Design of Fuzzy TS-PDC Controller for Electrical Power System via Rules Reduction Approach

Alshammari, Badr M.; Salah, Rim Ben; Kahouli, Omar; Kolsi, Lioua

doi:10.3390/sym12122068

Cited by 7 publications

(4 citation statements)

References 25 publications

(33 reference statements)

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“…The output obtained from the fuzzy set is expressed as a crisp value using the defuzzification process. In this case, the centroid was chosen as the defuzzification method [32].…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller Optimization Techniques

Eswararao¹,

Sundaram²,

Chenniappan

et al. 2022

IEEE Access

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The main aim of this paper is to introduce a framework for the design and modelling of a photovoltaic (PV)-wind hybrid system and its control strategies. The purpose of these control techniques is to regulate continuous changes in the operational requirements of the hybrid system;currently, in power system networks, the distribution of energy plays a major role in maintaining power reliability in distribution systems. In this study, the proposed hybrid system was incorporated with a combined PV and wind energy system. Maximum power point tracking (MPPT) methods have been proposed to achieve maximum efficiency from the designed system. In addition, this study focused on improving the stability of the hybrid system. To improve the power quality and transient stability of the proposed system, we introduce a novel control strategy called the distributed power flow controller (DPFC) implementation with an optimization technique called the lion optimization algorithm(LOA)technique. This LOA control technique was developed for the first time in the application of a DPFC controller in a grid-connected system. The control technique was developed using signals from the system parameters, that is, voltage and current. To tune these parameters, this study used fuzzy logic and lion optimization techniques. The proposed system with controllers was tested in MATLAB/Simulink and the results were compared.

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“…The output obtained from the fuzzy set is expressed as a crisp value using the defuzzification process. In this case, the centroid was chosen as the defuzzification method [32].…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller Optimization Techniques

Eswararao¹,

Sundaram²,

Chenniappan

et al. 2022

IEEE Access

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“…Careful consideration and analysis are required to ensure the optimal selection of premise variables and the appropriate number of fuzzy rules in the T-S fuzzy model. Moreover, researchers have developed innovative control strategies based on T-S fuzzy control, such as the parallel distributed compensation (PDC) controller 19 , which enables distributed control and effective handling of system complexity. The stability and performance of T-S fuzzy control systems are often analyzed and ensured using mathematical techniques like Linear Matrix Inequalities (LMI) [20][21][22] , which provide a powerful framework for stability analysis and controller design, guaranteeing robust stability and performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Stability Control of Inverted Pendulum Using Takagi-Sugeno Fuzzy Model with Disturbance Rejection and Input-Output Constraints

Nguyen

Dong

Bui

2023

Preprint

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The Takagi-Sugeno (T-S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T-S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T-S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T-S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method.

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“…Careful consideration and analysis are required to ensure the optimal selection of premise variables and the appropriate number of fuzzy rules in the T–S fuzzy model. Moreover, researchers have developed innovative control strategies based on T–S fuzzy control, such as the parallel distributed compensation (PDC) controller 21 , which enables distributed control and effective handling of system complexity. The stability and performance of T–S fuzzy control systems are often analyzed and ensured using mathematical techniques like Linear Matrix Inequalities (LMI) 22 – 24 , which provide a powerful framework for stability analysis and controller design, guaranteeing robust stability and performance.…”

Section: Introductionmentioning

confidence: 99%

Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

Nguyen,

Dong,

BUI

2023

Sci Rep

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The Takagi–Sugeno (T–S) fuzzy model is a versatile approach widely used in system control, often in combination with other strategies. This paper addresses key control challenges linked to the T–S system and presents important considerations to ensure its successful application using the Lyapunov theorem. One crucial aspect is determining the optimal number of premise variables and selecting accurate fuzzy rules for the T–S model. Additionally, the theorem based on Linear Matrix Inequality (LMI) is developed to enable effective disturbance rejection. To enhance stability control, constraints are imposed on the output angle and control input of a rotary inverted pendulum (RIP). By integrating T–S fuzzy control, disturbance rejection, and input/output constraints, robust stability in controlling the RIP is achieved. Extensive simulations are performed to showcase the efficiency of the suggested method, and the simulation results are thoroughly discussed and analyzed to verify the efficacy of the control method.

show abstract

Design of Fuzzy TS-PDC Controller for Electrical Power System via Rules Reduction Approach

Cited by 7 publications

References 25 publications

Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller Optimization Techniques

Performance Improvement of Grid Interfaced Hybrid System Using Distributed Power Flow Controller Optimization Techniques

Enhancing Stability Control of Inverted Pendulum Using Takagi-Sugeno Fuzzy Model with Disturbance Rejection and Input-Output Constraints

Enhancing stability control of inverted pendulum using Takagi–Sugeno fuzzy model with disturbance rejection and input–output constraints

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