光伏电站远程监控系统的研究与实现

发布时间：2018-10-24 21:44

【摘要】：在能源短缺和环境污染问题日益严峻的今天,大力发展新可再生能源如太阳能已成为全球共识,近年来光伏产业发展势头迅猛。太阳能光伏发电是利用太阳能的重要方向之一,然而光伏电站多建设在分散、偏远、环境恶劣的地区,不利于人员值守,从而严重制约了光伏发电技术的推广运用,同时也突出了光伏电站监控的重要性,对光伏电站的实时高效监控和定期维护管理已经成为光伏电站建设必须考虑的重要问题之一。本文综合了ZigBee无线通信和BP神经网络算法,设计了光伏电站远程监控系统。本文提出的远程光伏电站监控系统是集数据采集、数据分析显示、诊断故障为一体的智能应用系统。使用Jennic公司的EK000开发板作为硬件开发平台,JN5139为主要控制芯片,运用ZigBee协议,采用星型网络结构实现主从节点之间的数据传输和采集。软件使用CodeBlocks作为集成开发环境,将编译的代码下载到目标板中；选择了具有较强非线性映射能力、自学习能力和容错能力的BP神经网络算法进行光伏电站故障诊断,设计了光伏电站主要故障的输入与输出算法,并进行网络训练,建立了基于BP的光伏电站智能故障诊断系统,MATLAB训练结果证明该算法能够准确地诊断光伏电站故障的具体类型；最后通过LabVIEW虚拟仪器进行监控界面设计,实现了对采集到的数据进行显示与处理,利用VISA串口通信技术与ZigBee进行通信,并建立了基于LabVIEW的BP故障诊断系统,能够在监控界面上显示诊断过程,使用Access数据库存储数据,从而起到了对光伏电站的有效自动控制。实验结果表明,该远程光伏电站监控系统能够稳定可靠运行,并具有组网简单、花费少、维护性好等优点,达到了预期的效果。本文研究的光伏电站远程监控系统能够进行实时监测,快速确定故障原因,并存储监测的记录数据,有利于工作人员迅速准确地排除故障,实现了光伏电站监控的功能。最后提出本文可以改进的地方,并对它的发展作出了展望。
[Abstract]:Nowadays, energy shortage and environmental pollution are becoming more and more serious. It has become a global consensus to develop new renewable energy sources such as solar energy. In recent years, photovoltaic industry is developing rapidly. Solar photovoltaic power generation is one of the important directions of solar energy utilization. However, the construction of photovoltaic power plants in scattered, remote and harsh areas is not conducive to the personnel on duty, which seriously restricts the popularization and application of photovoltaic power generation technology. At the same time, it also highlights the importance of photovoltaic power station monitoring, real-time and efficient monitoring and regular maintenance management has become one of the important issues that must be considered in the construction of photovoltaic power station. In this paper, ZigBee wireless communication and BP neural network algorithm are integrated, and the remote monitoring system of photovoltaic power station is designed. The remote photovoltaic power station monitoring system proposed in this paper is an intelligent application system which integrates data acquisition, data analysis and fault diagnosis. The EK000 development board of Jennic Company is used as the hardware development platform, the JN5139 is the main control chip, and the star network structure is used to realize the data transmission and acquisition between the master and slave nodes by using ZigBee protocol. The software uses CodeBlocks as the integrated development environment, downloads the compiled code to the target board, selects the BP neural network algorithm with strong nonlinear mapping ability, self-learning ability and fault-tolerant ability for photovoltaic power station fault diagnosis. The input and output algorithms of the main faults of photovoltaic power station are designed, and the network training is carried out, and the intelligent fault diagnosis system of photovoltaic power station based on BP is established. The result of MATLAB training proves that the algorithm can accurately diagnose the specific type of fault of photovoltaic power station. Finally, the monitoring interface is designed by LabVIEW virtual instrument, the data collected is displayed and processed, the communication between ZigBee and ZigBee is realized by VISA serial port communication technology, and the BP fault diagnosis system based on LabVIEW is established. It can display the diagnosis process on the monitor interface and store the data by using Access database, thus playing an effective and automatic control of photovoltaic power station. The experimental results show that the remote photovoltaic power station monitoring system can operate stably and reliably, and has the advantages of simple networking, low cost and good maintenance. The remote monitoring system of photovoltaic power station studied in this paper can real-time monitor, quickly determine the cause of failure, and store the recorded data of monitoring, which is helpful for the staff to quickly and accurately troubleshoot the fault, and realize the function of photovoltaic power station monitoring. Finally, the paper puts forward some improvements and prospects for its development.
【学位授予单位】：扬州大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM615

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