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In this manuscript, an image analytics based deep learning framework for wind turbine blade surface damage detection is proposed. Turbine blade(s) which carry approximately one-third of a turbine weight are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. The surface damage detection of wind turbine blade requires a large dataset so as to detect a type of damage at an early stage. Turbine blade images are captured via aerial imagery. Upon inspection, it is found that the image dataset was limited and hence image augmentation is applied to improve blade image dataset. The approach is modeled as a multi-class supervised learning problem and deep learning methods like Convolutional neural network (CNN), VGG16-RCNN and AlexNet are tested for determining the potential capability of turbine blade surface damage., Comment: Introduction/Methodology section needs further review

Forecasting a particular variable can depend upon temporal or spatial scale. Temporal variations that indicate variations with time, reflect the stochasticity present in the variable. Spatial variation usually are dominant in climatology and meteorology. Temporal scale for a variable can be modeled in terms of time-series. A time series is a successively ordered sequence of numerical data points, and can be taken on any variable changing with time. Wind speed forecasting applications lie majorly in the area of electricity market clearing, economic load dispatch and scheduling, and sometimes to provide ancillary support. Thus, a proper classification based on the prediction horizon i.e. the duration of prediction becomes important for various transmission system operators., Comment: 11 pages

In this manuscript, we have investigated the response of the State of Charge (SoC) and the open-circuit voltage across the dynamic battery model under the variable voltage and current during the charging cycle of the battery. These variable input voltage and current have been obtained using the variable irradiance and surface temperature of a Solar PV array which is connected as an input of the dynamic battery model to store the energy within it. In order to match the Simulation result with reality, these variable irradiance and surface temperature of Solar PV Array with respect to time has been simulated. After forming and storing the energy within the dynamic battery model; the SoC of the battery has been estimated using the Kalman filter approach. After the successful estimation of SoC; the Open Circuit Voltage (OCV) and State of Charge (SoC) have been plotted using the polynomial regression technique. The regression plots between the OCV and SoC have been drawn for the polynomial degree of 2, 3,4, and 5. Results reveal that R$^2$ keeps increasing as we increase the degrees of regression. Simultaneously the value of RMSE keeps decreasing as we increase the degree of the polynomial regression., Comment: Results under progress