PSO and Fuzzy Based Tuning Mechanism for Optimization of Transient Response in High-Performance Drilling Machine

Saxena, Arti; Dubey, Y.M.; Kumar, Manish

doi:10.1109/spin48934.2020.9071215

Cited by 4 publications

(1 citation statement)

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“…In this light, the objective or cost function that permits the determination of optimal values of the parameters must be formulated. In the literature on control parameter optimization, numerous optimization cost functions can be spotted in the literature [26,27] such as the integral of absolute error (IAE), integral square error (ISE), and integral of time squared error (ITSE). These cost functions exhibit exceptional performance in terms of error minimization.…”

Section: Optimization Of the Pve Using Particle Swarmmentioning

confidence: 99%

A New High-Performance Photovoltaic Emulator Suitable for Simulating and Validating Maximum Power Point Tracking Controllers

Harrison

Alombah

2023

International Journal of Photoenergy

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Photovoltaic (PV) research is rapidly growing, and the need for controlled environments to validate new MPPT controllers is becoming increasingly important. Currently, researchers face several challenges in testing MPPT algorithms due to the unpredictable nature of solar PV power generation. In this paper, we propose a new photovoltaic emulator (PVE) that could replace solar panels and ensure a highly controllable environment suitable for testing photovoltaic (PV) systems. In this PVE, the complex nonlinear equations of the PV cell/module are fast computed and resolved by a new linearization technique which involves the systematic breakdown of the current-voltage ( I - V ) curve of the PV into twelve linear segments. Based on input environmental conditions, an artificial neural network (ANN) was constructed to assist the linearization process by predicting the current-voltage boundary coordinates of these segments. Using simple linear equations, with the segment boundary coordinates, a reference voltage was generated for the PVE. A nonlinear backstepping controller was designed to exploit the reference voltage and stabilize the power conversion stage (PCS). The PVE was optimized using particle swarm optimization (PSO). Several tests have shown that the proposed nonlinear controller provides better dynamic and robust performance than the PI controller, the most reputable and recurrent control method in the area of PVE. The PVE was coupled with a recently proposed integral backstepping MPPT controller and analyzed under several dynamic conditions, including the MPPT test specified by EN 50530. It was found that the accuracy of the proposed PVE measured by its relative error is less than 0.5%, with an MPPT efficiency of greater than 99.5%. The attractive results achieved by this PVE make it especially suitable for simulating and validating MPPT controllers.

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Section: Optimization Of the Pve Using Particle Swarmmentioning

confidence: 99%