Fault prediction model on electrical power network using artificial neural network-based time series: A case study of Ayede-Eruwa/ Lanlate Feeder (original) (raw)
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Power System Fault Prediction Using Artificial Neural Networks
The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary 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. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector 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. Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port Π circuit) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher. 1.0 Introduction Adequate fault detection is vitally important to ensure reliable power system operation. Many system 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. To date, few studies have been made concerning early fault detection (EFD) techniques which facilitate the prediction of a major fault before it actually occurs. In a typical power system, the states (voltages and currents) of most bus bar nodes are monitored and gradual changes are analysed. However, because of the complexity of recorded data, faults at an early stage cannot be easily recognised. These faults can be disguised by the complexity of power system operational data [1-6]. The aim of the EFD method is to detect and alert the operator before a catastrophic fault actually occurs. In other words, this is an early warning fault prevention method. ANNs are employed to monitor the states of some important components in power networks, such as switchgear and transformers. The ANN is trained to detect minor changes to the internal parameters modelled as power system equivalent circuits [6]. The small variations of voltages and currents resulting from internal parameters changes, at sending end and receiving ends of the power system can be derived under simulation and then presented to the ANN for training. As some of the internal parameters of the power system do not physically exist, they cannot be measured directly by simple measurement methods. Thus, the application of an intelligent technique, such as an ANN method , is obviously required. The principle of the EFD can be applied to various sections of a power system. A typical extremely simplified example will now be given. Transmission lines in power systems carry high currents and voltages. Small changes in state, caused by partial faults, on transmission lines are often too insignificant to trigger the conventional protection systems. However, these small scale changes may develop and eventually lead to major faults. For example, in winter, snow may gradually accumulate on transmission lines. The impedance of transmission lines could change accordingly. The circuit breaker would trip when the snow formed a short-circuit and this could " blackout " a large area. With early warning fault monitoring, the interruption of power supply could possibly be prevented. The change of impedance of the transmission line provides vital information which can be analysed by EFD technique to provide an early detection capability. This technique 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. 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. 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
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Electric power Transmission lines are characterized by very lengthy transmission lines and thus are more exposed to the environment. Consequently, transmission lines are more prone to faults, which hinder the continuity of electric power supplied, increases the loss of electric power generated and loss of economy. Quick detection and classification of a fault hastens its Clearance and reduces system downtime thus, improving the security and efficiency of the network. Thus, this paper focuses on developing a single artificial neural network to detect and classify a fault on Nigeria 33-kV electric power transmission lines. This study employs feedforward artificial neural networks with backpropagation algorithm in developing the fault detector- classifier. The transmission lines were modeled using SimPowerSystems toolbox in Simulink and simulation is done in MATLAB environment. The instantaneous voltages and currents values are extracted and used to train the fault detector-classifier....
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