Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Abstract

Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV) generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM) technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC) maximum power point tracking (MPPT) technique that is based on proportional integral (PI) controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN), ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO) techniques and their performance are compared with existing models like back propagation (BP) forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network