Load Frequency Control Using Optimized Control Techniques

Hemeida, Ashraf Mohamed; Mohamed, S. B.; Mahmoud, M. F.

doi:10.21608/jesaun.2020.42349.1011

Cited by 8 publications

(10 citation statements)

References 25 publications

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“…Therefore, the gain parameters range has been narrowed iteratively until closed-loop stability condition of power system model is achieved within the range (−10, 150). PID controller produces best results and minimizes the error significantly within the selected range (−10, 150) as compared to very short range (0, 5) (Pain and Acharjee, 2014; Abd-Elazim and Ali, 2018) (Gözde et al, 2008;Selvakumaran et al, 2012;Modi et al, 2013;Rao and Rama Krishna Reddy, 2015;Shankar et al, 2015;Hazlee Azil et al, 2016;Jeyalakshmi and Subburaj, 2016;Singh and Ramesh, 2019;Hemeida et al, 2020;Veerasamy et al, 2020;Safari et al, 2021) (Sidi Brahim et al, 2021) (Mallesham et al, 2012)- (Kumari and Jha, 2014). Table 4 depicts the PSO operators used to find out the optimal PID controller parameters.…”

Section: Proposed Approaches For Tuning the Pid Controller Particle S...mentioning

confidence: 99%

“…Similarly, fuzzy logic requires hard work to get the influential signal response from the power system (Abd-Elazim and Ali, 2018). Optimization techniques such as genetic algorithm (GA) (Milani and Mozafari, 2009) (Chang et al, 1998;Rerkpreedapong et al, 2003;Das et al, 2010;Mallesham et al, 2012;Konar et al, 2014;Regad et al, 2019;Hemeida et al, 2020;Sidi Brahim et al, 2021), particle swarm optimization (PSO) (Pain and Acharjee, 2014;Abd-Elazim and Ali, 2018) (Gözde et al, 2008;Selvakumaran et al, 2012;Modi et al, 2013;Rao and Rama Krishna Reddy, 2015;Shankar et al, 2015;Hazlee Azil et al, 2016;Jeyalakshmi and Subburaj, 2016;Singh and Ramesh, 2019;Hemeida et al, 2020;Veerasamy et al, 2020;Safari et al, 2021) (Sidi Brahim et al, 2021 (Mallesham et al, 2012)- (Kumari and Jha, 2014), firefly algorithm (FA) (Naidu et al, 2013;Shakarami et al, 2013;Padhan et al, 2014;Chandra Sekhar et al, 2016;Abd-Elazim and Ali, 2018;Boddepalli and Navuri, 2018;Gupta et al, 2021), and artificial bee colony (Ghasemi and Shayeghi, 2011;Rathor et al, 2011;Gozde et al, 2012;Naidu et al, 2014;Elsisi et al, 2015;Kouba et...…”

Section: Introductionmentioning

confidence: 99%

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Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

Hassan

Ramli²,

Milyani³

2022

Front. Energy Res.

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It is necessary to predict solar photovoltaic (PV) output and load profile to guarantee the security, stability, and reliability of hybrid solar power systems. Severe frequency fluctuations in hybrid solar systems are expected due to the intermittent nature of the solar photovoltaic (PV) output and the unexpected variation in load. This paper proposes designing a PID controller along with the integration of a battery energy storage system (BESS) and plug-in hybrid electric vehicle (PHEV) for frequency damping in the hybrid solar power system. The solar PV output is predicted with high accuracy using artificial neural networks (ANN) given that solar irradiance and cell temperature are inputs to the model. The variation in load is also forecasted considering the factors affecting the load using ANN. Optimum values of the PID controller have been found using genetic algorithm, particle swarm optimization, artificial bee colony, and firefly algorithm considering integral absolute error (IAE), integral square error (ISE), and integral time absolute error (ITAE) objective functions. IAE, ISE, ITAE, Rise time, settling time, peak overshoot and maximum frequency deviation have been measured for comparison and effectiveness. The transient behavior has been further improved by utilizing the power from BESS/PHEV to the power system. The results demonstrate the efficacy of the suggested design for frequency control using the genetic algorithm method along with ISE objective function compared with those obtained from the conventional, particle swarm optimization, artificial bee colony, and firefly algorithm techniques.

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Section: Proposed Approaches For Tuning the Pid Controller Particle S...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

Hassan

Ramli²,

Milyani³

2022

Front. Energy Res.

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“…Fig. 3 shows the structure of the FPIDF controller where the error inputs to the controllers are given by Equations ( 19) and (20) as follows [37,48]: output gains, all of which are optimized using the MPA in addition to the shape of fuzzy membership functions. The optimal gains of both conventional and FPIDF controllers can be obtained by minimizing the fitness function (FF) which can be considered as follows [23,48]:…”

Section: Control Strategy and Problem Formulationmentioning

confidence: 99%

“…For example, a PI-LFC based on the particle swarm optimization (PSO) technique was suggested in [18] to improve the frequency response under different load disturbances. Moreover, different optimization algorithms were implemented for tuning the PID-LFC parameters such as the ant colony optimization in [19], the genetic algorithm in [20], the harmony search algorithm in [21,22], the socialspider optimizer algorithm in [23], the whale optimization algorithm in [24] and the PSO algorithm in [25,26]. Another LFC, based on the model predictive control for a hybrid power system taking into consideration the effect of governor and turbine parameters variation, was presented in [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

et al. 2021

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This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK. INDEX TERMSFuzzy control, load frequency control, marine predator algorithm, microgrid system, PIDF controller, renewable energy, storage systems. NOMENCLATURE A Rotor swept area ACE Area control error B1, B2 Frequency bias coefficients BES Battery energy storage CE Change of error Predator step size control ratio Power coefficient COR Competition over resources E Error FF Fitness function FLC Fuzzy logic controller FPIDF Fuzzy proportional-integral-derivative with filter G Solar radiation ( ) Controller transfer function Solar irradiance under standard test conditions 1 , 2 Input scaling gains 12

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Performance Analysis of Automatic Generation Control of Multi-Area Restructured Power System

Kumar

Prasad

2021

2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Load Frequency Control Using Optimized Control Techniques

Cited by 8 publications

References 25 publications

Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

Robust Load Frequency Control of Hybrid Solar Power Systems Using Optimization Techniques

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

Performance Analysis of Automatic Generation Control of Multi-Area Restructured Power System

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