基于神经网络的配网系统光伏输出功率控制分析

Analysis of Photovoltaic Output Power Control of Distribution Network System Based on Neural Network

摘要:

随着目前世界上的能源需求愈发扩大,光伏发电凭借其储备量大,且清洁无污染的特性,逐步成为目前新能源发电的主流,但是,由于光伏发电效率受到环境光照强度的影响,因此,其输出功率时时发生变化,所以,目前光伏电池的最大功率跟踪(Most Power Point Trace,MPPT)与控制技术已经成为了业界最为关注的问题。针对该问题,本文利用BP神经网络技术对光伏电池的最大输出功率进行检测以及控制,通过对光伏系统以及人工神经网络的基本原理进行介绍,引入了BP神经网络的基本概念,最后搭建了基于BP神经网络的配网光伏输出功率控制系统,通过仿真,证明了其理论的可行性与正确性,能够为我国光伏产业提供一定帮助。

Abstract:

With the increasing demand for energy in the world, photovoltaic power generation has gradually become the mainstream of new energy power generation due to its large reserve volume and clean and non-polluting characteristics. However, because the efficiency of photovoltaic power generation is affected by the environmental lighting intensity, so, Its output power changes from time to time, so at present, the maximum power tracking(MPPT) and control technology of photovoltaic cells have become the most concerned issues in the industry. Aiming at this problem, this paper uses BP neural network technology to detect and control the maximum output power of photovoltaic cells. By introducing the basic principles of photovoltaic systems and artificial neural networks, the basic concept of BP neural network is introduced. Finally, a power control system based on BP neural network is built. Through simulation, the feasibility and correctness of the theory are proved, which can provide some help to the photovoltaic industry in China.