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Application of transformer partial discharge on-line monitoring in Smart Grid

Add time:2022-02-18 11:41:31   Number of views:160  

1、 Introduction

Electromagnetic interference in a broad sense includes not only the interference that enters the monitoring system through the current sensor together with the partial discharge signal, but also the interference that affects the monitoring system itself, such as grounding, shielding, and dry s interference caused by improper circuit processing. The latter can be solved by improving the system design, reasonably selecting circuits and components, and improving the manufacturing level of the system. Field electromagnetic interference, especially the former, is the focus of research. It can be divided into continuous periodic interference, pulse interference and white noise. Periodic interference includes system high-order harmonics, carrier communication and radio communication. Pulse type interference is divided into periodic pulse type interference and random pulse type interference. Periodic pulse interference is mainly caused by high-frequency inrush current generated by the action of power electronic devices. Random pulse type interference includes corona discharge on high-voltage line, partial discharge generated by other electrical equipment, discharge generated by tap changer action, arc discharge generated by motor operation, suspended potential discharge generated by poor contact, etc. White noise includes coil thermal noise, ground grid noise, power supply line and all kinds of noise coupled into transformer relay protection signal line.

Electromagnetic interference generally enters the measuring point through space direct coupling and line conduction. Different measurement points will lead to different interference coupling paths and different effects on measurement; The types and intensity of interference vary with different measuring points.

The selection principle of transformer partial discharge monitoring points is that the partial discharge signal strength is large, the signal-to-noise ratio is high, and the measurement is simple. It mainly includes shell grounding wire and casing end screen grounding wire, and some also choose neutral grounding wire, iron core grounding wire and high-voltage outlet end. Sometimes, in order to suppress interference, the reference interference signal is also measured from the power supply line of the transformer. Since it is inconvenient to install sensors at the neutral point and high-voltage outlet end, and some transformer cores are grounded internally, the monitoring system mostly selects the shell and bushing end screen grounding wire as the measurement point.

2、 Common suppression methods

Interference suppression is always considered from three aspects: interference source, interference path and signal post-processing. Finding out the interference source and directly eliminating or cutting off the corresponding interference path is the fundamental method to solve the interference effect. However, it is required to analyze the interference source and interference path in detail, and it is generally not allowed to change the original transformer operation mode. Therefore, the measures that can be taken in these two aspects are always very limited. Various signal processing techniques are adopted to suppress various interferences coupled into the monitoring system through the current sensor. Generally, partial discharge signals and interference signals are distinguished from each other from the following aspects:; Power frequency phase, frequency spectrum, pulse amplitude and amplitude distribution, signal polarity, repetition rate and physical position, and a large number of anti-interference technologies are proposed.

There are two different ideas in anti-interference technology: one is based on narrowband (the frequency band is generally 10kHz to several 10kHz) signals. It picks up the signal through the narrow-band current sensor and band-pass filter circuit with appropriate frequency band, avoids all kinds of continuous periodic interference, and improves the signal-to-noise ratio of the measurement signal. This method is only suitable for a specific substation and is inconvenient to use. In addition, because the partial discharge signal is a wide-band pulse, the narrow-band measurement will cause the distortion of the signal waveform, which is not conducive to the later digital processing. The other is a processing method based on broadband (the frequency band is generally 10 to 1000khz) signals. The detection signal contains most of the energy of partial discharge and a lot of interference, but the signal-to-noise ratio is low. The processing steps for these interferences are generally: A. suppress continuous periodic interference; b. Suppress periodic pulse interference; c. Suppress random pulse interference. With the development of digital technology and the application of pattern recognition method in partial discharge, this processing method can often achieve good results.

According to the above two ideas, the detection signals with different signal-to-noise ratios can be obtained. In the post-processing, many processing methods are consistent. It can be summarized into frequency domain processing and time domain processing methods. The frequency domain method is based on the discrete characteristics of periodic interference in the frequency domain; The time domain processing method is based on the discrete characteristics of pulse interference in the time domain. There are two implementation modes: hardware and software. The following are introduced respectively.

3、 Suppression of periodic interference

Periodic interference, also known as narrowband interference, accounts for a large proportion of all kinds of interference. The suppression and elimination of interference should also start from this. Because of its high intensity and fixed phase distribution, it is mostly processed by frequency domain method. It mainly includes FFT threshold filter, adaptive filter, fixed coefficient filter and ideal multi passband digital filter (imdf).

There are many and mature algorithms for narrowband interference suppression. From the application effect, fixed coefficient filter and ideal multi band-pass filter are ideal. Because imdf needs to perform FFT and IFFT many times when processing data, it will cost a lot of calculation time, which is not conducive to real-time processing. However, according to the monitoring frequency band found by imdf, a fixed coefficient finite impulse response (FIR) digital filter can be formed to process directly in time domain, which simplifies the operation and speeds up the processing speed.

The above methods can be realized by software or hardware circuits. The narrow-band interference can not be adjusted flexibly after the on-site test, but the hardware can not suppress the narrow-band interference. Although the software method is flexible, it has the disadvantage of slow real-time operation speed.

4、 Suppression of periodic pulse interference

When the periodic interference is removed from the signal, other interference rises to the main contradiction. For the suppression of periodic pulse interference, there are two kinds of processing methods: analog method and digital method. The simulation methods include differential balance method, directional coupling method and reference signal method; The first two methods are also applicable to the suppression of random pulse interference, which will be introduced later. Select the distribution line containing only pulse interference but not discharge pulse to measure the pulse interference signal, and use the measured interference pulse as the control signal. When the signal level exceeds the set threshold and is determined as interference, stop the operation of analog-to-digital converter (ADC) to eliminate the interference pulse from the distribution line.

The principle of digital method is to use the characteristics of different phase distribution of interference and partial discharge signals for processing. For example, Konig G. And Kopf U. A method is proposed. Firstly, the signals of multiple cycles are recorded, and then the data in the same phase of each cycle are averaged to form a template to subtract from the original signal, so as to eliminate the periodic interference signal. This method has a better effect of removing interference when the government has less signals and clear distribution characteristics, but not when the government has more and strong signals.

V. Nagesh and B. I. Gururaj of India proposed a method, which draws lessons from some achievements of biological signal processing. Its basic principle is that starting from the different shapes of the partial discharge signal and the periodic interference signal, the data is segmented first, the pulse is separated from the waveform signal to form a single pulse sequence, and the FFT algorithm is used to calculate the cross-correlation of each pulse in the frequency domain, Judge their similarity and group them according to certain standards, obtain the class signal template according to these groups of pulses, and then synthesize each class of signals in time domain. It is found that the phase of partial discharge signal is scattered, while the interference is very concentrated. Using this feature, the periodic pulse interference signal is eliminated and the remaining signal is reconstructed to obtain the signal after removing the periodic pulse interference. Therefore, it is feasible to suppress the interference by using the difference of waveform and phase between partial discharge and periodic pulse interference. This method can also be used for location. It can be identified by analyzing the characteristics of pulse waveform caused by different discharge points. The disadvantages of this method are: when the repetition rate is high, it is possible to regard two adjacent pulses as one, which affects the effect of recognition; In addition, when there are many pulse waveforms, the operation speed is affected, but with the significant improvement of microcomputer operation ability, this influence will be ignored more and more.

5、 Suppression of random pulse interference

This kind of interference is difficult to eliminate. Because the characteristics of interference and partial discharge signals are similar in frequency domain, a large number of existing methods are considered in time domain. Common methods include hardware circuit method, software waveform recognition method and artificial intelligence method.

1. Hardware circuit method

Its basic idea is to remove the pulse interference by using the characteristics that the external pulse interference in the output signals of two measurement points is in the same direction and the internal discharge pulse direction is opposite. The common methods include the polarity identification method and the differential circuit identification method.

In practical application, the effect of the first two is not ideal. This is because for the differential balance method, due to different propagation paths, the two signals constituting the differential often can not correspond well, so the differential effect is poor. The concept of differential "balance pair" is proposed and improved, which can eliminate the interference and obtain the amplitude and number of partial discharge pulses at the same time. The limitation of pulse polarity discrimination is that the analog delay and polarity discriminator are more affected by external factors, which will cause electronic gating misoperation and reduce the accuracy of polarity discrimination.

The directional coupling method was proposed by borsi h in Germany in 1987. The schematic diagram is shown in Figure 1. It uses a specially wound Rogowski coil to couple the partial discharge signal near the flange at the bottom of the high-voltage bushing, and judge whether it is partial discharge signal or external electromagnetic interference according to the voltage at both ends of the coil. This method connects the middle tap of Rogowski coil with the measuring terminal of the end screen of transformer bushing. At this time, the end screen measuring terminal string is grounded with a small resistance, which can be regarded as the low-voltage arm of the capacitor voltage divider composed of the end screen and the end screen capacitance to ground. After being grounded with a small resistance, a high pass filter is formed, and only high-frequency signals can pass through. Rogowski coil is connected with the end screen measuring terminal of high voltage bushing to form a directional coupling circuit. When the current I is in the direction shown in the figure, u (1) = u c u 1, u (2) = UC - U 2 = UC - U1. At this time, u (1) > U (2); If the current I is reverse, u (1) < U (2). Thus, the direction and nature of the signal can be determined, and the partial discharge signal can be clearly distinguished from external electromagnetic interference.

In practical application, people have improved this. Two Rogowski coils are used to replace the original measurement coil, and the frequency selection method is used to improve the signal-to-noise ratio of the measurement signal. According to the introduction of the paper, good results are obtained.

2. Software waveform recognition method

With the development of computer technology and digital signal processing technology, using pulse signal characteristics for logical judgment can also suppress interference. Its premise is pulse recognition, that is to judge whether the pulse exists, pulse duration and corresponding starting and ending points, so as to accurately determine the discharge phase and acoustic delay.

At present, the threshold recognition method is mostly used in pulse recognition. The pulses measured in the field are mostly attenuated oscillation waves, which is easy to be misjudged and the pulse duration cannot be determined. A method combining pulse amplitude threshold and waveform characteristics to identify oscillation pulse is proposed, and good results are obtained in practice.

3. Application of pattern recognition

The essence of this method is still to distinguish by using the phase characteristics of the signal. Although the amplitude of partial discharge signals varies greatly, their phases are concentrated near 45 ° and 225 ° respectively. For example, because the occurrence phase of arc discharge is different from that of partial discharge, the amplitude change is small, and the pulse shape is also slightly different, according to these characteristics, an experienced expert can easily distinguish the interference of arc discharge signal. Pattern recognition method is the software implementation of expert experience. It has been confirmed in Ciger's report, and some corresponding software have appeared. Common methods include fuzzy logic method, Kohonen network classification method, KLT transformation method and artificial neural network method based on small distance. Generally speaking, the difficulty of pattern recognition method is that it needs to accumulate a lot of prior knowledge and find out the specific differences between interference and partial discharge. In on-line measurement, it is difficult to find these differences in strong interference signals. Several of these methods are described below.

(1) Karhunen Loeve transform method

It is found that when the dimension of the input vector used for pattern recognition is high, the classification is difficult and the effect is not good; After reducing the dimension, the classification effect can be improved. In other words, in order to improve the recognition rate and highlight the characteristics of the signal, it is first necessary to remove the interference or noise information in the signal. The principle of KLT transformation is shown in Figure 2. It can be seen from the figure that if X1 - x2 coordinate system is adopted, X1 and X2 coordinates must be adopted at the same time for classification; If this is orthogonally transformed, it is transferred to W1 - W2 coordinate system. Then only w 2 coordinates are needed for classification. It can be seen that the interference can be removed by KLT transformation.

(2) Pulse sequence analysis -- Kohonen network

The algorithm is an unsupervised algorithm (as shown in Figure 3). Its principle is to find the node with short Euclidean distance from the input vector to the output layer, and take it as the output. Through the self-organizing algorithm, it can carry out adaptive classification and distinguish the partial discharge signal and interference signal, so as to achieve the purpose of interference elimination and suppression.

(3) Pulse sequence analysis

It is reported that this method is simple, effective and has high recognition rate: it consists of the analysis sequence composed of the discharge voltage difference or phase difference between partial discharges, which can distinguish different discharge modes and interferences to achieve the purpose of interference suppression; In addition, the fault point can be located.

6、 Summary

A large number of research results show that with the improvement of a / D conversion rate and the development of computer technology, the on-line monitoring system of transformer partial discharge using broadband (10k-1000khz) sensor combined with high-speed sampling has become the mainstream of development. Signal processing has developed from traditional spectrum analysis to time domain analysis of partial discharge waveform.

Some achievements in the field of digital processing technology and artificial intelligence have been widely used in interference suppression in on-line monitoring, and are expected to achieve breakthrough results.

In order to further improve the effectiveness of anti-interference measures, the research on the propagation law of interference and pulse should be strengthened, including the research on the propagation in substation and the internal propagation of transformer. Therefore, it may be found that they have different characteristics in waveform, phase and direction.


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