本文選題：電能質(zhì)量　切入點：擾動分類識別　出處：《華北電力大學》2017年碩士論文

【摘要】：隨著科技的不斷進步,大量非線性負荷接入電力系統(tǒng)中,造成了大量復雜的電能質(zhì)量問題,因此電能質(zhì)量擾動分類識別問題日益成為關注的焦點。識別各類單一及復合電能質(zhì)量擾動是解決電力系統(tǒng)故障的首要前提,也是諧波源責任分攤、電能質(zhì)量評估等后續(xù)研究的基礎,有著重要的科研意義。首先,本文介紹了電能質(zhì)量的基本指標,對電壓暫降、諧波等常見擾動進行建模仿真,利用S變換的電能質(zhì)量擾動檢測分析方法,基于其多分辨率特性對信號進行時頻分析,得到基頻幅值和頻率最大值曲線描述信號的幅值、起止時間以及諧波等時頻特征。其次,本文對S變換窗函數(shù)進行改進,提出引入幅度和指數(shù)調(diào)節(jié)系數(shù)的改進S變換,針對不同擾動的時頻特征,綜合考慮噪聲等影響因素,提出四個波形指標對時域波形進行描述,并利用主客觀賦權(quán)法結(jié)合的粗糙集理論確定各波形指標權(quán)重,從而找到最優(yōu)調(diào)節(jié)系數(shù)得出最優(yōu)時頻波形,為電能擾動信號的分類識別奠定基礎。最后對最優(yōu)波形進行特征量的精確提取,采用將全局與局部核函數(shù)結(jié)合的混合核函數(shù)支持向量機進行分類,通過改變混合核函數(shù)調(diào)節(jié)系數(shù)可以實現(xiàn)不同數(shù)據(jù)類型下分類精度的最大化,有效提高了分類器的泛化能力和分類準確率。基于Lab VIEW環(huán)境,編寫電能質(zhì)量擾動分類識別軟件,能夠直觀的對電力系統(tǒng)擾動信號進行檢測分析,然后對實際變電站各支路電壓信號進行分析驗證,結(jié)合工程實際情況,證明所提方法的可行性。
[Abstract]:With the development of science and technology, a large number of nonlinear loads are connected to the power system, resulting in a large number of complex power quality problems. Therefore, the problem of classification and identification of power quality disturbances has become the focus of attention day by day. Identifying all kinds of single and complex power quality disturbances is the first prerequisite to solve power system faults, and it is also the responsibility allocation of harmonic sources. Power quality evaluation and other follow-up studies have important scientific significance. Firstly, this paper introduces the basic indicators of power quality, modeling and simulation of common disturbances, such as voltage sag, harmonic, etc. Using S-transform power quality disturbance detection and analysis method, based on the multi-resolution characteristic of the signal, time-frequency analysis is carried out, and the amplitude of the fundamental frequency and the maximum frequency curve are obtained to describe the time-frequency characteristics of the signal, such as amplitude, starting and ending time and harmonic wave. Secondly, In this paper, the window function of S-transform is improved, and an improved S-transform with amplitude and exponential adjustment coefficient is proposed. Considering the time-frequency characteristics of different disturbances and considering the influence factors such as noise, four waveform indexes are proposed to describe the time-domain waveforms. The weight of each waveform index is determined by rough set theory combined with subjective and objective weighting method, and the optimal time-frequency waveform can be obtained by finding the optimal adjustment coefficient. It lays a foundation for the classification and recognition of power disturbance signals. Finally, the optimal waveform is extracted accurately, and the hybrid kernel support vector machine (SVM), which combines global and local kernel functions, is used to classify the signal. By changing the adjustment coefficient of mixed kernel function, the classification accuracy can be maximized under different data types, and the generalization ability and classification accuracy of classifier can be improved effectively. Based on Lab VIEW environment, the power quality disturbance classification recognition software is developed. It can detect and analyze the disturbance signal of power system intuitively, and then analyze and verify the voltage signal of each branch in actual substation. Combined with the actual situation of the project, the feasibility of the proposed method is proved.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM711

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