Transformer partial discharge on-line monitoring system

Abstract

The invention relates to a transformer partial discharge on-line monitoring system, and belongs to the technical field of transformer discharge monitoring. Comprising an ultrahigh frequency sensor and a partial discharge analysis device IED, and the partial discharge analysis device IED is connected with a monitoring platform through a remote communication module. At least three ultrahigh-frequency sensors are arranged at different positions of the transformer, the transformer is monitored through the ultrahigh-frequency sensors, and according to electromagnetic wave signals monitored by the ultrahigh-frequency sensors, whether a partial discharge phenomenon exists in the transformer or not is judged, and the position of a partial discharge source of the transformer is preliminarily determined. According to the application, the partial discharge phenomenon of the transformer can be monitored, the partial discharge position of the transformer can be comprehensively analyzed through the ultrahigh frequency sensors arranged at multiple positions on the transformer, the fault position searching time of an operator is shortened, and the maintenance time of the transformer is further shortened.

Description

Technical Field

[0001] The present application belongs to the technical field of transformer discharge monitoring, and specifically relates to an online monitoring system for partial discharge of a transformer. Background Art

[0002] Partial discharge can occur when certain areas of the transformer's insulation structure are exposed to excessive electric field strength. For example, sharp corners or burrs on the surface of the insulated conductors, or sharp corners on the oil tank or certain metal components, can occur. Furthermore, "wedge-shaped" oil gaps exist between turns and at the contact points between turns and spacers, which have low breakdown strength. Improper handling of these areas can also cause partial discharge. Failure statistics indicate that transformer insulation failures often originate from the aging of the oil-paper insulation caused by partial discharge.

[0003] Partial discharge in transformers generates electrical pulses, electromagnetic radiation, ultrasound, light, and other new products, which can cause local overheating. Consequently, various detection methods have emerged, including electrical pulse detection, ultrasound, optical detection, and infrared detection.

[0004] The recently developed ultra-high frequency (UHF) detection method, which studies partial discharge in transformers, overcomes the low measurement frequency and narrow bandwidth of traditional pulse current methods and allows for a more comprehensive study of the intrinsic characteristics of partial discharge. For example, a transformer partial discharge online monitoring system, published in China with patent authorization number CN207114700U, utilizes the UHF detection method to monitor partial discharge in transformers online.

[0005] The aforementioned patent includes an ultra-high frequency (UHF) sensor and a noise sensor, each of which is connected to a partial discharge (PD) IED host. The PD IED host includes a detector, an analog-to-digital converter, and a data collector. The detector is connected to the UHF sensor and the noise sensor, which are then connected to the data collector via an analog-to-digital converter. The data collector is connected to a first fiber optic transceiver via a network signal converter. The first fiber optic transceiver is connected to a second fiber optic transceiver via an optical fiber, and the second fiber optic transceiver is connected to a host computer. This patent uses UHF sensors and noise sensors to monitor the operation of the transformer and uses signal analysis to determine whether the transformer is experiencing partial discharge. However, this only determines whether the transformer is experiencing partial discharge; it cannot determine the specific location of the partial discharge, requiring further inspection of the transformer by operators. Summary of the Invention

[0006] The technical problem to be solved by this application is: to overcome the shortcomings of the existing technology and provide an online monitoring system for partial discharge of transformers. This application can not only monitor the partial discharge phenomenon of the transformer, but also comprehensively analyze the location where the partial discharge of the transformer is generated through ultra-high frequency sensors arranged at multiple positions on the transformer, thereby shortening the time for operators to find the fault location and thus shortening the maintenance time of the transformer.

[0007] The technical solution adopted by this application to solve the problems existing in the prior art is: A transformer partial discharge online monitoring system includes an ultra-high frequency sensor and an IED. The ultra-high frequency sensor provided on the transformer is connected to the IED, and the IED is connected to a monitoring platform via a remote communication module.

[0008] At least three UHF sensors are installed at different positions on the transformer. The transformer is monitored by each UHF sensor. Based on the electromagnetic wave signals detected by each UHF sensor, it is determined whether there is partial discharge inside the transformer and the location of the transformer partial discharge source is preliminarily determined.

[0009] Preferably, the position of the signal source is calculated by the time delay difference of the electromagnetic wave signals monitored by each ultra-high frequency sensor, and the position of the signal source is used to determine whether there is partial discharge inside the transformer and preliminarily determine the position of the transformer partial discharge source.

[0010] Preferably, four ultra-high frequency sensors are provided at different positions on the transformer, and the method for determining the position of the signal source is as follows: Construct a coordinate system for the transformer. The four UHF sensors are located at S1(x1,y1,z1), S2(x2,y2,z2), S3(x3,y3,z3), and S4(x4,y4,z4). The coordinate system set by the signal source is P(x,y,z). The propagation speed of the electromagnetic wave emitted by the signal source is c; The time when the signal reaches the four UHF sensors S1, S2, S3 and S4 are t1, t2, t3 and t4 respectively; Calculate the time difference as: Δt 21 =t2-t1, Δt 31 =t3-t1, Δt 41 =t4-t1; The relationship between distance difference and time difference is: cΔt 21 =d2-d1, cΔt 31 =d3-d1, cΔt 41 =d4-d1; Substitute into the distance formula , we get the nonlinear equations: ;

[0011] Solve the above nonlinear equations to obtain x, y, z, and bring x, y, z into the coordinate system constructed by the transformer; If P(x,y,z) is in the coordinate system constructed by the transformer, the signal source is the partial discharge source inside the transformer, and the location of the partial discharge source is determined at the same time; If P(x,y,z) is not in the coordinate system constructed by the transformer, there is no partial discharge inside the transformer.

[0012] Preferably, the propagation speed of the electromagnetic wave emitted by the signal source is c≈3×10 8 m / s.

[0013] Preferably, a background noise sensor is provided around the transformer, the noise signal is monitored by the background noise sensor, the signal monitored by the ultra-high frequency sensor is subjected to noise reduction processing, and the signal after noise reduction processing is used to determine whether it is an electromagnetic wave signal generated by partial discharge of the transformer.

[0014] Preferably, the background noise sensor collects external interference signals of the transformer, and the interference signals mainly include mobile phone signals.

[0015] Preferably, when the transformer is not started, the background noise sensor collects signals, and the collected signals are all interference signals, which are stored in the interference signal feature library; After the transformer is started, the signal monitored by the ultra-high frequency sensor is compared with the signal stored in the interference signal feature library. After eliminating the interference signal in the interference signal feature library, the remaining signal is the signal generated inside the transformer. Whether the transformer has partial discharge is determined based on whether the frequency of this part of the signal is within the frequency band of the electromagnetic wave signal generated by partial discharge inside the transformer.

[0016] Compared with the prior art, this application has the following beneficial effects: The location of the signal source is determined by monitoring the time difference of the signals through multiple ultra-high frequency sensors arranged on the transformer. The location of the signal source is then used to judge whether there is partial discharge inside the transformer and the location of the partial discharge source is determined. This makes it easier for operators to quickly locate the partial discharge area during maintenance, thus shortening the maintenance cycle.

[0017] There is no need to perform noise reduction processing on the signals monitored by the UHF sensor, which makes online monitoring of partial discharge of transformers more convenient and efficient, while reducing R&D and usage costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present application is further described below with reference to the accompanying drawings and examples.

[0019] Figure 1 This is a schematic diagram of an online monitoring system for partial discharge of a transformer in this application. DETAILED DESCRIPTION

[0020] The online monitoring system for partial discharge of transformers of the present application is further described in detail with reference to the accompanying drawings, but is not intended to limit the present application.

[0021] Example 1: Figure 1 As shown, a transformer partial discharge online monitoring system includes an ultra-high frequency sensor and an IED. The ultra-high frequency sensor installed on the transformer is connected to the IED. The IED is connected to a monitoring platform via a remote communication module.

[0022] The ultra-high frequency sensor has a sampling frequency of 300MHz to 2000MHz and an N-type output connector. Installed on the transformer's oil drain valve or manhole cover, it provides real-time online monitoring of partial discharge within the transformer. The partial discharge analysis device (IED) has a sampling frequency of 500MHz to 2000MHz, a real-time sampling bandwidth of 200MHz, and uses 485 or IEC61850 communication protocols. The monitoring interface is N-type. The IED channel uses multiple high-frequency channels plus multiple noise channels. This embodiment uses four high-frequency channels or four high-frequency channels plus one noise channel.

[0023] At least three UHF sensors are installed at different locations on the transformer. Each UHF sensor monitors the transformer and detects differences in the electromagnetic wave signals from the signal source, which can be used to preliminarily determine the location of the signal source. The difference in the electromagnetic wave signals detected by each UHF sensor represents the time delay difference between the signals reaching each sensor.

[0024] In order to calculate the position of the signal source by using the time delay difference, in this embodiment, four ultra-high frequency sensors are provided at different positions on the transformer. The method for determining the position of the signal source is as follows: Construct a coordinate system for the transformer. The four UHF sensors are located at S1(x1,y1,z1), S2(x2,y2,z2), S3(x3,y3,z3), and S4(x4,y4,z4). The coordinate system set by the signal source is P(x,y,z). The propagation speed of the electromagnetic wave emitted by the signal source is c, c≈3×10 8 m / s.

[0025] The time when the signal reaches the four UHF sensors S1, S2, S3 and S4 are t1, t2, t3 and t4 respectively; Calculate the time difference as: Δt 21 =t2-t1, Δt 31 =t3-t1, Δt41 =t4-t1; The relationship between distance difference and time difference is: cΔt 21 =d2-d1, cΔt 31 =d3-d1, cΔt 41 =d4-d1; Substitute into the distance formula , we get the nonlinear equations: ;

[0026] Solve the above nonlinear equations to obtain x, y, and z. Bring x, y, and z into the coordinate system to determine the position of the signal source.

[0027] If P(x,y,z) is in the coordinate system constructed by the transformer, then the signal source is the partial discharge source inside the transformer, and the location of the partial discharge source is also determined; If P(x, y, z) is not in the coordinate system constructed by the transformer, then the signal source is a partial discharge source outside the transformer, and there is no partial discharge phenomenon inside the transformer.

[0028] The above method can determine whether a transformer is experiencing partial discharge (PD) without requiring noise reduction processing of the signals detected by the ultra-high frequency (UHF) sensor. If PD is present, the source of the PD can also be located. By omitting the noise reduction process, some hardware modules within the PD analysis device (IED) can be omitted, such as the preamplifier, adjustable bandpass filter, and high-speed ADC. These modules can be reduced based on actual usage, thereby reducing hardware costs. Furthermore, there's no need to integrate noise reduction algorithms into the system, reducing R&D costs and energy consumption.

[0029] Example 2: For some common interference signals, such as mobile phone signals, radar signals, carrier signals, and engine signals, background noise sensors can be installed around the transformer to collect them. The background noise sensor has a collection frequency of 300MHz to 3000MHz and an N-type output connector. The background noise sensor monitors the noise signal and performs simple noise reduction processing on the signal detected by the ultra-high frequency sensor to reduce the calculation amount of the above-mentioned signal source location determination method and further reduce energy consumption. The specific method is as follows: When the transformer is not running, the background noise sensor collects signals. These signals are all interference signals and are stored in an interference signal feature library for subsequent identification and elimination. Common interference signal features found in transformer operating locations are collected and stored in the interference signal feature library.

[0030] After the transformer is started, the signal monitored by the ultra-high frequency sensor is compared with the signal stored in the interference signal feature library. After eliminating the interference signal in the interference signal feature library, the remaining signal is the signal generated inside the transformer. Whether the transformer has partial discharge is determined based on whether the frequency of this part of the signal is within the frequency band of the electromagnetic wave signal generated by partial discharge inside the transformer.

[0031] If partial discharge occurs in the transformer, the location of the partial discharge source is determined using the signal source location determination method in Example 1.

[0032] Mobile phone signals are 0.3-3GHz pulse modulated signals, radar signals are high-frequency pulse signals, and carrier signals are power frequency harmonics. These interference signals overlap with the frequency domain of partial discharge signals, making them difficult to separate using traditional filtering. Filter comparison requires signal comparison and screening to be completed within milliseconds to support online monitoring.

[0033] Therefore, in the signal comparison process, the following intelligent anti-interference system combining dynamic feature library learning and multimodal spectrum subtraction and noise reduction is used for comparison.

[0034] The intelligent anti-interference system, which uses multimodal spectral subtraction and noise reduction, collects data using background noise sensors and ultra-high frequency (UHF) sensors. The signals detected by the UHF sensors are fed into a multimodal interference feature extraction engine embedded within the partial discharge (PD) analysis device (IED) via a real-time signal synchronization acquisition module. Data collected by the background noise sensors is stored in an interference signal feature library, from which interference signals are extracted and fed into the multimodal interference feature extraction engine. These two sets of data are processed within the multimodal interference feature extraction engine using an adaptive spectral subtraction and low-rank decomposition collaborative noise reduction module, and then judged by a partial discharge signal detector.

[0035] The interference signal feature library is updated using the incremental learning method. During the construction of the interference signal feature library, the feature extraction method is as follows: When the transformer is not started, the feature extraction formula is: Time domain characteristics: ; K: kurtosis; X: the original time-domain sampling sequence of the vibration signal or electromagnetic interference signal (such as the voltage / current value sequence collected by the sensor); μ: the mean of the signal sequence, reflecting the DC offset of the signal; σ: standard deviation of the signal sequence, representing the intensity of signal fluctuation; E[]: Mathematical expectation operator, which represents the probability-weighted average of the random variables in the brackets.

[0036] Kurtosis measures the sharpness or impulse characteristics of a signal distribution. When K > 3, the signal contains high-frequency pulse interference (such as mobile phone signals or radar pulses); when K ≈ 3, it indicates a Gaussian distribution (white background noise); and when K < 3, it indicates a flat distribution (such as low-frequency engine vibration).

[0037] Frequency domain characteristics: ; ; H f :spectral entropy; S k : The complex amplitude of the kth frequency component of the signal after Fourier transform (FFT); ∣S k ∣ 2 : power spectral density (energy) of the kth frequency component; P k : the normalized power of the kth frequency component (as a proportion of the total signal energy); N: The total number of frequency points in FFT analysis (determined by the sampling rate and window length).

[0038] Spectral entropy quantifies the complexity or randomness of the signal spectrum, H f →0: Signal energy is concentrated in a few frequencies (such as carrier signals); H f →log2N: Energy is evenly distributed (such as broadband noise).

[0039] Time-frequency domain features: ; ; E jk : wavelet packet energy ratio; x jk (p): the p-th wavelet coefficient in the j-th layer and the k-th sub-band after the signal is decomposed by wavelet packet; E total : the total energy of the signal in all sub-bands; p: time domain index of the wavelet coefficient (reflecting the local time window of the signal).

[0040] The wavelet packet energy ratio locates the dominant frequency band of the interference signal, E jk Other sub-bands: Interference is concentrated in the frequency band [f j ,f j+1 ] (e.g., 2.4GHz Wi-Fi signals correspond to specific sub-bands). It is also used to construct the time-frequency fingerprint of interference and support frequency-domain filtering of the dynamic feature library.

[0041] When the transformer is running, the interference signal is screened in real time. During the interference signal comparison and elimination process, the weighted KL divergence feature matching is used: ; : is the KL divergence in the interference signal feature library; P(i): real-time signal wavelet packet energy distribution; Q(i): Feature distribution of the cluster center of the interference feature library; α(i), β(i): frequency band energy weights.

[0042] After removing the interference signal, the partial discharge signal judger is used to determine whether a partial discharge phenomenon exists. The judgment method is as follows: ; Δf: frequency band tolerance (typical value 10MHz); γ: Energy threshold (dynamically calibrated according to transformer type).

[0043] The online monitoring system page of the monitoring platform displays a coordinate system with a virtual box diagram of the transformer within it. The locations of the UHF sensors are displayed within the virtual box diagram, along with the location of the signal source determined when the UHF sensors detect a signal. The signal source is marked with a red dot. If the signal source is located within the virtual box diagram of the transformer, it indicates a partial discharge source. The red dot flashes, and an alarm is issued. The alarm signal can be transmitted wirelessly to the operator's mobile app or the corresponding page on their work computer.

[0044] Furthermore, the virtual box diagram of the transformer is correlated with the transformer assembly drawing. After the location of the partial discharge source inside the virtual box diagram is marked, it can be correlated with the transformer assembly drawing, indicating the structural parts of the partial discharge source location and reporting them through a list, making it easier for operation and maintenance personnel to carry relevant accessories and maintenance tools to the site, further shortening the maintenance cycle.

[0045] The online monitoring system has information storage and organization functions, provides trend analysis with different update cycles such as "hours, days, weeks, months, years", and the display time period of data trends can be selected.

[0046] The above describes the implementation methods of the present application in detail in conjunction with the accompanying drawings, but the present application is not limited to the above implementation methods. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A transformer partial discharge online monitoring system, comprising an ultra-high frequency sensor and an IED (internal discharge analysis device), wherein the ultra-high frequency sensor provided on the transformer is connected to the IED, and wherein: The partial discharge analysis device IED is connected to the monitoring platform via a remote communication module; At least three UHF sensors are installed at different positions on the transformer. The transformer is monitored by each UHF sensor. Based on the electromagnetic wave signals detected by each UHF sensor, it is determined whether there is partial discharge inside the transformer and the location of the transformer partial discharge source is preliminarily determined.

2. A transformer partial discharge online monitoring system according to claim 1, characterized in that: The time delay difference of the electromagnetic wave signals monitored by each ultra-high frequency sensor is used to calculate the position of the signal source. The position of the signal source is used to determine whether there is partial discharge inside the transformer and to preliminarily determine the location of the transformer partial discharge source.

3. The transformer partial discharge online monitoring system according to claim 2, characterized in that: Four UHF sensors are located at different positions on the transformer. The method for determining the signal source position is as follows: Construct a coordinate system for the transformer. The four UHF sensors are located at S1(x1,y1,z1), S2(x2,y2,z2), S3(x3,y3,z3), and S4(x4,y4,z4). The coordinate system set by the signal source is P(x,y,z). The propagation speed of the electromagnetic wave emitted by the signal source is c; The time when the signal reaches the four UHF sensors S1, S2, S3 and S4 are t1, t2, t3 and t4 respectively; Calculate the time difference as: Δt 21 =t2-t1, Δt 31 =t3-t1, Δt 41 =t4-t1; The relationship between distance difference and time difference is: cΔt 21 =d2-d1, cΔt 31 =d3-d1, cΔt 41 =d4-d1; Substitute into the distance formula , we get the nonlinear equations: ； Solve the above nonlinear equations to obtain x, y, z, and bring x, y, z into the coordinate system constructed by the transformer; If P(x,y,z) is in the coordinate system constructed by the transformer, the signal source is the partial discharge source inside the transformer, and the location of the partial discharge source is determined at the same time; If P(x,y,z) is not in the coordinate system constructed by the transformer, there is no partial discharge inside the transformer.

4. The transformer partial discharge online monitoring system according to claim 3, characterized in that: The propagation speed of the electromagnetic wave emitted by the signal source is c≈3×10 8 m / s.

5. A transformer partial discharge online monitoring system according to any one of claims 1 to 4, characterized in that: Background noise sensors are installed around the transformer. The noise signals are monitored by the background noise sensors, and the signals monitored by the ultra-high frequency sensors are subjected to noise reduction processing. The signals after noise reduction processing are used to determine whether they are electromagnetic wave signals generated by partial discharge of the transformer.

6. The transformer partial discharge online monitoring system according to claim 5, characterized in that: The background noise sensor collects external interference signals from the transformer, which mainly include mobile phone signals.

7. The transformer partial discharge online monitoring system according to claim 6, characterized in that: When the transformer is not started, the background noise sensor collects signals. The collected signals are all interference signals and are stored in the interference signal feature library. After the transformer is started, the signal monitored by the ultra-high frequency sensor is compared with the signal stored in the interference signal feature library. After eliminating the interference signal in the interference signal feature library, the remaining signal is the signal generated inside the transformer. Whether the transformer has partial discharge is determined based on whether the frequency of this part of the signal is within the frequency band of the electromagnetic wave signal generated by partial discharge inside the transformer.

8. The transformer partial discharge online monitoring system according to claim 7, characterized in that: When the transformer is running, the interference signal is screened in real time. During the interference signal comparison and elimination process, the weighted KL divergence feature matching is used: ； : is the KL divergence in the interference signal feature library; P(i): real-time signal wavelet packet energy distribution; Q(i): Feature distribution of the cluster center of the interference feature library; α(i), β(i): frequency band energy weights; After removing the interference signal, the partial discharge signal judger is used to determine whether a partial discharge phenomenon exists. The judgment method is as follows: ； Δf: frequency band tolerance (typical value 10MHz); γ: Energy threshold (dynamically calibrated according to transformer type).

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