Dynamic State Estimator Based on Wide Area Measurement System During Power System Electromechanical Transient Process

Qin, Xiaohui; Li, Baiqing; Liu, Nan

doi:10.5772/37574

Cited by 1 publication

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is used for a wide variety of applications in industry, most famously being used by NASA as part of the Apollo project to aid the navigation of manned spacecraft going to the moon and back [21]. A range of Kalman filters have been proposed for power system state estimation [22,23,24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…As 3 (2) using straightforward matrix manipulation. These assumptions have been made previously for MPC in [9], and for Kalman filtering in [22]. In turn by zeroing these faster dynamics in the state-space representation for the control, it allows larger sample times to be used for the discretisation of the state-space for control and estimation.…”

Section: Implementation Of Mpc For Agcmentioning

confidence: 99%

See 1 more Smart Citation

Efficient implementation of MPC-based AGC for real-world systems with low inertia

Namara

Milano

2018

Electric Power Systems Research

View full text Add to dashboard Cite

The paper discusses practical implementation and computational aspects of an AGC scheme based on model predictive control (MPC) for a real-world power system, namely the all-island Irish transmission system. This system is particularly interesting from the frequency regulation point of view due to the high penetration of wind power generation. This leads to a significant reduction in system inertia, which in turn impacts on the ability of the system operator to regulate the system frequency. The paper compares the performance of the MPC-based AGC with a conventional AGC based on PI controllers and draws relevant conclusions. The case study is a high-resolution simulation of a 1,479-bus model of the Irish electrical grid with significant wind penetration, stochastic loads, and controller delays.

show abstract