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-This paper presents an exploration on the effect of wind turbine contribution to the frequency control of individual systems that can be used for efficient power production in India. The research includes the study of Permanent Magnet Synchronous Generator (PMSG), in wind farms. The WTs are tested for inertia and for droop responses with intelligent fuzzy logic controllers (FLC) that choose Double Input Single Output (DISO) strategy that automatically sets gain constants, as well as combined responses for the WTs. Quantitative analyses are presented for the WTs for benefits and drawbacks including appropriate selection parameters. The analysis includes inertia, droop and combined inertia, droop schemes. The reconnaissance also incorporates inertia with FLC, droop with FLC, inertia and droop with FLC schemes for detailed study of WTs, so as to forecast and achieve proper frequency control. Moreover, the analysis provides the best suited method for frequency control in PMSG.

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in:Pattern Recognition Letters, 34.16 (2013) AbstractThis paper presents an approach for skin detection which is able to adapt its parameters to image data captured from video monitoring tasks with a medium field of view. It is composed of two detectors designed to get high and low probable skin pixels (respectively, regions and isolated pixels). Each one is based on thresholding two color channels, which are dynamically selected. Adaptation is based on the agreement maximization framework, whose aim is to find the configuration with the highest similarity between the channel results. Moreover, we improve such framework by learning how detector parameters are related and proposing an agreement function to consider expected skin properties. Finally, both detectors are combined by morphological reconstruction filtering to keep the skin regions whilst removing wrongly detected regions. The proposed approach is evaluated on heterogeneous human activity recognition datasets outperforming the most relevant state-of-the-art approaches.

Electroencephalogram (EEG) is the recording of electrical activities of the brain. It is contaminated by other biological signals, such as cardiac signal (electrocardiogram), signals generated by eye movement/eye blinks (electrooculogram) and muscular artefact signal (electromyogram), called artefacts. Optimisation is an important tool for solving many real-world problems. In the proposed work, artefact removal, based on the adaptive neuro-fuzzy inference system (ANFIS) is employed, by optimising the parameters of ANFIS. Artificial Immune System (AIS) algorithm is used to optimise the parameters of ANFIS (ANFIS-AIS). Implementation results depict that ANFIS-AIS is effective in removing artefacts from EEG signal than ANFIS. Furthermore, in the proposed work, improved AIS (IAIS) is developed by including suitable selection processes in the AIS algorithm. The performance of the proposed method IAIS is compared with AIS and with genetic algorithm (GA). Measures such as signal-to-noise ratio, mean square error (MSE) value, correlation coefficient, power spectrum density plot and convergence time are used for analysing the performance of the proposed method. From the results, it is found that the IAIS algorithm converges faster than the AIS and performs better than the AIS and GA. Hence, IAIS tuned ANFIS (ANFIS-IAIS) is effective in removing artefacts from EEG signals.

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