Enhancing legacy OLTC performance through Cost-effective retrofitting: a field-tested approach
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Abstract
Legacy On-Load Tap Changer (OLTC) systems in distribution substations are often vulnerable to phase reversal errors, voltage fluctuations, and mechanical degradation, particularly in resource-constrained environments. These challenges degrade voltage regulation accuracy and accelerate wear in OLTC components due to excessive or incorrect tap operations. This paper presents a cost-effective retrofit control strategy designed to enhance the reliability and performance of mechanical OLTC systems. The proposed solution integrates real-time phase sequence detection, adaptive tap control logic using hysteresis thresholds, and automatic motor direction correction to prevent erroneous tap movements. A comprehensive MATLAB-based simulation validates the methodology under various operational scenarios, including reverse power flow, phase reversal, and voltage sags/swells. The strategy is also experimentally deployed in a live 11 kV/433 V university distribution substation. It covers diverse loads such as administrative cum academic blocks, laboratories, guest house cum hostels, and HVAC systems. In this university campus scenario, it demonstrates a 66 % reduction in tap changes, enhanced Mean Time Between Failures (MTBF), and 80 % fewer manual interventions. The outcomes confirm that the proposed retrofit enhances voltage stability, operational efficiency, and system longevity, making it a scalable and economically viable solution for ageing power distribution infrastructure.
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Research ethics: Not applicable.
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Informed consent: All three author have the consent for the submission.
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Author contributions: Sadheesh Kumar S. J: Idea, Conceptualization, Experimentation, Drafting. Ram Jethmalani C.H : Conceptualization, Revision, Experiment design. Navin Sam K : Idea, Revision, Supervision. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: NIL.
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Conflict of interest: Authors have no conflict of intrest.
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Research funding: NIL.
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Data availability: Will be provided on request.
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