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To effectively guarantee a secure and stable operation of a smart substation, it is essential to develop a relay protection system considering the real-time online operation state evaluation and the risk assessment of that substation. In this paper, based on action data, defect data, and network message information of the system protection device (PD), a Markov model-based operation state evaluation method is firstly proposed for each device in the relay protection system (RPS). Then, the risk assessment of RPS in the smart substation is carried out by utilizing the risk transfer network. Finally, to highly verify the usefulness and the effectiveness of the proposed method, a case study of a typical 220 kV substation is provided. It follows from the case study that the developed method can achieve a better improvement for the maintenance plan of the smart substation.
Dongliang Nan; Weiqing Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente; Lu Zhang. Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies 2020, 13, 1777 .
AMA StyleDongliang Nan, Weiqing Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, Pierluigi Siano, Mimmo Parente, Lu Zhang. Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network. Energies. 2020; 13 (7):1777.
Chicago/Turabian StyleDongliang Nan; Weiqing Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano; Mimmo Parente; Lu Zhang. 2020. "Risk Assessment of Smart Substation Relay Protection System Based on Markov Model and Risk Transfer Network." Energies 13, no. 7: 1777.
Dynamic state estimation (DSE) for generators plays an important role in power system monitoring and control. Phasor measurement unit (PMU) has been widely utilized in DSE since it can acquire real-time synchronous data with high sampling frequency. However, random noise is unavoidable in PMU data, which cannot be directly used as the reference data for power grid dispatching and control. Therefore, the data measured by PMU need to be processed. In this paper, an adaptive ensemble square root Kalman filter (AEnSRF) is proposed, in which the ensemble square root filter (EnSRF) and Sage–Husa algorithm are utilized to estimate measurement noise online. Simulation results obtained by applying the proposed method show that the estimation accuracy of AEnSRF is better than that of ensemble Kalman filter (EnKF), and AEnSRF can track the measurement noise when the measurement noise changes.
Dongliang Nan; Weiqing Wang; Kaike Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano. Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter. Applied Sciences 2019, 9, 5200 .
AMA StyleDongliang Nan, Weiqing Wang, Kaike Wang, Rabea Jamil Mahfoud, Hassan Haes Alhelou, Pierluigi Siano. Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter. Applied Sciences. 2019; 9 (23):5200.
Chicago/Turabian StyleDongliang Nan; Weiqing Wang; Kaike Wang; Rabea Jamil Mahfoud; Hassan Haes Alhelou; Pierluigi Siano. 2019. "Dynamic State Estimation for Synchronous Machines Based on Adaptive Ensemble Square Root Kalman Filter." Applied Sciences 9, no. 23: 5200.