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Decision fusion of voltage stability indicators for small sized power systems

In this paper, the authors consider a probabilistic approach to at the issue of voltage stability of a small power system. They do this via a data fusion technique using the Bayesian rule, which is well suited for such applications. They use three proximity indicators in this study. On their own, each of these indicators have some amount of probability of error when used in deciding whether the system is stable or not. A data fusion center is used here to minimize the probability of error in deciding whether the system is stable or not. The authors employ a data fusion center that fuses the decision made by each individual proximity indicator (rule) in an optimal way using the Bayesian technique. Monte Carlo runs are employed to generate 10,000 different realizations of load levels from which they find the conditional probability density functions for each of the three proximity indicators (rules). The study is conducted on a 5-bus system.