發(fā)布時(shí)間：2018-04-15 01:24

  本文選題：預(yù)測(cè)控制 + GA-BP神經(jīng)網(wǎng)絡(luò)��； 參考：《湖南工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著全球能源和環(huán)境問(wèn)題的日益突出,全世界各國(guó)都開(kāi)始重視能源危機(jī)、環(huán)境破壞等問(wèn)題,并著力于尋找清潔能源補(bǔ)充和改善當(dāng)下境況。近些年來(lái),我國(guó)利用可再生能源的比重逐年增加,分布式發(fā)電項(xiàng)目在海島和偏遠(yuǎn)地區(qū)也不斷投產(chǎn)建設(shè)。此外,由分布式發(fā)電單元組成的微電網(wǎng)系統(tǒng)與網(wǎng)絡(luò)控制系統(tǒng)相結(jié)合組成的智能微電網(wǎng),也大大降低了微電網(wǎng)系統(tǒng)的運(yùn)營(yíng)成本,提高了微電網(wǎng)的穩(wěn)定性、靈活性,擴(kuò)寬了其應(yīng)用領(lǐng)域。然而,基于網(wǎng)絡(luò)控制的微電網(wǎng)系統(tǒng)也存在不足之處,比如分布式單元的獨(dú)立穩(wěn)定運(yùn)行、各分布式單元的能量切換、微電網(wǎng)的低壓穿越等問(wèn)題。如果這些問(wèn)題在微電網(wǎng)實(shí)際運(yùn)行過(guò)程中得不到有效地解決,那么微電網(wǎng)系統(tǒng)將陷入不穩(wěn)定運(yùn)行或者癱瘓,同時(shí)也會(huì)給用戶(hù)側(cè)用電設(shè)備造成傷害。另外,網(wǎng)絡(luò)控制是通過(guò)周期性地檢測(cè)系統(tǒng)信號(hào),然后將數(shù)據(jù)傳送至中央控制器進(jìn)行控制決策,由于網(wǎng)絡(luò)通信固有的缺陷,如通信丟包、遲滯等,難免會(huì)出現(xiàn)通信失誤,并且微電網(wǎng)主要分布式發(fā)電單元受氣象因素的影響,其波動(dòng)性和隨機(jī)性也使得發(fā)電功率輸出具有不確定性,故在精準(zhǔn)控制上需要引入新的控制方法。鑒于此,本文分析國(guó)內(nèi)外許多專(zhuān)家和學(xué)者在微電網(wǎng)結(jié)構(gòu)和控制策略的相關(guān)文獻(xiàn),提出了基于神經(jīng)網(wǎng)絡(luò)預(yù)測(cè)控制的微電網(wǎng)能量?jī)?yōu)化管理,文中在已有基礎(chǔ)上加入了預(yù)測(cè)控制,為微電網(wǎng)的合理調(diào)度和控制決策提供了參考值;文中還引入了新的微電網(wǎng)能量管理結(jié)構(gòu),使得資源配置更加合理,能量管理更加優(yōu)化。最后,具體的創(chuàng)新性研究工作從以下三個(gè)方面介紹:(1)預(yù)測(cè)控制模型采用的主預(yù)測(cè)工具是神經(jīng)網(wǎng)絡(luò),并加入反向傳播途徑,即為BP神經(jīng)網(wǎng)絡(luò),為了縮短收斂周期,文中還引入遺傳算法,但仍然不能完全避免其收斂周期過(guò)長(zhǎng),故引入了回歸分析預(yù)測(cè)來(lái)填補(bǔ)收斂周期過(guò)長(zhǎng)的情況,最后文中將神經(jīng)網(wǎng)絡(luò)與回歸分析相結(jié)合構(gòu)成預(yù)測(cè)的控制機(jī)制實(shí)現(xiàn)了預(yù)測(cè)控制的最優(yōu)化。(2)每個(gè)分布式單元都是采用電力電子接口輸出,且輸出端都有U、I檢測(cè),將檢測(cè)信息送至控制器反饋調(diào)節(jié)電力電子器件,實(shí)現(xiàn)單分布式單元的相對(duì)穩(wěn)定控制輸出。此外,文中還將這些檢測(cè)信號(hào)送至中央控制器,并對(duì)分布式單元協(xié)調(diào)控制,使得微電網(wǎng)能量管理更加合理。(3)文中的微電網(wǎng)能量管理結(jié)構(gòu)是多層次結(jié)構(gòu),且具體分為上層決策層和下層決策層,上層決策層具有一組固定的參數(shù)(參考值)函數(shù),并通過(guò)特定的控制策略結(jié)構(gòu)來(lái)適應(yīng)較低的決策水平,根據(jù)這一參數(shù),每個(gè)較低級(jí)別的決策者通過(guò)跟蹤上層決策者所提供的參考值解決自身的優(yōu)化問(wèn)題。結(jié)構(gòu)建模上還考慮了最小碳排放和降低生產(chǎn)成本,提高了市場(chǎng)競(jìng)爭(zhēng)力,使得本次研究更具工程實(shí)踐價(jià)值。
[Abstract]:With the increasingly prominent global energy and environmental problems, countries all over the world have begun to pay attention to energy crisis, environmental damage and other issues, and focus on finding clean energy to supplement and improve the current situation.In recent years, the proportion of renewable energy used in China has increased year by year, and distributed power generation projects have been put into production in islands and remote areas.In addition, the smart microgrid, which is composed of the microgrid system and the network control system, greatly reduces the operating cost of the micro-grid system, and improves the stability and flexibility of the micro-grid system.It widens its application field.However, the microgrid system based on network control also has some shortcomings, such as the independent and stable operation of the distributed unit, the energy switching of each distributed unit, the low-voltage traversing of the micro-grid, and so on.If these problems can not be effectively solved in the actual operation of microgrid, then the microgrid system will be unstable or paralyzed, and will also cause harm to the consumer side power equipment.In addition, network control detects system signals periodically and then transmits data to the central controller for control decision. Due to the inherent defects of network communication, such as packet loss and delay, communication errors occur inevitably.And the main distributed generation units of microgrid are affected by meteorological factors, its volatility and randomness also make the generation power output uncertain, so it is necessary to introduce a new control method in precise control.In view of this, this paper analyzes many domestic and foreign experts and scholars in microgrid structure and control strategy related literature, proposed based on neural network predictive control of microgrid energy optimization management, this paper added predictive control on the basis of the existing.The paper also introduces a new energy management structure of microgrid, which makes resource allocation more reasonable and energy management more optimized.Finally, the specific innovative research work introduces the following three aspects: 1) the main predictive tool used in the predictive control model is the neural network, and the BP neural network is added to the back propagation path. In order to shorten the convergence period, the main predictive tool used in the predictive control model is the BP neural network.Genetic algorithm (GA) is also introduced in this paper, but the convergence period is still too long. Therefore, regression analysis is introduced to replace the case of long convergence cycle.Finally, the neural network and regression analysis are combined to form the predictive control mechanism to realize the optimization of predictive control. (2) every distributed unit is output with power electronic interface, and the output end has UFI detection.The detection information is sent to the controller to feedback and adjust the power electronic device to realize the relatively stable control output of the single distributed cell.In addition, these detection signals are sent to the central controller, and the distributed unit is coordinated to make the energy management of microgrid more reasonable.The upper decision layer has a set of fixed parameters (reference value) function and adapts to the lower decision level through the specific control strategy structure.Each decision maker at the lower level solves the optimization problem by tracking the reference values provided by the upper decision makers.In structural modeling, the minimum carbon emission and production cost are considered, and the market competitiveness is improved, which makes this study more valuable in engineering practice.
【學(xué)位授予單位】：湖南工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP183;TM727

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