Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine

Fathy, Ahmed; Kassem, Ahmed M.

doi:10.1016/j.isatra.2018.11.035

Cited by 108 publications

(44 citation statements)

References 48 publications

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“…Authors argued that ALO outperform metaheuristic algorithms. Fathy and Kassem [193] trained adaptive neuro fuzzy inference system (ANFIS) using ALO to control multi-interconnected plants comprise wind turbine and photovoltaic.…”

Section: G Control Engineeringmentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

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Section: G Control Engineeringmentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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“…12 is constructed with the aid of models given in Refs. [3,27]. A load disturbance of 1% is applied on area 1.…”

Section: Nonlinear Multi-interconnected Systemmentioning

confidence: 99%

“…This helps in keeping the system reliability in the event of failure of generation in one area [1,2]. Recently, renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT) are integrated with traditional energy sources in multi-interconnected system [3]. Many reported studies were performed on designing LFC incorporated in the multiinterconnected system.…”

Section: Introductionmentioning

confidence: 99%

Multi-Verse Optimizer for Model Predictive Load Frequency Control of Hybrid Multi-Interconnected Plants Comprising Renewable Energy

et al. 2020

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This paper presents a recent metaheuristic optimization approach of multi-verse optimizer (MVO) to design load frequency control (LFC) based model predictive control (MPC) incorporated in large multi-interconnected system. The constructed system comprises six plants with renewable energy sources (RESs). MVO is employed to determine the optimal parameters of MPC-LFC to achieve the desired output of the interconnected system in case of load disturbances. The presented system comprises reheat thermal, hydro, photovoltaic (PV) model with maximum power point tracker (MPPT), wind turbine (WT), diesel generation (DG), and superconducting magnetic energy storage (SMES). The integral time absolute error (ITAE) of the frequencies and tie-line powers deviations is proposed as objective function. The effects of governor dead zone and generation rate constraint (GRC) of thermal plants are considered. The performance of the proposed MPC optimized via MVO is compared with the other designed via intelligent water drops (IWD) and genetic algorithm (GA). Additionally, the robustness of the proposed MPC-LFC based MVO with variation of the system parameters is presented. The obtained results confirmed the superiority and reliability of the proposed controller compared to the others.

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“…For the improvement of power system stability with LFC, various control techniques are adopted apart from conventional classical control techniques. Some of them are distributed model predictive control [9], fuzzy PID controller [10], I minus PDF controller [15], artificial neuro-fuzzy interface controller [18]. Further the fractional order controllers are powerful than classical controllers.…”

Section: Introductionmentioning

confidence: 99%

“…Novel of them are fractional order PID controller [5,19], PID μ F controller [7], cascade PI-FOPD controller [14], cascade tilted-integral-derivative controller [20], fractional-order PI-PID cascade controller [21] and cascade tiltintegral-tilt-derivative controller [22]. The controller gains are optimised by various meta-heuristic techniques like bacterial foraging optimisation [3,5], opposition-based harmonic search optimisation [6], lightning search algorithm [7], modified sine cosine algorithm [10], volleyball premier league algorithm [14], stochastic fractal search optimisation [15], hybrid bacteria foraging-particle swarm optimisation (PSO) [16], modified group search optimisation [17], antlion optimiser [18], PSO [19], salp swarm algorithm (SSA) [20], sine cosine algorithm [21] and water cycle algorithm [22].…”

Section: Introductionmentioning

confidence: 99%