Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Jumani, Touqeer Ahmed; Mustafa, Mohd Wazir; Hamadneh, Nawaf N.; Atawneh, Samer; Rasid, Madihah Md.; Mirjat, Nayyar Hussain; Bhayo, Muhammad Akram; Khan, İlyas

doi:10.3390/en13164063

Cited by 19 publications

(14 citation statements)

References 89 publications

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“…In [34], it is concluded that there is no universal optimisation algorithm to be the best in solving optimisation problems. Hybrid optimisation techniques can utilize the strong advantages of each optimisation algorithm to reach an optimal solution.…”

Section: • Artificial Neural Network (Ann)mentioning

confidence: 99%

Design and Simulation of Low-Cost Microgrid Controller in Off-Grid Remote Areas

Bangtit¹

2022

Electric Power Conversion and Micro-Grids

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This study presents the microgrid controller with an energy management strategy for an off-grid microgrid, consisting of an energy storage system (ESS), photovoltaic system (PV), micro-hydro, and diesel generator. The aim is to investigate the improved electrical distribution and off-grid operation in remote areas. The off-grid microgrid model and the control algorithms developed using MATLAB Simulink and State flow. The energy management system is focusing on the state of charge of the energy storage system. The microgrid controller controls the operation mode and power generation from the distributed generations’ local controller, i.e., PV, micro-hydro, and diesel. It also controls the smart meters of the loads to be connected or disconnected to the microgrid. The simulation results show that the proposed microgrid control can control the target off-grid microgrid in given possible scenarios. The off-grid microgrid managed to meet the energy demand with the lowest power outage and the diesel generator operation’s lowest cost.

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Section: • Artificial Neural Network (Ann)mentioning

confidence: 99%

Design and Simulation of Low-Cost Microgrid Controller in Off-Grid Remote Areas

Bangtit¹

2022

Electric Power Conversion and Micro-Grids

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“…The fitness function opted for the current study is an error integrating expression called integral time absolute error (ITAE) and is given by Equation ( 17). 33 ITAE ¼…”

Section: Optimization and Its Necessity In Proposed Control Designmentioning

confidence: 99%

Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

Memon

Mustafa

Jumani

et al. 2021

Int Trans Electr Energ Syst

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Summary Considering the superior performance and comparatively lower maintenance cost of the brushless double‐fed induction generator (BDFIG) than the conventional double‐fed induction generator (DFIG), this article explores its application in wind energy conversion systems (WECS) with a novel control strategy. The proposed control strategy utilizes the intelligence of the Salp swarm algorithm (SSA)–based vector control scheme to optimally regulate the speed, torque, current, active, and reactive power of the proposed WECS during the sharp changes in the wind speed and load. To overcome the demerits associated with the conventional “trial and error” method of PI regulator tuning, the SSA is utilized to optimally select their proportional and integral gains automatically. SSA accomplishes the mentioned task by iteratively minimizing the considered error integrating objective function through an offline optimization method; thus, it provides the least error and consequently optimal dynamic response of the proposed BDFIG‐based WECS at the end of the optimization process. The effectiveness and performance of the proposed control strategy are validated in terms of optimal speed, torque, and active and reactive power regulation and is compared with the internal model control and particle swarm optimization algorithm–based vector control schemes under identical operating conditions and system configurations. The proposed control scheme for the considered BDFIG‐based WECS obtains the best optimal dynamic response among the considered control schemes; thus, it proves its efficacy and essence.

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“…The evolution, features and validation of these three techniques are clearly mentioned in [37][38][39]. Different optimization methods for power quality and transient stability are discussed in [40][41][42]. The optimized values of controller gains, leaky factor and the step size are determined by these techniques.…”

Section: Optimization Techniquementioning

confidence: 99%

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

Kodakkal

Rajagopal

Raju

et al. 2021

Designs

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A power generating system should be able to generate and feed quality power to the loads which are connected to it. This paper suggests a very efficient controlling technique, supported by an effective optimization method, for the control of voltage and frequency of the electrical output of an isolated wind power harnessing unit. The wind power unit is modelled using MATLAB/SIMULINK. The Leaky least mean square algorithm with a step size is used by the proposed controller. The Least Mean Square (LMS) algorithm is of adaptive type, which works on the online modification of the weights. LMS algorithm tunes the filter coefficients such that the mean square value of the error is the least. This avoids the use of a low pass filter to clean the voltage and current signals which makes the algorithm simpler. An adaptive algorithm which is generally used in signal processing is applied in power system applications and the process is further simplified by using optimization techniques. That makes the proposed method very unique. Normalized LMS algorithm suffers from drift problem. The Leaky factor is included to solve the drift in the parameters which is considered as a disadvantage in the normalized LMS algorithm. The selection of suitable values of leaky factor and the step size will help in improving the speed of convergence, reducing the steady-state error and improving the stability of the system. In this study, the leaky factor, step size and controller gains are optimized by using optimization techniques. The optimization has made the process of controller tuning very easy, which otherwise was carried out by the trial-and-error method. Different techniques were used for the optimization and on result comparison, the Antlion algorithm is found to be the most effective. The controller efficiency is tested for loads that are linear and nonlinear and for varying wind speeds. It is found that the controller is very efficient in maintaining the system parameters under normal and faulty conditions. The simulated results are validated experimentally by using dSpace 1104. The laboratory results further confirm the efficiency of the proposed controller.

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Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Cited by 19 publications

References 89 publications

Design and Simulation of Low-Cost Microgrid Controller in Off-Grid Remote Areas

Design and Simulation of Low-Cost Microgrid Controller in Off-Grid Remote Areas

Salp swarm algorithm–based optimal vector control scheme for dynamic response enhancement of brushless double‐fed induction generator in a wind energy conversion system

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

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