Leakage Current Prediction of Composite Insulator using Artificial Neural Network
Krishna Patel1, Bhupendra Parekh2, Dinesh Kumar3
1Krishna Patel, Department of Electrical Engineering, Marwadi University, Rajkot, India.
2Dr. Bhupendra Parekh, Department of Electrical Engineering, Birla Vishvkarma Mahavidyalaya, V.V. Nagar, India.
3Dr. Dinesh Kumar, Department of Electrical Engineering, Marwadi University, Rajkot, India.
Manuscript received on 17 March 2019 | Revised Manuscript received on 23 March 2019 | Manuscript published on 30 July 2019 | PP: 6258-6266 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3746078219/2019©BEIESP | DOI: 10.35940/ijrte.B3746.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The uninterrupted power supply is one of the main concerns for the power utility which is often adversely affected by the flashover of the outdoor insulators. The main cause of the flashover of outdoor insulator is the contaminated pollution on its surface which is more severe for the insulators located nearer to the seashore. At such sites, insulators have to be washed regularly to avoid flashover which is an expensive and time-consuming process. So it is required to optimize the washing schedule. When the contamination is severe on the surface of the insulator, it allows the flow of the leakage current (LC) which turns into the flashover. The LC is a good indication for predicting the flashover. LC measurement and instrumentation system in real tower insulator is complex and expensive. In this paper, a prediction method is developed which predicts the LC at the level of starting of the arc which may turns into a flashover. An artificial neural network based model is developed which predicts the leakage current for the different polluted condition and humidity level. An experimental setup is prepared and subsequently data is taken to acquire LC on different relative humidity and equivalent salt deposit density (ESDD). Subsequently, a neural network model is constructed with the experimental data to predict LC. A single layer feed forward network based a predictive model performs LC prediction with the average error of 5.46% from the real values which is acceptable in case of alarming situations.
Index Terms: ANN, ESDD, Flashover, Insulator, Leakage Current
Scope of the Article: Artificial Intelligence