Optimized fuzzy self-tuning PID controller design based on Tribe-DE optimization algorithm and rule weight adjustment method for load frequency control of interconnected multi-area power systems

Jalali, Neda; Razmi, Hadi; Doagou‐Mojarrad, Hasan

doi:10.1016/j.asoc.2020.106424

Cited by 60 publications

(27 citation statements)

References 30 publications

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“…Another adaptive droop controller with the fuzzy-PI controller has been optimized using the genetic algorithm optimizer (GA) [44]. A modified fuzzy-PID controller optimized with the tribe-DE optimizer (TDE) has been introduced in [45].…”

Section: A Literature Reviewmentioning

confidence: 99%

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

et al. 2021

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Several issues have been risen due to the recent vast installations of renewable energy sources (RESs) instead of fossil fuel sources in addition to the replacement of electric vehicles (EVs) for fuel-powered vehicles. Mitigating frequency deviations and tie-line power fluctuations has become driving challenge for the control design of interconnected power systems. RESs represent continuously varying power generators due to their nature and dependency on the environmental conditions. In this context, this article presents a new modified hybrid fractional order controller for load frequency and EVs control in interconnected power systems. The new controller combines the benefits of two widely employed fractional order controllers, including the FOPID and TID controllers. In addition, a new practical application of recent artificial ecosystem optimization (AEO) method has been proposed in this article for determining simultaneously the optimum controller parameters. The proposed controller and optimization method are validated on two areas interconnected power system with different types of RESs and with considering the natural characteristics of sources, EVs and load variations. Obtained simulation results verify the superior performance of the proposed controller and optimization method for achieving high mitigation of frequency fluctuations and tie-line power deviations, increased robustness, enhanced system stability over a wide range of parameters uncertainty and fast response during transients.INDEX TERMS Artificial ecosystem optimization, electric vehicles (EVs), fractional order controller, load frequency control, renewable energy sources.

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Section: A Literature Reviewmentioning

confidence: 99%

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

et al. 2021

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“…A whale optimization algorithm has been suggested to tune proportional-integral-derivative controllers and utilized for AVR systems (Mosaad et al, 2019). A fuzzy self-tuning PID controller has been optimized using a tribe-DE optimization algorithm and a rule weight adjustment method for load frequency control of interconnected multi-area power systems (Jalali et al, 2020). Finally, an optimal fuzzy adaptive robust PID control system has been implemented via a particle swarm optimization scheme for an active suspension system (Mahmoodabadi & Nejadkourki, 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimal design of an adaptive robust proportional-integral-derivative controller based upon sliding surfaces for under-actuated dynamical systems

2021

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This paper presents a novel adaptive robust proportional-integralderivative (PID) controller for under-actuated dynamical systems via employing the advantages of the PID control and sliding surfaces. The related control gains as adjustable parameters are computed during the control process using gradient descent techniques and the chain derivative rule. Based on the design of control rules, the genetic algorithm optimization is utilized in order to find the optimal values of the learning rates and initial conditions of the control gains. The suggested strategy is implemented successfully to stabilize the cart-pole and ball-beam systems. Lastly, the simulation results demonstrate the efficacy of the proposed controller to control such under-actuated dynamical systems as well as its superiority in comparison with other recently introduced methods.

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“…Therefore, a robust controller is obligatory to overcome these limitations of conventional control techniques. Artificial intelligence (AI) techniques such as artificial neural network (ANN) [22] and fuzzy logic control (FLC) [23] approaches have presented promising results in load frequency control (LFC) problems. Although fuzzy gives a model‐free description, it also has few limitations about the selection of membership functions (MFs) and rules [24].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is a crucial and key challenge in controller design to choose an efficient optimisation technique. To upgrade the performance of different controllers, till now several researchers have employed distinct optimisation techniques such as Moth‐flame optimisation [2], multi‐verse optimiser (MVO) [16], bacterial foraging optimisation algorithm (BFOA) [18], sine–cosine algorithm [21], tribe‐differential evolution (TDE) [23], imperialist competitive algorithm (ICA) [24, 26, 29, 33], salp swarm algorithm (SSA) [34], whale optimisation algorithm [35], Jaya optimisation [36] etc. Moreover, the literature survey reveals that the execution of cascade FLC does not just rely upon the kind of optimisation algorithms employed but also depends upon the configuration of the cascaded controller.…”

Section: Introductionmentioning

confidence: 99%

Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems

Sharma¹,

Dhundhara

Arya³

et al. 2020

IET Renewable Power Generation

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The uncertain demeanour from wind generators and loads adversely affect the grid operational stability. Various control approaches have been explored to remedy the system uncertainties while maintaining generation and load demand balance. This study proposes a fuzzy‐based proportional–fractional integral–derivative with filter controller to sustain frequency stability in wind integrated power systems having different configurations. The controller parameters have been tuned using a recently developed coyote optimisation algorithm (COA). The proposed control approach is executed and validated on three distinct configurations of two‐area power systems. All test models are integrated with a doubly fed induction generator (DFIG) type wind turbine units (WTUs). Different case scenarios have been considered to analyse the efficacy of the proposed control strategy in the presence of WTU. Furthermore, the impact of inertial support delivered by the DFIG‐WTU and higher penetration of wind energy in the power system has been studied. The analysis reveals that the control scheme in coordination with WTU support reduces the stress on a wind turbine during the inertial control scheme and maintains the grid frequency stability under unexpected load disturbances. Stability and robustness analysis are also conducted to verify the validity of the introduced control approach.

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Optimized fuzzy self-tuning PID controller design based on Tribe-DE optimization algorithm and rule weight adjustment method for load frequency control of interconnected multi-area power systems

Cited by 60 publications

References 30 publications

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

Optimal design of an adaptive robust proportional-integral-derivative controller based upon sliding surfaces for under-actuated dynamical systems

Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems

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