Search

The load analysis for the distribution system and facilities has relied on measurement equipment. Moreover, load monitoring incurs huge costs in terms of installation and maintenance. This paper presents a new model to analyze wherein facilities load under a feeder every 15 min using meter-reading data that can be obtained from a power consumer every 15 min or a month even without setting up any measuring equipment. After the data warehouse is constructed by interfacing the legacy system required for the load calculation, the relationship between the distribution system and the power consumer is established. Once the load pattern is forecasted by applying a clustering and classification algorithm of temporal data-mining techniques for the power customer who is not involved in automatic meter reading, a single-line diagram per feeder is created, and power-flow calculation is executed. The calculation result is analyzed by using various temporal and spatial analysis methods, such as the Internet geographic information system, single-line diagram, and online analytical processing. [ABSTRACT FROM AUTHOR]

Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [ABSTRACT FROM AUTHOR]

A fundamental aspect in the regulation of the continuity of electricity supply is the identification of faults that could be caused by an exceptional event and, therefore, that are outside the utility control and responsibility. Different methods have been proposed during the years: the interpretation of the observed faults as a signal of an underlying system naturally leads to the analysis of the problem by means of a hidden Markov model. These models, in fact, are widely used for introducing dependence in data and/or for modeling observed phenomena depending on hidden processes. The application of this method shows that the model is able to identify exceptional events; moreover, the study of the estimated model parameters gives rise to reality-linked considerations. [ABSTRACT FROM AUTHOR]