本文選題：電能質(zhì)量 + 擾動檢測與分類��； 參考：《燕山大學(xué)》2015年碩士論文

【摘要】：目前,智能化精密儀表的廣泛運用,對供電質(zhì)量提出了更加嚴(yán)格的要求;同時由于大量擾動負(fù)荷接入電網(wǎng)或其它擾動源存在,使得供電質(zhì)量問題日益凸顯。因此,研究分析電能質(zhì)量擾動信號具有非常重要的意義。本文采用原子分解技術(shù)來分析各種電能質(zhì)量擾動信號。為克服傳統(tǒng)原子分解技術(shù)存在計算量過大的不足,本文將原子離散參數(shù)連續(xù)化,大大減少重構(gòu)信號需要的原子數(shù)且使迭代結(jié)果更準(zhǔn)確;針對頻率范圍較大的諧波、衰減振蕩等擾動信號,采用快速傅里葉變換對最優(yōu)原子頻率進行預(yù)求解,從而降低原子庫的規(guī)模;采用粒子群算法及遺傳算法對匹配追蹤算法進行優(yōu)化。仿真算例表明,粒子群優(yōu)化匹配追蹤算法的性能優(yōu)于遺傳算法優(yōu)化匹配追蹤算法。采用粒子群優(yōu)化的匹配追蹤算法及基于擾動信號特征的連續(xù)相關(guān)原子庫,對6種單一電能質(zhì)量擾動(暫降信號、暫升信號、閃變信號、諧波、衰減振蕩、尖峰信號)進行分析。仿真研究表明,該方法可快速準(zhǔn)確地提取電能質(zhì)量信號的擾動特征,且有較好的抗噪性能。由于連續(xù)相關(guān)原子庫具有針對性,所以本文采用連續(xù)相關(guān)原子庫及粒子群優(yōu)化的匹配追蹤算法對擾動信號進行分類。通過多重擾動信號的仿真算例驗證,該分類方法可以很好的完成擾動信號的分類,并獲取擾動信號的所有參數(shù)。
[Abstract]:At present, with the wide application of intelligent precision instruments, the quality of power supply is required more strictly, and the problem of power supply quality is becoming more and more serious because a large number of disturbance loads are connected to the power network or other disturbance sources. Therefore, it is very important to study and analyze the power quality disturbance signal. In this paper, atomic decomposition technique is used to analyze various power quality disturbance signals. In order to overcome the disadvantages of the traditional atomic decomposition technique, the discrete parameters of atoms are continuous, which greatly reduces the number of atoms needed to reconstruct the signal and makes the iterative results more accurate. The fast Fourier transform (FFT) is used to pre-solve the optimal atomic frequency so as to reduce the scale of atomic library. Particle swarm optimization and genetic algorithm are used to optimize the matching and tracking algorithm. Simulation results show that the performance of particle swarm optimization matching tracking algorithm is better than that of genetic algorithm. Using the matching tracking algorithm of particle swarm optimization and the continuous correlation atomic library based on the characteristic of disturbance signal, six kinds of single power quality disturbances (sag signal, hoisting signal, flicker signal, harmonic wave, attenuation oscillation, peak signal) are analyzed. The simulation results show that the proposed method can extract the disturbance characteristics of power quality signals quickly and accurately, and has good anti-noise performance. Because of the pertinence of the continuous correlation atom library, this paper uses the continuous correlation atomic library and the matching tracking algorithm of particle swarm optimization to classify the disturbance signal. The simulation results show that the classification method can achieve the classification of the disturbance signal and obtain all the parameters of the disturbance signal.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM711

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