Enhancement of a Hybrid Electric Shipboard Microgrid’s Frequency Stability With Triangulation Topology Aggregation Optimizer-Based 3DOF-PID-TI Controller

This work addresses the important issue of frequency stability in shipboard microgrids (SMGs), which are confronted with difficulties because of the alternating power insertion from green sources and low system inertia. In order to sustain a consistent frequency (despite system uncertainties), a study and precisely calibrated regulator is necessary. Therefore, this article proposed a new combined three degree of freedom proportional integral derivative (3DOF-PID) and tilted-integral (TI) regulators for achieving enhanced system performance, robustness, and enhance the frequency performance of a power grid. The superior performance of the suggested 3DOF-PID-TI has been assured by comparing it with three distinct controller architectures (FOPID, PDPID2 and 2DOF-PID) for the multi-energy SMG system. The investigation also took into account the temporal delays brought on by the communication links between the sensor and the regulator. The triangulation topology aggregation optimizer (TTAO), a relatively new meta-heuristic technique that hasn’t been employed to load frequency control (LFC) issues until recently, was employed to adjust the controllers’ parameters. The TTAO’s frequency performance outcomes were thoroughly contrasted to those of other optimization algorithms (i.e., Chimp, Whale, and Gradient-Based Optimization Algorithms) so as to evaluate the optimization efficacy of the suggested optimizer. The findings showed that the proposed 3DOF-PID-TI controller, whose parameters has been tuned by TTAO, outperformed its competitors in terms of overshoot (15.87mHz), undershoot (-29.04mHz), settling time (2.49sec) and performance index (i.e., ITAE(0.0171)). Additionally, the suggested controller’s robustness was assessed in a range of SMG operating situations as ex. (Random Multi-Step Variation in Load, Real Data on Stochastic Power Fluctuations, Energy Storage System Impact on the system and sensitivity analysis). The acquired data amply proved that when crucial system parameters experience a substantial variation, the controller’s gains set under normal circumstances do not need to be re-tuned.