Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle

Mohanty, Debidasi; Panda, Sidhartha

doi:10.1007/s40313-020-00683-9

Cited by 28 publications

(15 citation statements)

References 35 publications

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“…A sudden surge in load of 10% is happened at t = 0 s in area‐1 and the MSSA tuned FO‐FPPDPI controller parameters are found to be,

K_{11} = 1.2829

K_{21} = 1.3467

α = 0.8162

K_{P} = 1.4651

K_{D} = 0.8550

λ = 0.4990

K_{P 1} = 1.3546

K_{I} = 1.6219

μ = 1.1154

.The objective function values are found to be 0.0544 and 1.6237 for

J_{1}

and

J_{2}

, respectively.The performance of proposed controller is validated with conventional and several other optimization techniques like: Ziegler Nichols (ZN), 44 genetic algorithm (GA), 44 bacterial foraging optimization algorithm (BFOA), 44 PSO, 45 hybrid BFOA‐PSO, 45 NSGA‐II‐based PI controller, 38 pattern search, particle swarm optimization (PSO) optimized fuzzy PI controllers, 46 hybrid PSO‐PS based fuzzy PI controller, 46 and modified salp swarm algorithm (SSA) optimized Fractional Order Adaptive Fuzzy PID controller with Filter (FOAFPIDF) 23 . For all these optimization techniques, ITAE values are provided in the literatures.…”

Section: Resultsmentioning

confidence: 99%

“…Fuzzy PI controller, 19,20 fuzzy PID controller, 21 fuzzy FOPID controller 14 have proposed for LFC in similar kind of HPS and shown superior performance over other controllers. An adaptive fuzzy PID controller, 22 Fractional order adaptive fuzzy PID controller, 23 Type II fuzzy PID controller, 24 Fuzzy PD-PI controller, 25 Fuzzy predictive PID controller 26 have suggested in the literatures and outperforms other controllers. Another advanced controller known as model predictive controller (MPC) 27,28 is proposed for robotic manipulation of non-linear and uncertain system as well as LFC of multi area power system with ESCs.…”

mentioning

confidence: 99%

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Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

Mohanty

Panda

2023

Int J Numerical Modelling

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Significant number of renewable sources when integrated with distributed generations and energy storage components form a sustainable power system called hybrid power system (HPS). But the stochastic nature of the sources and continuous variation of load cause serious issues in the power systems like frequency instability. Our work proposes a novel approach for load frequency control (LFC) of the proposed system. For the work, both frequency deviation and control signal are used to give the input signal to energy storage components. A new fuzzy‐based controller called fractional order fuzzy precompensated PDPI (FO‐FPPDPI) controller for LFC of the mentioned HPS is suggested in this paper. The controller is optimized by a nature inspired multi‐objective salp swarm algorithm (MSSA). The comparison of the proposed approach with the classical PID controller and Fuzzy PID (FPID) controller is carried out by taking several system operating conditions. Robustness of the FO‐FPPDPI controller is also validated for system parameter variations and the superiority of the proposed control strategy over PID and FPID controllers is validated for all the situations. Finally, the control strategy is realized in OPAL‐RT to measure its fidelity with the simulation results.

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“…A sudden surge in load of 10% is happened at t = 0 s in area‐1 and the MSSA tuned FO‐FPPDPI controller parameters are found to be,

K_{11} = 1.2829

K_{21} = 1.3467

α = 0.8162

K_{P} = 1.4651

K_{D} = 0.8550

λ = 0.4990

K_{P 1} = 1.3546

K_{I} = 1.6219

μ = 1.1154

.The objective function values are found to be 0.0544 and 1.6237 for

J_{1}

and

J_{2}

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

Mohanty

Panda

2023

Int J Numerical Modelling

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“…The authors of [13] employed particle swarm optimization to control and simulate a wind-biomass isolated hybrid power system. For frequency regulation of hybrid power systems with electric vehicles, the authors in [14] developed the fractional-order adaptive fuzzy PID controller with modified salp swarm algorithm optimization. The fractional order fuzzy PID controller for frequency regulation of a solar-wind integrated power system with a hydrogen aqua equalizer-fuel cell unit was presented by the authors in [15].…”

Section: Introductionmentioning

confidence: 99%

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant

et al. 2023

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In this paper, the electrical parameters of a hybrid power system made of hybrid renewable energy sources (HRES) generation are primarily discussed. The main components of HRES with energy storage (ES) systems are the resources coordinated with multiple photovoltaic (PV) cell units, a biogas generator, and multiple ES systems, including superconducting magnetic energy storage (SMES) and pumped hydro energy storage (PHES). The performance characteristics of the HRES are determined by the constant power generation from various sources, as well as the shifting load perturbations. Constant power generation from a variety of sources, as well as shifting load perturbations, dictate the HRES’s performance characteristics. As a result of the fluctuating load demand, there will be steady generation but also fluctuating frequency and power. A suitable control strategy is therefore needed to overcome the frequency and power deviations under the aforementioned load demand and generation conditions. An integration in the environment of fractional order (FO) calculus for proportion-al-integral-derivative (PID) controllers and fuzzy controllers, referred to as FO-Fuzzy-PID controllers, tuned with the opposition-based whale optimization algorithm (OWOA), and compared with QOHSA, TBLOA, and PSO has been proposed to control the frequency deviation and power deviations in each power generation unites. The results of the frequency deviation obtained by using FO-fuzzy-PID controllers with OWOA tuned are 1.05%, 2.01%, and 2.73% lower than when QOHSA, TBLOA, and PSO have been used to tune, respectively. Through this analysis, the algorithm’s efficiency is determined. Sensitivity studies are also carried out to demonstrate the robustness of the technique under consideration in relation to changes in the sizes of the HRES and ES system parameters.

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“…The salp swarm algorithm (SSA), which is one of the most contemporary swarm algorithms used in many domains, including engineering design, wireless networking, image processing and power energy (Abualigah et al , 2020), is chosen in this paper as an optimization algorithm to estimate the motor’s physical, electrical and mechanical parameters instead of the ordinary methods that identify the mathematical parameters. Furthermore, its flexibility and superiority were proved in many works (El-Fergany, 2018; Mohanty and Panda, 2021; Yang et al , 2019; Ferahtia et al , 2021; Pan et al , 2021). Also, it is used to extract the parameters of polymer exchange membrane fuel cells, where it provides better performance in this application (Yousri et al , 2019); SSA is also used to identify a lithium battery model (Ferahtia et al , 2021), which proved its efficiency for parameter estimation of these systems.…”

Section: Introductionmentioning

confidence: 99%

New identification of induction machine parameters with a meta-heuristic algorithm based on least squares method

Zorig,

Belkheiri,

Bendjedia

et al. 2023

COMPEL

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Purpose The great value of offline identification of machine parameters is when the machine manufacturer does not provide its parameters. Most machine control strategies require parameter values, and some circumstances in the industrial sector only require offline identification. This paper aims to present a new offline method for estimating induction motor parameters based on least squares and a salp swarm algorithm (SSA). Design/methodology/approach The central concept is to use the classic least squares (LS) method to acquire the majority of induction machine (IM) constant parameters, followed by the SSA method to obtain all parameters and minimize errors. Findings The obtained results showed that the LS method gives good results in simulation based on the assumption that the measurements are noise-free. However, unlike in simulations, the LS method is unable to accurately identify the machine’s parameters during the experimental test. On the contrary, the SSA method proves higher efficiency and more precision for IM parameter estimation in both simulations and experimental tests. Originality/value After performing a primary identification using the technique of least squares, the initial intention of this study was to apply the SSA for the purpose of identifying all of the machine’s parameters and minimizing errors. These two approaches use the same measurement from a simple running test of an IM, and they offer a quick processing time. Therefore, this combined offline strategy provides a reliable model based on the identified parameters.

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Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle

Cited by 28 publications

References 35 publications

Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant

New identification of induction machine parameters with a meta-heuristic algorithm based on least squares method

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