Testing low voltage network state estimation in RTDS environment

Purpose The purpose of this study is to propose a control scheme based on state estimation algorithm to improve zero or low-voltage ride-through capability of permanent magnet synchronous generator (PMSG) wind turbine. Design/methodology/approach Based on the updated grid codes, during and after faults, it is necessary to ensure wind energy generation in the network. PMSG is a type of wind energy technology that is growing rapidly in the network. The control scheme based on extended Kalman filter (EKF) is proposed to improve the low voltage ride-through (LVRT) capability of the PMSG. In the control scheme, because the state estimation algorithm is applied, the requirement of DC link voltage measurement device and generator speed sensor is removed. Furthermore, by applying this technique, the extent of possible noise on measurement tools is reduced. Findings In the proposed control scheme, zero or low-voltage ride-through capability of PMSG is enhanced. Furthermore, the requirement of DC link voltage measurement device and generator speed sensor is removed and the amount of possible noise on the measurement tools is minimized. To evaluate the ability of the proposed method, four different cases, including short and long duration short circuit fault close to PMSG in the presence and absence of measurement noise are studied. The results confirm the superiority of the proposed method. Originality/value This study introduces EKF to enhance LVRT capability of a PMSG wind turbine.

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Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm

Energies ◽

10.3390/en12234457 ◽

2019 ◽

Vol 12 (23) ◽

pp. 4457 ◽

Cited By ~ 1

Author(s):

Antončič ◽

Papič ◽

Blažič

Keyword(s):

Kalman Filter ◽

State Estimation ◽

Low Voltage ◽

Least Squares Method ◽

Weighted Least Squares ◽

Measurement Data ◽

Estimation Algorithm ◽

Distribution Networks ◽

Voltage Distribution ◽

State Estimator

This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

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