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LWT Based ANN with Ant Lion Optimizer for Detection and Classification of High Impedance Faults in Distribution System

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Abstract

In this paper, the proposed wavelet-based methodology is developed to identify and classify the High Impedance Fault (HIF) in the Power Distribution System (PDS). The planned technique is based on the combination of Ant Lion Optimizer (ALO) and Artificial Neural Network (ANN), which is performed to accurately isolate the HIF. The change in phase current waveforms caused by faults and normal switching events has been used in this methodology. In order to develop the method to detect high impedance arcing faults under the linear conditions. The faults are identified through the computation of the basic electric descriptions of current and voltage signals. From the voltage and current signals, the harmonic components also computed. From the voltage, current signals, the fault are identified and classified in the system which can be able to solve the problem in the system. The harmonics level also analyzed which also detected and able to correct it for enabling the stable operation in the system. ANN is an Artificial Intelligence (AI) method that applied for optimizing precise generation limits as blocking happened. The neural network contains two stages: training stage and testing stage. Here, the ALO algorithm is utilized to improve the performance of the ANN training process. ALO is a new nature-inspired algorithm mimicking the hunting behavior of ant lions. The design of Lifting Wavelet Transform (LWT) is suitable for the classification process. The main objective of ANN with the aid of the ALO algorithm is the detection and classification of the HIF in PDS and analyzed the delay time of different locations. From the evaluation of the proposed technique, the inputs and their corresponding outputs are noted. The performance of the work is implemented in MATLAB/Simulink platform and the presentation of this model is investigated on the basis of the two cases of analysis. The results show that the projected algorithm detects the HIFs accurately and compared with the existing methods ALO, GSA and ANN, and GA and Fuzzy, respectively.

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Abbreviations

HIF:

High impedance fault

PDS:

Power distribution system

AI:

Artificial intelligence

LWT:

Lifting wavelet transform

DG:

Distributed generators

LDCs:

Local distribution companies

SCADA:

Supervision control and data acquisition

ANN:

Artificial neural network

IDE:

Integrated development environment

PQDSZ:

Quasi-differential zero sequence protection

MV:

Medium-voltage

ALO:

Ant lion optimizer

MLP:

Multilayer perceptron

BP:

Back-propagation

MSE:

Mean square error

\(V_{q\left( k \right)}^{abc}\) :

\(V_{{q\left( {HIF} \right)}}^{abc}\)HIF fault voltages

\(Z_{qq}^{abc}\) :

Driving point impedance

\(Z_{HIF}^{abc}\) :

HIF fault impedance

\(\,I_{HIF}^{abc}\) :

HIF fault current

\(V_{{g\left( {HIF} \right)}}^{abc}\) :

Voltage HIF fault point g

\(F\left( S \right)\) :

Fitness function

\(\alpha_{i} = \left\{ {\alpha_{1} ,\,\alpha_{2\,,\,} ....\alpha_{N} } \right\}\) :

ANN output

\(N_{H\,}\) :

Number of hidden neurons

\(d_{OUT}\) :

Output from jth output neuron

\(w_{ij}\) :

Weight of i–j link of the network

\(\alpha_{i}\) :

Output of ith the hidden neurons.

\(c\) :

Input variable

\(\eta\) :

Learning rate

\(\gamma^{t}\) :

Minimum of all variable of tth variable

\(\lambda^{t}\) :

Maximum of all variables in tth an iteration

\(t\) :

Current iteration

\(Antlion_{j}^{t}\) :

Position of selected jth ant-lion at tth iteration

\(Ant_{j}^{t}\) :

Position of ith ant at tth iteration

\(RM_{antlion}^{t}\) :

Random walk around the antlion selected using the roulette wheel at tth iteration

\(R_{Elite}^{t}\) :

Random walk around the elite at tth iteration

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Authors and Affiliations

  1. JNTUA, Ananthapuram, Andra Pradesh, India

    N. Narasimhulu & M. Vijay Kumar

  2. Administration and Placements RGMCET, Nandyal, Andra Pradesh, India

    D. V. Ashok Kumar

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  1. N. Narasimhulu
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Correspondence to N. Narasimhulu.

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Narasimhulu, N., Kumar, D.V.A. & Kumar, M.V. LWT Based ANN with Ant Lion Optimizer for Detection and Classification of High Impedance Faults in Distribution System. J. Electr. Eng. Technol. 15, 1631–1650 (2020). https://doi.org/10.1007/s42835-020-00456-z

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