Load Frequency Control of Multi Area Interconnected Microgrid Power System using Grasshopper Optimization Algorithm Optimized Fuzzy PID Controller

Lal, Deepak; Barisal, Ajit Kumar; Tripathy, M.

doi:10.1109/raetcs.2018.8443847

Cited by 54 publications

(33 citation statements)

References 27 publications

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“…Step 1: Input a discrete-time state space model with sampling period Ts for the frequency control issue of an islanded microgrid system described by Equations (7) and (8). In conclusion, the detailed steps of the proposed FOMPC-based optimal frequency control method for an islanded microgrid are summarized as follows:…”

Section: Of 21mentioning

confidence: 99%

“…When a microgrid is islanded, an optimal controller should be designed to minimize the frequency deviations. In recent years, some research works have been reported related to the optimal frequency control issue of an islanded microgrid [7][8][9][10], but most works have focused on proportional-integral-derivative (PID) control method and its different kinds of improved versions based on intelligent optimization algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

et al. 2018

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Optimal frequency control of an islanded microgrid has been a challenging issue in the research field of microgrids. Recently, fractional-order calculus theory and some related control methods have attempted to handle this issue. In this paper, a novel fractional-order model predictive control (FOMPC) method is proposed to achieve the optimal frequency control of an islanded microgrid by introducing a fractional-order integral cost function into model predictive control (MPC) algorithm. Firstly, a discrete state-space model is derived for the optimal frequency control problem of an islanded microgrid. Afterward, a fractional-order integral cost function is designed to guide the FOMPC algorithm to obtain optimal control law by borrowing the Grünwald-Letnikov (GL) definition of fractional order calculus. Six simulation studies have been carried out to illustrate the superiority of FOMPC to conventional MPC under dynamical load disturbances, perturbed system parameters and random dynamical power fluctuation of wind turbines.

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Section: Of 21mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

et al. 2018

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“…20 Table 7 shows the optimization values of the scaling factors obtained via FP-PID controller and it is compared with PID controller. 41 System performance is observed based on dynamic parameters, such as settling time, frequency oscillation and undershoot. Our proposed method succeeded in achieving the stable and best performance compared to PID controller.…”

Section: Case 1: Contract Violation Along With Uncontrolled Loadmentioning

confidence: 99%

Automatic generation controller for multi area multisource regulated power system using grasshopper optimization algorithm with fuzzy predictive PID controller

Kumar

Sharma

2020

Int J Numerical Modelling

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This paper presents a fuzzy predictive-proportional integral derivative (FP-PID) controller approach for automatic generation controller (AGC). This new AGC approach aims to balance the total generating system without any power losses and load changes in keeping constant system frequency per tie-lie power flow. But, a sudden load variation in multi-area Interconnected power system (MIPS) creates nonlinearities (frequency deviation & tie-line) in all control areas. Because, penetrating of renewable sources to the power system, the sluggish control action may cause inefficiency in migrating frequency and tie-line power flow. Hence, an accurate and fast-acting controller is required to maintain the nominal value also the quality and stability of power system, because traditional AGC is not feasible for further process. Here, we propose an FP-PID controller in MIPS for controlling large parametric uncertainties. Modeling error is taken into account to reduce the maximum deviation and time of oscillation. The main purpose of FP-PID based AGC is to ensure stable and reliable power system operation. A grasshopper optimization algorithm (GOA) is used to tune the parameters of FP-PID controller with Integral of time multiplied squared error (ITSE) as the objective function. The proposed method is compared with conventional two-area and three-area system, the result are built-in Simulink/MATLAB show that the proposed method has a good dynamic response, fast operation reduced magnitude error and minimized frequency transients for three power areas. The robustness of the controller is, it need not be retuned for wide variations in system parameters and random step load power.

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“…Recently Seyedali Mirjalili proposed multi solutions based metaheuristic approach named as grasshopper optimization algorithm (GOA) which mimics swarming behavior of grasshopper in nature to solve the optimization problems. This is used to solve the multiple real value and binary optimization problems such as optimal reconfiguration for partial shaded photo voltaic array [16], frequency control of the load for interconnected multi area micro grid power system by tuning the gains of Fuzzy proportional integral derivative (Fuzzy PID) controller through GOA [17], economic load dispatch (ED) with renewable energy (wind mill) integration [18], optimal selection of conductor for radial distribution system [19], optimal control of voltage and frequency for an islanded micro grid [20], feature selection problem and short term load forecasting specific to the region [21] etc. Inspired by the successful applications of GOA and BGOA (binary grasshopper optimization algorithm) to research and industrial problems, this paper proposes BGOA to solve the combinatorial generation selection problem with a better solution quality as compared to the traditional GOA.…”

Section: Introductionmentioning

confidence: 99%

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

Shahid¹,

Malik²,

Said³

2021

Turk J Elec Eng & Comp Sci

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The unit commitment problem in power system is a highly nonlinear, non-convex, multi-constrained, complex, highly dimensional, mixed integer and combinatorial generation selection problem. The phenomenon of committing and de-committing represents a discrete problem that requires binary/discrete optimization techniques to tackle with unit commitment optimization problem. The key functions of the unit commitment optimization problem involve deciding which units to commit and then to decide their optimum power (economic dispatch). This paper confers a binary grasshopper optimization algorithm to solve the unit commitment optimization problem under multiple constraints. The grasshopper optimization algorithm is a metaheuristic, multiple solutions-based algorithm inspired by the natural swarming behavior of grasshopper towards food. For solving the binary unit commitment optimization problem, the real/continues value grasshopper optimization algorithm is mapped into binary/discrete search-space by using an Sshaped sigmoid function. The proposed algorithm is tested on IEEE benchmark systems of 4,

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Load Frequency Control of Multi Area Interconnected Microgrid Power System using Grasshopper Optimization Algorithm Optimized Fuzzy PID Controller

Cited by 54 publications

References 27 publications

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

Automatic generation controller for multi area multisource regulated power system using grasshopper optimization algorithm with fuzzy predictive PID controller

Heuristic based binary grasshopper optimization algorithm to solve unit commitment problem

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