Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System

130

Summary Power quality (PQ) studies have gained huge attention from the academics and the industry over the past three decades. The main objective of this article is to provide a comprehensive review on the state‐of‐the‐art techniques based on digital signal processing (DSP) and machine learning for automatic recognition of PQ events. It is aimed to present extensive information on the status of detection and classification of PQ events to the academics following a line of investigation on the similar domain. On the other hand, microgrid is one of the emerging architecture under the umbrella of smart grid infrastructure. In microgrid environment, the integration of renewable energy sources and distributed generators boosts the application of power electronic technology, which is the main cause of various PQ issues. Therefore, various PQ detection and classification (PQD&C) schemes for microgrid application using DSP and machine learning techniques have been reviewed in this article. Moreover, a comparative assessment on various PQD&C techniques has been carried out in details considering several criteria, such as type of data used (synthetic or real world), type of PQ disturbance studied (single or multiple), and performance in terms of accuracy (both noiseless and noisy environment). Hence, several types of research work in PQD&C area, such as the detection principles, benefits, and weaknesses of former works related to each PQD&C technique, are tinted in this manuscript. Therefore, from this review, the opportunities in PQD&C research domain in the power system can be explored further.

Section: Wavelet Transform and Its Variants' Based Methodsmentioning

confidence: 99%

Section: Variational Mode Decomposition-based Methodsmentioning

confidence: 99%

Section: Pq Issues Influenced By Dgs and Application Of Various Pqdmentioning

confidence: 99%

Section: Pq Issues Influenced By Dgs and Application Of Various Pqdmentioning

confidence: 99%

See 2 more Smart Citations

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

Mishra

2019

130

“…Relative energy ratio (RER), mode instantaneous amplitude (IA), number of zero crossing, and center frequency (CF) from the each of the modes. 18 Abbreviations: HHT, Hilbert-Huang transform; ST, S-transform; VMD, variational mode decomposition; WT, wavelet transform.…”

Section: Vmdmentioning

confidence: 99%

Advanced signal processing and machine learning techniques for voltage sag causes detection in an electric power system

Mishra

Panigrahi

2019

Summary In this manuscript, voltage sag causes (VSCs) detection in an electric power system is studied using S‐transform (ST) and variational mode decomposition (VMD) techniques. The advantages of these approaches are compared in detail against earlier implemented wavelet transform (WT), empirical mode decomposition (EMD), and Hilbert‐transform (HT) techniques, as a novel contribution to previous studies. Voltage sag can trigger periods of downtime, considerable damage of product and moreover, it can attribute to malfunctions, instabilities, and decrease the lifespan of the connected loads. Therefore, accurate detection of VSCs can avoid the loss and problems instigated by voltage sag in an electric power system. In this work, initially, the extracted disturbance voltage signals from the relaying point are passed through the aforementioned signal processing algorithms to extract unique features. Then, these extracted features are used as inputs to the extreme learning machine (ELM) classifier for recognition of VSCs. Additionally, to augment the precision of the output result, the performance of ELM classifier is compared with the artificial neural network (ANN), K‐nearest neighbors (KNN), and support vector machine (SVM) classifier. Three types of VSCs are used in this analysis such as (a) sag due to incipient fault, (b) sag due to induction motor stating, and (c) sag due to transformer energization. The simulation results show good performance and feasibility of the stated methods for VSCs recognition even under in cooperation of noisy condition.

Islanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression

Dash

Satapathy

Nayak

2020

Summary This article presents the detection and classification of islanding and power quality (PQ) disturbances for a multiple distributed generation based using an adaptive cross variational mode decomposition (XVMD) with reduced kernel ridge regression (RKRR). This article considers photovoltaic as the primary DG and studied the effect of the solar irradiation variation. Further the considered microgrid is subjected to different operating conditions pertaining to both islanding and non‐islanding disturbances. The proposed XVMD is an extension of VMD and with RKRR, it is a new contribution to the earlier studies to deal dynamic detection threshold requirement. The XVMD based RKRR needs simple features to classify the disturbances in less time as compared to the VMD based methods. The features to classify the events are calculated from the optimal mode selected by the hybrid firefly algorithm. For the performance analysis, voltage signals across the PV based DGs are obtained by initiating various power qualities and islanding disturbances. A validation case study through TMS 320C6713 Starter Kit (DSK) is presented for capacitor switching. The robustness of the proposed XVMD‐RKRR is observed from a large number of numerical experimentation by comparing it with other well‐known approaches.