Causative Fault Classification and Optimal Placement of Lightning Arrester for Minimizing the Risk of Failure in Transmission Systems
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Date
2019
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Department of Electrical Engineering, NITS
Abstract
This project deals with classification and mitigation of faults arising in a transmission network. Response of operators to a fault depends upon the type of fault that has occurred itself. Response to a vegetative fault will be much different than that of lightning induced faults. Currently causative fault classification is only possible by physically visiting a place where the fault has occurred. However if the fault can be classified from the fault data itself, it would massively speed up system response and enhance smoother operation of transmission apparatus. Causative fault classification has been done in this project by utilizing Artificial Neural Network (ANN). With the help of ANN, fault classification with an exceedingly high accuracy (99.5% accuracy) has been achieved in this project. The project also describes an economical solution for transmission network protection against lightning surges and strokes by selective installation of arresters. To achieve this cherished goal, a probabilistic methodology based on IEEE Std. 1410 has been applied to the Northern Grid of West Bengal and an optimisation process based on Binary Bat Algorithm has been developed and applied to solve the stated problem. The proposed method considers technical, meteorological, geographical and economic criteria to achieve the best solution. Consequently, the costs of investment and the total annual costs including line investment, line maintenance and outage costs are being decreased taking into consideration the cost of protective devices. In the optimisation procedure, lightning occurrence and network characteristics as well as transmitted power are considered. The achieved outcomes confirm effectiveness of the suggested approach from technical, meteorological, geographical and economical point of view.