A predictive OLTC control strategy for voltage regulation with reduced tap changes in solar PV – integrated 11 kV/433 V distribution substation

Abstract

The mass deployment of solar photovoltaic generation in distribution networks induces rapid voltage variations, reverse power flow, and frequent on-load tap changes, thereby undermining voltage quality and the longevity of on-load tap changing units. Classical on-load tap-changing controllers can only act on instantaneous voltage readings and, as a result, exhibit a reactive response to disturbances caused by solar photovoltaic generation, inevitably leading to unnecessary tap changes and tap-hunting behaviour. The current work presents a predictive control algorithm based on a family of rules that follows the principles of model predictive control logic and tap-budget control, similar to the token-bucket algorithm used in computer networks. This on-load tap changing-predictive control algorithm model includes: (i) a low-price, field-tested on-load tap change retrofit controller with phase-sequence detection and hysteresis-based intelligent tap logic, and (ii) short-term solar photovoltaic generation voltage prediction based on different deep learning techniques. The tap actions are also activated when the measured and predicted deviations exceed their respective limits, making predictive voltage regulation possible and reducing mechanical wear and tear. When testing, validation is performed using a 1-year dataset for an 11 kV/433 V campus feeder with a 2 MW peak load and optional 0.5, 1, and 1.5 MW distributed solar photovoltaic plants. Findings indicate improved voltage regulation performance, with approximately 57 % fewer tap operations, while maintaining stable voltage regulation under reverse power flow conditions and phase reversal conditions. The suggested methodology provides a scalable, cost-effective solution for modernizing on-load tap-changing systems in distribution networks in developing regions.