Design and implementation of SSA based fractional order PID controller for automatic generation control of a multi-area, multi-source interconnected power system

Mohapatra, Tapas Kumar; Sahu, Binod Kumar

doi:10.1109/icsesp.2018.8376697

Cited by 21 publications

(11 citation statements)

References 16 publications

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“…The best optimization algorithm has the characteristics of avoiding local optima, fast convergence rate (CR) and less complexity [25]. Researchers have adopted SSA in different field such as emission estimation of CO2 [26], for feature sections [10], chemical compound activities [27], power system stabilizer [28], fraction order PID controller [29] and PIDfuzzy controller [30].…”

Section: Introductionmentioning

confidence: 99%

Estimating PV models using multi-group salp swarm algorithm

Al-Shabi

Ghenaï

Bettayeb

et al. 2021

IJ-AI

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<span id="docs-internal-guid-ea798321-7fff-3e0c-24d7-776c9b1165b3"><span>In this paper, a multi-group salp swarm algorithm (MGSSA) is presented for estimating the photovoltaic (PV) solar cell models. The SSA is a metaheuristic technique that mimics the social behavior of the salp. The salps work in a group that follow a certain leader. The leader approaches the food source and the rest follows it, hence resulting in slow convergence of SSA toward the solution. For several groups, the searching mechanism is going to be improved. In this work, a recently developed algorithm based on several salp groups is implemented to estimate the single-, double-, triple-, Quadruple-, and Quintuple-diode models of a PV solar cell. Six versions of MGSSA algorithms are developed with different chain numbers; one, two, four, six, eight and half number of the salps. The results are compared to the regular particle swarm optimization (PSO) and some of its newly developed forms. The results show that MGSSA has a faster convergence rate, and shorter settling time than SSA. Similar to the inspired actual salp chain, the leader is the most important member in the chain; the rest has less significant effect on the algorithm. Therefore, it is highly recommended to increase the number of leaders and reduce the chain length. Increasing the number of leaders (number of groups) can reduce the root mean squared error (RMSE) and maximum absolute error (MAE) by 50% of its value.</span></span>

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Section: Introductionmentioning

confidence: 99%

Estimating PV models using multi-group salp swarm algorithm

Al-Shabi

Ghenaï

Bettayeb

et al. 2021

IJ-AI

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“…In artificial intelligencebased methods, the design process depends on the training data and analysis for determining the optimal parameters of the controller. From another side, the meta-heuristic based algorithms have found great concerns for determining the optimal controller parameters through proper tuning of various parameters [20], [34].…”

Section: Introductionmentioning

confidence: 99%

“…Several optimization algorithms have been presented in the literature, wherein the classical genetic algorithm (GA) and particle swarm optimization (PSO) methods are the most widely applied techniques [33]. Many other techniques have been reported in the literature, such as salp swarm algorithm (SSA) [34], differential evolution (DE) [20], firefly algorithm (FA) [35], teaching learning-based optimization (TLBO) [21], ant-lion optimizer algorithm (ALO) [23], imperialist competitive algorithm (ICA) [36], etc. In addition, hybrid and combined optimization techniques have been addressed in the literature for designing the controller parameters.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

et al. 2020

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Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.

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“…With its good performance, SSA has been widely used in several areas. In [6] and [7], SSA has been used in multilevel color image segmentation, T. K. Mohapatra and B. K. Sahu [8] have used SSA to optimize PID control, and SSA has also been implied in [9] to design power system stabilizer.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

Hóng

Zhu

et al. 2019

IEEE Access

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As a representative of lightweight virtualization, container technology has been widely used in cloud services and edge computing applications. However, in the resource pool scenario composed by multiple intelligent terminal devices, considering the limited resources and poor stability of these devices, it is necessary to split the overall service into multiple microservices and deploy the backups of them in respective containers. Traditional container scheduling policies tend to be less effective in solving such problems. Therefore, the article used a meta-heuristic algorithm to solve this kind of problems. Based on a newly proposed salp swarm algorithm (SSA), the paper presented a comprehensive improved SSA (CISSA). CISSA improved the performance of the original SSA by 4 steps. To verify the performance of CISSA in different kinds of test functions, the algorithm was compared with 7 commonly-used meta-heuristic algorithms in 29 benchmark functions provided by the author of SSA. In addition, the article constructed three container cluster models of different sizes, all these algorithms were used to solve these redundant container deployment problems, the experimental results indicate that the CISSA is superior to other algorithms in such problems of different dimensions.

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Design and implementation of SSA based fractional order PID controller for automatic generation control of a multi-area, multi-source interconnected power system

Cited by 21 publications

References 16 publications

Estimating PV models using multi-group salp swarm algorithm

Estimating PV models using multi-group salp swarm algorithm

An Optimized Hybrid Fractional Order Controller for Frequency Regulation in Multi-Area Power Systems

A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

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