Determination of the optimal number of clusters in harmonic data classification

In many of clustering algorithms, such as K-means and Fuzzy C-mean, the value of the expected numbers of clusters is often needed in advance as an input parameter to the algorithm. Other clustering algorithms estimate this number as the clustering process progresses using various heuristic techniques; however such techniques can also lead to a local minima within the solution space without finding the optimum number of clusters. In this paper, a method has been developed to determine the optimum number of clusters in power quality monitoring data using a data mining algorithm based on the minimum message length technique. The proposed method was tested using data from known number of clusters with randomly generated data points, with data from a simulation of a power system, and with power quality data from an actual harmonic monitoring system in a distribution system in Australia. The results from the tests confirm the effectiveness of the proposed method in finding the optimum number of clusters

Identification of Load Power Quality Characteristics using Data Mining

The rapid increase in computer technology and the availability of large scale power quality monitoring data should now motivate distribution network service providers to attempt to extract information that may otherwise remain hidden within the recorded data. Such information may be critical for identification and diagnoses of power quality disturbance problems, prediction of system abnormalities or failure, and alarming of critical system situations. Data mining tools are an obvious candidate for assisting in such analysis of large scale power quality monitoring data. This paper describes a method of applying unsupervised and supervised learning strategies of data mining in power quality data analysis. Firstly underlying classes in harmonic data from medium and low voltage (MV/LV) distribution systems were identified using clustering. Secondly the link analysis is used to merge the obtained clusters into supergroups. The characteristics of these super-groups are discovered using various algorithms for classification techniques. Finally the a priori algorithm of association rules is used to find the correlation between the harmonic currents and voltages at different sites (substation, residential, commercial and industrial) for the interconnected supergroups

Maximum allowable delay bound estimation using Lambert W function

The widespread of communication networks make them very promising to play a great role in future control systems. The communication networks will be present in the feedback control system which makes it a kind of time delay system. Closing the feedback system through a communication network introduces many challenges for the controller designers. Communication networks induce inherent time delay and some of the data may be lost which can destabilize the control system or result in poor system performance. It is important to identify the maximum time delay that the control system can withstand. In this paper, we report the application of the Lambert W function for calculating the maximum allowable delay bound in linear time delay control systems. The results of the calculation are compared with the most widely used Linear Matrix Inequalities based method. © 2017 IEEE

Identification of Load Power Quality Characteristics using Data Mining

The rapid increase in computer technology and the availability of large scale power quality monitoring data should now motivate distribution network service providers to attempt to extract information that may otherwise remain hidden within the recorded data. Such information may be critical for identification and diagnoses of power quality disturbance problems, prediction of system abnormalities or failure, and alarming of critical system situations. Data mining tools are an obvious candidate for assisting in such analysis of large scale power quality monitoring data. This paper describes a method of applying unsupervised and supervised learning strategies of data mining in power quality data analysis. Firstly underlying classes in harmonic data from medium and low voltage (MV/LV) distribution systems were identified using clustering. Secondly the link analysis is used to merge the obtained clusters into supergroups. The characteristics of these super-groups are discovered using various algorithms for classification techniques. Finally the a priori algorithm of association rules is used to find the correlation between the harmonic currents and voltages at different sites (substation, residential, commercial and industrial) for the interconnected supergroups

Classification and Explanatory Rules of Harmonic Data

Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. One of the paramount issues in clustering process is to discover the natural groups in the data set. A method based on the minimum message length (MML) has been developed to determine the optimum number of clusters (or mixture model size) in a power quality data set from an actual harmonic monitoring system in a distribution system in Australia. Once the optimum number of clusters is determined, a supervised learning algorithm, C5.0, is used to uncover the fundamental defining factors that differentiate the various clusters from each other. This allows for explanatory rules of each cluster in the harmonic data to be defined. These rules can then be utilised to predict which cluster any new observed data may best be described

Analyzing harmonic monitoring data using supervised and unsupervised learning

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

Analyzing harmonic monitoring data using data mining

Harmonic monitoring has become an important tool for harmonic management in distribution systems. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical MV 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 has been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that impact significantly on 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 data mining based on clustering techniques which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. The paper shows how clustering can be used to identify interesting patterns of harmonic measurement data and how these relate to their associated operational issues

Clustering, classification and explanatory rules from harmonic monitoring data

A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and for the calculation of means, variances and relative abundance of the member clusters. In this work a novel technique has been developed using the MML method to determine the optimum number of clusters (or mixture model size) during the clustering process. Once the optimum model size is determined, a supervised learning algorithm is employed to identify the essential features of each member cluster, and to further utilize these in predicting which ideal clusters any new observed data may best described by. This chapter first describes the design and implementation of the harmonic monitoring program and the data obtained. Results from the harmonic monitoring program using both unsupervised and supervised learning techniques are then analyzed and discussed