Abstract
The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.
Highlights
In recent years, the proliferation of the grid integrated renewable energy (RE) sources are increasing in the low and medium voltage utility grids to meet the energy demand
The researchers are focused on the advancement of signal processing based detection techniques and artificial intelligence-based classification techniques for smart utility grids, which promises an effective solution to the monitoring of power quality (PQ) challenges in the smart grid [4]
Tabled data explains the details of hardware/real-time system, which include technical parameters, features used in various PQ monitoring techniques even in noisy conditions for benefiting the beginners and engineers for selecting specific PQ detection and classification technique and other equipment based on the hardware data used in previous researches
Summary
The proliferation of the grid integrated renewable energy (RE) sources are increasing in the low and medium voltage utility grids to meet the energy demand. The most desired features in smart grid monitoring and operation are fast response and adaptation of detection and classification techniques with the changes associated with renewable energy penetration, noise and loads. The researchers are focused on the advancement of signal processing based detection techniques and artificial intelligence-based classification techniques for smart utility grids, which promises an effective solution to the monitoring of PQ challenges in the smart grid [4]. The new scheme can remove disturbance features altogether, and has more eminent signal processing performance compared with conventional ST and EMD methods [7] These image recognition methods have been observed as another possible solution for PQ monitoring with RE integration. The motivation of this article is to present a comprehensive review of detection and classification techniques for PQDs with RES in the utility grid.
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