Quantum Machine Learning for Secondary Frequency Control

TL;DR

This paper presents a quantum machine learning approach using a variational quantum circuit for real-time secondary frequency control in power systems, improving response accuracy and stability.

Contribution

It introduces a purely quantum variational circuit model for frequency control, eliminating classical-quantum latency and enhancing real-time applicability.

Findings

Achieves over 90% prediction accuracy with sufficient measurement shots

Generalizes well across diverse test scenarios

Significantly improves transient response and reduces frequency fluctuations

Abstract

Frequency control in power systems is critical to maintaining stability and preventing blackouts. Traditional methods like meta-heuristic algorithms and machine learning face limitations in real-time applicability and scalability. This paper introduces a novel approach using a pure variational quantum circuit (VQC) for real-time secondary frequency control in diesel generators. Unlike hybrid classical-quantum models, the proposed VQC operates independently during execution, eliminating latency from classical-quantum data exchange. The VQC is trained via supervised learning to map historical frequency deviations to optimal Proportional-Integral (PI) controller parameters using a pre-computed lookup table. Simulations demonstrate that the VQC achieves high prediction accuracy (over 90%) with sufficient quantum measurement shots and generalizes well across diverse test events. The quantum-optimized PI parameters significantly improve transient response, reducing frequency fluctuations and settling time.