本文選題：光伏電站 + 遠程監(jiān)控　； 參考：《遼寧工程技術大學》2014年碩士論文

【摘要】：隨著科學技術的迅速發(fā)展和能源危機的不斷加重,人們越來越重視尋找新能源。光伏由于有種種優(yōu)點被人們普遍利用。隨著光伏電站的大規(guī)模興起,配套的監(jiān)控設備也在逐步完善。人們已經不僅僅滿足于現(xiàn)場觀測各種實時數(shù)據(jù),而是希望更科學智能地管理電站。電站作為一種新能源,不同于傳統(tǒng)的常規(guī)能源,由于其發(fā)電功率的間歇性波動,成為并網的一大瓶頸。如何在保證安全的情況下,最大限度的消除功率波動給電網帶來的影響已經成為研究的熱點。如何將功率預測與電站的監(jiān)控管理有機的聯(lián)系在一起是本文研究的重點。本文首先介紹了光伏電站監(jiān)控系統(tǒng)的研究現(xiàn)狀,論述了功率預測作為光伏電站監(jiān)控有機組成的重要性。結合現(xiàn)場實際情況對系統(tǒng)總體框架和功能進行了詳細設計。其次,考慮到光伏電站布線不方便,選用了無線通信模塊WiFi和GPRS進行數(shù)據(jù)的傳輸,詳細敘述了通信過程。通過協(xié)議TCP、UDP的比較,提出了一種通信協(xié)議,來解決通信過程中存在的數(shù)據(jù)丟包和實時性問題,再次,利用灰色關聯(lián)法尋找歷史數(shù)據(jù)中與待測日相似度較高日期,通過BP神經網絡訓練后,得出待預測日的發(fā)電功率。通過與傳統(tǒng)的算法與物理算法比較,得出此算法的適用范圍。最后,利用C++builder6.0構建了電站本地監(jiān)控,完成了科學管理電站的要求。
[Abstract]:With the rapid development of science and technology and the aggravation of energy crisis, people pay more and more attention to finding new energy.Photovoltaic is widely used because of its various advantages.With the large-scale rise of photovoltaic power plants, supporting monitoring equipment is also gradually improving.People are not only satisfied with the field observation of real-time data, but also want to manage the power station more scientifically and intelligently.As a new energy, power station is different from conventional energy, because of its intermittent fluctuation of power generation, it becomes a bottleneck of grid connection.How to eliminate the influence of power fluctuation on power grid to the maximum extent has become a hot topic in the case of ensuring safety.How to combine power prediction with monitoring and management of power station is the focus of this paper.This paper first introduces the research status of photovoltaic power station monitoring system, and discusses the importance of power prediction as an organic component of photovoltaic power plant monitoring system.The overall frame and function of the system are designed in detail according to the actual situation in the field.Secondly, considering the inconvenient wiring of photovoltaic power station, the wireless communication module WiFi and GPRS are selected for data transmission, and the communication process is described in detail.Based on the comparison of TCP / UDP protocol, a communication protocol is proposed to solve the data packet loss and real-time problems in the process of communication. Thirdly, the grey correlation method is used to find the date of high similarity between the historical data and the day to be tested.After BP neural network training, the generation power of the day to be predicted is obtained.By comparing with the traditional algorithm and the physical algorithm, the application range of the algorithm is obtained.Finally, the local monitoring of the power station is constructed by using C builder6.0, and the requirement of scientific management of the power station is fulfilled.
【學位授予單位】：遼寧工程技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM615

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