculation result of the discharge source is determined according to the detection signals of each NV color electrocardiogram field sensor; In one possible implementation, S103 may include: S1031: Time synchronization of each detection signal; Time synchronization is the foundation of TDOA positioning, and its purpose is to ensure that the signal acquisition time axis of all NV color ECG field sensors is strictly aligned.

[0043] Based on the above, this application sets a GPS / BeiDou precision clock source to provide a unified time reference for the acquisition card, achieving hardware synchronization. Simultaneously, compensation can be performed using known calibration signals (such as power frequency signals) to ensure signal synchronization across all channels.

[0044] S1032: Determine the time difference between each NV color ECG field sensor based on the synchronized detection signal; This application can accurately calculate the time difference between sensors through signal correlation analysis.

[0045] In one possible implementation, S1032 may include: 1. Determine the signal-to-noise ratio (SNR) of each detection signal after synchronization, and use the detection signal with the highest SNR as the reference signal; Calculate the signal-to-noise ratio (SNR) of each synchronized detection signal, and select the signal with the highest SNR as the benchmark. Using this as a reference can reduce the error in time difference calculation.

[0046] 2. Determine the cross-correlation function between each detection signal (excluding the reference signal) and the reference signal; 3. For any cross-correlation function corresponding to any detection signal other than the reference signal, determine the peak point of the cross-correlation function, and take the delay time corresponding to the peak point as the time difference between the NV color ECG field sensor corresponding to the detection signal and the NV color ECG field sensor corresponding to the reference signal.

[0047] For each of the remaining sensor signals, a cross-correlation function is calculated with the reference signal. By finding the peak position of the cross-correlation function, the time difference between the arrival of the signal at different sensors can be estimated.

[0048] Furthermore, in low signal-to-noise ratio environments, phase transform weighting (GCC-PHAT) can be used to improve the robustness of time delay estimation, or bispectral and machine learning (such as convolutional neural networks) methods can be used to directly regress the time difference from the original waveform to suppress the effects of noise and multipath propagation.

[0049] S1033: Based on the various time differences, the location calculation results of the discharge source are obtained by using TDOA technology to locate and solve the source.

[0050] In one possible implementation, S1033 may include: 1. Establish a system of hyperbolic equations based on the various time differences; Let the coordinates of the power source be ( The coordinates of the reference sensor are ( ), No. The coordinates of the sensors are ( The speed of electromagnetic waves is The time difference between the two sensors is ,but:

[0051] electromagnetic wave speed This is a preset constant, the value of which is determined by the insulating medium inside the electrical equipment cavity, and the calculation formula is:

[0052] in, The speed of light in a vacuum This is the relative permittivity of the insulating medium. This value is preset during system deployment based on the type of target equipment (such as SF6 gas, transformer oil, epoxy resin).

[0053] Four sensors can establish three independent equations, forming a hyperbolic equation system.

[0054] 2. Transform the hyperbolic equation system into a pseudo-linear equation system; By introducing intermediate variables to eliminate the nonlinear terms with square roots in the equations, the hyperbolic equation system is transformed into a linear equation system.

[0055] 3. The weighted least squares method is used to solve the pseudo-linear system of equations to obtain the initial solution; 4. Based on the initial solution, the weight matrix of the weighted least squares method is corrected, and the pseudo-linear equation system is solved using the weighted least squares method based on the corrected weight matrix to obtain the location solution of the discharge source.

[0056] The pseudo-linear equation system is solved by weighted least squares method to obtain the initial solution. Then, based on the initial solution, the solution is optimized and a second solution is performed to obtain the location calculation result of the discharge source.

[0057] Specifically, the initial solution is calculated, and the theoretical distance difference between each sensor is compared with the distance difference converted from the actual time difference to obtain the residual. The weight matrix is ​​then corrected based on the magnitude of the residual. The system of equations is re-solved using the corrected weight matrix to obtain a more accurate solution. This process is repeated multiple times until the change in the solution is less than a set threshold, and finally, the positioning solution is output.

[0058] When the sensor layout is reasonable and the noise is Gaussian distributed, the above method can provide the optimal solution of approximate maximum likelihood estimation. It has high computational efficiency and excellent performance in line-of-sight (LOS) environments.

[0059] Furthermore, optimization algorithms such as Taylor series expansion, least squares (LS), and Newton's iteration algorithm can be used to solve the above hyperbolic equations to obtain the optimal estimate of the spatial coordinates of the discharge source. For complex environments, intelligent algorithms such as particle swarm optimization (PSO) can be introduced to cope with non-line-of-sight (NLOS) errors.

[0060] When using the Newton-Raphson iterative algorithm, the initial value can be coarsely located using the phase distribution of the PRPD spectrum (e.g., corona discharge is concentrated in ±90°, and air gap discharge is symmetrically distributed), reducing the number of iterations. Simultaneously, compressed sensing algorithms can be introduced to utilize the sparsity of the discharge signal to reconstruct the three-dimensional electric field distribution under low signal-to-noise ratio conditions, achieving a positioning accuracy better than 100μm and overcoming the physical limitations of sensor spacing (1-2 meters) in the hardware layer.

[0061] Based on the above, this application combines TDOA technology and an NV color electrocardiogram sensor array to achieve three-dimensional localization of partial discharge sources with an accuracy better than 100 μm. For example, when deploying an NV color electrocardiogram sensor on the surface of a basin insulator to detect air gap discharge, the PRPD spectrum shows a symmetrical double-cluster distribution (around phases 120° and 240°), and the TDOA localization error is <80 μm.

[0062] S104: The discharge mode recognition results and the location calculation results of the discharge source are fused to obtain the partial discharge detection results.

[0063] In one possible implementation, S104 may include: S1041: Establish a fusion decision-making model based on DS evidence theory or Bayesian inference; S1042: Input the discharge mode recognition results and the location calculation results of the discharge source into the fusion decision model to obtain the partial discharge detection results.

[0064] The discharge pattern recognition results (such as discharge type and confidence level) and the localization solution results (such as three-dimensional coordinates and error) are input into the fusion decision model and then fused.

[0065] The model, in conjunction with auxiliary information (e.g., sensor coordinates, device structure model, historical data, etc.), first determines the logical correlation between the pattern and location (e.g., "surface discharge" should correspond to a device surface location; if the location is inside the device, the confidence of the combination is reduced), eliminating physically contradictory propositions. Then, multi-dimensional fusion is performed. For example, if the pattern recognition confidence is high but the positioning error is large, the model will emphasize pattern information and narrow the positioning range based on device structure characteristics; if the positioning accuracy is high but the pattern recognition is ambiguous, the model will optimize the pattern judgment based on the device component type at the positioning point (e.g., surface discharge is more likely to occur on the surface of an insulator). Finally, the partial discharge detection result is output. For example, at the root of the A-phase bushing of the transformer (coordinates: x1, y1, z1), there is a 95% confidence level that surface discharge occurred.

[0066] This application improves the reliability of test results by more than 30% through result fusion compared to single information sources. Especially in complex electromagnetic environments or scenarios with special equipment structures, it can effectively avoid misjudgments caused by single technical errors, and provides comprehensive, reliable and interpretable fault diagnosis results, providing an authoritative basis for the insulation status assessment of high-voltage equipment.

[0067] Furthermore, the partial discharge detection results can be visualized on a PC or mobile interface, presenting them intuitively to the user.

[0068] In summary, this application acquires detection signals using multiple NV color electrocardiogram (ECG) sensors deployed on the target device, providing a precise data source for subsequent analysis. Then, PRPD technology is used to extract discharge characteristics from the sensor signals, clarifying the discharge mode and achieving accurate identification of the discharge type. Simultaneously, TDOA technology is used to calculate the specific location of the discharge source within the device, completing spatial positioning calculations while maintaining accuracy under low signal-to-noise ratios. Finally, the discharge mode identification results and the discharge source positioning calculation results are fused to comprehensively determine the type, severity, and location of the partial discharge, forming a comprehensive and reliable partial discharge detection result. This provides a precise basis for assessing the insulation status and troubleshooting of high-voltage equipment.

[0069] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0070] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0071] Figure 2 A schematic diagram of the partial discharge detection device based on NV color centers provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 2 As shown, the partial discharge detection device based on NV color centers includes: The signal acquisition module 21 is used to acquire the detection signals of multiple NV color electrocardiogram field sensors arranged on the target device; The first detection module 22 is used to determine the discharge mode identification result based on the detection signals of each NV color electrocardiogram sensor using PRPD technology. The second detection module 23 is used to determine the location calculation result of the discharge source based on the detection signals of each NV color electrocardiogram field sensor using TDOA technology. The fusion module 24 is used to fuse the discharge mode recognition results and the location calculation results of the discharge source to obtain the partial discharge detection results.

[0072] In one possible implementation, the first detection module 22 may include: The first feature vector extraction unit is used to construct a PRPD map based on any detection signal, and extract features from the PRPD map to obtain the PRPD feature vector corresponding to the detection signal. The second feature vector extraction unit is used to obtain the target feature vector based on the PRPD feature vectors corresponding to each detection signal; The identification result output unit is used to input the target feature vector into the classification model to obtain the discharge pattern identification result.

[0073] In one possible implementation, the first feature vector extraction unit may include: The preprocessing subunit is used to filter and reduce noise in the detection signal to obtain the preprocessed detection signal. The discharge pulse extraction subunit is used to extract effective partial discharge pulses from the preprocessed detection signal; The feature extraction subunit is used to determine the phase angle and peak amplitude of any partial discharge pulse, based on the power frequency voltage reference signal. The spectrum output subunit is used to determine the phase angle and peak amplitude of each partial discharge pulse, forming the PRPD spectrum corresponding to the detection signal.

[0074] In one possible implementation, the second detection module 23 includes: The synchronization unit is used to synchronize the time of each detection signal; The time difference determination unit is used to determine the time difference between each NV color electrocardiogram sensor based on the synchronized detection signal. The positioning calculation unit is used to calculate the positioning result of the discharge source by using TDOA technology based on various time differences.

[0075] In one possible implementation, the positioning solution unit may include: The first equation establishes a sub-unit, which is used to establish a system of hyperbolic equations based on each time difference; The second equation establishes a sub-unit, which is used to transform the hyperbolic equation system into a pseudo-linear equation system. The first solution subunit is used to solve the pseudo-linear equation system using the weighted least squares method to obtain the initial solution; The second solution subunit is used to correct the weight matrix of the weighted least squares method based on the initial solution, and to solve the pseudo-linear equation system using the weighted least squares method based on the corrected weight matrix to obtain the location solution of the discharge source.

[0076] In one possible implementation, the time difference determination unit includes: The reference determination subunit is used to determine the signal-to-noise ratio of each detection signal after synchronization, and to take the detection signal with the highest signal-to-noise ratio as the reference signal. The cross-correlation function establishes a sub-unit, which is used to determine the cross-correlation function between each of the other detected signals and the reference signal, except for the reference signal; The time difference output subunit is used to determine the peak point of the cross-correlation function corresponding to any detection signal other than the reference signal, and to use the delay time corresponding to the peak point as the time difference between the NV color ECG field sensor corresponding to the detection signal and the NV color ECG field sensor corresponding to the reference signal.

[0077] In one possible implementation, the fusion module 24 may include: The fusion model building unit is used to build a fusion decision model based on DS evidence theory or Bayesian inference. The detection result output unit is used to input the discharge mode recognition result and the location calculation result of the discharge source into the fusion decision model to obtain the partial discharge detection result.

[0078] In one possible implementation, the number of NV color electrocardiogram (ECG) field sensors can be at least four, with each NV color ECG field sensor forming a triangular or quadrilateral array.

[0079] Figure 3 This is a schematic diagram of the electronic device 3 provided in an embodiment of the present invention. Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0080] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0081] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0082] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0083] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0084] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A partial discharge detection method based on NV color centers, characterized in that, include: Acquire detection signals from multiple NV color electrocardiogram field sensors deployed on the target device; Based on PRPD technology, the discharge mode identification result is determined according to the detection signals of each NV color electrocardiogram field sensor; Based on TDOA technology, the location calculation result of the discharge source is determined according to the detection signals of each NV color electrocardiogram field sensor; The discharge pattern recognition result and the location calculation result of the discharge source are fused to obtain the partial discharge detection result.

2. The partial discharge detection method based on NV color centers according to claim 1, characterized in that, The method based on PRPD technology, which determines the discharge mode identification result according to the detection signals of each NV color electrocardiogram sensor, includes: For any given detection signal, a PRPD map is constructed based on the detection signal; features are extracted from the PRPD map to obtain the PRPD feature vector corresponding to the detection signal. The target feature vector is obtained based on the PRPD feature vector corresponding to each detection signal; The target feature vector is input into the classification model to obtain the discharge pattern recognition result.

3. The partial discharge detection method based on NV color centers according to claim 2, characterized in that, The process of constructing the PRPD map based on the detection signal includes: The detection signal is filtered and denoised to obtain a preprocessed detection signal. Effective partial discharge pulses are extracted from the preprocessed detection signals; For any given partial discharge pulse, the phase angle and peak amplitude of the pulse are determined based on the power frequency voltage reference signal. The phase angle and peak amplitude of each partial discharge pulse form the PRPD spectrum corresponding to the detection signal.

4. The partial discharge detection method based on NV color centers according to claim 1, characterized in that, The method for determining the location calculation result of the discharge source based on the detection signals of each NV color electrocardiogram field sensor, using TDOA technology, includes: Synchronize the time of each detection signal; Based on the synchronized detection signals, the time difference between each NV color electrocardiogram field sensor is determined; Based on the various time differences, the location calculation results of the discharge source are obtained by using the TDOA technique.

5. The partial discharge detection method based on NV color centers according to claim 4, characterized in that, The step of obtaining the location calculation result of the discharge source by using TDOA technology based on various time differences includes: Based on the various time differences, establish a system of hyperbolic equations; Transform the hyperbolic equation system into a pseudo-linear equation system; The initial solution is obtained by solving the pseudo-linear equation system using the weighted least squares method. The weight matrix of the weighted least squares method is corrected based on the initial solution, and the pseudo-linear equations are solved using the weighted least squares method based on the corrected weight matrix to obtain the location solution of the discharge source.

6. The partial discharge detection method based on NV color centers according to claim 4, characterized in that, The step of determining the time difference between each NV color electrocardiogram sensor based on the synchronized detection signal includes: Determine the signal-to-noise ratio (SNR) of each detection signal after synchronization, and use the detection signal with the highest SNR as the reference signal; Determine the cross-correlation function between each of the other detection signals besides the reference signal and the reference signal; For any cross-correlation function corresponding to any detection signal other than the reference signal, determine the peak point of the cross-correlation function, and use the delay time corresponding to the peak point as the time difference between the NV color ECG field sensor corresponding to the detection signal and the NV color ECG field sensor corresponding to the reference signal.

7. The partial discharge detection method based on NV color centers according to claim 1, characterized in that, The partial discharge detection result is obtained by fusing the discharge pattern recognition result and the location calculation result of the discharge source, including: Establish a fusion decision-making model based on DS evidence theory or Bayesian inference; The discharge pattern recognition result and the location calculation result of the discharge source are input into the fusion decision model to obtain the partial discharge detection result.

8. The partial discharge detection method based on NV color centers according to any one of claims 1 to 7, characterized in that, The number of NV color electrocardiogram field sensors is at least 4, and each NV color electrocardiogram field sensor forms a triangular or quadrilateral array.

9. A partial discharge detection device based on NV color centers, characterized in that, include: The signal acquisition module is used to acquire the detection signals of multiple NV color electrocardiogram field sensors arranged on the target device; The first detection module is used to determine the discharge mode identification result based on the detection signals of each NV color electrocardiogram sensor using PRPD technology. The second detection module is used to determine the location calculation result of the discharge source based on the detection signals of each NV color electrocardiogram field sensor using TDOA technology. The fusion module is used to fuse the discharge mode recognition result and the location calculation result of the discharge source to obtain the partial discharge detection result.

10. An electronic device, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the partial discharge detection method based on NV color centers as described in any one of claims 1 to 8.

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