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Photovoltaic (PV) power classification model has become a significant alternative to PV power point forecasting model in which classes of future PV powers are estimated. In this work, a novel PV power classification model which could be realized using feed forward neural network (FFNN) trained with a hybrid meta-heuristic approach is proposed to forecast the classes of future PV powers. The insisted classification model is implemented to classify three classes of PV power for the location Oregon city of United States of America (USA) with latitude ${43.8041^0}N$ 43.8041 0 N , longitude ${120.5542^0}W$ 120.5542 0 W , and altitude of $1124$ 1124 m. The meta-heuristic approach comprising of Levy Flight (Levy)-Sine Cosine Algorithm (SCA)-Particle Swarm Optimization (PSO) is utilized for training the parameters of FFNN by minimizing the root mean square error $\left({{\rm{RMSE}}} \right)$ RMSE . The results disclose that the proposed approach attained the most accurate prediction of PV power with $\% {\rm{MAPE}} = 2.38$ % MAPE = 2.38 for the month of November as well as high classification accuracy with ${\rm{PCA}} = 94.96\% $ PCA = 94.96 % in the month of April. Based on the obtained results, the proposed LF-SCA-PSO trained FFNN outperformed the existing hybrid models in both prediction and classification and hence possess the potential to be a new alternative to assist engineers in predicting the PV power of solar systems at short- and long-time horizons. [ABSTRACT FROM AUTHOR]

This paper presents skin color enhancement based on favorite skin color to agree with user‐defined favorite skin color using improved histogram equalization with variable enhancement degree (IHEwVED) and machine learning methods. The skin color to be adjusted in the input image is shifted to favorite skin color by using novel control parameters of the proposed IHEwVED method. Three different novel display device‐dependent color image processing methods are introduced based on hsv and yiq color space to obtain the desired enhanced output images. A reduced convolutional neural network and the novel ensemble extreme learning machine (EELM) architectures are developed and implemented in a field‐programmable gate array to test, synthesize, and validate the recognition capability of the user‐defined favorite skin color. The less computational complex proposed IHEwVED‐EELM method recognizes 45 to 50 favorite skin color per second of test images by consuming 0.035 second training time with training root mean square error (RMSE) of 0.0048 and testing RMSE of 0.01208. Finally, a stand‐alone favorite skin color restoration system is developed using the high‐speed video processor NI‐PXI‐1031 based on the IHEwVED‐EELM method in the Python‐OpenCV environment. The laboratory experimental performances ascertain the real‐time ability of the proposed favorite skin color restoration method. [ABSTRACT FROM AUTHOR]

This study presents a novel approach using adaptive variational mode decomposition with detrended fluctuation analysis to detect the islanding disturbances for photovoltaic based DC micro grid. DC parameters are simple to estimate in comparison to AC profile. Thus DC parameters are recorded under islanding scenario, and processed through proposed adaptive variational mode decomposition which decomposes the signals into intrinsic mode functions. These segregated intrinsic mode functions are further selected optimally by choosing the significant weighted kurtosis index. This optimal selection (maximisation of weighted kurtosis index) is ensured by modified particle swarm optimisation in terms of number of modes (K) and penalty factor (σ). For detection and monitoring (D&M) accurate islanding scenario the significant intrinsic mode functions are subjected to detrended fluctuation analysis, where power exponent (α) values are utilised for correct detection (i.e. distinguishing islanding out of other grid contingencies by two and three dimensional scattering plots). The effectiveness of the proposed D&M for DC micro grid is established in this paper in terms of classification accuracy and relative computational time. The proposed DC side islanding D&M method is less complex (as compared to AC signals) to be implemented. Fastness and accuracy of proposed D&M is established and performed in MATLAB/Simulink platforms. [ABSTRACT FROM AUTHOR]