【摘要】：光伏系統(tǒng)的孤島運(yùn)行會導(dǎo)致電能質(zhì)量惡化、電力系統(tǒng)設(shè)備損壞等問題,國內(nèi)外的相關(guān)標(biāo)準(zhǔn)均規(guī)定光伏系統(tǒng)需要配備孤島保護(hù)裝置。傳統(tǒng)的孤島保護(hù)研究主要集中在單逆變器系統(tǒng),對于多逆變器并聯(lián)的光伏電站的孤島保護(hù)研究甚少,傳統(tǒng)的孤島保護(hù)策略應(yīng)用于光伏電站時則會存在孤島檢測失效的問題,需要研究適用于光伏電站的孤島保護(hù)策略。論文根據(jù)光伏電站的實(shí)際系統(tǒng)結(jié)構(gòu)建立了簡化的光伏電站等效仿真模型,以此作為仿真驗(yàn)證的基礎(chǔ)。分析了影響并網(wǎng)點(diǎn)處電壓幅值和頻率在光伏電站孤島運(yùn)行前后變化的因素,得出了傳統(tǒng)被動式孤島檢測法的失效機(jī)理。以頻率-無功反饋法為例,論證了傳統(tǒng)主動法應(yīng)用于光伏電站孤島檢測時,由于反饋系數(shù)的選擇、擾動信號互相干擾等原因而會檢測失效。論文提出了適用于光伏電站的基于SVG調(diào)節(jié)無功功率的孤島檢測法,在光伏電站輸出的有功功率低于閾值時,以光伏電站輸出到并網(wǎng)點(diǎn)的功率因數(shù)在指定區(qū)間循環(huán)波動為約束量,調(diào)節(jié)SVG輸出的無功功率主動形成光伏電站和本地負(fù)載的無功不匹配,SVG輸出的無功功率不影響光伏電站的并網(wǎng)運(yùn)行,在光伏電站孤島運(yùn)行時,無功功率不匹配會導(dǎo)致并網(wǎng)點(diǎn)頻率迅速超出閾值,可以以此快速可靠地檢測出孤島運(yùn)行。為了達(dá)到避免人工設(shè)定參考量閾值、縮小孤島檢測盲區(qū)的目的,論文提出了適用于光伏電站的一種基于Adaboost智能算法的孤島檢測法。Adaboost算法利用若干低精度分類器組合成高精度分類器,學(xué)習(xí)由光伏電站不同運(yùn)行情況下的特征量數(shù)據(jù)組合成的學(xué)習(xí)數(shù)據(jù)集,生成一個判斷系統(tǒng)當(dāng)前處于并網(wǎng)運(yùn)行或孤島運(yùn)行狀態(tài)的二分類模型,利用與學(xué)習(xí)數(shù)據(jù)集同源的檢驗(yàn)數(shù)據(jù)集校驗(yàn)該模型的分類精度,將系統(tǒng)當(dāng)前運(yùn)行狀態(tài)下的特征數(shù)據(jù)輸入該模型即可判斷系統(tǒng)是否處于孤島運(yùn)行。該方法不需人工設(shè)定參考量閾值,用多參考量同時判別孤島,具有很高的檢測精度。
[Abstract]:The isolated island operation of the photovoltaic system will lead to the deterioration of the power quality and the damage of the power system equipment. The relevant standards both at home and abroad stipulate that the photovoltaic system needs to be equipped with isolated island protection. The traditional island protection research is mainly concentrated in the single inverter system, and the Research on the island protection of the photovoltaic power station with multiple inverters is very small. When the isolated island protection strategy is applied to the photovoltaic power station, there will be a problem of isolated island detection failure. It is necessary to study the island protection strategy suitable for the photovoltaic power station. The paper establishes a simplified equivalent simulation model of the photovoltaic power station based on the actual system structure of the photovoltaic power station, which is the basis of the imitation true verification. The factors of amplitude and frequency variation before and after the operation of the photovoltaic power station before and after the island operation are obtained. The failure mechanism of the traditional passive island detection method is obtained. Taking the frequency reactive power feedback method as an example, it is demonstrated that the traditional active method is applied to the island detection of the photovoltaic power station, and the failure is detected due to the selection of the feedback coefficient and the interference of the disturbance signal to each other. In this paper, an isolated island detection method for reactive power based on SVG for photovoltaic power station is proposed. When the active power output of a photovoltaic power station is lower than the threshold, the power factor output to the node in the photovoltaic power station is circulated as a constraint in the specified range, and the reactive power of the SVG output is actively adjusted to form the photovoltaic power station and the local load. The reactive power of the SVG output does not affect the grid operation of the photovoltaic power station. When the photovoltaic power station is running on the island, the reactive power mismatch will lead to the rapid and reliable detection of the island operation. A.Adaboost algorithm based on Adaboost intelligent algorithm for photovoltaic power station is proposed in this paper. A high precision classifier combined with a number of low precision classifiers is used to learn a learning data set composed of the characteristic data of different operating conditions of the photovoltaic power station, and a judgment system is currently in the parallel operation. The two classification model of the running state of the isolated island is used to check the classification accuracy of the model by using the test data set homologous with the learning data set. The model can be used to judge whether the system is in the isolated island. The method does not need to set the threshold value manually and discriminating the island with the multi reference amount simultaneously. It has high detection precision.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM615

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