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This paper presents a study on a grid-connected and islanded multiple distributed generation (DG) system for frequency and voltage regulation. The multiple DG system includes solar cells, wind turbine, fuel cell, and battery storage. The H-infinity controller is used whose weighting parameters are optimized to minimize voltage and frequency deviation. The performance of the system is analyzed under different conditions for both grid-connected and islanded modes of operation. In case of the load variations, the inner voltage and current loop react based on the H-infinity control strategies. The outer power loop uses the droop characteristic controller. The design is simulated using MATLAB/SIMULINK. The simulation results show that the multiple DG system can supply high-quality power both in grid-connected and islanded modes. Also, we show that the proposed control methodology will make the system to transit smoothly between the islanded mode and the grid-connected mode. The results indicate that the frequency and voltage deviations meet the nominal values as per IEEE standard.

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Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localiza-tion of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellu-lar localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.

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