Author
W. Ngamkham
Bio: W. Ngamkham is an academic researcher from Mahanakorn University of Technology. The author has contributed to research in topics: PIC microcontroller & Maximum power principle. The author has an hindex of 2, co-authored 2 publications receiving 43 citations.
Papers
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01 Nov 2005
TL;DR: In this paper, a maximum power point tracking algorithm using an artificial neural network (ANN) for a solar power system is presented. But, it is not shown that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power.
Abstract: This paper presents the development of a maximum power point tracking algorithm using an artificial neural network for a solar power system. By applying a three layers neural network and some simple activation functions, the maximum power point of a solar array can be efficiently tracked. The tracking algorithm integrated with a solar-powered battery charging system has been successfully implemented on a low-cost PIC16F876 RISC-microcontroller without external sensor unit requirement. The experimental results with a commercial solar array show that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power in steady state operation. The overall system efficiency is well above 90%.
26 citations
28 Nov 2005
TL;DR: In this article, the authors presented the development of a maximum power point tracking algorithm using an artificial neural network for a solar power system by applying a three layers neural network and some simple activation functions.
Abstract: This paper presents the development of a maximum power point tracking algorithm using an artificial neural network for a solar power system By applying a three layers neural network and some simple activation functions, the maximum power point of a solar array can be efficiently tracked The tracking algorithm integrated with a solar-powered battery charging system has been successfully implemented on a low-cost PIC16F876 RISC-microcontroller without external sensor unit requirement The experimental results with a commercial solar array show that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power in steady state operation The overall system efficiency is well above 91%
17 citations
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TL;DR: A genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive to eliminate dc–dc converter and its accompanying losses is proposed.
148 citations
01 Oct 2006
TL;DR: Experimental results with a commercial PV array show that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power in steady state operation.
Abstract: This paper describes FPGA implementation of a Maximum Power Point Tracking (MPPT) for Photovoltaic (PV) applications. By slightly modifying the original algorithm, an improved variable step-size P&O algorithm is realized and efficiently implemented using a hard-ware description language (VHDL). Subsequently, the new MPPT algorithm integrated with a solar-powered battery charging system is implemented on the XC2C384 FPGA without external sensor unit requirement. Experimental results with a commercial PV array show that the proposed algorithm outperforms the conventional controller in terms of tracking speed and mitigation of fluctuation output power in steady state operation. The overall system efficiency is well above 96%.
116 citations
24 Dec 2012
TL;DR: The proposed two-stage off-line trained ANN based MPPT technique offers enhanced performance even under rapidly changing environmental conditions, no need for temperature/irradiance measurement, in addition to reduced required training sets because of the presented ANN cascaded structure.
Abstract: The dependency of photovoltaic (PV) arrays on temperature and irradiance levels shapes their known nonlinear behavior; hence maximum power point tracking (MPPT) is mandatory. Traditional MPPT techniques, like Perturb and Observe (P&O) and Incremental Conductance (IncCond), offer acceptable performance with a trade-off between accuracy and fast operation. Moreover, moderate operation is remarked at rapidly changing environmental conditions. On the contrary, off-line trained artificial neural network (ANN) is considered as accurate, fast and robust estimation technique. In this paper, a two-stage off-line trained ANN based MPPT technique is proposed where two cascaded ANNs are utilized. The first estimates the temperature and irradiance levels from the array voltage and current signals while the other network determines the optimum peak operating point from the temperature and irradiance, estimated by the first ANN. The proposed technique offers enhanced performance even under rapidly changing environmental conditions, no need for temperature/irradiance measurement, in addition to reduced required training sets because of the presented ANN cascaded structure.
41 citations
TL;DR: In this paper, the authors present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN).
Abstract: To enhance the production of solar photovoltaic (SPV)-based cleaner energy, the maximum power point (MPP) tracking (MPPT) schemes are utilized. To ensure a reliable and effective MPP extraction from SPV systems, the exploitation and implementation of different MPPT schemes are of great significance. This article intends to present a rigorous and comprehensive review of MPPT schemes in SPV systems under partial shading (PS) conditions based on a meta-heuristic approach and artificial neural network (ANN). In recent years, modern optimization-based global MPP (GMPP) extraction schemes are gaining much attention from researchers. In this review article, thirteen modern optimizations and ANN-based GMPP tracking techniques are vividly described with their flowchart and detailed mathematical modeling. This work assesses all the schemes according to parameters like tracking efficacy, tracking time, application, sensed parameters, converter utilized, steady-state oscillations, experimental setup, and key notes. Based on the rigorous review, a novel GMPP extraction scheme based on a recently introduced meta-heuristic approach named artificial gorilla troops optimizer is proposed. This review work serves as a source of comprehensive information about applying these MPPT techniques to extract GMPP from the SPV system under PS conditions; furthermore, it can be considered a one-stop handbook for further study in this field.
22 citations
01 Nov 2007
TL;DR: In this article, the authors used an adjustable self-organizing fuzzy logic controller (SOFLC) for a solar-powered traffic light equipment (SPTLE) with an integrated maximum power point tracking (MPPT) system on a low-cost microcontroller.
Abstract: This paper presents the development of maximum power point tracking (MPPT) using an adjustable self-organizing fuzzy logic controller (SOFLC) for a solar-powered traffic light equipment (SPTLE) with an integrated maximum power point tracking (MPPT) system on a low-cost microcontroller The proposed system is integrated with a boost converter for realizing of high performance SPTLE, whose adaptability properties are very attractive for operation of a solar array power tracking in dynamic environments The proposed MPPT scheme obtained by varying the duty ratio for DC- DC boost converter has been successfully implemented on a low-cost PIC16F876A RISC-microcontroller Experimental results of the hardware prototypes for SPTLE, light flasher and light chevron, with commercial solar array show that our proposed MPPT using SOFLC as compared with fuzzy logic controller (FLC) in terms of tracking speed with 92% of overall system efficiency
17 citations