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应用科学学报 >

2013 , Vol. 31 >Issue 6: 613 - 618

DOI: https://doi.org/10.3969/j.issn.0255-8297.2013.06.010

信号与信息处理

改进脉冲耦合神经网络及二维Otsu算法的光伏阵列阴影检测

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  • 1. 重庆大学自动化学院，重庆400044
    2. 国家电网重庆市电力工司南岸供电分公司，重庆400060
胡蓓，博士生，研究方向：新能源系统先进控制与应用、图像识别与处理；段其昌，教授，博导，研究方向：新能源系统先进控制与应用、图像智能计算与处理等，E-mail: qc_d@sina.com

收稿日期: 2012-05-06

  修回日期: 2012-12-27

  网络出版日期: 2012-12-27

基金资助

国家自然科学基金(No.51377187)；重庆市科技攻关项目基金(No.2011AB6054)资助

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Shadow Detection for PV Array Using Improved PCNN and Two-Dimensional Otsu Algorithm

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  • 1. Automation, Chongqing University, Chongqing 400044, China
    2. Chongqing Nan0an Power Company, State Grid Electric Power Company, Chongqing 400060, China

Received date: 2012-05-06

  Revised date: 2012-12-27

  Online published: 2012-12-27

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摘要

阴影对太阳能发电系统输出功率有极大的抑制作用，该文针对光伏阵列局部遮荫现象提出一种基于改进的脉冲耦合神经网络的阴影检测方法. 设置合适的初始参数，根据unit-linking PCNN（ULPCNN）算法进行阴影分割，利用二维Otsu算法自动选取迭代次数，以循环迭代过程中具有最优阈值的分割图像为最终分割结果. 仿真结果表明：该算法可检测出光伏阵列局部阴影，与传统的脉冲耦合神经网络算法及ULPCNN算法相比分割结果更好，操作更简洁.

关键词： 阴影检测; 脉冲耦合神经网络; unit-linking PCNN; 二维Otsu算法

本文引用格式

胡蓓1， 隆霞1， 胡超1， 段盼2， 唐若笠1， 段其昌1 . 改进脉冲耦合神经网络及二维Otsu算法的光伏阵列阴影检测[J]. 应用科学学报, 2013 , 31(6) : 613 -618 . DOI: 10.3969/j.issn.0255-8297.2013.06.010

Abstract

Shadows cause serious reduction of power generation in a photovoltaic (PV) power plant. This paper proposes a shadow detection method based on improved pulse coupled neural networks (PCNN) for partially shaded PV module images. Suitable initial parameters of unit-link PCNN are set. The ULPCNN is applied to the gray shading images, and the two-dimensional Otsu method is used to automatically determine the numbers of iterations. The segmentation that achieves the best threshold in the iteration is selected as an optimal result. Simulations verify that the test images are well segmented, and the method has better performance compared to the conventional PCNN and ULPCNN.

Key words： shadow detection; pulse coupled neural network (PCNN); unit-linking PCNN; two-dimensional Otsu algorithm

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