Extracting power metering automation terminal flows features and forecasting fault classification

Power metering automation terminals communicate to main station by mobile cellular network. Hence the terminals occur communication and internal faults in normal case and part of faults have apparent statistical features in data flow term. Based on the power metering automation terminals´ historical communication flows records, this dissertation studies on the method of preprocessing and multiply clustering the raw data, and observing the differences among multidimensional data and proposes methods to preprocess power metering automation terminal data and extract the features. Combining the extracted features and historical troubleshooting records, it studies and tests the classifying and forecasting model to terminal faults and marks the reliability of model. Meanwhile, this dissertation proposes the algorithm of faults probability.