Optimally Designed PID Controller for a DC-DC Buck Converter via a Hybrid Whale Optimization Algorithm with Simulated Annealing

Curvelet transform is a multiscale directional transformer, which allows optimal non-adaptive sparse representation of object with edge. In this paper, a new image fusion technique has been developed by combination of whale optimization algorithm (WOA) and simulated annealing (SA) along with curvelet transform. The resulting combined algorithm is abbreviated as hybrid whale optimization algorithm with simulated annealing. Initially, hWOA-SA has been applied to enhancing the quality of image using de-noising scheme. Afterwards, the curvelet transform has been employed to carry out the fusion of images. In terms of PSNR, the curvelet transform exhibits the better performance. The effectiveness and validation of the proposed scheme has been carried-out using quality matrices. The performance analysis is carried out after checking the effectiveness of proposed approach by evaluating the various parameters such as: RSME, PFE, MAE, CORR, SNR, PSNR, MI, UQI and SSIM and compared with numerous techniques. Simulation results obtained from proposed hWOA-SA based image fusion are very competitive and better than other image fusion technique available in the literature.

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Operation of Whale Optimization Algorithm in PID Controllers for AVR System

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst060901 ◽

2020 ◽

Vol 06 (09) ◽

pp. 1-5

Author(s):

Sujatha Nebarthi

Keyword(s):

Transient Response ◽

Optimization Algorithm ◽

Pid Controller ◽

Optimization Technique ◽

Optimization Techniques ◽

Whale Optimization Algorithm ◽

Pid Controllers ◽

Avr System ◽

Whale Optimization ◽

Strong Control

In the present paper presents the Whale Optimization Algorithm technique (WOA) it is a partial search algorithm. To advance the improved the performance of the PID controller uses whale optimization algorithm as the optimization technique. The proposed algorithm is used to tuning the controllers very fast and tuning is very high quality in PID Controllers is most effectively. It growths the system by its main transient response and by comparing the all terms of rise time (tr), settling time (ts) and peak overshoot (% Mp). More over the three gains are (proportional (kP), integral (ki) and derivative (Kd)) of the PID controller have been enhanced by the WOA technique to control the Automatic Voltage Regulator system. In this the transient response of the terminal voltage may be observed from the well-conditioned analysis they can be suggest WOA established PID Controller and which reveal a very most upgrade strong control structure for the managing the AVR system in the Electrical Power System. The simulation result of the propounded controller has shown superior result to the other optimization techniques on PID controller along with the transient response parameters and improve and supervise the performance of the System

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Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function

Electrical Engineering & Electromechanics ◽

10.20998/2074-272x.2021.2.09 ◽

2021 ◽

pp. 64-69

Author(s):

M. F. Mehdi ◽

A. Ahmad ◽

S. S. Ul Haq ◽

M. Saqib ◽

M. F. Ullah

Keyword(s):

Simulated Annealing ◽

Differential Evolution ◽

Optimization Algorithm ◽

Optimal Solution ◽

Whale Optimization Algorithm ◽

Power Network ◽

Multi Objective ◽

Whale Optimization ◽

Emission Dispatch ◽

Economic Emission Dispatch

Introduction. Dynamic Economic Emission Dispatch is the extended version of the traditional economic emission dispatch problem in which ramp rate is taken into account for the limit of generators in a power network. Purpose. Dynamic Economic Emission Dispatch considered the treats of economy and emissions as competitive targets for optimal dispatch problems, and to reach a solution it requires some conflict resolution. Novelty. The decision-making method to solve the Dynamic Economic Emission Dispatch problem has a goal for each objective function, for this purpose, the multi-objective problem is transformed into single goal optimization by using the weighted sum method and then control/solve by Whale Optimization Algorithm. Methodology. This paper presents a newly developed metaheuristic technique based on Whale Optimization Algorithm to solve the Dynamic Economic Emission Dispatch problem. The main inspiration for this optimization technique is the fact that metaheuristic algorithms are becoming popular day by day because of their simplicity, no gradient information requirement, easily bypass local optima, and can be used for a variety of other problems. This algorithm includes all possible factors that will yield the minimum cost and emissions of a Dynamic Economic Emission Dispatch problem for the efficient operation of generators in a power network. The proposed approach performs well to perform in diverse problem and converge the solution to near best optimal solution. Results. The proposed strategy is validated by simulating on MATLAB® for 5 IEEE standard test system. Numerical results show the capabilities of the proposed algorithm to establish an optimal solution of the Dynamic Economic Emission Dispatch problem in a several runs. The proposed algorithm shows good performance over the recently proposed algorithms such as Multi-Objective Neural Network trained with Differential Evolution, Particle swarm optimization, evolutionary programming, simulated annealing, Pattern search, multi-objective differential evolution, and multi-objective hybrid differential evolution with simulated annealing technique.

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