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Maximum power point tracking using hybrid fuzzy based p&o and back propagation (BP) neural network for photovoltaic system
(2014-10-01)
Konghuayrob, Poom
;
Kaitwanidvilai, Somyot
Photovoltaic system is one of the most popular renewable energy sources to solve the problem of energy crisis. The important problem of solar PV systems is their low efficiency and nonlinear output characteristics in the changing weather that causes the difficulty in tracking of maximum power. To overcome this problem, this paper proposes “Hybrid Fuzzy based P&O and Back Propagation (BP) neural network” for improving the efficiency and reducing the power oscillation of PV system. The proposed system composes of two parts which are fuzzy based P&O and Neural Network. The fuzzy based P&O is used to find the maximum power point (MPP) while the neural network is used to find the appropriate modulation index (ma). The proposed algorithm is adopted in the AC module flyback inverter in which modulation index (Δma) is used as the control variable to track the MPP of the PV array. Simulation results verify that the proposed technique can effectively track the power from PV system, and is better than the conventional P&O techniques
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Maximum power point tracking using fuzzy logic control for photovoltaic systems
(2011-07-26)
Takun, Pongsakor
;
Kaitwanidvilai, Somyot
;
Jettanasen, Chaiyan
In this paper, a fuzzy logic control (FLC) is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.
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A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control
(2010-04-01)
Khaehintung, Noppadol
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Kunakorn, Anantawat
;
Sirisuk, Phaophak
This paper presents a novel fuzzy logic control technique tuned by particle swarm optimization (PSO-FLC) for maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed PV system composes of a current-mode boost converter (CMBC) with bifurcation control. An optimal slope compensation technique is used in the CMBC to keep the system adequately remote from the first bifurcation point in spite of nonlinear characteristics and instabilities of this converter. The proposed PSO technique allows easy and more accurate tuning of FLC compared with the trialand-error based tuning. Consequently, the proposed PSO-FLC method provides faster tracking of maximum power point (MPP) under varying light intensities and temperature conditions. The proposed MPPT technique is simple and particularly suitable for PV system equipped with CMBC Experimental results are shown to confirm superiority of the proposed technique comparing with the conventional PVVC technique and the trial-and-error based tuning FLC. © ICROS, KIEE and Springer 2010.
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Optimal fuzzy logic-based PID controller for load-frequency control including superconducting magnetic energy storage units
(2008-10-01)
Pothiya, Saravuth
;
Ngamroo, Issarachai
This paper proposes a new optimal fuzzy logic-based-proportional-integral-derivative (FLPID) controller for load frequency control (LFC) including superconducting magnetic energy storage (SMES) units. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the multiple tabu search (MTS) algorithm is applied to simultaneously tune PID gains, membership functions and control rules of FLPID controller to minimize frequency deviations of the system against load disturbances. The MTS algorithm introduces additional techniques for improvement of search process such as initialization, adaptive search, multiple searches, crossover and restarting process. Simulation results explicitly show that the performance of the optimum FLPID controller is superior to the conventional PID controller and the non-optimum FLPID controller in terms of the overshoot, settling time and robustness against variations of system parameters. © 2008 Elsevier Ltd. All rights reserved.
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Design of optimal fuzzy logic-based PID controller using Multiple Tabu Search algorithm for AGC including SMES units
(2007-12-01)
Pothiya, Saravuth
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Ngamroo, Issarachai
;
Kongprawechnon, Waree
This paper proposes a new optimization approach of a Fuzzy Logic based-Proportional-Integral-Derivative (FLPID) controller by the Multiple Tabu Search (MTS) algorithm for Automatic Generation Control (AGC) including Superconducting Magnetic Energy Storage (SMES) units. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the MTS is applied to simultaneously tune PID gains, membership functions and control rules of FLPID controller to minimize frequency deviations of the system against load disturbances. The MTS algorithm introduces additional techniques for improvement of search process such as initialization, adaptive search, multiple searches, crossover and restarting process. Simulation results explicitly show that the performance of the proposed optimum FLPID controller is superior to the conventional PID controller and the non-optimum FLPID controller in terms of the overshoot, settling time and robustness against variations of system parameters. © 2007 RPS.
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Self-tuning fuzzy logic controller for direct torque control of slip energy recovery system
(2001-12-01)
Tunyasrirut, Satean
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Ngamwiwit, Jongkol
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Furuya, Tadayoshi
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Yamamoto, Yoshiicshi
This paper proposes a design self-tuning fuzzy logic controller to control the slip energy recovery system. The control system is designed to maintain efficiency of motor by keeping the speed constant and the good transient response by using direct torque control. Fuzzy logic controller has been designed by genetic algorithm optimization technique as a means to determine and optimize the fuzzy logic controller design is discussed. In the proposed approach normalization factors and/or membership function parameters and/or the controller policy, are translated into bit-strings. These bit-strings are processed by the genetic algorithm and if the selection process as well as the objective function is chosen properly, a near-optimal solution can be found. To examine the efficiency of the proposed approach, a self-tuning fuzzy logic controller for direct torque control the wound rotor induction motor drive is designed the current in dc link circuit. A particular objective function is chosen to achieve a high dynamic performance. The simulation results demonstrate a significant enhancement in shortening the development time, and improving system performance over a conventional fuzzy logic controller.

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