Open Access

ARTICLE

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

by Saurabh Awasthi^*, Gagan Singh, Nafees Ahamad

Department of Electrical and Electronics & Communication Engineering, DIT University, Dehradun, 248001, India

* Corresponding Author: Saurabh Awasthi. Email:

(This article belongs to the Special Issue: Hybrid Artificial Intelligence and Machine Learning Techniques in Renewable Energy Systems )

Energy Engineering 2023, 120(4), 811-829. https://doi.org/10.32604/ee.2023.026863

Received 29 September 2022; Accepted 12 December 2022; Issue published 13 February 2023

Abstract

A distributed generation system (DG) has several benefits over a traditional centralized power system. However, the protection area in the case of the distributed generator requires special attention as it encounters stability loss, failure re-closure, fluctuations in voltage, etc. And thereby, it demands immediate attention in identifying the location & type of a fault without delay especially when occurred in a small, distributed generation system, as it would adversely affect the overall system and its operation. In the past, several methods were proposed for classification and localisation of a fault in a distributed generation system. Many of those methods were accurate in identifying location, but the accuracy in identifying the type of fault was not up to the acceptable mark. The proposed work here uses a shallow artificial neural network (sANN) model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators. Firstly, a distribution network consisting of two similar distributed generators (DG1 and DG2), one grid, and a 100 Km distribution line is modeled. Thereafter, different voltages and currents corresponding to various faults (line to line, line to ground) at different locations are tabulated, resulting in a matrix of 500 × 18 inputs. Secondly, the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train, validate, and test the neural network. The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

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Keywords

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