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This paper proposes a novel approach to estimate the steady-state angle stability limit (SSASL) by using the nonlinear power system dynamic model in the modal space. Through two linear changes of coordinates and a simplification introduced by the steady-state condition, the nonlinear power system dynamic model is transformed into a number of single-machine-like power systems whose power-angle curves can be derived and used for estimating the SSASL. The proposed approach estimates the SSASL of angles at all machines and all buses without the need for manually specifying the scenario, i.e. setting sink and source areas, and also without the need for solving multiple nonlinear power flows. Case studies on 9-bus and 39-bus power systems demonstrate that the proposed approach is always able to capture the aperiodic instability in an online environment, showing promising performance in the online monitoring of the steady-state angle stability over the traditional power flow-based analysis.
In this paper, we propose a data-driven energy storage system (ESS)-based method to enhance the online small-signal stability monitoring of power networks with high penetration of intermittent wind power. To accurately estimate inter-area modes that
The integration of renewables into electrical grids calls for optimization-based control schemes requiring reliable grid models. Classically, parameter estimation and optimization-based control is often decoupled, which leads to high system operation
Fast and accurate optimization and simulation is widely becoming a necessity for large scale transmission resiliency and planning studies such as N-1 SCOPF, batch contingency solvers, and stochastic power flow. Current commercial tools, however, prio
This paper develops a grey-box approach to small-signal stability analysis of complex power systems that facilitates root-cause tracing without requiring disclosure of the full details of the internal control structure of apparatus connected to the s
This paper studies the distributed state estimation in sensor network, where $m$ sensors are deployed to infer the $n$-dimensional state of a linear time-invariant (LTI) Gaussian system. By a lossless decomposition of optimal steady-state Kalman filt