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  1. Here, traditional synchrophasors rely on CTs and PTs physically connected to transmission lines or buses to acquire input signals for phasor measurement. However, it is challenging to install and maintain traditional phasor measurement units in some remote areas due to lack of facilities. Since transmission lines naturally generate alternating electrical and magnetic fields in the surrounding atmosphere, this paper presents two innovative designs for non-contact synchronized measurement devices (NCSMD), including an electric field sensor based non-contact SMD (E-NCSMD) and a magnetic field sensor based non-contact SMD (M-NCSMD). Compared with conventional synchrophasors, E-NCSMD and M-NCSMD are much more flexible to bemore » deployed and have much lower costs, making E-NCSMDs and M-NCSMD highly accessible and useful for a wide array of phasor measurement applications. Laboratory and field experiment results verified the effectiveness of the designs of both E-NCSMD and M-NCSMD.« less
  2. Here, this paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step ofmore » the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

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