Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Jumani, Touqeer Ahmed; Mustafa, Mohd Wazir; Rasid, Madihah Md.; Mirjat, Nayyar Hussain; Baloch, Mazhar Hussain; Salisu, Sani

doi:10.3390/electronics8010111

Cited by 44 publications

(29 citation statements)

References 31 publications

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“…Usually, time is represented by the iteration number in the optimization analysis; therefore, a step size of 1 was chosen for the time variable. A simplified version of Equation (10) is shown in Equation (11).…”

Section: Salp Swarm Optimization Algorithm and Its Implementation In mentioning

confidence: 99%

“…Despite their superior performance over the traditional methods of FOPID tuning, all the mentioned optimization algorithms are bound with major drawbacks. For example, the localized solutions by GA restrict its usage to static data sets [11,12]. The PSO suffers from high uncertainty in its parameter selection and suffers local optimum stagnation for high dimensional search spaces [13].…”

Section: Introductionmentioning

confidence: 99%

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Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

et al. 2019

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Owing to the superior transient and steady-state performance of the fractional-order proportional-integral-derivative (FOPID) controller over its conventional counterpart, this paper exploited its application in an automatic voltage regulator (AVR) system. Since the FOPID controller contains two more control parameters (µ and λ ) as compared to the conventional PID controller, its tuning process was comparatively more complex. Thus, the intelligence of one of the most recently developed metaheuristic algorithms, called the salp swarm optimization algorithm (SSA), was utilized to select the optimized parameters of the FOPID controller in order to achieve the optimal dynamic response and enhanced stability of the studied AVR system. To validate the effectiveness of the proposed method, its performance was compared with that of the recently used tuning methods for the same system configuration and operating conditions. Furthermore, a stability analysis was carried out using pole-zero and bode stability criteria. Finally, in order to check the robustness of the developed system against the system parameter variations, a robustness analysis of the developed system was undertaken. The results show that the proposed SSA-based FOPID tuning method for the AVR system outperformed its conventional counterparts in terms of dynamic response and stability measures.

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Section: Salp Swarm Optimization Algorithm and Its Implementation In mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

et al. 2019

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“…Therefore, instead of selecting those gains manually, an intelligent AI‐based method can select optimal gain values and thus reduces the level of overshoots to their minimum level. In this context, Chung et al utilize PSO for the very first time in MG control architectures and developed a PSO‐based controller for both MG operating modes that is grid‐tied and islanded. In the quoted research, the parameters of the PI regulator were optimized by using the PSO algorithm which in turn improved the system's transient and steady response.…”

Section: Background and Justification Of The Current Research Workmentioning

confidence: 99%

“…In addition, the optimization process of PI gains was very slow as it took nearly 480 iterations for the minimization process of FF. Chung et al have further extended their work in the year 2010 and have successfully expedited the PSO convergence rate to achieve the minimization of FF in only 10 iterations. They also successfully managed to improve steady‐state behavior by reducing the steady‐state power oscillations.…”

Section: Background and Justification Of The Current Research Workmentioning

confidence: 99%

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Dynamic response enhancement of grid‐tied ac microgrid using salp swarm optimization algorithm

Jumani

Mustafa

Rasid

et al. 2019

Int Trans Electr Energ Syst

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Summary To alleviate the overloads in the power system and to reduce the exponential growth in carbon dioxide (CO2) emissions, deployment of the renewable energy sources (RES) into the power system is the need of the hour. However, injecting these RES into the current power system network causes large voltage and power overshoots hence deteriorate the transient response and power quality of the overall power system. In this paper, an efficient solution of the above‐mentioned issues is explored by developing an optimal microgrid (MG) controller using one of the most modern and intelligent artificial intelligence (AI) techniques named the salp swarm optimization algorithm (SSA). The intelligence of the SSA is exploited to select the optimal controller gains and dc‐link capacitance value by minimizing a time integrating error fitness function (FF) which in‐turn enhances the dynamic response and power quality of the studied MG system. The proposed grid‐tied MG controller is designed to achieve the preset active and reactive power sharing ratio between distributed generator (DG) and utility grid during DG and load switching conditions. To validate the superiority of the proposed controller, its performance is compared with that of its precedent grasshopper optimization algorithm (GOA)‐based controller for the identical operating conditions and system configuration. The outcomes of the study show that the proposed MG controller outperforms its competitor in terms of transient response and quality of power.

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A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

Arfeen

Sheikh

Azam

et al. 2020

Concurrency and Computation

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Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic methods. Solar PV/Battery grid‐connected MG is modeled to achieve optimum size, supreme power quality, reduced fluctuations in voltage and frequency, reduced settling time, eliminate short transient currents, seamless power, least annual cost and high reliability as an objective function under wavering weather condition and dynamic load changes. Four broad categorizations of metaheuristic algorithms, that is, evolutionary, swarm intelligence, physics, and human intelligence‐based algorithms are well elaborated in this study. The optimal solution to the fitness function by using a hybrid optimization method also directed in the study. This paper gives deep insight to readers working in the area.

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Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Cited by 44 publications

References 31 publications

Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

Salp Swarm Optimization Algorithm-Based Fractional Order PID Controller for Dynamic Response and Stability Enhancement of an Automatic Voltage Regulator System

Dynamic response enhancement of grid‐tied ac microgrid using salp swarm optimization algorithm

A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems

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