Maximum power extraction from wind energy system based on fuzzy logic control

Eltamaly, Ali M.; Farh, Hassan M. H.

doi:10.1016/j.epsr.2013.01.001

Cited by 148 publications

(64 citation statements)

References 9 publications

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“…FLC can simulate human knowledge and reasoning and is robust even when measurements are imprecise or when input characteristics change [34][35][36].To explore the influence of different factors, such as power demand and discharge current, this study uses fuzzy control strategy in the PCCEM strategy. FLC calculates the optimal number of cells connected in the circuit and the value of ΔSOC.…”

Section: Fuzzy Logic Control Strategymentioning

confidence: 99%

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

et al. 2017

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Abstract:Inconsistencies that are associated with parallel-connected cells used in electric vehicles induce varied states of charge (SOCs) in each cell. Thus, loop current in the battery pack is inevitable, and this reduces overall capacity, energy utilization rate, and pack lifetime. However, no method is available to address loop current. To reduce loop current and the resulting battery inconsistency, a parallel-connected cell pack (PCCP) model that considers thermal effects is established, and a novel Simscape model that is based on PCCP is successfully constructed. Furthermore, the strategy of parallel-connected cell energy management (PCCEM) is proposed to utilize fuzzy logic control (FLC) strategy, which automatically adjusts the number of cells in a circuit in accordance with the load demand, and turns on the first N switches in the corresponding SOC order. The New European Driving Cycle (NEDC) driving cycle simulation shows that the PCCEM strategy considerably reduces loop current and improves the consistency of battery performance and the utilization rate of battery power.

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Section: Fuzzy Logic Control Strategymentioning

confidence: 99%

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

et al. 2017

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“…In WECS overall performance, due to larger filter size and lack of the intelligence level, conventional proportional integral (PI), proportional integral derivative (PID), proportional resonant integral (PRI) and quasi-proportional resonance (QPR) controls methods are not being effective. Hence researchers are tending to use intelligent controllers like fuzzy logic controllers (FLC), neural networks, adaptive neuro fuzzy interfacing system (ANFIS) and genetic algorithm (GA) [4][5][6]. Smart grid requires a fast convergence of error, grid fault and power balance to supply uninterrupted power supply to the consumers [7][8][9], thus soft computing techniques are generally employed to support these parameters.…”

Section: Introductionmentioning

confidence: 99%

Modified Fuzzy Logic Based Control Strategy for Grid Connected Wind Energy Conversion System

Sekhar

Babu

2017

JGE

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This paper analyses the modelling and control of the grid connected wind energy conversion system (WECS). The required power for grid connected load is effectively supplied by the proposed wind turbine, permanent magnet. And at the grid connected voltage source inverter utilizes a simple FLC with hysteresis current controller (HCC), which suits well for variable speed operation of a wind turbine with direct driven PMSG. Detailed modelling and controller strategies of overall system performance during the transient and dynamic conditions of the proposed system are analyzed to show the effectiveness of the controller strategies. The simulations have been done using MATLAB/Simulink.

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“…Fuzzy logic controller (FLC) is developed for maximum power extraction at below rated wind speed [7]. The main aim of the FLC is to track the optimal power by adjusting the rotational speed of the turbine.…”

Section: Introductionmentioning

confidence: 99%

Backstepping sliding mode control of a variable speed wind turbine for power optimization

Saravanakumar

Jena

2015

J. Mod. Power Syst. Clean Energy

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To optimize the energy capture from the wind, wind turbine (WT) should operate at variable speed. Based on the wind speed, the operating regions of the WT are divided into two parts: below and above the rated wind speed. The main aim at below rated wind speed is to maximize the energy capture from the wind with reduced oscillation on the drive train. At above rated wind speed, the aim is to maintain the rated power by using pitch control. This paper presents the control of WT at below rated wind speed by using backstepping sliding mode control (BSMC). In BSMC, generator torque is considered as the control input that depends on the optimal rotor speed. Usually, this optimal rotor speed is derived from effective wind speed. In this paper, effective wind speed is estimated from aerodynamic torque and rotor speed by using the modified Newton Rapshon (MNR) algorithm. Initially, a conventional sliding mode controller (SMC) is applied to the WT, but the performance of the controller was found to be less robust with respect to disturbances. Generally, WT external disturbance is not predictable. To overcome the above drawback, BSMC is proposed and both the controllers are tested with mathematical model and finally validated with the fatigue, aerodynamics, structures, and turbulence (FAST) WT simulator in the presence of disturbances. From the results, it is concluded that the proposed BSMC is more robust than conventional SMC in the presence of disturbances.

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Maximum power extraction from wind energy system based on fuzzy logic control

Cited by 148 publications

References 9 publications

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

Energy Management of Parallel-Connected Cells in Electric Vehicles Based on Fuzzy Logic Control

Modified Fuzzy Logic Based Control Strategy for Grid Connected Wind Energy Conversion System

Backstepping sliding mode control of a variable speed wind turbine for power optimization

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