Decection Algorithms Of Power Quality Relatd Signals Baed On S Transform Andd Applications

With extensive use of non-linear loads and high-precision equipments, power energy(PQ) is deteriorating , the problem have been the attentive focus by power unit and users in recent years. To improve the power quality and ensure the power network working safely, it is necessary to detect the power quality problems and find their reasons.Power quality disturbances are external behaviors of PQ caused by nonlinear loads and fault of the power network. Partial discharge(PD) in transformers and other electric devices is one potential reason causing power quality deterioration, the acoustic emission(AE) signals, which occur when PD happens, can estimate the PD quantity and locate the location of PD accurately. So in general meaning, power quality disturbances and acoustic emission signals of transformers'PD are the related signal of PQ. S transform(ST) is developed on the foundation of Fourier transform and wave transform, this paper researches the analyzing methods of power quality disturbances and AE signals based on S transform. The main contributions of this thesis are as follows:(1) A new algorithm based on S transform is presented to analyze PQ disturbances and locate the disturbance time. In the algorithm, the time average of amplitude quadratic sum in ST module matrix gives the existing time of PD , the frequency average of amplitude quadratic sum estimates the frequency components, the base frequency curve is used to show the voltage amplitude of the base frequency. The simulation results shows ST is superior to the short time fourier transform, and the algorithm detection can satisfy the accuracy requirement of PD disturbance.(2) The main task is to analyze and classify for the PQ disturbance detection. To obtain more precise disturbance parameters, a new approach based on generalized S transform(GST) is proposed to analyze the disturbances, in which parameterλcan be adjusted adaptively according to the frequency components. The computing formula is derived and the implementation process of GST is described .The simulation analysis of the disturbances are done using GST and the feature components are extracted for the classifier. Two classification methods are given to classify the PQ disturbances: decision tree method and artificial immune classifier, and they are tested to identify 13 kinds of disturbances. The results show that two methods are effective ,the decision tree method is more understandable and has fast recognition speed, artificial immune classifier has more high classification correct ration and is not sensitive to the noise.(3) A novel harmonic and interharmonic measurement algorithm based on modified GST (MGST) and clonal selection algorithm(CSA) is put forward. The variable parameters in MGST is selected to satisfy the high frequency resolution of harmonics computing. The harmonics amplitudes and phases given by MGST are with some error, so the clonal selection algorithm with good global searching abilities is used to optimize them, and the detail optimization process is described. The simulation results and application in large-scale inverter power supplies show that the method could analyze the harmonics and interharmonics accurately, and its performance is far better than FFT especial for the interharmonics and transient harmonics.(4) To overcome the effect of noise, modified S transform (MST) is adopted to detect the voltage sag. The effect of parameter in MST on detection accuracy of sag amplitude is analyzed, the method for obtaining the amplitudes, times, harmonic components and phases is described. The sag models of short-circuit fault, induction motor starting and transformer unload excitation are constructed, and their data are analyzed by using MST to exact features. The decision tree is used to distinguish different sag sources and the simulation results show MST can depress the noise effectively and the correct recognition rate of sag sources is good.(5) Aimed at solving the analysis and location of acoustic emission(AE), an approach combining generalized S transform and three-dimension location is proposed. After analyzing the principle and characteristics of partial discharge acoustic emission signal, the de-noising method is applied to eliminate the noise in acoustic signals, which works through opening a window near the main frequency in the GST module matrix and other elements outside the window being valued zero. The de-noised acoustic emission signals are analyzed by utilizing GST, some feature parameters are exacted, which include main frequency band energy and the arrival times of acoustic wave received by the sensors on the transform , then the time difference between sensors are computed. The location equation is deduced, these time difference are put into the equation and the acoustic source of transformers'partial discharge is located. The proposed algorithm can be extended to the location of other electrical equipments'PD such as insulators and the AE location of rocks in mines. The results show that the method could extract effective features of acoustic emission and give accurate location with little error and good reliability.These algorithms proposed for detecting the power quality disturbances in this dissertation will improve the accuracy and reliability for on-line PQ detection and off-line analysis. The algorithm of AE analyzing and locating provide an important theoretical basis and implement way for the PD monitor system.