Power systems wide-area voltage stability assessment considering dissimilar load variations and credible contingencies

Abstract This paper reveals that the existing techniques have some deficiencies in the proper estimation of voltage stability margin (VSM) when applied to a power system with different load change scenarios. The problem gets worse when credible contingencies occur. This paper proposes a real-time wide-area approach to estimate VSM of power systems with different possible load change scenarios under normal and contingency operating conditions. The new method is based on an artificial neural network (ANN) whose inputs are bus voltage phasors captured by phasor measurement units (PMUs) and rates of change of active power loads. A new input feature is also accommodated to overcome the inability of trained ANN in prediction of VSM under N−1 and N−2 contingencies. With a new algorithm, the number of contingencies is reduced for the effective training of ANN. Robustness of the proposed technique is assured through adding a random noise to input variables. To deal with systems with a limited number of PMUs, a search algorithm is accomplished to identify the optimal placement of PMUs. The proposed method is examined on the IEEE 6-bus and the New England 39-bus test system. Results show that the VSM could be predicted with less than 1% error

Data-driven classifier for extreme outage prediction based on bayes decision theory

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix