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The empirical mode decomposition (EMD) is a good time-frequency analysis method, which can deal with nonlinear and non-stationary signals. Aiming at improving modal aliasing problem brought by the traditional EMD, white noise is introduced into the improved aided analysis algorithm namely ensemble empirical mode decomposition (EEMD), instantaneous amplitude and frequency can be obtained by using teager energy operator (TEO), which is adopted to identify the type of power quality disturbance. The anti-aliasing of EEMD and real-time detection of TEO are verified by the signal simulation in Matlab. Simulation and experimental results show that the proposed algorithm can detect and locate power quality disturbances accurately and quickly, with excellent detection effects.

Power flow shift being one of the main reasons that cause a large-scale blackout of power system, it is important to get the power flow shift under control at the beginning of the failure. In view of multidrop overload which frequently occurs in power outage, a comprehensive evaluation method is proposed to control the power flow shift. This method synthetically considers the control nodes' ability to eliminate the overload of the transmission in combination with PCA in which nodes' sensitivity of branch is selected as the evaluation index, divides them into two types (nodes that increase the power output to branch and others that decrease the power output to branch) and then obtains the control nodes' operation sequence. Additionally, a reverse equal matching adjustment is used to realize the control over multidrop overload. Typical numerical example is used to verify the effectiveness of the comprehensive evaluation method.