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The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies. [ABSTRACT FROM AUTHOR]

Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI) control method is generally used for speed control of BLDC motor drivers. Although this method is easy to apply, the determined controller coefficients are generally constant and hence insufficient in dynamic changes. For this reason, methods that respond faster to dynamic changes, such as fuzzy-PI, have been proposed in the literature. Although the rule-based fuzzy controller increases its response ability to dynamic changes, determined rule-based coefficients affects the system performance completely. Therefore, the determination of the rule base values of the fuzzy controller is critical. In this paper, meta-heuristic Cuckoo Optimization Algorithm (COA) is proposed to determine the rule base values of the fuzzy controller for BLDC motor. Additionally, the rule-based table values of the fuzzy controller used for BLDC motor is determined using other meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA), Invasive Weed Optimization (IWO) and the results are compared. Finally, experimental studies for Pittman44 series BLDC motor are also carried out and the results are obtained. [ABSTRACT FROM AUTHOR]

The use of renewables can make it more challenging to maintain a balance between supply and demand in microgrids due to their variable generation profiles. To address this issue, microgrids can be designed to be grid-interactive or to include energy storage units, or both. Additionally, demand-side management (DSM) strategies can be implemented to facilitate control during peak periods. This paper presents a control system for a grid-interactive microgrid with photovoltaic (PV) panels and energy storage units. The proposed system uses a fuzzy-based algorithm to control the energy storage units and provides DSM through the use of a hybrid daily pricing model that combines multi-time rate and inclining block rate pricing methods. To optimize the global maximum power point of the PV panels under partial shading conditions, the microgrid model is tested with several metaheuristic optimization algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), and dragonfly algorithm (DA). A hybrid algorithm that combines PSO and DA is also proposed. The resulting integrated control system includes both demand-side and grid-side control operations. [ABSTRACT FROM AUTHOR]

Developing an optimization algorithm with independent algorithm-specific parameters is crucial in tuning the controller parameters of Automatic Generation Control (AGC). In 2020, Rao developed a robust and newly proposed algorithm independent of algorithm-specific parameters with faster convergence and less computational complexities. This paper presents the AGC of a sample two-area interconnected multi-source power systems with fractional-order PID controllers optimized using the Rao algorithm. The objective function selected for tuning the optimal gain of the controller using the Rao algorithm is based on Integral Squared Error (ISE). The robustness of the Rao algorithm for parameter variations and load changes is compared with other metaheuristic algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The efficacy of the proposed tuning method for AGC application is validated using the dSPACE 1103 hardware-in-loop simulation. [ABSTRACT FROM AUTHOR]

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly Algorithm. [ABSTRACT FROM AUTHOR]