Study On Data Stream Techniques And Its Application In Electric Power Information Processing

Data Stream Management techniques have become hot spots in the fields of information processing and database, it can provide effective support for real-time information processing and analysis. With the expansion of power grids and the raising of automation level, large amount of real-time data generated by the power system operation forms data streams, these data streams include important information related with equipment failures and system stability. Study on data stream techniques and its application in electric power information processing has important theoretical value and practical value. The main content of our research includes:A general data stream processing model is established according to the real-time requirement for data stream processing, it can be used to process recent data using sliding window, and can be suitable for different processing needs by way of synopsis data structure and data stream processing algorithm. How to establish a synopsis data structure using wavelet transforms and update the results incrementally is investigated. An improved incremental update algorithm for wavelet decomposition is proposed, which can improve the handling accuracy and reduce the response time.A new method for constructing a synopsis data structure based on parameters estimation is proposed to meet the need of system identification applications, which provide a new way for the construction of data streams synopsis. A simulation experiment of the transformer fault diagnosis using temporal characteristics and parameters estimation are finished, the experimental results show effectiveness of proposed method.The issues of continuous query and anomaly detection in data stream and related problems are analyzed. The anomaly detection algorithm based on shifted wavelet tree in data streams have been studied, and an improved algorithm was proposed. For the improved algorithm of shifted wavelet tree, monotonous search space was constructed for binary detection which has improved the detection efficiency; and the incremental algorithm of updating Wavelet tree is use to reduce response time. A simulation experiment of detecting voltage sag was finished, the experimental results show that the anomaly detection algorithm has low requirements in processing time and has high detection accuracy, it provides a new approach to real-time voltage sag detection.Clustering and classification of data streams were implemented by combining traditional data mining algorithms and the idea of data stream processing. A method of load classification for power distribution transformer based on data stream mining is proposed which can meet the need of TOU( time-of-use) electricity price, and a way of identifying power quality disturbances online using data stream mining is proposed which can provide decision support for the power quality improvement.A data stream prediction method is proposed by combining traditional prediction methods and the idea of data stream processing. In this method, wavelet decomposition and least squares support vector machines (LS-SVM) are combined to ensure the accuracy of the prediction, sliding window model in data stream processing is used to follow the data changing, and incremental algorithms for wavelet decomposition and online LS-SVM are used to save time. Simulation experiment using real power load dataset and the generator power angle dataset in the transient stability analysis of power system proves the effectiveness of the proposed method. This method can be used to ultra-short-term load prediction and power system transient stability prediction.