Modelling and Prediction of Surface Roughness and Power Consumption Using Parallel Extreme Learning Machine Based Particle Swarm Optimization

Prediction model allows the machinist to determine the values of the cutting performance before machining. Modelling using improved extreme learning machine based particle swarm optimization, IPSO-ELM has less parameters to adjust and also takes real number as particles while decreasing the norm of output weights and constraining the input weight and hidden biases within a reasonable range to improve the ELM performance. In order to solve the multi objectives modelling problem, we have proposed a parallel IPSO-ELM. In this research work, the best input weights and hidden biases for different performance were identified. The proposed method was able to model the training and the testing set with minimal error. The predicted result from the designed model was able to match the experimental data very closely

Experimental investigation of soiling impact on grid connected PV power

WOS:000518455400047Algeria has large territory which is around 2,382 million km with an average solar power of 2650 kWh/m(2)/year on more than 80 % of the total surface of country. Back in 2015, the Algerian government has adopted a program for electricity production with an objective to insert 13GW of photovoltaic (PV) systems, which corresponds to an estimated modules area of 90 km(2) distributed over the national territory. The PV systems choice is justified by the availability of a great solar potential. Nonetheless, the Sahara regions are characterized by frequent sandstorms. But the Algerian Northern regions are characterized by exhaust emissions of carbon particles. The main objective of our study is to show the impact of the soil on grid connected PV performance in coastal regions. Hence, experiments have been conducted on clean and dirty PV modules glazing in natural conditions to determine the electrical characteristics. It was found, that the dirt can significantly minimize the power production during the day, for an exposure period of one month and half after the last cleaning. It is very important to indicate that, this experimental investigation can help forecasting the PV generator energy production by taking into account of dirt effects. (C) 2019 Published by Elsevier Ltd