Dynamic Control of Isolated Network Microgrids: A Resilient Backpropagation Neural Network-Based Virtual Inertia Control Approach

In isolated networked microgrid with high penetration of solar and wind-based generation, maintaining system stability and achieving optimal dynamic performance poses significant challenges due to reduced mechanical inertia traditionally provided by synchronous generators. This paper introduces a novel Resilient Back Propagation Bayesian Neural network-based virtual inertia control strategy to enhance frequency response and overall stability of the considered network microgrid. By leveraging robust control techniques, the proposed approach provides virtual inertia to respond effectively to varying system conditions and disturbances, improving system robustness and minimising the isolated networked microgrid’s tie-line power and frequency deviations. Comprehensive simulations, including case studies under varying disturbance conditions such as load fluctuations and renewable energy variations, prove the efficacy of the introduced virtual inertia control strategy. The proposed method outperforms conventional proportional integral derivative, and artificial bee colony-optimised proportional integral derivative control techniques in key dynamic performance metrics. It achieves the lowest integral time absolute error of 18.3912, compared to 30.8946 for proportional integral derivative and 20.8212 for optimised proportional integral derivative control techniques, demonstrating superior frequency response. Additionally, it achieves a mean square error of 4.0897e-07, significantly lower than 4.239e-06 for neural network-based fractional order proportional integral derivative control and 3.072e-06 for feed-forward neural network, confirming improved accuracy. Results indicate improved dynamic performance metrics, including faster frequency stabilization and reduced overshoot with minimal tie-line power and frequency deviation, compared to proportional integral derivative and optimal control techniques. The strategy’s adaptability and computational efficiency offer practical benefits for real-world implementation, contributing to more resilient and efficient microgrid operations.