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A two-stage circuit configuration with 3-phase utility grid assisted solar power generation system is designed. In order to track the solar PV arrays maximum peak power (MPP), a DC boost converter with Composite Incremental Conductance (InC) MPP technique is employed in first stage. In Second stage, a grid-tied four-leg DC-AC converter, is employed to provide the solar power that was extracted into the utility grid and to enhancing power quality. An adaptive hybrid Multi-Second Order Generalized Integrator-Quadrature (MSOGI-Q) control algorithm, in conjunction with Neuro Fuzzy system (NFS) is proposed for controlling this DC-AC converter. MSOGI-Q reference current generation strategy is designed to mitigate current harmonics by retrieving the fundamental constituents from non-linear load. The Neuro Fuzzy system along with PID controller, by adjusting the voltage from dc link keeps consistent power in between AC and DC sides for varying isolation condition. To get the peak power out of a PV panel even in different environmental conditions, the composite incremental conductance algorithm is used. To upgrade the dynamic performance of voltage variation at Point of Common Coupling (PCC) a feedforward descriptor for Photovoltaic contributions has been implemented. In addition, a comparison between the proposed NFS-MSOGIQ control technique and the existing adaptive control technique is done to assess the effectiveness of the proposed adaptive control technique. The proposed adaptive control technique for the distributed generation system is designed and stimulated in MATLAB/SIMULINK and the simulation outcomes verify the effectiveness of the proposed control technique under various transition circumstances. A laboratory prototype model of the proposed system is developed for theoretical analysis and its results are found to be compliant with IEEE standards. Furthermore, THD values of voltage and current are found to be within standard limits. [ABSTRACT FROM AUTHOR]

PID controllers play an important function in determining tuning parameters in any process sector to deliver optimal and resilient performance for nonlinear, stable and unstable processes. The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller. The Direct Multi Search (DMS) algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model. A Metaheuristics Algorithm such as, SA (Simulated Annealing), MBBO (Modified Biogeography Based Optimization), BBO (Biogeography Based Optimization), PBIL (Population Based Incremental Learning), ES (Evolution Strategy), StudGA (Stud Genetic Algorithms), PSO (Particle Swarm Optimization), StudGA (Stud Genetic Algorithms), ES (Evolution Strategy), PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) are used to tune the PID controller and Smith predictor design. The effectiveness of the suggested algorithms DMS-SA, DMS-BBO, DMSMBBO, DMS-PBIL, DMS-StudGA, DMS-ES, DMS-ACO, and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response. When compared to other optimizations, the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning. [ABSTRACT FROM AUTHOR]

In this research, UPQC (Unified Power Quality Conditioner) with optimized hybrid fuzzy controller based GBSSA (Gaussian Barebone Salp Swarm Algorithm) with EPLL(Enhanced Phase Locked Loop) have been proposed for power quality enhancement in power distribution networks. Using the proposed method, the difficulties in major of the power distribution system networks can be solved, related to power quality issues. GBSSA has been employed in this research, to improve solution accuracy and optimization efficiency. Given that, it is permissible to add some extra time cost to acquire a better solution, based on the Non-Free Lunch (NFL) theory, and that the time consumption of function evaluation is rather large, when addressing actual optimization problems, the extra time consumption can be overlooked to some extent. The EPLL control method improves the standard PLL, by reducing its fundamental flaw, which is the occurrence of main frequency errors, as well as double frequency errors. It controls the DC-bus voltage of unified power quality conditioners, during supply voltage and load voltage turbulences. The proposed UPQC control technique has been found to be resilient, to a variety of source and load perturbations, including unbalanced, transient distorted supply, voltage sag, unbalanced load and voltage swell. The proposed optimized GBSSA hybrid fuzzy controller with EPLL has been proven to be more effective in reducing the THD (Total Harmonic Distortion) to 3.22%. Moreover, comparative analysis with a conventional TSF-PLL has been performed with that of Takagi-Sugeno fuzzy controller and implemented using MATLAB (MATrix Laborator [ABSTRACT FROM AUTHOR]

This paper compares the effectiveness of the proposed hybrid metaheuristic algorithms for a class of unstable systems with time delay to that of the existing ones. The local search and global methods of optimization are combined to yield more effective hybrid metaheuristic algorithms. These algorithms are used to tune the proportional-integral-derivative (PID) controllers, satisfying the robust stabilizing vector gain margin (VGM). Six global heuristic algorithms namely ant colony optimization (ACO), particle swarm optimization (PSO), biogeography-based optimization (BBO), population-based incremental learning (PBIL), evolution strategy (ES), and stud genetic algorithms (StudGA) are combined with the local search property of derivative free optimization methods such as simplex derivative based pattern search (SDPS) and implicit filtering (IMF) to yield hybrid metaheuristic algorithms. The efficacy of the proposed control schemes in terms of various time domain specifications and stabilizing VGM are compared with some existing methods for unstable process with time delay (UPTD) systems. The performance of the proposed control schemes particularly in the context of uncertainty in the plant is demonstrated using a case study. The efficacy of the proposed control scheme is illustrated with a nontransfer function based multibody vehicle autosteer control design problem. [ABSTRACT FROM AUTHOR]

The tuning of controllers is a field of intrinsic research in the control and automation industry. Proportional-integral-derivative (PID) controllers and their variant forms have been much studied for automatic controller tuning owing to their simplicity and performance. The challenge in autotuning controllers is increased in systems that involve delay. Use of metaheuristics and soft computing methods are increasingly dominant in the autotuning of PID controllers. This paper deals with the design of a PID controller and Smith’s predictor for processes with delay. The algorithm employs a Stud genetic algorithm that is search intensified using derivative free search methods, namely simplex derivative pattern search and implicit filtering. The resulting algorithms are named as DFSGStudGA1 and DFSGStudGA2 respectively. The effectiveness of the discussed schemes are compared against some existing methods of controller tuning for robustness in terms of vector gain margin (VGM) and tolerance to plant uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a system for tuning PI controller of cascade control system. The tuning approach is based on Evolutionary Multiobjective Optimization Algorithm (EMOA). The EMOA used are NSGA-II (Non Dominated Sorting Genetic Algorithm-II) and NSPSO (Non Dominated Sorting Particle Swarm Optimization). In the proposed method, steady state analysis and disturbance rejection are the objectives considered. Therefore Multiobjective optimization technique is used. PI controllers are used for controlling both level and flow process. PI controllers are preferred for a fast process like flow, the reason being its faster than other conventional controllers. The multiobjective optimization results in approximation of Pareto fronts. The final optimal solution is selected based on the objectives. Simulation results obtained using NSGA-II and NSPSO are compared. [Copyright &y& Elsevier]