Modified sine cosine algorithm-based fuzzy-aided PID controller for automatic generation control of multiarea power systems

Sahu, Prakash Chandra; Prusty, Ramesh Chandra; Sahoo, Buddhadeva

doi:10.1007/s00500-020-04716-y

Cited by 53 publications

(18 citation statements)

References 19 publications

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“…The fundamental characteristic of the SSCO algorithm is that the algorithm's procedure is slightly simple mechanism where the design variable is updated using only the mathematical modeling of the sine cosine functions to guide the population to search for global optimal solutions. In SSCO algorithm, the position's updating rule of an agent's population in the design space is formulated in accordance to the following equation [19][20][21]:…”

Section: Ssco Algorithmmentioning

confidence: 99%

“…The SCO algorithm can disclose proficient accuracy in comparison with other well-known nature-inspired optimization algorithms; it is not qualified for very complex problems and is still may face the difficulty of becoming trapped in local optima [21,22]. The modified algorithm is proposed to overcome these shortcomings and to step-up its search capability for solving different real-life problems.…”

Section: Msco Algorithmmentioning

confidence: 99%

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Controller design for gantry crane system using modified sine cosine optimization algorithm

Abbas

Mahmood

2021

TELKOMNIKA

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The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane. Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements.

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Section: Ssco Algorithmmentioning

confidence: 99%

Section: Msco Algorithmmentioning

confidence: 99%

Controller design for gantry crane system using modified sine cosine optimization algorithm

Abbas

Mahmood

2021

TELKOMNIKA

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“…When the studies on the use of LFC to ensure system stability in power systems were examined, it was observed that the determination of the controller parameters plays a major role in achieving successful results. Various evolutionary algorithms, such as genetic algorithm (GA) [3], re y algorithm (FA) [3], population extremal optimization (PEO) [5], particle swarm optimization (PSO) [4,6], hybrid shu ed frog leaping algorithm, teaching-learning based optimization (hybrid SFLA-TLBO) [7], equilibrium optimization algorithm (EOA) [8], grey wolf optimization (GWO) [9], hybrid gravitational-re y (hGFA) [10], differential evolution-arti cial electric eld algorithm (DE-AEFA) [11], Jaya algorithm [12,13], sine-cosine algorithm (SCA) [14,15], lightning ash algorithm (LFA) [16], ant-lion optimization (ALO) [17], and gravitational search algorithm (GSA) [18], have been used to tune the controller parameters. Additionally, different optimization algorithms, such as mine blast algorithm (MBA) [19], salp swarm algorithm (SSA) [20], hybrid moth ame optimization-generalised Hop eld neural network (MFO-GHNN) [21], whale optimization algorithm (WOA) [22], crow search algorithm (CSA) [23,24], marine predator algorithm (MPA) [25],…”

Section: Introductionmentioning

confidence: 99%

Ant-Lion Optimizer-Based Load Frequency Controller for a Hybrid Power System with PV and Thermal Power Plant

Can

Öztürk

Kotb

2022

Preprint

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The process of damping frequency fluctuations in power systems, known as load frequency control (LFC), is an important issue that directly affects the balance between production and consumption for maintaining the stable operation of an interconnected power system. LFC is more complex in multi-area interconnected power systems due to the increasing penetration of renewable energy sources, such as wind turbines and photovoltaic (PV) panels. This study proposes to determine the proportional integral derivative (PID) controller parameters for LFC in a two-area reheat thermal power system, including the photovoltaic system, using ant-lion optimization (ALO). The two-area power system model, consisting of a thermal generator and a PV system, was modelled in MATLAB/SIMULINK. To analyse the effectiveness of the optimization method, the system behavior was examined by considering different case studies, such as the change in load demand and solar irradiation values. The simulation results were compared with the results of different heuristic algorithms, such as the firefly algorithm (FA), genetic algorithm (GA), and population extremal algorithm (PEO). The results revealed that the suggested ALO-PID controller provides about 60% improvement in overshoot/undershoot values and settling time compared to other heuristic optimization methods mentioned.

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“…The active disturbance rejection control and MPC have been combined with maintaining the fuel efficiency of TGSOFCs to an expected constant [19]. The PID could not be suitable for the constraints of the nonlinear TGSOFCs [20]. The maximum power point tracking algorithms could not effectively improve the fuel efficiency of the TGSOFC with nonlinear constraints [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive multistep model predictive control for tubular grid-connected solid oxide fuel cells

Yin

Liu

2021

Preprint

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In the research of renewable energy power generation, tubular grid-connected solid oxide fuel cells with the apparent advantage in voltage regulation have been widely applied in power systems. Recently, a model predictive control has been applied to consider the nonlinear constraints of tubular grid-connected solid oxide fuel cells, which cannot be considered by a proportional-integral-derivative controller. While both model predictive control and proportional-integral-derivative controller achieve only 80% fuel efficiency, which should be improved. An adaptive multistep model predictive control (AMMPC) is proposed to improve the fuel efficiency of tubular grid-connected solid oxide fuel cells and simultaneously consider systemic thermodynamics and electrochemistry constraints. The AMMPC contains the advantages of adaptive control and multistep model predictive control. Both adaptive two-step model predictive control and three-step model predictive control are designed for tubular grid-connected solid oxide fuel cells. With the more accurate prediction ability, the AMMPC improves the fuel efficiency of tubular grid-connected solid oxide fuel cells with higher fuel efficiency (86.5%) than model predictive control (80%) and proportional-integral-derivative (80%). Both feasibility and effectiveness of the AMMPC are verified with high fuel efficiency under both simple and complex power demands cases.

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Modified sine cosine algorithm-based fuzzy-aided PID controller for automatic generation control of multiarea power systems

Cited by 53 publications

References 19 publications

Controller design for gantry crane system using modified sine cosine optimization algorithm

Controller design for gantry crane system using modified sine cosine optimization algorithm

Ant-Lion Optimizer-Based Load Frequency Controller for a Hybrid Power System with PV and Thermal Power Plant

Adaptive multistep model predictive control for tubular grid-connected solid oxide fuel cells

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