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Khem Raj Mahawar
Dr. Bharat Bhushan Jain
Keywords:
Distribution Network; Hilbert Transform; Power Quality; Solar Energy; Stockwell Transform; Rule Based Decision Tree; Wind Energy.
Abstract:
Faults in transmission and distribution networks can significantly affect the reliability, stability, and continuity of electrical power supply, potentially resulting in service interruptions and large-scale blackouts. Rapid fault detection, accurate fault classification, and precise estimation of fault location are therefore essential for improving power-system protection, reducing restoration time, and enhancing network reliability. The increasing integration of renewable energy sources, such as solar photovoltaic and wind energy systems, introduces distributed generation (DG) into conventional distribution networks and significantly changes their electrical characteristics and fault behavior. The bidirectional power flow, variable generation, and altered fault-current characteristics associated with DG can also influence the performance of conventional protection and fault-detection schemes. This paper presents a comparative analysis of transformation-based fault detection techniques for renewable energy-integrated distribution networks. Various signal transformation methods are investigated for extracting significant time-domain and frequency-domain characteristics from electrical signals during normal and fault conditions. The study considers the applicability of transformation techniques for detecting and distinguishing different types of power-system faults under changing network conditions. The extracted features are further analyzed using suitable decision-based fault detection approaches to improve the identification of fault events. The comparative evaluation highlights the capabilities and limitations of the investigated transformation techniques in terms of fault detection accuracy, sensitivity, and suitability for renewable-integrated distribution systems. The study demonstrates that transformation-based signal analysis can provide an effective approach for reliable and rapid fault detection in modern distribution networks with distributed renewable energy generation.
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International Journal of Recent Research and Review
ISSN: 2277-8322
Vol. XIX, Issue 3
September 2026
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PUBLISHED
September 2026
ISSUE
Vol. XIX, Issue 3
SECTION
Articles
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