Research On Comprehensive Power Quality Detection And Analysis Systems

Power quality information detection and analysis is the precondition to monitor and control the power quality. So it has significant theoretical and practical value for ensuring the safe operation of power systems and safe use of the consumers. Aiming at the problem of the power quality detection and analysis, new methods to classify the power quality disturbances, to compress the power quality data, to comprehensively evaluate the power quality are studied in the paper. Meanwhile, the comprehensive power quality information detection and analysis system is designed for detecting and analysing the power quality information of the power system including microgrid system.Nowadays, the demand for data compression has increased considerably due to the the huge amount of the power quality data detected. Aiming at the problem that the current power quality data compression methods do not consider the correlation of the three-phase power quality data, a new method of data compression of three-phase power quality data based on image coding algorithm is presented in the paper. The nature of power quality data compression is to remove the redundancy including the spatial redundancy, the time redundancy, and other redundancy. The three-phase power quality data are transformed by dq0 transform to eliminate the redundancy of the three-phase data. The obtained one-dimensional power quality data are transformed to two-dimensional matrix according to the integral multiples of period to eliminate the redundancy between cycles. The two-dimensional image is smoothed based on image smoothing algorithm in order to make it more suitable for two-dimensional wavelet transform coding. Meanwhile, data compression is processed based on two-dimension wavelet transform, image set partitioning in hierarchical tree coding and DEFLATE coding algorithms. The simulation results show that the proposed methods can not only get higher signal noise ratio relative to the existing methods of the power quality data compression under the same compression ratio, but also control the power quality compression performance according to the practical requirement flexibly.Aiming at the problem that it is difficult to classify the category of multiple power quality disturbances for the current power quality disturbance classification methods, a new multiple power quality disturbances classification method is presented based on multi-label classification in the paper. In terms of the feature extraction, the signal of multiple power quality disturbances is decomposed by discrete wavelet transform, and the norm energy entropy of the wavelet coefficients of each level are extracted as eigenvector for classfication. In terms of the classifier design, the methods of the k-nearest neighbor Bayesian rule and the C-means RBF neural network based on ranking approach of the multi-label classification are introduced respectively to solve the problem of the multiple power quality disturbances classification. At the same time the simulation experiment is conducted based on the evaluation metrics of the multi-label classification. The simulation results show that the proposed methods can effectively recognize the multiple power quality disturbances including voltage sag, voltage swell, interruption, impulsive transient, harmonics, voltage fluctuation and their compound ones effectively under different noise conditions relative to the existing methods and the average precision achieves 95%.Aiming at the problem of inconsistencies in the conclusions, which is caused by adopting many different single comprehensive evaluation methods to evaluate the same objective, a new method of power quality comprehensive evaluation based on the combined evaluation method is presented in the paper. Firstly, the power quality is evaluated respectively based on four single comprehensive evaluation methods including the analytic hierarchy process, fuzzy comprehensive evaluation, artificial neural network evaluation and gray comprehensive evaluation. Secondly, in order to ensure the consistency of different single comprehensive evaluation results, the statistics is constructed based on Kendall correlation to check up the coherence of different evaluation methods and eliminate the inconsistencies results of the single comprehensive evaluation methods. Then, the arithmetic mean, Borda and Copeland methods are used respectively to combine the single comprehensive evaluation results which have passed the pre-test. Finally, the final result of the power quality combined evaluation is obtained according to the size of the statistics t which is established based on the Spearman's rank correlation coefficient after post-test. The effectiveness of the proposed method is proved through evaluating the power quality of several locations.The comprehensive power quality information detection and analysis system is designed using the virtual instrument technology based on the former proposed methods of power quality classification, power quality data compression and power quality comprehensive evaluation. This system can not only detect and analyse the power quality information of the power grid even large power customers on-line, but also evaluate the power quality of the power system reasonably off-line. This system enables the systematic, intelligent networked detection of the electric energy. So it can improve the efficiency of the electric energy measurement and provide reference for accurate electrical energy measurement. This system can not only record the whole power quality information data of the power grid to provide rich real information for accident analysis, but also classify the power quality disturbance to provide important evidence for the corresponding improvement.