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Optimal predictive load frequency control with multi-objective PID-based search algorithm
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DOI：10.1016/j.swevo.2025.102214.png)
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Abstract
En 中文
Maintaining frequency stability in modern interconnected power systems (IPSs) has become increasingly challenging due to growing system complexity, the integration of variable energy sources, and frequent load fluctuations, particularly in manufacturing and industrial environments where power quality is critical. To address this challenge, we propose an optimal predictive load frequency control (LFC) framework that combines real-time forecasting with multi-objective controller optimization. An online extreme learning machine (ELM) predicts short-term load deviations, enabling proactive regulation. The control structure adopts a cascaded fractional-order design, integrating FOPI and FOPID controllers. These controllers are optimized using a multi-objective PID-based search algorithm (MOPSA), which simultaneously minimizes the integral of time-weighted absolute error (ITAE), enhances the damping ratio, and reduces the cost of energy storage operation. Fast-response energy storage devices (ESDs) are further coordinated to buffer transient imbalances. Simulation results demonstrate that the proposed FOPI–FOPID controllers with ESDs reduce ITAE by 90% (from 464.99 to 44.65) and shorten frequency settling time by 81% (from 83.03 s to 15.49 s), significantly outperforming benchmark methods such as DSA:FOPI–FOPD. These findings confirm the proposed framework’s ability to deliver precise control in dynamic multi-region IPS environments.
Keywords:
Load frequency control
Multi-objective optimization
Predictive control
Extreme learning machine
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