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Padmavathi N.,Chilambuchelvan A.,Shanker N. R. 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.2
Maximum Power Point Tracking (MPPT) algorithm performs for maximizing the effi ciency of solar Photo Voltaic (PV) system. The solar photovoltaic system effi ciency reduces due to partial shading and ambient atmospheric condition, which varies with geographic locations. Traditional MPPT systems solve the above problem through diff erent soft computing algorithms such as Perturb and observe (P&O), Flower pollination algorithm (FPA) and Particle swarm optimization (PSO). In P&O, FPA and PSO algorithms, duty cycle of boost converter varies to attain MPPT. The soft computing algorithms in MPPT perform less during the partial shading eff ect or rapid insolation, fl uctuation condition of solar energy. The performance of MPPT with traditional algorithms is reduced due to slow convergence speed and oscillations in tracking by computing algorithms. In this paper, Regression controller based MPPT achieve maximum peak voltage during partial shading eff ect is developed. The regression controller predicts the duty cycle for boost converter based on stored dataset of PV system output voltage and load, during partial shading eff ect or rapid isolation for that particular geographic location. The regression based duty cycle prediction controller is programmed in MATLAB R2018a Simulink. Furthermore, Regression controller is implemented in PV system test bed. The simulation and hardware results of Regression controller based MPPT perform more of about 20%, 16.96% and 15% in effi ciency respectively than PSO, FPA and P&O algorithms during partial shading condition in PV.
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NPFAM: Non-Proliferation Fuzzy ARTMAP for Image Classification in Content Based Image Retrieval
( Anitha K ),( Chilambuchelvan A ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7
A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.
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