Classification of input and output variables for a Bayesian model to analyze animal-related outages in overhead distribution systems
ABSTRACT Animals, such as squirrels, cause significant outages in overhead distribution systems. Models that would accurately estimate outages caused by animals would be very useful for utilities for year-end analysis of reliability performance of the distribution system. Large increase in outages caused by animals would require the utility to do further evaluation and take remedial actions. A two-layer Bayesian network model with Month-Type, Level of Fair Weather Days in the week, and Outage Level in the previous week as input and Outage Level in the week is presented in this paper for estimation of weekly animal-related outages. Results of different approaches for classification of inputs and output are presented, which are then compared to select the best classification of input and output variables for the model.
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ABSTRACT: This paper extends previous research on using a Bayesian network model to investigate impacts of time (month) and weather (number of fair weather days in a week) on an- imal-related outages in distribution systems. Outage history (outages in the previous week) is included as an additional input to the model, and inputs and outputs are classified systematically to reduce errors in estimates of outputs. Conditional probability table obtained from the historical data are used to estimate weekly animal-related outages which is followed by a Monte Carlo simu- lation to find estimates of mean and confidence limits for monthly animal-related outages. Comparison of results obtained for four cities of different sizes in Kansas with those obtained using a hybrid wavelet/neural network model shows consistency between the two models. The methodology presented in this paper is simple to implement and useful for the utilities for year-end analysis of the outage data to identify specific reliability-related concerns.IEEE Transactions on Power Systems 08/2011; 26(3):1618-1624. · 3.53 Impact Factor