Power Quality Disturbances Classification Based On Multi-resolution Fast S-transform

In recent years, rectifier, adjustable speed drive, electric arc furnace and electrified railway are widely used in all walks of life. As a result, power grid is severely disrupted, power quality is getting worse. At the same time, to protect sensitive modern industry equipment and the normal running of efficient production process, users start to ask for high quality power supply to power sector. As the premise of the power quality comprehensive evaluation and management of disturbance source location, the power quality disturbance signal recognition is of great significance. To improve identification accuracy and analysis ability to the power quality disturbance signals, this paper proposes a multi-resolution fast S-transform method which is used for power quality disturbance signal recognition and disturbance parameter detection under the high noise industrial environment.In order to meet the requirement of type recognition and parameter estimation in power quality disturbance signal analysis, firstly, the relationship between the disturbance parameter estimation errors and the kurtosis at the start and end locations of the disturbances in the time-amplitude curve and frequency-amplitude curve under different time-frequency resolutions is analyzed; Secondly, The optimal window width adjustment factors in different frequency ranges are determined according to the deviation maximization method, and the cubic spline interpolation method is used for fitting, the optimal window width required for the type recognition and parameter estimation of different disturbance signals is adjusted automatically; Finally, according to the characteristics of redundancy computation of generalized S transform, multi-resolution fast s-transform method is designed to meet the computation requirement of real time processing.The time-frequency modular matrix obtained from S-transform has the characteristics of gray image. Therefore, the classification accuracy of disturbances can be improved by two-dimensional mathematical morphology de-noising method. Firstly, morphological open operator with a line type, zero angle structure element was used in the high frequency area of the modular matrix to immune noise affection after threshold filtering; Secondly, a decision tree classifier was designed based on 5 features which were extracted from the original signals, Fourier spectrums of original signals and time-frequency modular matrix of multi-resolution fast S-transform, the decision tree can recognize the noise signal without disturbances and 13 types of disturbances including 6 types of complex disturbances; Finally, the minimum classification faults rule is designed to get the optimum threshold of each node. The comparison of simulation experiments shows that the new method has better noise immunity and more suitable for disturbances recognition in the noise environments.On the base of power quality disturbance signal processing, parameters are detected according to the result of recognition. The information of amplitude, start-stop time and main frequency of disturbance can be fully reflected by the original signal, Fourier spectrum, fundamental frequency amplitude curve, time-amplitude curve and frequency-amplitude curve of the power quality disturbance signal. Based on the analysis of short-term disturbances, periodic disturbances and transient disturbances, this paper proposes a set of parameter detection method suitable for the single and compound kinds of disturbance. Simulation and actual test data analysis show that the new method can meet the requirement of the parameter estimation of compound power quality disturbance signal. The parameter estimation error is lower than the generalized S-transform method and etc.Simulation results show that the new method improves the ability of feature presentation significantly and reduces the computation under high sampling rate effectively. The new method can identify the type of disturbance and detect the parameters of disturbance accurately under high noise environment.