High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.
Various maximum power point tracking (MPPT) methods used in solar generation systems have one common point, that is, they all select the output of the solar array as their control target and try to make it maximum. However, a typical solar generation system consists of a solar array and a DC/DC converter. Making the output of the solar array maximum does not certainly result in the maximum DC/DC converter output because the efficiency of the DC/DC converter is dependent upon its input voltage and current and will affect the overall output of the system. This paper describes a new MPPT technique which considers the solar array and the DC/DC converter as one system and maximizes the overall system output. The experiment results verified the validity of the method and some discussions on the new method are made.I.
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