Detection And Recognition Algorithms Study On Power Quality Disturbances

Nowadays, our country is stepping into an accelerating period of industrialization and urbanization. The requirement of power supply keeps fast increasing all the while, which brings huge opportunities to the development of interconnected power grid and the scale-up of power system. However, the coming substantive power electronic equipments, impactive and nonlinear loads in power system lead to a series of power quality problems, such as voltage sag, voltage swell, voltage fluctuation, harmonics, voltage notch, impulse, oscillation transient and so on. Hence, it is significative to real-time monitor, intelligently analyze those power quality disturbances (PQD), and to build the analysis frame of them. It can provide solutions and methods for PQD detection, recognition and source location. Besides, it can supply decisions for the PQ management. Thus, the dissertation researches on the detection, recognition and source location of PQD based on the study of steady disturbances, transient disturbances and multiple disturbances.About disturbances detection, firstly, the dissertation proposes a new detection scheme on basis of generalized morphologic filter and difference-entropy. Power quality disturbances are processed by generalized morphologic filter, which can maintain their original features during the denoising process. Then, the complexity of disturbances is measured by self-defined difference-entropy to locate the starting and ending time by checking the entropy values of singular points. Secondly, a new morphological undecimated wavelet (MUDW) is defined and applied for power quality disturbances detection. The proposed MUDW composed of open-close-plus-close-open morphology filter and morphology gradient operators, which can detect the verges of disturbances, besides, the MUDW satisfies the signal reconstruct condition. Simulations are done in MATLAB and a comparison with other MUDW is given. The results show that the proposed MUDW has good depicting-characteristics ability and resisting-noise performance, also, it can direct the start-end time and variation polarity rightly even in the strong noise environment.About single disturbance recognition, on one hand, the dissertation presents a novel algorithm based on mathematical morphology (MM) and dynamic time warping (DTW). In this algorithm, firstly, morphological filter is used in disturbances filtering. Secondly, a dq transform method is used in feature extraction of those disturbances. Thirdly, it calculated distances matrix between testing disturbances and six kinds of reference disturbances, then, DTW algorithm is used to search the optimum path which needs to be shortest in every distances matrix to guarantee the testing signals and reference signals resemble most. Finally, it selectes the shortest path as classification result. Simulation results show that this novel algorithm has an excellent recognition effect, which is not influenced by the amplitude, start-end time (including zero-crossing disturbance), disturbances duration time and noise strength. On the other hand, a new recognition method based on High-order Cumulants (HOC) and Support Vector Machines (SVM) is proposed for recognizing two kinds of high-frequency disturbances: oscillation transient and impulse transient. This method utilizes HOC to extract the 3rd order and 4th order statistic features of impulse transient and oscillation transient, and selects 8 features including the amount of local maxima and local minima, the value of maximum and minimum for each cumulants as input of SVM. The proposed method is available for classifying two kinds of disturbances without influencing by strong noise and other disturbances.About multiple disturbances recognition, a classification system is constructed using wavelet transform, teager energy operators (TEO) and prony algorithm. In the First place, the system uses wavelet transform to decompose the multiple disturbances into different frequency-space. In the next place, the low-frequency component is analyzed by TEO (Teager Operators) algorithm in order to distinguish whether the signal contains frequency variation, sag, swell or interruption. Furthermore, prony algorithm is adopted to analyze the signal without low-frequency component, and it can estimate the existence of harmonics, inter-harmonics or oscillation transients. Finally, it detects the start-end instants and windowed disturbance energy of high-frequency component to identify whether it is composed of impulse or notch disturbance. A mass of single disturbances and multiple disturbances are generated for testing the proposed recognition system and the results show that the system has good adaptability and high recognition rate for those kinds of signals.About disturbance source location, on one hand, it researches on locating switched capacitors on two different buses in distribution system. It calculates the average disturbance energy and average branch current variation as features by the web-based monitoring information, and those features are put into the probabilistic neural network to get the position of capacitors. On the other hand, the dissertation focuses on the source location of sag disturbance. It presents an algorithm to determine the position and reason of sag source by computing the value and direction of active power and reactive power. Simulation results indicate that this method can locate and distinguish the sag disturbances generated by capacitor clearing, symmetrical line fault, non-symmetrical line fault and induction motor starting.From the study above, the dissertation finally forms a PQD analysis frame including disturbances detection, disturbances recognition (single disturbance and multiple disturbances) and disturbances sources location.This dissertation is supported by the Scientific Research Foundation for the Returned Overseas Chinese Scholars—'Research on the power quality disturbances detection, recognition and location methods and system based on the network-based information'...