【摘要】：在全球能源問題日益嚴(yán)峻,國(guó)家大力發(fā)展清潔能源的趨勢(shì)下,徑流式小水電站發(fā)電功率的合理調(diào)度管理具有十分重大的意義。貴州省是我國(guó)水資源豐富的省區(qū)之一,河流密布,水量豐富,省內(nèi)有相當(dāng)數(shù)量的小水電站。依托國(guó)家科技支撐項(xiàng)目,華北電力大學(xué)與貴州電網(wǎng)公司研發(fā)了小水電集群功率預(yù)測(cè)系統(tǒng)負(fù)責(zé)全省徑流式小水電的日前功率預(yù)測(cè)。然而,隨著未來小水電站的數(shù)量將不斷增多,發(fā)電功率數(shù)據(jù)量也將成倍增加,基于傳統(tǒng)關(guān)系型數(shù)據(jù)庫的原有系統(tǒng),由于其固有的系統(tǒng)擴(kuò)展性差、并發(fā)難度大等多種問題,將難以滿足海量徑流式小水電功率數(shù)據(jù)的高效存儲(chǔ)管理的需求。此外,系統(tǒng)中現(xiàn)有的功率預(yù)測(cè)以高性能計(jì)算機(jī)串型計(jì)算為主,在面對(duì)巨大數(shù)據(jù)量時(shí)存在數(shù)據(jù)處理時(shí)間長(zhǎng),預(yù)測(cè)速度慢,風(fēng)險(xiǎn)控制不足以及容錯(cuò)率低等問題,給小水電功率預(yù)測(cè)計(jì)算帶來了極大的不便。為了解決上述問題并實(shí)現(xiàn)對(duì)徑流式小水電功率數(shù)據(jù)的高效存儲(chǔ)、訪問和快速計(jì)算,首先,本文通過考慮各級(jí)電網(wǎng)調(diào)度的層次關(guān)系以及分析徑流式小水電功率數(shù)據(jù)特點(diǎn),將徑流式小水電功率數(shù)據(jù)遷移至Hadoop大數(shù)據(jù)平臺(tái)上。充分考慮實(shí)際徑流式小水電功率數(shù)據(jù)量的產(chǎn)生速度,設(shè)計(jì)了徑流式小水電功率數(shù)據(jù)存儲(chǔ)方案,并提出徑流式小水電功率數(shù)據(jù)的追加策略和基于多衡量指標(biāo)評(píng)價(jià)的數(shù)據(jù)副本放置策略。其次,分析了徑流式小水電發(fā)電功率的影響因素,結(jié)合徑流式小水電功率數(shù)據(jù)與氣象信息的相關(guān)性特點(diǎn),設(shè)計(jì)了基于氣象信息的徑流式小水電功率預(yù)測(cè)模型,并采用分布式計(jì)算方式實(shí)現(xiàn)。最后,利用所提方案搭建了實(shí)驗(yàn)仿真平臺(tái),應(yīng)用真實(shí)數(shù)據(jù)算例得到預(yù)測(cè)結(jié)果并與原有系統(tǒng)進(jìn)行對(duì)比。實(shí)驗(yàn)結(jié)果表明分布式預(yù)測(cè)算法的效率與比原有系統(tǒng)預(yù)測(cè)效率更高,驗(yàn)證了方案的可行性和有效性。
[Abstract]:With the global energy problem becoming more and more serious, and the development of clean energy in the country, it is of great significance to manage the power generation power of small runoff hydropower stations. Guizhou Province is one of the provinces with abundant water resources. Relying on the national science and technology support project, North China Electric Power University and Guizhou Power Grid Company have developed a small hydropower cluster power forecasting system, which is responsible for the pre-day power prediction of runoff small hydropower in the province. However, as the number of small hydropower stations will continue to increase in the future, the amount of generating power data will increase exponentially. The original system based on traditional relational database has many problems, such as poor system expansibility and great concurrency difficulty. It will be difficult to meet the demand of efficient storage and management of large amount of runoff small hydropower power data. In addition, the existing power prediction in the system is based on high performance computer string computing. In the face of large amount of data, there are many problems, such as long data processing time, slow prediction speed, insufficient risk control and low fault-tolerant rate, etc. It brings great inconvenience to the prediction and calculation of small hydropower power. In order to solve the above problems and realize the efficient storage, access and fast calculation of runoff small hydropower power data, first of all, this paper analyzes the characteristics of runoff small hydropower power data by considering the hierarchical relationship of grid dispatching at all levels. The runoff type small hydropower power data will be transferred to the Hadoop big data platform. Considering the generation speed of the actual run-off small hydropower power data, the paper designs a runoff type small hydropower power data storage scheme. The supplementary strategy of runoff type small hydropower power data and the data replica placement strategy based on multi-measure index evaluation are proposed. Secondly, the influence factors of runoff small hydropower power generation are analyzed. Based on the characteristics of correlation between runoff small hydropower power data and meteorological information, a runoff small hydropower power prediction model based on meteorological information is designed. And the distributed computing method is adopted. Finally, the experimental simulation platform is built by using the proposed scheme, and the prediction results are obtained by using real data examples and compared with the original system. The experimental results show that the efficiency of the distributed prediction algorithm is higher than that of the original system, and the feasibility and effectiveness of the scheme are verified.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TV737;TP311.13

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