【摘要】：隨著電力電子技術的飛速發(fā)展,大容量與非線性電子元件在電力系統(tǒng)中的廣泛應用會引起電網電壓和電流波形的畸變,由此帶來的電能質量問題越來越突出,引起了人們的廣泛的關注。電網諧波不僅降低了電力設備的利用效率,而且影響用電設備的正常工作,特別是引發(fā)起局部電路諧振,使電壓升高、諧波放大,危害用戶的用電安全。然而,越來越多的敏感負荷,如可編程控制器、計算機和精密儀器等,卻對電能質量提出了更高的要求。因此,有必要準確地檢測并給出電網諧波參數,從而準確進行電網諧波評估和電網諧波治理。對電網諧波信號進行及時、準確的檢測分析,減少由諧波導致的繼電保護和自動裝置的誤動,從而提高電力設備的效率,降低用電成本。本文首先分析了4種常用的電能質量分析方法:有效值法、傅立葉變換法、小波變換法和自適應的最小二乘法,并分別對以上算法進行仿真分析。然后,闡述和分析了卡爾曼濾波、無跡卡爾曼濾波基本原理并分別進行了算例仿真。無跡卡爾曼濾波算法將狀態(tài)噪聲協(xié)方差和觀測噪聲協(xié)方差視為常量,不能準確反映實時變化的噪聲環(huán)境,估計效果差。本文提出利用基于種群分類與動態(tài)學習因子的改進粒子群優(yōu)化算法,對無跡卡爾曼濾波的狀態(tài)噪聲協(xié)方差和觀測噪聲協(xié)方差進行優(yōu)化,結合無跡卡爾曼濾波對電網動態(tài)諧波進行估計。給出了基于粒子群優(yōu)化的無跡卡爾曼濾波(particle swarm optimized unscented Kalman filter,PSOUKF)算法流程,運用MATLAB進行編程,對電網動態(tài)諧波估計進行仿真分析,并將本文所提算法與卡爾曼濾波算法、無跡卡爾曼濾波算法進行比較。仿真結果表明,本文所提方法比傳統(tǒng)分析方法更有效,在沒有增加計算復雜度的情況下,能夠提高動態(tài)諧波估計精度。
[Abstract]:With the rapid development of power electronics technology, the wide application of large capacity and nonlinear electronic components in power system will lead to the distortion of voltage and current waveforms of power network, and the problems of power quality are becoming more and more prominent. Has aroused the widespread concern of people. Harmonics not only reduce the utilization efficiency of power equipment, but also affect the normal operation of electric equipment, especially the local circuit resonance, which makes the voltage rise, harmonic amplifies, and endangers the safety of users. However, more and more sensitive loads, such as programmable controllers, computers and precision instruments, require higher power quality. Therefore, it is necessary to accurately detect and give the harmonic parameters of the power network, so as to accurately evaluate and treat the harmonic of the power network. In order to improve the efficiency of power equipment and reduce the cost of electricity consumption, the harmonic signals are detected and analyzed in time and accurately to reduce the relay protection caused by harmonics and the misoperation of automatic devices. In this paper, four commonly used power quality analysis methods are analyzed firstly: effective value method, Fourier transform method, wavelet transform method and adaptive least square method, and the above algorithms are simulated and analyzed respectively. Then, the basic principle of Kalman filter and unscented Kalman filter are described and analyzed. The unscented Kalman filter takes the state noise covariance and the observation noise covariance as constants and can not accurately reflect the real time changing noise environment. In this paper, an improved particle swarm optimization algorithm based on population classification and dynamic learning factor is proposed to optimize the state noise covariance and observation noise covariance of unscented Kalman filter. The unscented Kalman filter is used to estimate the dynamic harmonics of the power system. The flow chart of unscented Kalman filter (particle swarm optimized unscented Kalman filter,PSOUKF) algorithm based on particle swarm optimization is presented. The dynamic harmonic estimation of power network is simulated and analyzed by using MATLAB, and the algorithm proposed in this paper and Kalman filter algorithm are presented. The unscented Kalman filtering algorithm is compared. Simulation results show that the proposed method is more effective than the traditional analysis method and can improve the accuracy of dynamic harmonic estimation without increasing computational complexity.
【學位授予單位】：深圳大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN713;TM935

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本文編號：2359212