Partial discharge detection device and method integrating quantum sensing and mems sensing

Abstract

The application belongs to the technical field of power equipment monitoring, and discloses a partial discharge detection device and method integrated with quantum sensing and MEMS sensing; the partial discharge signal is collected by a quantum sensor and an electric field sensor respectively; data is transmitted to a microprocessor by a data acquisition card; the microprocessor judges the discharge type by using a wavelet transform method, extracts characteristic parameters, and transmits the data to a computer; according to characteristic parameter analysis, the calculation weight of different sensors is determined for data fusion, and the partial discharge electric field intensity is calculated and displayed; the application realizes data fusion of the quantum sensor and the MEMS sensor, provides more comprehensive and accurate information, can better identify the source and characteristics of the partial discharge, improves the accuracy of diagnosis and the reliability of the detection system, reduces false positives and false negatives, and provides reliable data for the operation state, fault diagnosis and maintenance of power equipment.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, specifically to a partial discharge detection device and method integrating quantum sensing and MEMS sensing. Background Technology

[0002] In the field of power systems, partial discharge detection is a crucial task. Power equipment operates under high voltage and high current conditions for extended periods, making it prone to high-frequency pulsed electric fields at certain insulation locations, leading to partial discharge (PD). PD is characterized by short rise times and high amplitude. The long-term effects of partial discharge are a significant factor contributing to insulation aging and deterioration in power equipment. Early detection of internal insulation defects through partial discharge detection devices and methods can prevent operational failures or even equipment burnout caused by sudden or latent accidents. Therefore, real-time and rapid partial discharge detection, especially accurate location of discharge points, is of great significance for power equipment operation monitoring and rapid fault diagnosis.

[0003] Currently, some important equipment is equipped with partial discharge detection devices. Existing partial discharge detection methods mainly include optical detection, electrical detection, and quantum detection. Among them, optical detection methods are mainly based on the Pockels effect or Kerr effect, which have the advantages of wide frequency response range and high accuracy. However, each optical element is susceptible to the influence of ambient temperature and humidity, and the switching electromagnetic interference signals present are very similar to partial discharge signals in terms of frequency spectrum and amplitude. Electrical detection methods are mainly based on electrostatic induction, such as MEMS electric field sensors, which have the advantages of small size, fast response, and good linearity and frequency response. However, the metal structure of the electrodes can cause electric field distortion, leading to inaccurate detection. Quantum detection methods mainly use the coupling between the electric field and quantum systems, quantum properties, or quantum phenomena to perform detection, such as diamond NV color centers and other sensor devices. This provides new opportunities for quantum detection in the field of partial discharge, especially in terms of high sensitivity and high accuracy. With the increasing demand for partial discharge detection in recent years, none of the above methods can achieve high stability and high accuracy detection of partial discharge on their own, and inaccurate detection problems sometimes occur. In particular, while quantum sensing technology offers high precision, it is also responsive to ambient temperature and magnetic fields, resulting in insufficient accuracy when measuring electric fields alone.

[0004] To address the aforementioned issues, this invention proposes an integrated approach using two detection methods for partial discharge detection. This method leverages the characteristic of the quantum energy spectrum of diamond NV centers changing with the electric field, employing optical detection magnetic resonance spectroscopy to detect the variation of the ground-state zero-field splitting peak of the NV centers with the electric field. Simultaneously, a MEMS electrode is designed as the sensing electrode to achieve real-time detection of high-frequency partial discharge pulse signals, thus mitigating the impact of environmental factors on the quantum sensor. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a partial discharge detection device and method integrating quantum sensing and MEMS sensing, thereby resolving the issues in the prior art. To achieve the above-mentioned objective, the technical solution adopted by this invention is as follows:

[0006] A partial discharge detection device integrating quantum sensing and MEMS sensing includes a quantum sensor, a MEMS electric field sensor, a data acquisition card, a microprocessor, and a computer; the quantum sensor and the MEMS electric field sensor synchronously detect the same partial discharge signal; the data acquisition card acquires the analog signals from the quantum sensor and the MEMS electric field sensor and converts them into digital signals; the microprocessor and the computer perform data fusion and signal analysis on the digital signals.

[0007] Furthermore, the quantum sensor is a diamond NV center quantum sensor, which includes: a diamond NV center probe, an optical path module, a microwave module, and a photodetector;

[0008] The microwave module includes a microwave antenna and a microwave source; the microwave antenna is positioned 1 mm directly below the diamond NV color center probe.

[0009] Furthermore, the MEMS electric field sensor includes: MEMS electrodes and signal processing circuits; wherein, the MEMS electrodes use silicon wafers or glass wafers as substrates and Ti / Cu as metal layers, and the electrode structure is distributed with two spiral lines; the signal circuits include: I / V conversion circuits, differential signal amplification circuits, and filtering circuits; the signal circuits acquire the voltage signals of the MEMS electrodes and amplify and filter the voltage signals.

[0010] Furthermore, when the quantum sensor and the MEMS electric field sensor detect the same partial discharge, they are placed facing each other, with a distance of 20-40 mm between the quantum sensor probe and the MEMS electric field sensor probe.

[0011] Furthermore, during microprocessor signal analysis, the discrete wavelet transform method is used to perform multi-scale decomposition and reconstruction through the filter bank algorithm to obtain the approximation coefficients and detail coefficients of the signal; then, time-domain signal and feature parameter extraction is performed. Based on the waveforms of the same discharge signal from different sensors, the microprocessor extracts the discharge signals from the two sensors in the time domain and calculates their feature parameters respectively.

[0012] The approximation coefficients and detail coefficients are based on the decomposition formula of the discrete wavelet transform, which is:

[0013]

[0014] Reconstruction formula of discrete wavelet transform:

[0015]

[0016] Where x[n] is the original signal, φ j,k [n] is the scaling function, ψ j,k [n] represents the wavelet basis function, W(j,k) represents the wavelet coefficients, j represents the approximation coefficients, and k represents the detail coefficients.

[0017] Furthermore, the characteristic parameters are the skewness coefficient and the kurtosis coefficient;

[0018] Wherein, the skewness coefficient:

[0019]

[0020] Kurtosis coefficient:

[0021]

[0022] Where N is the number of signal points collected, x[n] is the amplitude of the signal at the corresponding point, μ is the average value of the signal, and σ is the standard deviation of the signal.

[0023] Furthermore, during computer signal analysis, weights are first selected, and characteristic parameters, spectral density, and power spectral density are analyzed to determine the energy of partial discharge in different frequency bands. Based on the different measurement ranges of quantum sensors and MEMS electric field sensors for high-frequency pulse signals and the different interferences from ambient temperature and magnetic fields, the weights w of the two sensing systems are determined. qt and w mems ;

[0024] Calculate the weighted average to determine the output voltage amplitude V of the quantum sensor for the same discharge signal. qt The output voltage amplitude V of the MEMS electric field sensor mems After weighted averaging, the partial discharge electric field intensity data are calculated using the following formula:

[0025] E = q qt V qt w qt +q mems V mems w mems

[0026] Where, q qt Let q be the coefficient between the voltage and electric field strength of the quantum sensor. mems w is the coefficient between the voltage and electric field strength of the MEMS sensor. qt +w mems =1.

[0027] A partial discharge detection method integrating quantum sensing and MEMS sensing, applied to the aforementioned partial discharge detection device integrating quantum sensing and MEMS sensing, includes the following steps:

[0028] Step 1: The quantum sensor and the MEMS electric field sensor simultaneously detect the same partial discharge signal;

[0029] Step 2: Perform linear fitting on the calculation results of the quantum sensor and the MEMS electric field sensor to obtain the output voltage amplitude relationship between different sensors, determine the linear coefficients of the sensor output linear relationship for the same discharge at the same time in the partial discharge region, and determine the quantum sensor output voltage V for the same discharge signal. qt MEMS sensor output voltage V mems .

[0030] Furthermore, in step 1, the detection by the quantum sensor includes the following steps:

[0031] Step 1.1a: Place the quantum sensor probe in the region to be detected to acquire signals; the ground-state spin Hamiltonian of the quantum sensor in the partial discharge region is:

[0032]

[0033] Where h is Planck's constant; D is the zero-field splitting microwave frequency of the optically detected magnetic resonance spectrum; γ is the spin operator; E is the applied electric field strength; e The gyromagnetic ratio of the NV spin; The strength of the applied magnetic field;

[0034] Step 1.2a: Control the semiconductor laser to generate a 532nm pulsed laser; the microwave source outputs a microwave signal; the microwave antenna generates a microwave signal; scan the signal frequency range of 2.7GHz-2.9GHz to obtain the center frequency of zero-field splitting under the measurement environment conditions;

[0035] Step 1.3a: Set the microwave frequency to the center frequency of zero-field splitting. According to the Stark effect, the center frequency will change under the condition of a change in the applied electric field, which will cause the fluorescence signal intensity to change, and thus cause the output voltage of the quantum sensor to change.

[0036] Step 1.4a: Establish a standard curve based on the relationship between the standard electric field and the output signal of the quantum sensor, and then determine the partial discharge signal based on the quantum sensor signal.

[0037] Furthermore, in step 1, the detection of the MEMS electric field sensor includes the following steps:

[0038] Step 1.1b: Place the MEMS electric field sensor probe in the area to be detected to acquire signals;

[0039] Step 1.2b: The MEMS electrodes of the MEMS electric field sensor sense the partial discharge signal and generate an induced voltage signal; wherein, under the action of electrostatic principle, the MEMS electrodes generate an induced current for the high-frequency pulse voltage present in the partial discharge, and the formula for the induced current is as follows:

[0040]

[0041] Where ε0 is the free space dielectric constant, E(t) is the electric field to be measured, a is the effective sensing area of ​​the MEMS electrode, and C is the capacitance value of the MEMS sensor.

[0042] Step 1.3b: Acquire the voltage signal of the simulated MEMS electric field sensor, plot the MEMS electrode output voltage signal in the time domain, and obtain the relationship between pulse signal and time;

[0043] Step 1.4b: Calculate the partial discharge signal at the test location based on the principle of electrostatic induction and the relationship between pulse signal and time.

[0044] The present invention has the following beneficial effects:

[0045] (1) A dual-mode partial discharge detection method integrating quantum sensing and MEMS sensing was constructed and a related device was developed, which solved the problems of poor sensitivity and narrow bandwidth of partial discharge detection.

[0046] (2) The introduction of MEMS sensing signals solves the problem of quantum sensors being affected by ambient temperature and magnetic field interference, and automatically adapts to different detection scenarios through dynamic adjustment of weight coefficients;

[0047] (3) The introduction of the discrete wavelet transform algorithm solves the problem of feature extraction of sensor signals. This method can analyze the local frequency characteristics of partial discharge signals and retain the time-domain details of discharge intensity at different times. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the partial discharge detection method integrating quantum sensor and MEMS sensor of the present invention;

[0049] Figure 2 Schematic diagram of NV color center quantum sensor;

[0050] Figure 3 This is the electrode structure for a MEMS electric field sensor. Detailed Implementation

[0051] The following will be based on embodiments of the present invention. Figures 1-3The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0052] This invention provides a partial discharge detection device and method integrating quantum sensing and MEMS sensing, which fully utilizes the advantages of quantum sensors and MEMS sensors to achieve high spatial resolution, high sensitivity, and non-contact partial discharge detection.

[0053] Quantum sensors utilize diamond with high concentrations of NV centers coupled to optical fibers, achieving fluorescence excitation and collection through the same fiber, thus enabling highly sensitive detection of partial discharges. MEMS sensors utilize the principle of electrostatic induction between electrodes and a spatial electric field, converting changes in the spatial electric field into induced charges through MEMS electrodes, thereby achieving stable detection of partial discharges.

[0054] like Figure 1 A partial discharge detection device integrating quantum sensing and MEMS sensing includes a quantum sensor, a MEMS electric field sensor, a data acquisition card, a microprocessor, and a computer; the quantum sensor and the MEMS electric field sensor synchronously detect the same partial discharge signal; the data acquisition card acquires the analog signals from the quantum sensor and the MEMS electric field sensor and converts them into digital signals; the microprocessor and the computer perform data fusion and signal analysis on the digital signals.

[0055] Furthermore, the quantum sensor is a diamond NV center quantum sensor, which includes: a diamond NV center probe, an optical path module, a microwave module, and a photodetector; the structure of the NV center quantum sensor is as follows: Figure 2 .

[0056] The microwave module includes a microwave antenna and a microwave source; the microwave antenna is positioned 1 mm directly below the diamond NV color center probe.

[0057] Furthermore, the MEMS electric field sensor includes: MEMS electrodes and signal processing circuitry; wherein, the MEMS electrodes use a silicon wafer or glass sheet as a substrate and Ti / Cu as a metal layer, and the electrode structure is a distribution of two spiral lines; the signal circuitry includes: an I / V conversion circuit, a differential signal amplification circuit, and a filtering circuit; the signal circuitry acquires the voltage signal from the MEMS electrodes and amplifies and filters the voltage signal. The structure of the MEMS electric field sensor is as follows. Figure 3 .

[0058] Furthermore, when the quantum sensor and the MEMS electric field sensor detect the same partial discharge, they are placed facing each other, with the microwave antenna and the MEMS electrode spaced 20-40 mm apart.

[0059] A partial discharge detection method integrating quantum sensing and MEMS sensing, applied to the aforementioned partial discharge detection device integrating quantum sensing and MEMS sensing, includes the following steps:

[0060] Step 1: The quantum sensor and the MEMS electric field sensor simultaneously detect the same partial discharge signal;

[0061] Step 2: Perform linear fitting on the calculation results of the quantum sensor and the MEMS electric field sensor to obtain the output voltage amplitude relationship between different sensors, determine the linear coefficients of the sensor output linear relationship for the same discharge at the same time in the partial discharge region, and determine the quantum sensor output voltage V for the same discharge signal. qt MEMS sensor output voltage V mems .

[0062] Furthermore, in step 1, the detection by the quantum sensor includes the following steps:

[0063] Step 1.1a: Place the quantum sensor in the region to be detected to acquire the signal; the ground state spin Hamiltonian of the quantum sensor in the partial discharge region is:

[0064]

[0065] Where h is Planck's constant; D is the zero-field splitting microwave frequency of the optically detected magnetic resonance spectrum, with a value of 2870MHz; γ is the spin operator; E is the applied electric field strength; e The gyromagnetic ratio of the spin of the NV color center is 28MHz / mT; The strength of the applied magnetic field;

[0066] Step 1.2a: Control the semiconductor laser to generate a 532nm pulsed laser; the microwave source outputs a microwave signal; the microwave antenna generates a microwave signal and scans the signal frequency range of 2.7GHz-2.9GHz to obtain the center frequency of zero-field splitting under geomagnetic conditions;

[0067] Step 1.3a: Set the microwave frequency to the center frequency of zero-field splitting. According to the Stark effect, the center frequency will change under the condition of a change in the applied electric field, which will cause the fluorescence signal intensity to change, and thus cause the output voltage of the quantum sensor to change.

[0068] Step 1.4a: Establish a standard curve based on the relationship between the standard electric field and the output signal of the quantum sensor, and then determine the partial discharge signal based on the quantum sensor signal;

[0069] In the specific implementation of the quantum sensor detection of the present invention: firstly, the quantum sensor probe with NV color centers ( Figure 2 The laser (as shown) is placed in the area to be detected. The laser intensity is 5mW, the laser pulse is a square wave, the controller adjusts the single pulse time to 10us, and the duty cycle to 0.5. When the laser is turned on, green light shines along the optical fiber onto the 15um NV color center. The green fluorescence generated by the NV color center is collected and reflected back to the excitation light path by the optical fiber, and then reflected by the dichroic mirror into the photodetector, where the optical signal is converted into an electrical signal.

[0070] The power of the microwave source was adjusted to 5dBm, and the signal was applied to the microwave antenna of the color center to be NV. The controller adjusted the microwave source to quickly scan the frequency around 2.87GHz, and at the same time the data acquisition card was set to a acquisition rate of 1MSPS to quickly collect the photodetector signal. The signal was then processed by the computer to obtain the photodetector magnetic resonance spectrum.

[0071] The optically detected magnetic resonance spectrum was measured under known waveform conditions, and the linear relationship between voltage and frequency was obtained based on the microwave frequency of 2.87 GHz at the split center.

[0072] The change in the center microwave frequency is calculated, and based on the linear relationship between the electric field and frequency obtained from the calibration, the relationship between the discharge electric field and time is analyzed and calculated.

[0073] Furthermore, in step 1, the detection of the MEMS electric field sensor includes the following steps:

[0074] Step 1.1b: Place the MEMS electric field sensor in the area to be detected to acquire signals;

[0075] Step 1.2b: The MEMS electrodes of the MEMS electric field sensor sense the partial discharge signal, generating an induced current. A 1MΩ resistor is connected in parallel to convert the current into a voltage. Specifically, under the influence of electrostatics, the MEMS electrodes generate an induced current for the high-frequency pulse voltage present in the partial discharge. The formula for the induced current is as follows:

[0076]

[0077] Where ε is the free space dielectric constant, E(t) is the electric field to be measured, A is the effective sensing area of ​​the MEMS electrode, and C is the capacitance value of the MEMS sensor.

[0078] Step 1.3b: Acquire the voltage signal of the simulated MEMS electric field sensor, plot the MEMS electrode output voltage signal in the time domain, and obtain the relationship between pulse signal and time;

[0079] Step 1.4b: Calculate the partial discharge signal at the test location based on the principle of electrostatic induction and the relationship between pulse signal and time.

[0080] In the specific detection process of the MEMS electric field sensor of this invention: First, the MEMS sensor probe is placed in the area to be detected. The amplification factor of the differential signal amplification circuit is set, and the filtering frequency of the filtering circuit is set. The MEMS electrode is connected to the circuit module via a coaxial cable, and a data acquisition card is connected to collect partial discharge signals. The linear relationship between induced voltage and time is then calibrated to obtain the results.

[0081] The partial discharge detection device in this solution can operate automatically to detect the partial discharge status of power equipment. It is particularly suitable for high-frequency pulse signals within a certain frequency band and can effectively assist in the diagnosis of transformer operation faults.

[0082] The microprocessor uses wavelet analysis to calculate an analysis algorithm that determines whether a high-frequency pulse signal is generated based on sensor data, and matches ordinary signals with high-frequency pulse signals; it analyzes the degree of agreement between the characteristic parameters of quantum sensors and MEMS electric field sensors, and transmits the results to a computer.

[0083] The computer analyzes the partial discharge detection capabilities of quantum sensors and MEMS electric field sensors based on the degree of agreement of their characteristic parameters. By comparing the degree of agreement of characteristic parameters between different sensors, the consistency of detection of the same partial discharge signal by different sensors is determined; the partial discharge quantity is calculated based on the determination results.

[0084] This invention targets sudden high-frequency pulse signals. The microprocessor uses wavelet transform to extract the output voltage amplitude of the sensor for the signal; specifically, discrete wavelet transform is used for discrete data output by the data acquisition card.

[0085] Furthermore, multi-scale decomposition and reconstruction are performed using a series of filtering algorithms to obtain the approximate coefficients and detail coefficients of the signal. The decomposition formula for the discrete wavelet transform is as follows:

[0086]

[0087] The reconstruction formula of the discrete wavelet transform is as follows:

[0088]

[0089] Where x[n] is the original signal, φ j,k [n] is the scaling function, ψ j,k [n] represents the wavelet basis function, W(j,k) represents the wavelet coefficients, j represents the approximation coefficients, and k represents the detail coefficients.

[0090] Wavelet analysis coefficients can determine the characteristics of time-domain signals, and the detail coefficients can be used to determine whether high-frequency pulse signals are generated, thus matching the analysis algorithms for ordinary signals and high-frequency pulse signals.

[0091] In a partial discharge signal, the characteristic parameters of the quantum sensor and the MEMS sensor are analyzed, and the degree of agreement of the characteristic parameters is analyzed. Based on the degree of agreement, the detection capabilities of the two sensors for the same partial discharge signal are analyzed, reflecting the partial discharge detection capabilities of quantum detection and optical detection under different voltage waveforms. The detection characteristic parameters include: skewness coefficient and kurtosis coefficient.

[0092] The formula for the skewness coefficient is as follows:

[0093]

[0094] The formula for the kurtosis coefficient is as follows:

[0095]

[0096] Where N is the number of signal points collected, x[n] is the amplitude of the signal at the corresponding point, μ is the average value of the signal, and σ is the standard deviation of the signal.

[0097] The detection signal and characteristic parameters are transmitted to a computer for weighted averaging to determine the output voltage amplitude V of the quantum sensor for the same discharge signal. qt The output voltage amplitude V of the MEMS electric field sensor mems After weighted averaging, the partial discharge electric field intensity data are calculated using the following formula:

[0098] E = q qt V qt w qt +q mems V mems w mems

[0099] Where, q qt q is the coefficient between the voltage and charge of the quantum sensor. mems w is the coefficient between the voltage and charge of a MEMS sensor. qt +w mems =1.

[0100] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, substitutions, or variations made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A partial discharge detection device integrating quantum sensing and MEMS sensing, characterized in that, It includes quantum sensors, MEMS electric field sensors, data acquisition cards, microprocessors, and computers; the quantum sensors and MEMS electric field sensors synchronously detect the same partial discharge signal; the data acquisition card acquires the analog signals from the quantum sensors and MEMS electric field sensors and converts them into digital signals; the microprocessor and computer perform data fusion and signal analysis on the digital signals. In computer signal analysis, weights are first selected, and then characteristic parameters, spectral density, and power spectral density are analyzed to determine the energy of partial discharge in different frequency bands. Based on the different measurement ranges of quantum sensors and MEMS electric field sensors for high-frequency pulse signals and the different interferences from ambient temperature and magnetic fields, the weights of the two sensing systems are determined. and ; Calculate the weighted average to determine the output voltage amplitude of quantum sensors with the same discharge signal. Output voltage amplitude of MEMS electric field sensor After weighted averaging, the partial discharge electric field intensity data are calculated using the following formula: in, This is the coefficient between the voltage and electric field strength of the quantum sensor. This is the coefficient between the voltage and electric field strength of the MEMS sensor. .

2. The partial discharge detection device integrating quantum sensing and MEMS sensing according to claim 1, characterized in that, The quantum sensor is a diamond NV center quantum sensor, which includes: a diamond NV center probe, an optical path module, a microwave module, and a photodetector; The microwave module includes a microwave antenna and a microwave source; the microwave antenna is positioned 1 mm directly below the diamond NV color center probe.

3. The partial discharge detection device integrating quantum sensing and MEMS sensing according to claim 1, characterized in that, The MEMS electric field sensor includes: MEMS electrodes and signal processing circuits; wherein, the MEMS electrodes use silicon wafers or glass sheets as substrates and Ti / Cu as metal layers, and the electrode structure is distributed with two spiral lines; the signal circuits include: I / V conversion circuits, differential signal amplification circuits, and filtering circuits; the signal circuits acquire the voltage signals of the MEMS electrodes and amplify and filter the voltage signals.

4. The partial discharge detection device integrated with quantum sensing and MEMS sensing according to claim 1, wherein, When the quantum sensor and the MEMS electric field sensor detect the same partial discharge, they are placed facing each other, with a distance of 20-40 mm between the quantum sensor probe and the MEMS electric field sensor probe.

5. The partial discharge detection device integrating quantum sensing and MEMS sensing according to claim 1, characterized in that, When performing signal analysis by the microprocessor, the discrete wavelet transform method is used to perform multi-scale decomposition and reconstruction through the filter bank algorithm to obtain the approximation coefficients and detail coefficients of the signal. Then, time-domain signal and feature parameter extraction is performed. Based on the waveforms of the same discharge signal from different sensors, the microprocessor extracts the discharge signals from the two sensors in the time domain and calculates their feature parameters respectively. The approximation coefficients and detail coefficients are based on the decomposition formula of the discrete wavelet transform, which is: Reconstruction formula of discrete wavelet transform: in, The original signal, For scaling function, For wavelet basis functions, denoted as wavelet coefficient, j as approximation coefficient, and k as detail coefficient.

6. The partial discharge detection device integrating quantum sensing and MEMS sensing according to claim 5, characterized in that, The characteristic parameters are skewness coefficient and kurtosis coefficient; Wherein, the skewness coefficient: Kurtosis coefficient: Where N is the number of signal points collected, x[n] is the amplitude of the signal at the corresponding point, μ is the average value of the signal, and σ is the standard deviation of the signal.

7. A partial discharge detection method integrating quantum sensing and MEMS sensing, applied to the partial discharge detection device integrating quantum sensing and MEMS sensing as described in any one of claims 1-6, characterized in that, Includes the following steps: Step 1: The quantum sensor and the MEMS electric field sensor simultaneously detect the same partial discharge signal; Step 2: Perform linear fitting on the calculation results of the quantum sensor and the MEMS electric field sensor to obtain the output voltage amplitude relationship between different sensors, determine the linear coefficients of the sensor output linear relationship for the same discharge in the partial discharge region at the same time, and determine the output voltage of the quantum sensor for the same discharge signal. MEMS sensor output voltage .

8. The partial discharge detection method integrating quantum sensing and MEMS sensing according to claim 7, characterized in that, Step 1, the detection by the quantum sensor, includes the following steps: Step 1.1a: Place the quantum sensor probe in the region to be detected to acquire signals; the ground-state spin Hamiltonian of the quantum sensor in the partial discharge region is: Where h is Planck's constant; D is the zero-field splitting microwave frequency of the optically detected magnetic resonance spectrum; Here, E is the spin operator; E is the applied electric field strength. The gyromagnetic ratio of the NV spin; The strength of the applied magnetic field; Step 1.2a: Control the semiconductor laser to generate a 532nm pulsed laser; the microwave source outputs a microwave signal; the microwave antenna generates a microwave signal; scan the signal frequency range of 2.7GHz-2.9GHz to obtain the center frequency of zero-field splitting under the measurement environment conditions; Step 1.3a: Set the microwave frequency to the center frequency of zero-field splitting. According to the Stark effect, the center frequency will change under the condition of a change in the applied electric field, which will cause the fluorescence signal intensity to change, and thus cause the output voltage of the quantum sensor to change. Step 1.4a: Establish a standard curve based on the relationship between the standard electric field and the output signal of the quantum sensor, and then determine the partial discharge signal based on the quantum sensor signal.

9. The partial discharge detection method integrating quantum sensing and MEMS sensing according to claim 7, characterized in that, Step 1, the detection of the MEMS electric field sensor includes the following steps: Step 1.1b: Place the MEMS electric field sensor probe in the area to be detected to acquire signals; Step 1.2b: The MEMS electrodes of the MEMS electric field sensor sense the partial discharge signal and generate an induced voltage signal; wherein, under the action of electrostatic principle, the MEMS electrodes generate an induced current for the high-frequency pulse voltage present in the partial discharge, and the formula for the induced current is as follows: in, Let E(t) be the free space permittivity, and E(t) be the electric field to be measured. The effective sensing area of ​​the MEMS electrode. The capacitance value of the MEMS sensor; Step 1.3b: Acquire the voltage signal of the simulated MEMS electric field sensor, plot the MEMS electrode output voltage signal in the time domain, and obtain the relationship between pulse signal and time; Step 1.4b: Calculate the partial discharge signal at the test location based on the principle of electrostatic induction and the relationship between pulse signal and time.

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