Cohort intelligence optimization based controller design of isolated and interconnected thermal power system for automatic generation control

Murugesan, D; Shah, Pritesh; Jagatheesan, K.; Sekhar, Ravi; Kulkarni, Anand J.

doi:10.1109/iccsea54677.2022.9936499

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…These are obtained by an optimization algorithm, which calculates the optimal parameters by minimization of the cost function J. Different metaheuristic optimization algorithms like the Cohort Intelligence (CI) algorithm [92]- [95], Particle Swarm Optimization (PSO) [96], Artificial Bee Colony (ABC) optimization [97] and the Genetic Algorithm (GA) [98] were tested on the controller for comparison. The process of tuning the controller is summarized below:…”

Section: Methodsmentioning

confidence: 99%

Design of an Optimal Fractional Complex Order PID Controller for Buck Converter

Warrier,

Shah

2023

Journal of Robotics and Control

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Dynamic and robust controllers are the inherent requirement of power electronic converters, which are subjected to dynamic variations and nonlinearities. The effectiveness of fractional order controllers in non-linear system control has been well-established by studies in the past few decades. Various forms of fractional order controllers have been used in power-electronic control. Recent research indicates that complex order controllers, extensions of fractional controllers, are more robust against uncertainties and non-linearities than their integer and fractional order counterparts. Though complex order controllers have been employed in various nonlinear plants, they have not been extensively tested on power electronic applications. Also, the design and tuning of the controller is difficult. This paper investigates the effectiveness of a complex order PID controller on a typical power electronic DC-DC buck converter for the first time. Two types of complex order controllers of the form PI^{a+ib}D^c and PI^{a+ib}D^{c+id} were designed for a power electronic buck converter. The complex order controllers were implemented in Simulink and the optimal tuning of the complex order controller parameters for various performance indices was performed using different optimization algorithms. The Cohort Intelligence algorithm was found to give the most optimal results. Both the complex controllers showed more robustness towards uncertainties than the linear and fractional PID controllers. The PI^{a+ib}D^c controller gave the smoothest and fastest response under non-linearities. The dynamic performance of the complex order controller is the best and can be expected to be useful for more power electronic applications.

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Section: Methodsmentioning

confidence: 99%

Design of an Optimal Fractional Complex Order PID Controller for Buck Converter

Warrier,

Shah

2023

Journal of Robotics and Control

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“…Another study [13] focused on a thyristor controller series-based FOPID controller, with its parameters tuned using Particle Swarm Optimization (PSO) to enhance controller performance for Automatic Generation Control (AGC) in an interconnected power grid with multiple sources. The Controller Gain Parameters (CGPs) were optimized using the Cohort Intelligence Optimization (CIO) technique [14] to achieve improved response in AGC for a thermal power network with the FOPID controller acting as an auxiliary controller. Additionally, a multiple source integrated power system was examined using a PSO-PID controller for LFC [15], and the performance was compared with a conventional PID controller.…”

Section: Introductionmentioning

confidence: 99%

Stability Control Analysis of a Dung Beetle Optimizer-Based PI λ D μ Controller for a Multisource Interlinked Power Grid

Boopathi¹,

Jagatheesan²,

Khamies

et al. 2023

Preprint

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This paper presents a novel approach to enhance the frequency stability of an interlinked power system considering the integration of renewable energy sources and energy storage systems. The proposed method utilizes an optimal fractional Proportional-Integral-Derivative (PID) controller, with its parameters determined by the Dung Beetle Optimizer (DBO). The interlinked power system consists of two areas: Area 1 incorporates Photovoltaic (PV) systems with energy storage units (ESUs), while Area 2 comprises thermal, hydro, nuclear, and ESUs (Hydro Aqua Electrolyser (HAE) and Fuel Cell (FC)). The effectiveness of the proposed controller is validated through a comparison of its performance with other optimization techniques, such as the Ant Lion Optimizer (ALO) and Particle Swarm Optimization (PSO). The comparative analysis demonstrates the superior performance of the DBO, as it exhibits a 25% and 4% improvement in settling time over PSO and ALO, respectively. Overall, the proposed controller proves to be highly capable of enhancing system stability during scenarios involving high renewables penetration and abnormal cases.

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“…In recent years, researchers have explored many techniques and optimization approaches for effective load frequency control of microgrid power structures. Literature shows that various soft computing schemes have been employed for this purpose such as neural networks [3], fuzzy logic [4], adaptive neuro-fuzzy logic control [5], Fractional order controller [6], Grey wolf optimization [7], differential evolution [8], particle swarm optimization [9], ant colony optimization [10], artificial bee colony [11], hybrid optimization [12], imperialist competitive algorithm [13], genetic algorithm [14,15], teaching-learning based optimization [16][17][18][19], cohort intelligence optimization [20][21][22][23][24]. The following section 2 gives details of the multi-power interconnected microgrid architecture considered for load frequency control in the present study.…”

Section: Introductionmentioning

confidence: 99%

Teaching Learning based Optimization and Cohort Intelligence for Stochastic Microgrid Power System

Murugesan¹,

K²,

Shah

et al. 2023

Preprint

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Interconnected multi-area microgrids are the future of sustainable and reliable power systems. Microgrids require effective load frequency control for stable operation. This paper presents a proportional–integral–derivative (PID)based load frequency controller(LFC) for a stochastic microgrid power system having multiple power sources viz. 6 solar, wind, diesel engine generator, and electrical battery. The gain parameters of the proposed microgrid PID LFC 7 regulator were optimized by using a genetic algorithm (GA), teaching learning-based optimization (TLBO), and cohort 8 intelligence algorithms. The integral time-multiplied absolute error (ITAE) and integral time-squared error(ITSE) were 9 used as the cost functions for all algorithms. The performances of all optimized microgrid PID LFC architectures 10 were examined by applying random step load disruption conditions. Primary results show that the cohort intelligence 11 optimized PID LFC controller minimizes the computation time and attains superior robust response characteristics. The 12 cohort intelligence algorithm also consumes a lesser number of iterations and improves the power supply quality in the 13 multi-power micro-grid electrical power framework with regard to effective load frequency control characteristics.

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Cohort intelligence optimization based controller design of isolated and interconnected thermal power system for automatic generation control

Cited by 9 publications

References 21 publications

Design of an Optimal Fractional Complex Order PID Controller for Buck Converter

Design of an Optimal Fractional Complex Order PID Controller for Buck Converter

Stability Control Analysis of a Dung Beetle Optimizer-Based PI λ D μ Controller for a Multisource Interlinked Power Grid

Teaching Learning based Optimization and Cohort Intelligence for Stochastic Microgrid Power System

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