The Open University ’ s repository of research publications and other research outputs Early warning fault detection using artificial intelligent methods Conference Item

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper. 1.0 INTRODUCTION As a result of increasing competition in the electrical power industry and the requirement for quality power supply, adequate fault detection is becoming vitally important to both companies and consumers. One of the strategies to ensure low running cost is the general avoidance of supply interruption wherever possible. Conventional fault studies are concerned mainly with a ‘what if ’ scenario i.e. on considering what would happen after a fault occurred, identifying its location and accessing the nature and degree of damage. In contrast, if potential faults could be identified by an appropriate early warning system before a catastrophic fault actually occurs, the chance of power interruption would be greatly reduce. However, few studies have been made concerning early fault detection (EFD) techniques as used by Wong [1]. In a typical power system, the states (voltages and currents) of most bus bar nodes are monitored and gradual changes are analysed. However, in general, because of the complexity of recorded data, faults cannot be easily recognised at an early stage. These faults can often be disguised initially by the complexity of power system operational data.The purpose of using the EFD system is to provide an early warning to the operator when potential faults are identified. The heart of the EFD system is a hierarchical ANNs structure [2], which consists a network of ANNs, which can be employed to monitor the states of some important components in power networks, such as switchgear and transformers. Each of the individual ANNs is trained to detect minor changes to a component or a equivalent circuit component in a power system model. In the training process, a sufficient number of training patterns are presented to the ANNs repeatedly until the problem is generalised. A training pattern effectively consists of a series of numbers which inform the ANNs what the outputs states should be when identical, or similar, patterns are presented at the input of the ANN. In this case, the training patterns are the states at both ends of the monitored power system section under slightly different ‘known’ working conditions. The small variations of voltages and currents resulting from small degradation of component values, at sending and receiving ends of the monitored power system section, can be derived, under simulation, or selected from the power industry recorded data. As some of the equivalent components of a power system model do not physically exist, or are inaccessible, they cannot be measured directly by simple measurement methods. Thus, the application of an intelligent technique, such as an ANN method , is obviously desirable. The principle of the EFD method can be applied to various sections of a power system. A typical simplified example will now be given. High voltages and currents carried by transmission lines in power systems are subject to small changes in state, caused by partial faults, often too trivially small to trigger the conventional protection warming systems. However, these small scale changes may develop and eventually lead to major faults. If, for example, the protective layer for an underground transmission line had an undiscovered partial fault, due to road works damage, the cable could become progressively corroded and, in time, its electrical characteristics could change gradually. A circuit breaker would trip if underground flooding caused a short-circuit and this fault could “black-out” a large area. However, for a network with early warning fault monitoring, the interruption of power supply to certain sections of network could possibly be prevented. The gradual change of impedance of the transmission line provides vital information which can be continuously monitored and analysed by the EFD technique to provide an early detection capability. This approach could alert the operator before the main fault actually occurs enabling, in some situations, appropriate action to be taken, e.g. providing power supply from another circuit and switching out the endangered line prior to a more detailed investigation. 2.0 ARTIFICIAL NEURAL NETWORK An ANN may be considered as a greatly simplified model of the human brain which can be used to perform a particular task or function of interest. [3] The network is usually implemented using electronic components or simulated in software on a digital computer. The massively parallel distributed structure and the ability to learn and generalise makes it possible for ANNs to solve complex problems that otherwise are currently intractable. A brief description of what and how neural networks are being employed is given below. For more information relating to ANNs, see [3-6]. 2.1 Neurons And Synapses A neuron is an information processing unit that is fundamental to the operation of an ANN. Two basic elements can be identified from a neuron; an adder and an activation function. An adder is used to sum up the input signals, weighted by the respective synapses of the neuron. The activation function limits the amplitude of the output of a neuron. It compresses the permissible amplitude range of the output signal to some finite value [3]. For some gradient descent learning algorithms, such as the Back-Propagation learning method (BP), the activation functions are required to be bounded and differentiable. [4] In this work, the standard sigmoid function was selected which bounds its output range between zero and one. Synapses are simple connection that can either impose excitation or inhibition on the receptive neuron. Knowledge is acquired by the network through a learning process. The synaptic weights are used to store the knowledge. Through the learning process, the synaptic weights of the network are modified in such a way to map the input patterns to the output patterns. [3] 2.2 Structure of Artificial Neural Networks The standard Multi-Layer Feedforward (MLF) network is employed as the network architecture in this project. The MLF network is a network of neurons and synapses organised in the form of layers; the input layer, hidden layer and output layer. Figure 1. A 3-2-4 multi-layer feedforward network The function of the input layer is simply to buffer the external inputs to the network. The hidden neurons have no direct connections to the outside world. However, they empower the extraction of higherorder statistics as the network acquires a global perspective despite its local connectivity by virtue of the extra set of synaptic connections and the extra dimension of neural interactions (Churchland and Sejnowski, 1992)[5]. Figure 1 shows the structure of a MLF network. The source nodes in the input layer of the network supply respective elements of the activation pattern, which constitute the input signals applied to the neurons (computation nodes) in the second layer. The output signals of the second layer are used as inputs to the third layer, and so on, for the rest of the network. 2.3 Learning Algorithm The procedure used to perform the learning process is called a learning algorithm, the function of which is to modify the synaptic weights of the network in an orderly fashion so as to attain a desired design objective. Many learning methods have been developed in the last few decades. Detailed information concerning possible ANN learning algorithms can be obtained from [3-5]. Two learning algorithms have been used for this project; the standard back-propagation (BP) method and the genetic algorithm (GA). BP has already been successfully applied by several researchers to solve some difficult and diverse problems by training ANNs in a supervised manner. With regard to the BP method, a training set is applied to the input of the network, signals propagate through the network and emerge as a set of output states. An error term is derived from the difference between the desired and actual output values and synaptic weights are then adjusted in accordance with an error correction rule. As the iteration proceeds, the overall error normally approaches zero. [3] However, the slow rate of learning and the possible premature convergence are limitations of the standard BP learning method. An alternative to the BP is the GA which is an evolutionary algorithm based on the concept of natural selection and evolution, described in [7], [8]. Evolutionary computing techniques are based upon Darwin’s theory of evolution where a population of individuals, in this case potential solutions, compete with one another over successive generations, ‘survival of the fittest’. After a number of generations, the best solutions survive and the less fit are gradually eliminated from the population [8]. As the GAs can prevent the local solutions when guided by the parallel search strategy, premature convergence can be avoided. The synaptic weights of ANNs are considered to be the chromosomes of an individual. A population of individuals constitutes a pool of potential solutions. Chromosomes are traditionally represented by binary numbers and standard crossover and mutation are employed as the re