# A hybrid fuzzy-PIDD² control strategy for coordinated LFC and AVR in renewable-integrated multi-area power systems

## TL;DR

This paper introduces a hybrid control strategy combining fuzzy and conventional controllers to improve power system stability with high renewable energy.

## Contribution

The novel hybrid FPIDD²+PIDD² control strategy improves dynamic stability and robustness in renewable-integrated power systems.

## Key findings

- The hybrid controller reduces ITAE by up to 94% compared to conventional PID controllers.
- It achieves a 90% improvement in ITAE over standalone PIDD² controllers.
- The controller shows enhanced damping and robustness against system uncertainties.

## Abstract

This paper presents a novel hybrid control strategy that integrates a Fuzzy Proportional–Integral–Derivative Double Derivative (FPIDD²) controller with a conventional PIDD² controller for Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) in multi-area interconnected power systems, respectively. The proposed FPIDD²+PIDD² hybrid scheme enhances the overall dynamic stability and robustness of power systems operating under high renewable energy penetration. Controller parameters are optimally tuned using different metaheuristic algorithms, namely the Particle Swarm Optimization (PSO), Gorilla Troops Optimizer (GTO), and Marine Predators Algorithm (MPA). The proposed hybrid controller’s performance is evaluated against conventional PID and standalone PIDD² controllers under various disturbances, including step load changes, random load variations, and renewable energy fluctuations, where control performance is evaluated based on the Integral of Time-weighted Absolute Error (ITAE) criterion. Within the simulated test cases, the proposed FPIDD²+PIDD² controller achieves notable performance improvement, reducing ITAE by up to 94% and 90% compared to conventional PID and standalone PIDD² controllers, respectively. These results confirm the hybrid controller’s smooth transient response, enhanced damping, and improved robustness against nonlinearities and system uncertainties.