Topology error processing based on forecast measurement errors

Topology errors can cause significant state estimation error or even divergence of power flow solution, which undermines the reliable operation of power grids. Undetected topology errors lead to dangerous situational unawareness in the control center; for example, an unknown transmission line outage may cause a cascading failure that leads to a rolling blackout. Existing detection methods are based on DC state estimation with residual analysis. These detection techniques use only instantaneous measurement data and require convergence of numerical solution. In this paper, a new approach that utilizes both historical and instantaneous measurement data is proposed to detect topology errors. The key idea is to track the dynamics of the power flow measurements by forecasting the measurements. Topology errors will cause the forecast measurements to be significantly different from the actual measurements. To implement this approach, a time-forward kriging-based load forecasting technique is used to forecast bus load for the next time step. The forecast load is then converted to forecast state through power flow analysis using network parameters stored at the control center. The forecast measurements can be further calculated from the forecast state. Topology errors can be detected by comparing the forecast and actual measurements. The forecast measurement errors are used as input to train Support vector machine (SVM) classifier offline. Several common SVM kernel models are compared to find the most suitable kernel for detecting topology errors. The SVM classifier can be applied in real-time to detect a topology error. The proposed approach is tested on IEEE 14 bus system with NYISO load data from Year 2011 and 2012. Our analysis shows that the proposed approach can accurately detect a single-line topology error.