• Energy Research

A thyristor-controlled series capacitor (TCSC) is employed to a transmission line in order to enhance the usable capacity of the present as well as upgraded lines, improve system stability, reduce losses, and improve power flow control capability. However, in an abnormal situation, the TCSC may transit from the existing operation mode to the other mode according to its control system and protection strategy. There is much difference in the impedance of the TCSC between each mode. This threatens the reliability of the conventional protection system, especially the distance relay, that works based on the measurement of line impedance. In this paper, we suggest a new protection scheme for a distance relay of a transmission line equipped with a TCSC. In the suggested method, in order to mitigate the effect of the TCSC in the fault loop, the TCSC injected voltage is subtracted from the measured phase voltage before supplying the voltage signal to the distance relay. The suggested scheme was verified by a real time digital simulator (RTDS)-based closed-loop test bed of a protective relay. The effect of the TCSC in the fault loop was completely mitigated. The distance relay works properly with the suggested scheme.

Abstract Along with the increased installation of distributed energy resources (DER), long‐term voltage stability can be improved if proper coordination is developed between DERs and grid controllers, for example, Load Tap Changer (LTC). In most of the proposed methods, the steady‐state voltage‐sensitivity analysis has been implemented to predict the voltage state, which is the state's dynamic evolution in abnormal operations of the grid with high shares of DERs. This paper presents a system identification‐based model predictive control (MPC), which can coordinate DERs and LTC to restore the voltage to the pre‐fault condition after emergencies. First, the online voltage evolution is predicted based on the sparse identification of the nonlinear dynamics (SINDY) technique. Then, the SINDY‐based voltage prediction is combined with an adaptive MPC in the centralized controller. In addition, the nonlinear of DERs have been modeled to avoid the MPC‐based coordination jeopardizing the local constraints of DERs. The proposed method has been tested in a modified CIGRE benchmark network. Simulation results show that the dynamic voltage is effectively estimated by the SINDY method. Furthermore, the developed MPC model smoothly supports faster voltage recovering time with the least number of control actions of LTCs.

This paper presents a detailed analysis results of the effect of a thyristor-controlled series capacitor (TCSC) on subsynchronous resonance (SSR), which was first applied to a Korean power system. First, the TCSC parameters were calculated, the structure of TCSC with synchronous voltage reversal (SVR) controller was presented, and the torsional characteristics of Hanul nuclear power generator rotor were studied to investigate the natural frequency and mode shape. The test signal method was used to determine the electrical damping in the frequency range of SSR operation through an electromagnetic transient analysis program in various system configurations. The SSR phenomenon was analyzed by comparing the electrical and mechanical damping of a conventional fixed series capacitor (FSC), and the case of a TCSC installed, and the effectiveness of the TCSC without any risk of SSR was demonstrated. As a result, when installing FSC, SSR occurred under sensitive operating conditions, but SSR was prevented in the case of TCSC compensation with SVR. The results obtained in this study can be effectively applied to the installation of TCSC in real power systems.

IEEE transactions on smart grid (2023). doi:10.1109/TSG.2023.3260721 Published by IEEE, New York, NY

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.