华电技术

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基于动态代理模型的光伏系统最大功率追踪

谭恬   

  1. 汕头大学
  • 收稿日期:2021-01-25 修回日期:2021-02-14 发布日期:2021-05-18
  • 通讯作者: 谭恬
  • 基金资助:
    国家自然科学基金青年项目;广东省自然科学基金面上项目

Dynamic surrogate model based optimization for maximum power pointtracking of PV systems

Tian TAN   

  1. College of Engineering, Shantou University
  • Received:2021-01-25 Revised:2021-02-14 Published:2021-05-18
  • Contact: Tian TAN

摘要: 光伏发电系统在部分阴影条件下,其功率-电压特性输出曲线会出现多个峰值点。针对该问题,传统的最大功率点追踪算法容易陷入局部最优点且稳定性较差而不再适用。为实现部分阴影遮蔽下最大功率点跟踪,本文设计了一种基于光伏系统的动态代理模型优化方法 。该方法为避免盲目搜索,根据光伏系统的实时数据,采用径向基函数网络构建输入/输出特征的动态代理模型。在动态代理模型的基础上,采用贪婪搜索加速收敛。本文通过三个算例对该方法的实用性和优越性进行了评估,包括恒温恒光照强度启动实验、恒温光照强度阶跃变化和变温变光照强度。并与蚁群算法、灰狼算法、扰动观察法和粒子群算法相比,本文所提出方法在部分阴影条件下能快速稳定地使光伏系统产生更多的能量和更小的功率波动。

关键词: 光伏发电系统, 部分阴影条件, 最大功率点追踪, 动态代理模型优化, 贪婪搜索

Abstract: Under the partial shading condition (PSC), the power-voltage (PV) characteristic output curve of photovoltaic system will have multiple peak points. In order to solve this problem, the traditional maximum power point tracking (MPPT) algorithm is no longer applicable because it is easy to fall into the local optimal point and has poor stability.In order to achieve maximum power point tracking under partial shadow shadowing, a dynamic surrogate model based optimization (DSMO) method for photovoltaic system is designed in this paper. In order to avoid blind search, the radial basis function network is adopted to construct the dynamic agent model of input/output feature base on the real-time data of PV system. Based on the dynamic agent model, greedy search is used to accelerate convergence. In this paper, the practicability and superiority of the method are evaluated by three examples, including constant temperature and constant light intensity start-up experiment, constant temperature light intensity step change and variable temperature light intensity. Compared with ant colony algorithm (ASO), gray wolf optimizer (GWO), perturbation and observation method (P&O) and particle swarm optimization algorithm (PSO), the DSMO method proposed in this paper can quickly and stably generate more energy and smaller power fluctuation in the photovoltaic system under partial shading conditions.

Key words: photovoltaic system, partial shading conditions, maximum power point tracking, dynamic surrogate model, greedy search