光伏電站遠程監(jiān)控系統(tǒng)的研究與實現(xiàn)
發(fā)布時間：2018-10-24 21:44
【摘要】：在能源短缺和環(huán)境污染問題日益嚴峻的今天,大力發(fā)展新可再生能源如太陽能已成為全球共識,近年來光伏產業(yè)發(fā)展勢頭迅猛。太陽能光伏發(fā)電是利用太陽能的重要方向之一,然而光伏電站多建設在分散、偏遠、環(huán)境惡劣的地區(qū),不利于人員值守,從而嚴重制約了光伏發(fā)電技術的推廣運用,同時也突出了光伏電站監(jiān)控的重要性,對光伏電站的實時高效監(jiān)控和定期維護管理已經成為光伏電站建設必須考慮的重要問題之一。本文綜合了ZigBee無線通信和BP神經網絡算法,設計了光伏電站遠程監(jiān)控系統(tǒng)。 本文提出的遠程光伏電站監(jiān)控系統(tǒng)是集數(shù)據(jù)采集、數(shù)據(jù)分析顯示、診斷故障為一體的智能應用系統(tǒng)。使用Jennic公司的EK000開發(fā)板作為硬件開發(fā)平臺,JN5139為主要控制芯片,運用ZigBee協(xié)議,采用星型網絡結構實現(xiàn)主從節(jié)點之間的數(shù)據(jù)傳輸和采集。軟件使用CodeBlocks作為集成開發(fā)環(huán)境,將編譯的代碼下載到目標板中；選擇了具有較強非線性映射能力、自學習能力和容錯能力的BP神經網絡算法進行光伏電站故障診斷,設計了光伏電站主要故障的輸入與輸出算法,并進行網絡訓練,建立了基于BP的光伏電站智能故障診斷系統(tǒng),MATLAB訓練結果證明該算法能夠準確地診斷光伏電站故障的具體類型；最后通過LabVIEW虛擬儀器進行監(jiān)控界面設計,實現(xiàn)了對采集到的數(shù)據(jù)進行顯示與處理,利用VISA串口通信技術與ZigBee進行通信,并建立了基于LabVIEW的BP故障診斷系統(tǒng),能夠在監(jiān)控界面上顯示診斷過程,使用Access數(shù)據(jù)庫存儲數(shù)據(jù),從而起到了對光伏電站的有效自動控制。實驗結果表明,該遠程光伏電站監(jiān)控系統(tǒng)能夠穩(wěn)定可靠運行,并具有組網簡單、花費少、維護性好等優(yōu)點,達到了預期的效果。 本文研究的光伏電站遠程監(jiān)控系統(tǒng)能夠進行實時監(jiān)測,快速確定故障原因,并存儲監(jiān)測的記錄數(shù)據(jù),有利于工作人員迅速準確地排除故障,實現(xiàn)了光伏電站監(jiān)控的功能。最后提出本文可以改進的地方,并對它的發(fā)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.
【學位授予單位】：揚州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM615
本文編號：2292643
[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.
【學位授予單位】：揚州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM615
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