Multi-BP expert system for fault diagnosis of powersystem

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Abstract

Fault diagnosis and assessment is a crucial and difficult problem for power system. Back propagation neural network expert system (BPES) is an often used method in fault diagnosis. However, with the layer numbers increasing, BPES becomes time consuming and even hard to converge. To solve this problem, we divide the whole networks into many sub-BP groups within a short depth and then propose a novel Multi-BP expert system (MBPES) based method for power system fault diagnosis. We use two real power system data sets to test the effectiveness of MBPES. Experimental results show that MBPES obtains higher accuracy than two commonly used methods.

Highlights

► This work proposes a novel MBPES based method for power system fault diagnosis. ► Construction algorithm and training algorithm of MBPES are developed in the paper. ► MBPES is efficient and obtains higher accuracy than two commonly used methods.

Introduction

In modern times, power system becomes larger and more complex than before. With its fast development, higher demand for the sustainability and stability of power system is of great requirement. However, some common faults in power system have never been resolved very well and are still hindering the stability of power system, such as transmission fault, network distribution fault, power variable fault (Mizutani et al., 2007). Sometimes, even only one fault could destroy the equipments in power system, and might affect the whole power system. An even worse damage could cause conflagration and casualties, and leading to a huge pecuniary loss. Therefore, it is of great significance to do researches for preventing those faults from power system. And fault diagnosis is a powerful tool to guarantee the safety and reliability of power system.

In power system, transformer is a kind of major equipment and plays an important role in power transmission. It can raise voltage so that power can be transported to the user with less loss. On the other hand, the transformer can reduce power into different voltage levels, which can satisfy variety needs of users. Because of its complex structure and function, transformer is tending to cause fault. Unfortunately, transformer fault is very difficult to predict. Moreover, if some accidents take place in transformer, the whole system has no choice but to stop to check and maintain the equipments. Therefore, it is believed that keeping the transformer running in perfect situation plays a key role in power system diagnosis (Lin and Zeng, 2009). High voltage circuit breaker (over 3 kV) is another important element in power system. It has two main functions, namely controlling and protecting. Firstly, it decides when and which parts of the power system should be started or stopped according to the requirements; secondly, when some errors occur in power networks or equipments, the high voltage circuit breaker will quickly break off the error parts from power system, so that other parts can work without influence. In other words, high voltage circuit breaker is able to control the normal current in power lines, and to deal with the overload current, short-circuit current and other abnormal current within a limited time. When a mistake happens in high voltage circuit breaker, it will usually expand to other parts of the power system and finally lead to a worse accident.

There are some classical artificial intelligence technologies have been used in power system fault diagnosis, for example: the expert system (Ma et al., 2010), artificial neural networks (El-madany et al., 2011, Zhu et al., 2006; Huang et al., 2002; Karthikeyan et al., 2005), decision tree theory (Qu and Gao, 2008) etc. In recent years, some new theories have been applied in this field, such as data mining (Athanasopoulou and Chatziathanasiou, 2009), fuzzy set theory (Lee et al., 2000; Zhang et al., 2010), rough set theory (Li and Wang, 2010, Li et al., 2011), petri-network (Yang et al., 2004), support vector machine (Eristi and Demir, 2010), multi-agent systems (Zaki et al., 2007), and so on. Li and Liu had performed a comprehensive review of the above-mentioned methods (Li and Liu, 2010). They pointed out that there are some problems in the existing intelligent fault diagnosis expert system theology, such as the difficulty for knowledge gaining and managing, low on-line usage of fault diagnosis, high error rate, poor efficiency of inference process, and so on. Back propagation neural network (BPNN) expert system is an often used method in fault diagnosis. In real applications, BPNN usually has many layers. However, the training time of BPNN will grow exponentially with the layer number increasing. While more serious problem is that it is difficult to converge when BPNN has a large number of layers. Another problem is that the diagnostic accuracy of BPNN is still not satisfied. To solve those problems, we propose a so called multi-BP expert system (MBPES) method. In MBPES, the whole BPNN networks are divided into many sub-BP groups within a short depth, saying about 5layers. In this manner, the consumed training time is greatly reduced and it is easy to achieve the convergence of the training process.

In the experiments, we firstly compare the performance of BPNN with different number layers according to a XOR problem. Numerical results show that when the layer number is more than 6, BPNN is very time consumed and even hard to converge. To test the effectiveness of the proposed MBPES method, it is applied to two real power system data sets, namely that the transformer data set and high voltage circuit breaker data set. Experimental results show that MBPES is very efficient, and it is more accurate than two other compared methods.

Section snippets

Back propagation expert system

Back propagation (BP) algorithm is one of the most classical and successful learning methods of feed forward artificial neural network, which is based on gradient descent algorithm. For its success, those feed forward neural networks using BP algorithm are always called BPNNs. Fig. 1 shows an architecture of BPNN model with K hidden layers. There are n nodes in the input layer, which is corresponding to the sample vector's dimension. And the inputs of the input layer are the components of the

The structure of MBPES

In a BPNN, a rule is corresponding to a substructure of the networks with 3layers. Accordingly, 2and 3nested rules are corresponding to a substructure with 5and 7layers, respectively. Because those BPNN networks with more than 5layers are difficult to train and even hard to converge, we divide the whole networks into sub-networks with 5or less layers. Fig. 3 shows the division of a BPNN in serial structure.

However, in real applications, rules are not always nested in serial. For example, the

Comparison for different network layer numbers

To test the performance of BPNNs with different layer number, we take XOR problem as an example. In the experiment, the main parameters are set as follows: the inertial weight α is 0.5, the momentum coefficient η is 0.9, the threshold β is −0.5, and the error threshold Ethr is 0.001, respectively. To get the statistical results, each experiment is repeated for 100 times. Numerical results are shown in Table 1 and Fig. 10. From Table 1 and Fig. 8, we can see that the training time is raising

Conclusions

In this paper, facing the real data in transformer and high voltage breaker, with serial, parallel and hybrid grouping algorithm, we proposed two typical new Multi-BP networks. Moreover, they successfully solved the problem of low convergence speed caused by the large number of network layers and also greatly increased the speed of diagnosis. Furthermore, by the comparison with BPNN and BPES, we can see that the results from Multi-BP are more accurate. Using multi-BP network to diagnosis power

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (61073075, 61103092), China Postdoctoral Science Foundation (2011M500613), the Science-Technology Development Project from Jilin Province (20120730, 201215022), Special Fund for Basic Scientific Research of Central Colleges, Jilin University (no. 201103036).

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