Open Access

ARTICLE

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

Nagendra Kumar Swarnkar¹, Om Prakash Mahela², Baseem Khan^3,*, Mahendra Lalwani¹

1 Department of Electrical Engineering, Rajasthan Technical University, Kota, India
2 Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Limited, Jaipur, India
3 Department of Electrical and Computer Engineering, Hawassa University, Awassa, Ethiopia

* Corresponding Author: Baseem Khan. Email:

Energy Engineering 2022, 119(2), 473-497. https://doi.org/10.32604/ee.2022.017703

Received 31 May 2021; Accepted 23 July 2021; Issue published 24 January 2022

Abstract

A multiple power quality (MPQ) disturbance has two or more power quality (PQ) disturbances superimposed on a voltage signal. A compact and robust technique is required to identify and classify the MPQ disturbances. This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality (MPQ) disturbance using stockwell transform (ST) and hilbert transform (HT). This will reduce the computational time to identify the MPQ disturbances, which makes the algorithm fast. A MPQ identification index (IPI) is computed using statistical features extracted from the voltage signal using the ST and HT. IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances. A MPQ time location index (IPL) is computed using the features extracted from the voltage signal using ST and HT. IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time. Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio (SNR). The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree (RBDT) is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances. MATLAB software is used to perform the study.

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Keywords

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