Neuro-Fuzzy Based High-Voltage DC Model to Optimize Frequency Stability of an Offshore Wind Farm

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A New Load Frequency Control Technique for Hybrid Maritime Microgrids: Sophisticated Structure of Fractional-Order PIDA Controller

This paper proposes an efficient load frequency control (LFC) technique based on a fractional-order proportional–integral–derivative–accelerator with a low-pass filter compensator (FOPIDA-LPF) controller, which can also be accurately referred to as the PIλDND2N2 controller. A trustworthy metaheuristic optimization algorithm, known as the gray wolf optimizer (GWO), is used to fine-tune the suggested PIλDND2N2 controller parameters. Moreover, the proposed PIλDND2N2 controller is designed for the LFC of a self-contained hybrid maritime microgrid system (HMμGS) containing solid oxide fuel cell energy units, a marine biodiesel generator, renewable energy sources (RESs), non-sensitive loads, and sensitive loads. The proposed controller enables the power system to deal with random variations in load and intermittent renewable energy sources. Comparisons with various controllers used in the literature demonstrate the excellence of the proposed PIλDND2N2 controller. Additionally, the proficiency of GWO optimization is checked against other powerful optimization techniques that have been extensively researched: particle swarm optimization and ant lion optimization. Finally, the simulation results performed by the MATLAB software prove the effectiveness and reliability of the suggested PIλDND2N2 controller built on the GWO under several contingencies of different load perturbations and random generation of RESs. The proposed controller can maintain stability within the system, while also greatly decreasing overshooting and minimizing the system’s settling time and rise time

Nonlinear coordination strategy between renewable energy sources and fuel cells for frequency regulation of hybrid power systems

This study proposes an advanced control strategy for the coordination of an energy storage system (ESS) based on fuel cells (FCs) and renewable energy sources (RESs) to enhance frequency dynamic performance in hybrid power systems (HPSs). The proposed coordination control strategy is based on the nonlinear proportional-integral (NPI) controller, which increases the system's flexibility in dealing with disturbances and changing operating conditions. In addition, it improves the system's dynamic response and attempts to address system weakness caused by highly penetrating RESs. The proposed NPI controller is optimally designed using a new optimization algorithm, called dandelion optimizer (DO), whose proficiency and effectiveness are verified by comparing its performance with other well-known optimization algorithms used in the literature; particle swarm optimization (PSO), grey wolf optimization (GWO), and ant lion optimization (ALO) algorithms considering various standard objective functions. Furthermore, the proposed NPI controller performs better than other control strategies used in the literature under load/RESs fluctuations. The effectiveness of the proposed nonlinear coordination control strategy is examined and investigated through a self-contained HPS that includes a diesel generator, RESs (i.e., photovoltaic and wind power plants), battery ESS, flywheel ESS, aqua electrolyzer for hydrogen production, FCs, electric vehicles, and customer loads. The simulation results carried out by the MATLAB software demonstrate the superior performance of the proposed DO-optimized NPI controller for HPS frequency regulation, even when the power system's parameters have substantial variations. Moreover, the results revealed that the proposed strategy significantly reduces the frequency deviation by approximately 95% compared to the conventional coordination strategy based on the fixed contribution of RESs and by 90% compared to the adaptive coordination control based on the PI controller