Study On Fault Line Detection Based On Intelligent Algorithm In Non-Effectively Grounding Neutral System

Many countries, including China, adopt non-effectively grounding (isolated, Pe-tersen coil or high resistance earthed) neutral in medium voltage distribution networks to reduce outages caused by single-phase-to-earth fault (small current earth fault). When single-phase-to-earth fault occurs, it is difficult to detect the line with an earth fault due to weak current, unstable fault arc and stochastic factors. To date electric utilities still use manual switching to detect an earthed line in the absence of a more reliable fault line detection method and a more accurate device for detecting earth fault line. The problem of fault line detection for single-phase-to-earth fault becomes more and more prominent and it is urgent to resolve this problem thoroughly in order to meet the requirements of distribution automation. Therefore, the study of the high accuracy and high reliable automatic fault line detection technology and the corresponding device is significant to the improvement of the power supply reliability, the reduction in the outage loss and the improvement of distribution automation system.Based on analyzing the fault characteristics for single-phase-to-earth fault in non-effectively grounding neutral system, the paper studies the properties of the fault line detection based on fault transient information, and the integrated fault line detection based on fault transient information and fault steady information, respectively. First, applying the fault transient information, the fault line detection based on rough set theory and wavelet packet analysis is proposed. Next, applying fault transient information and fault steady information, the paper proposes the integrated fault line detection based on rough set theory. Next, the integrated fault line detection based on neural networks is presented because of the lower accuracy of the integrated fault line detection based on rough set theory. Next, in order to avoid the longtime training of the fault line detection model based on neural networks, a novel sample normalization algorithm based on rough set theory is proposed. Finally, applying the integrated fault line detection criterion based on neural networks, the design scheme of the device for detecting grounded line in non-effectively grounding neutral system is developed. The main researches are as follows:(1) The fault characteristics for single-phase-to-earth in non-effectively grounding neutral system are analyzed systematically. Through analyzing the steady characteristics, transient characteristics and harmonic characteristics of zero sequence current, the paper reveals that the transient characteristics, active component characteristics, the fifth harmonic characteristics and fundamental characteristics of zero sequence current should be selected as fault features to perform fault line detection.(2) The problems of the fault line detection method based on wavelet packet analysis for single-phase-to-earth fault in non-effectively grounding neutral system are analyzed. Therefore, a novel fault line detection method based on rough set theory and wavelet packets analysis is proposed. The fault line detection based on wavelet packet analysis, which selects transient component of zero sequence current as fault feature, provides high accuracy of fault line detection due to the magnitude of transient component of zero sequence current is much bigger than the steady component. The sampling frequency rate cannot be very high due to the limitation of the hardware circuit. The magnitude of transient signal sampled by lower sampling frequency rate will decay in different degree. When the magnitude of the sampled transient zero sequence current is attenuated rigorously, the result of fault line detection may be wrong. So the sampling signal must be enhanced. The more rigorously the transient signal is attenuated and the larger the possibility of fault is, the bigger the transient signal enhancement ratio is. The paper proposes a novel fault line detection method based on rough set theory and wavelet packet analysis. Firstly, the transient zero sequence current signals are sampled in lower sampling frequency rate and in higher sampling frequency rate for a short time, respectively. The amplitude of fundamental component, decaying ratio of amplitude between before and after sampling and polarity of initial wavefronts are extracted from the zero sequence current signal sampled in lower sampling frequency rate and in higher sampling frequency rate. Then, the fault features extracted are selected as condition attributes and the signal enhancement ratios are selected as decision attributions and an information system is constructed. Af- ter attribute reduction and attribute value reduction, the minimum solution of decision rules can be obtained. These rules can be used to enhance the low frequency sampling signals. Finally, the enhanced low frequency sampling signals are decomposed by wavelet packet to realize fault line detection. The simulation results show that the proposed method can detect the fault line when the magnitude based criterion or the polarity based criterion is invalid, or when the phase voltage is close to its crossover point, or when the bus grounding fault or the high earth resistance grounding fault occurs. So the method improves markedly the accuracy of fault line detection.(3) The fault measures of various fault features for single-phase-to-earth in non-effectively grounding neutral system are defined. Therefore, the integrated fault line detection method based on rough set theory is presented. The fault features of transient component, active component, the fifth harmonic component and fundamental component are extracted from zero sequence current through wavelet packet analysis method, zero sequence current active component method, the fifth harmonic current method and fundamental current component amplitude comparison method, respectively. Then they are transformed into fault measures according to their characteristics. The integrated fault line detection method based on rough set theory, in which the condition attributes are these fault measures and the decision attribute is the integrated fault measure, is proposed. Having performed attribute reduction and attribute value reduction, the minimum solution of decision rules can be obtained. These rules can be used to realize fault line detection. This method reduces these redundant attributes and integrates the fault features of transient component, active component and the fifth harmonic component, which have more effect on fault detection result, and realizes the integrated fault line detection. The fault line detection method is verified by the simulation data and field data for single-phase-to-earth fault and the testing results show that the method can provide the higher accuracy of fault detection than the fault line detection method based on single fault characteristic.(4) To overcome the problem that the accuracy of the fault line detection based on rough set is low, the integrated fault line detection method based on neural network is proposed, in which the inputs are these fault measures reduced by rough set theory and the output is the integrated fault measure. The fault line detection method is verified by the simulation data and field data for single-phase-to-earth fault. The testing results show that the method can provide higher accuracy of fault line detection than the fault line detection method based on rough set theory.(5) A novel sample normalization algorithm based on rough set theory is proposed to avoid the longtime training of neural networks classifier caused by the smaller distances between samples of different classes. After the inputs of neural networks are selected as the condition attributes and the outputs of neural networks are selected as decision attribute, the decision table is constructed. Then the minimal distances between different samples of different classes in the decision table are calculated. According to these minimal distances, the samples are extended or contracted. The smaller the minimal distance is, the bigger the radio of extension or contraction is. After all the original samples are extended or contracted, they are normalized. Finally, the normalized samples are used to train and verify the neural network. The method is analyzed with an example of fault line detection in non-effectively grounding neutral system. The simulation results show that the training time of neural networks with the sample normalization method is shortened markedly.(6) Using Electromagnetic Transients Program EMTP-ATP, a lot of experiments of single-phase-to-earth fault for all kinds of fault types are carried out. The simulation data can be used to analyze every fault line detection method.(7) According to the integrated fault line detection criterion based on neural networks, the design scheme of the device for detecting grounded line in non-effectively grounding neutral system is introduced. Meanwhile, the block diagram of its main hardware and the flow of its main software are designed.