發(fā)布時(shí)間：2018-01-25 00:04

  本文關(guān)鍵詞： 暫態(tài)穩(wěn)定評(píng)估 廣域測(cè)量系統(tǒng) 特征選擇 極限學(xué)習(xí)機(jī) 在線學(xué)習(xí) 規(guī)則提取　出處：《華北電力大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：電力系統(tǒng)暫態(tài)穩(wěn)定評(píng)估(TSA)一直是關(guān)系到電力系統(tǒng)安全穩(wěn)定運(yùn)行的重要問(wèn)題。隨著大區(qū)電網(wǎng)互聯(lián)、電力市場(chǎng)化改革和大規(guī)�？稍偕�能源的接入,系統(tǒng)的動(dòng)態(tài)行為更加復(fù)雜多變,控制變得更加困難,電網(wǎng)暫態(tài)穩(wěn)定破壞的后果也更加嚴(yán)重。時(shí)域仿真法、直接法等現(xiàn)有的TSA方法,難以滿足電網(wǎng)運(yùn)行對(duì)在線穩(wěn)定評(píng)估的要求。近年來(lái),基于模式識(shí)別技術(shù)的TSA方法(PRTSA)受到各國(guó)學(xué)者的廣泛關(guān)注,取得了較大的進(jìn)展。其主要任務(wù)是建立系統(tǒng)變量和系統(tǒng)穩(wěn)定結(jié)果問(wèn)的關(guān)系映射,具有學(xué)習(xí)能力強(qiáng)、評(píng)估速度快、能提供潛在有用信息等優(yōu)勢(shì),在電網(wǎng)在線安全穩(wěn)定分析領(lǐng)域有著良好的應(yīng)用前景。 本文系統(tǒng)地研究了PRTSA的特征選擇、分類器構(gòu)建、在線學(xué)習(xí)、拓?fù)渥兓�適應(yīng)性及規(guī)則提取等問(wèn)題。首先,研究從廣域測(cè)量系統(tǒng)(WAMS)可提供的故障后信息中抽取有效表征系統(tǒng)暫態(tài)穩(wěn)定性的模式特征集,通過(guò)特征選擇方法篩選出最優(yōu)特征子集,降低輸入空間維數(shù)；然后研究暫態(tài)穩(wěn)定評(píng)估分類器的構(gòu)建,提出一種基于優(yōu)化極限學(xué)習(xí)機(jī)的暫態(tài)穩(wěn)定評(píng)估模型；接著研究評(píng)估模型的在線學(xué)習(xí)機(jī)制,提出一種基于集成在線序貫極限學(xué)習(xí)機(jī)的暫態(tài)穩(wěn)定評(píng)估方法；最后研究了暫態(tài)穩(wěn)定評(píng)估網(wǎng)絡(luò)拓?fù)渥兓�的適應(yīng)性及暫態(tài)穩(wěn)定規(guī)則的提取問(wèn)題。論文的主要研究成果包括： 1、提出一種基于改進(jìn)最大相關(guān)最小冗余(mRMR)判據(jù)的TSA特征選擇方法。首先,基于WAMS可提供的故障后信息,建立穩(wěn)定分類的原始特征集,然后對(duì)mRMR判據(jù)進(jìn)行改進(jìn)后應(yīng)用于特征選擇和特征集壓縮。通過(guò)增量搜索算法得到一組嵌套的候選特征子集,并使用支持向量機(jī)分類器驗(yàn)證各候選特征子集的分類性能,選擇得到具有最大分類正確率的最優(yōu)特征子集。 2、提出一種基于優(yōu)化極限學(xué)習(xí)機(jī)(ELM)的暫態(tài)穩(wěn)定評(píng)估模型。基于所選的最優(yōu)特征子集,采用極限學(xué)習(xí)機(jī)來(lái)構(gòu)建TSA分類器,并采用基于綜合混沌搜索策略的改進(jìn)細(xì)菌群體趨藥性算法優(yōu)化選取ELM模型的參數(shù),提升了評(píng)估模型的分類能力。 3、提出一種基于集成在線序貫ELM的評(píng)估模型在線學(xué)習(xí)機(jī)制。針對(duì)評(píng)估模型不能在線更新的不足,采用增量式學(xué)習(xí)的在線序貫ELM作為弱分類器、在線Boosting算法作為集成學(xué)習(xí)算法進(jìn)行多ELM模型的在線集成學(xué)習(xí),提高了在線序貫ELM的穩(wěn)定性和泛化能力。 4、對(duì)暫態(tài)穩(wěn)定評(píng)估方法的拓?fù)渥兓�適應(yīng)性進(jìn)行了研究。基于本文依據(jù)WAMS信息建立的原始特征集,構(gòu)造考慮網(wǎng)絡(luò)拓?fù)渥兓�的樣本�?并采用本文的特征選擇方法得到考慮系統(tǒng)拓?fù)渥兓�的最�?yōu)特征子集,然后采用ELM構(gòu)建TSA模型對(duì)本文提出方法的拓?fù)渥兓�適應(yīng)性進(jìn)行了研究評(píng)價(jià)。結(jié)果表明與已有PRTSA方法相比,本文方法適應(yīng)電網(wǎng)拓?fù)渥兓�的能力具有顯著改進(jìn)。 5、提出一種基于極限學(xué)習(xí)機(jī)和改進(jìn)蟻群挖掘算法的穩(wěn)定評(píng)估規(guī)則提取方法。為了克服“黑箱型”學(xué)習(xí)機(jī)可理解性差、解釋性差的缺陷,首先研究了蟻群挖掘算法進(jìn)行規(guī)則挖掘的基本原理；然后基于所選最優(yōu)特征子集,從訓(xùn)練好的ELM中產(chǎn)生示例樣本集；最后,采用改進(jìn)蟻群挖掘算法從示例樣本集產(chǎn)生一組可以替代原ELM網(wǎng)絡(luò)的分類規(guī)則。
[Abstract]:Power system transient stability assessment (TSA) has been an important issue for the security and stabilization of power system. With the relation to the regional power grid interconnection, access power market reform and large-scale renewable energy, the dynamic behavior of the system is more complicated, the control becomes more difficult, transient stability and failure consequences are more serious. The time domain simulation the TSA method, the existing method of direct method, it is difficult to meet the power grid operation evaluation of on-line stability. In recent years, the TSA method based on pattern recognition technology (PRTSA) has attracted wide attention of scholars, has made great progress. Its main task is to establish the mapping relationship between system variables and system stability results has asked, strong learning ability, evaluation speed, can provide useful information and other advantages, in the power grid online stability analysis has a good application prospect.
This paper systematically studies the PRTSA feature selection, classifier construction, online learning, topology adaptability and rule extraction. Firstly, from the research of wide area measurement system (WAMS) can provide the transient stability after fault information extraction system model effectively characterize the feature set, the best subset of features selected by feature selection method. To reduce the dimension of input space; and then study the transient stability classifier evaluation, put forward an evaluation model of transient stability optimization based on extreme learning machine; then study the evaluation model of online learning mechanism, a method is presented to assess the transient stability of integrated online sequential extreme learning machine based on the extraction of; finally the transient stability assessment and transient adaptability stable rules of network topology. The main contributions of this paper include:
1, put forward an improved optimization based on TSA feature selection method (mRMR) criterion. Firstly, WAMS can provide the fault information based on the original feature, establish a stable classification set, and then the mRMR criterion is applied to feature selection and feature set compression. Improved search algorithm to get a set of candidate feature subsets nested by incremental, and using a support vector machine classifier to verify each candidate feature subset selection, get the feature with the highest classification accuracy subset.
2, put forward a kind of learning machine based on Optimization limit (ELM) for transient stability assessment model. The optimal feature subset selection based on TSA classifier to construct the extreme learning machine, and using the improved bacterial colony chemotaxis integrated chaotic searching algorithm is used to select ELM resistance parameter model based on improved classification ability assessment model.
3, put forward an evaluation model of ELM integrated online sequential mechanism based on online learning. Aiming at the shortage of evaluation model can not be updated online, the online sequential ELM incremental learning as a weak classifier, online Boosting algorithm as the ensemble learning algorithm for multi ELM model integrated learning in line, improve the stability and generalization ability of online sequential ELM.
4, changes in topology adaptability to transient stability assessment methods were studied. The original features based on the WAMS information set is established based on the network topology changes the sample set by the consideration of the structure, and get the optimal feature selection method considering the system topology changes sign subsets, and then build on the proposed topology change adaptation method the study and evaluation of TSA model by ELM. The results showed that compared with the existing PRTSA method, this method has the ability to adapt to changes in network topology significantly improved.
5, put forward a method of extracting stability evaluation rules of extreme learning machine and improved ant colony algorithm based on mining. In order to overcome the "black box" learning machine understandable, explain the defects of the poor, the basic principle of the first ant colony mining algorithm of rule mining; then based on the feature subset is generated sample sample set from trained ELM; finally, the improved ant colony algorithm for mining classification rules generated a set of alternative ELM network from the sample set.

【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM712

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本文編號(hào)：1461413