Discussion about the Weather Impact on the Daily Outages in Urban Distribution System

I n this paper, an evaluation approach for analyzing the weather’s impact on the number of daily outages in the urban distribution system is explored. By dividing the number of outag es into two levels, the task could be carried out as a binary classification problem. In this study, the actual outage data from the distribution system operator is analyzed together with the local weat her condition records. First, the tendency of differen t outage levels to weather conditions is described by the Principal Component Analysis (PCA). Then, the Support Vector Machine (SVM) algorithm is adopted to build the classification model for predicting the outag e levels based on the weather condition. An oversampling method is introduced to manage the severe imbalance between the two outage levels. At the end, the performance of the classification model is assessed with the Receiver Operating Characteristic (ROC) curve

Data-driven Feature Description of Heat Wave Effect on Distribution System

During the last years, the effects of the climate change have become more and more evident. In particular, urban regions, where is more common the use of underground cables, are experiencing the strong effect of extremely high temperature conditions and low humidity. This phenomenon, known in literature as “heat wave”, should be properly evaluated for highlighting its effect on the system operation and planning, as well as for properly scheduling appropriate maintenance interventions. This paper presents a three-step procedure aiming to characterize the heat wave phenomenon in terms of “most significant features” and, on this basis, recognizing the days as “critical” and “non-critical”. The weather conditions of the city of Turin (Italy) and the faults that have affected the local network in the last 10 years have been considered. This approach will be useful for system operators for integrating the weather information in distribution system operation and planning procedures

Prediction of Power Outages in Distribution Network with Grey Theory

Annual power outages in distribution network are highly related to the reliability of the power grid and directly affect the customers' satisfaction. The severe weather conditions, increasing loads as well as aging equipment are all potential threatens to the electrical grid infrastructure. A good prediction of the number of outages is essential for the maintenance planning and cost benefit analysis of investment. In order to predict the out-of-service cases in the power grid, the GM (1,1) (first-order Grey Modelling) forecasting method is introduced in this paper. To improve the accuracy of the prediction, the PSO (particle swarm optimization) algorithm is applied for the parameter optimization in the modeling. The number of outages in the next two years of a medium-voltage urban distribution network are predicted based on the records in the past 7 years. The good performance of the simulation results verifies the proposed forecasting method

Abnormal Electricity Consumption Detection from Incomplete Records in Power System

Due to the limited channel bandwidth or interference signals in the advance metering infrastructures, there are usually some missing or human-revised data among the electricity consumption records of civilian customers. In order to make full use of this kind of records, machine learning techniques are introduced in this paper for electricity consumption sensitivity analysis regarding to the weather conditions. With the missing and revised records filtered out, each customer would have an individual regression model between weather conditions and the power demand. The importance of variables in the regression model is regarded as the sensitivity to various weather conditions. Then the abnormal consumption patterns are detected with a typical outlier identification algorithm based on different weather sensitivities among all the customers. The methods used in this paper show good results to identify the abnormal consumption patterns effectively regardless the quality of the original dat