Short-term photovoltaic power forecasting method based on CDA-BP for microgrid

Abstract

The invention relates to a short-term photovoltaic power forecasting method based on CDA-BP for a microgrid, belonging to the technical field of photovoltaic power generation. The randomness and fluctuation of photovoltaic power make it difficult to achieve the ideal precision of photovoltaic power forecasting, and improving the forecasting accuracy is the premise of optimal load distribution in amicro-grid. Therefore, the invention provides a short-term photovoltaic power forecasting method of a microgrid based on a chaotic dragonfly algorithm optimized BP neural network (CDA-BP neural network). Based on the influence of solar irradiation rate and temperature rate on photovoltaic output power, the chaotic dragonfly algorithm (CDA) is used to optimize the connection weight coefficient andthreshold of BP neural network model, and the optimal photovoltaic power prediction model is obtained. The CDA adopts chaotic search to prevent the algorithm from falling into local optimization, anduses adaptive inertial weighting factor to improve the convergence rate. The invention has good prediction accuracy for short-term photovoltaic power generation of a microgrid.

Description

technical field [0001] The invention belongs to the technical field of photovoltaic power generation, and relates to a method for predicting short-term photovoltaic power generation power of a microgrid based on CDA-BP. Background technique [0002] The microgrid can promote the access of distributed clean energy, reduce environmental pollution and reduce power transmission loss, and improve the reliability of power supply for users by switching between isolated and grid-connected modes. Photovoltaic power forecasting is an important part of the energy management system of the microgrid. It is the basis for optimal dispatching and optimal load distribution of controllable microsources such as microgas turbines, diesel engines, and energy storage. The prediction results will directly affect the microgrid operation strategy and electricity trading. Relevant studies have shown that higher prediction accuracy of photovoltaic power generation in microgrids can improve the overal...

AI Technical Summary

Problems solved by technology

BP neural network is a multi-layer feed-forward neural network based on error backpropagation. It has strong robustness, can approach nonlinearity infinitely and has strong learning ability, but the convergence speed is slow, which is affected by the weight coefficient and threshold parameter. has a greater influence, and may also converge to a local optimum

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