Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation

Abstract

This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system. INDEX TERMS distributed generators; sunflower optimization algorithm; microgrid; renewable energy; coot bird metaheuristic optimizer.

Consolidation of LVFRT capabilities of microgrids using energy storage devices

“…Further to the above-mentioned, the flowchart shown in Fig. 14 depicts the general procedures of the CBO and detailed instruction [57]. The objective is to minimize the ripple factor (RF) of the DC link voltage for three studied systems.…”

Integration of charging station to the smart grid power network by using robust immune feedback adaptive and Rao optimization approaches

“…The performance criteria of the Rao1 algorithm is identified by using the benchmark objective function which is estimated as the integrated time square error (ITSE), and it is given as, 25 min…”

“…The performance criteria of the Rao1 algorithm is identified by using the benchmark objective function which is estimated as the integrated time square error (ITSE), and it is given as, 25 where “e” is the error between the sensed and estimated DC bus voltage and “t” is the run time of the simulation. The objective function of the Rao algorithm is to minimize the error between the sensed and estimated DC bus voltage and the error between sensed battery current and reference battery ( e ).…”

“…The water cycle technique is presented to identify the gains for the PI and fractional‐order PI (FOPI) controllers in Reference 24. In Reference 25, the optimum values for PI controllers are obtained by using the coot bird metaheuristic optimizer for a microgrid with different conditions. In Reference 26, the authors have proposed the Rao1, Rao2, and Rao3 algorithms for solving the optimization base problems.…”

Hybrid Optimized PI Controller Design for Grid Tied PV Based Electric Vehicle

“…The issues faced by conventional PI controllers are solved and the performance is enhanced by using metaheuristic algorithms, which are simple, robust and easy to implement [18][19][20][21]. Some of the metaheuristic algorithms that are adopted to tune the PI parameters more accurately are Grey Wolf Optimization (GWO) [22], Whale Optimization Algorithm (WOA) [23], Ant Colony Optimization (ACO) [24], Coot Bird Metaheuristic Optimizer (CBMO) [25], etc. For increasing the optimization efficiency in a wider range the hybrid algorithms are introduced by combining different algorithms.…”