On Improving the Reliability of Power Grids for Multiple Power Line Outages and Anomaly Detection

Improving the reliability of smart grids is critical to not only cost-effectiveness of electricity delivery but also repair cost reduction. To efficiently improve the reliability, the real-time anomaly behavior detection and efficient location identification of multiple line outages play a major role in wide area monitoring of smart grids. However, capturing the features of anomalous interruption and then detecting them at real time is difficult for large-scale smart grids, because the measurement data volume and complexity increase drastically with the exponential growth of data from the immense intelligent monitoring devices to be rolled out. This is especially true for multiple line outage detection, as the methods of identifying the locations of multiple line outages face two major challenges: a limited number of Phasor Measurement Units (PMUs) and the high computational complexity. This chapter proposes an efficient real-time anomaly detection (ReTAD) algorithm to address these challenges, inspired in part by the ambiguity group theory. To characterize the performance of line outage identification, this chapter also introduces a statistical model to describe the average identification capability of multiple line outages. Under this model, we develop a global optimal PMUs placement strategy to maximize the average identification capability for a fixed budget of PMUs. Using 14-, 30-, 57-, 118- and 2383-bus systems, our experimental study demonstrates that our proposed ReTAD algorithm successfully detects the anomalous events in real-time and identifies the most likely multiple line outages with a 500 × speedup when compared to the method of exhaustive search. For the IEEE 14- and 57-bus systems, our experimental study also demonstrates that the proposed techniques can select optimal PMU locations while improving the average identification capability by about 10 % compared to random PMUs placement method.