Takagi–Sugeno fuzzy modeling for three-phase micro hydropower plant prototype

Hamdaouy, Achour El; Salhi, Issam; Belattar, Abdellatif; Doubabi, Saïd

doi:10.1016/j.ijhydene.2017.02.167

Cited by 12 publications

(9 citation statements)

References 30 publications

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“…Thus, a controller system is required. In the literature, there are several known PHPP frequency regulations which have been widely used [10,[24][25][26][27]. The first, the speed-flow controller (SFC) consists to adjust the water flow reaching the hydraulic turbine in order to keep constant turbine speed (frequency constant).…”

Section: Controller Systemmentioning

confidence: 99%

“…On the other hand, the presented system uses a PLC's proportional integral derivative (PID) bloc which maintains the frequency around its standard value. The system does not have the flexibility to improve the PHPP frequency response using advanced controllers [7][8][9], while the PHPP system is non-linear system [4,5,[10][11][12]. The proposed controller could not guarantee good performances for all operating points.…”

Section: Introductionmentioning

confidence: 99%

“…The renewable energies such as solar, hydro and wind, could be an attractive alternative. In this context, it is suitable to build runof-river pico-hydroelectric power plants (PHPPs) in remote mountainous regions that cannot be connected to national electric grid [4,5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of a Low-Cost Autonomous Controller, Management and Security System for Pico-Hydroelectric Power Plants

Hamdaouy¹,

Salhi²,

Dahbi³

et al. 2020

JESA

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In this paper, a new controller, management and security system (CMSS) was proposed to ensure the overall operation of pico-hydroelectric power plants (PHPPs) at low-cost, especially in off-grid sites in remote areas. The proposed system can be used to meet the requirements of the PHPP such as the start-up, the departures' management, the loadfrequency controller (LFC), the emergency stop (ES), and the normal stop procedures in order to guarantee good quality and sustainable services. The system was tested under various conditions to prove its reliability using PHPP prototype. Based on the prototype, the laboratory experimental results are presented. The design's details and the implementation of the system are given as well. The system is completely independent, and it achieved an initial cost of only 266 $. The flexibility makes the system more suitable for extended future improvement such as adding data-logger, adding communication aspects using wireless technologies, etc. Furthermore, the system could be easily adapted for different educational purposes, especially for universities with limited resources.

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Section: Controller Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Low-Cost Autonomous Controller, Management and Security System for Pico-Hydroelectric Power Plants

Hamdaouy¹,

Salhi²,

Dahbi³

et al. 2020

JESA

Self Cite

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“…The advantage of T-S fuzzy systems is that they allow us to use a set of local linear systems with corresponding membership functions to represent nonlinear systems. The T-S fuzzy model is widely accepted as a powerful modeling tool and it's applications to various kinds of non-linear systems can be found in [4][5][6][7][8][9] . Based on the T-S fuzzy model of a plant, papers [10][11][12][13] introduced a fuzzy control design method for nonlinear systems with a guaranteed H 2/ ∞ model reference tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive multilayer T-S fuzzy controller for nonlinear SISO system optimized by differential evolution algorithm

Kien

Ánh

Sơn

2020

Sci. Tech. Dev. J.- Eng. Tech.

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In this paper, the authors propose a novel adaptive multilayer T-S fuzzy controller (AMTFC) with an optimized soft computing algorithm for a class of robust control uncertain nonlinear SISO systems. First, a new multilayer T-S fuzzy was created by combined multiple simple T-S fuzzy models with a sum function in the output. The multi-layer fuzzy model used in nonlinear identification has many advantages over conventional fuzzy models, but it cannot be created by the writer's experience or the trial and error method. It can only be created using an optimization algorithm. Then the parameters of the multilayer fuzzy model are optimized by the differential evolution DE algorithm is used to offline identify the nonlinear inverse system with uncertain parameters. The trained model was validated by a different dataset from the training dataset to guarantee the convergence of the training algorithm. Second, for robustly and adaptive purposes, the authors have proposed an additional adaptive fuzzy model based on Lyapunov stability theory combined with the optimized multilayer fuzzy. The adaptive fuzzy based on the sliding mode surface is designed to guarantee that the closed-loop system is asymptotically stable has been proved base on a Lyapunov stability theory. Furthermore, simulation tests are performed in the Matlab/Simulink environment that controlling a water level of a coupled tank with uncertain parameters are given to illustrate the effectiveness of the proposed control scheme. The proposed control algorithm is implemented in simulation with many different control parameters, and it is also compared with the conventional adaptive control algorithm and inverse controller. The simulation results also show the superior of the proposed controller than an adaptive fuzzy control or inverse controller when using the least mean square error standard.

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“…In addition, the linear function or the constant function can be calculated, and the calculation efficiency is relatively high. At present, the T-S modeling and control method have been successfully applied to many nonlinear systems, such as inverted pendulum system [21][22][23], the ball bar system [24,25], solar photovoltaic power system [26], aircraft motion control system [27] and helicopter system [28], vehicle system [29][30][31], micro hydropower plant prototype system [32], nonlinear systems with persistent bounded disturbances [33], and so on. However, the modeling and control method is relatively rare in cranes.…”

Section: Introductionmentioning

confidence: 99%

Takagi‐Sugeno Fuzzy Modeling and PSO‐Based Robust LQR Anti‐Swing Control for Overhead Crane

Shao

Zhang

2019

Mathematical Problems in Engineering

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The dynamic model of overhead crane is highly nonlinear and uncertain. In this paper, Takagi-Sugeno (T-S) fuzzy modeling and PSO-based robust linear quadratic regulator (LQR) are proposed for anti-swing and positioning control of the system. First, on the basis of sector nonlinear theory, the two T-S fuzzy models are established by using the virtual control variables and approximate method. Then, considering the uncertainty of the model, robust LQR controllers with parallel distributed compensation (PDC) structure are designed. The feedback gain matrices are obtained by transforming the stability and robustness of the system into linear matrix inequalities (LMIs) problem. In addition, particle swarm optimization (PSO) algorithm is used to overcome the blindness of LQR weight matrix selection in the design process. The proposed control methods are simple, feasible, and robust. Finally, the numeral simulations are carried out to prove the effectiveness of the methods.

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Takagi–Sugeno fuzzy modeling for three-phase micro hydropower plant prototype

Cited by 12 publications

References 30 publications

Design of a Low-Cost Autonomous Controller, Management and Security System for Pico-Hydroelectric Power Plants

Design of a Low-Cost Autonomous Controller, Management and Security System for Pico-Hydroelectric Power Plants

Adaptive multilayer T-S fuzzy controller for nonlinear SISO system optimized by differential evolution algorithm

Takagi‐Sugeno Fuzzy Modeling and PSO‐Based Robust LQR Anti‐Swing Control for Overhead Crane

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