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- W4382118914 abstract "The online small-signal stability assessment of complex power systems is typically a challenging problem due to uncertainties and parameter variations of system dynamics as well as the incurred high computational complexity. This chapter proposes a novel theoretical framework for dynamic small-signal stability assessment of power systems by estimating the region of attraction for operating states in real time. By analyzing the latest sampling data of power grids in a fixed time window, an up-to-date training set is constructed with the aid of converse Lyapunov function, which enables us to develop an online learning approach based on Gaussian Process (GP) to assess the stability level of power grids. As a result, an iteration algorithm is designed to update the assessment parameters by learning the input-output pairs in the training set. Theoretical analysis is conducted to ensure the existence of converse Lyapunov function for differential-algebraic system that serves to describe power system dynamics, as well as to estimate the region of attraction for operating states with a given confidence level. In particular, a practical method is proposed to leverage phasor measurement unit (PMU) data of real power grids for validating the online GP approach. Moreover, experimental validations are taken to substantiate the proposed online assessment approach by using PMU data of smart-grid infrastructure on EPFL campus. The proposed assessment approach contributes to situational awareness of human operators in the control station, thereby taking proactive remedial actions prior to emergencies." @default.
- W4382118914 created "2023-06-27" @default.
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- W4382118914 date "2023-01-01" @default.
- W4382118914 modified "2023-09-24" @default.
- W4382118914 title "Online Gaussian Process Learning for Security Assessment" @default.
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- W4382118914 doi "https://doi.org/10.1007/978-981-99-3053-1_5" @default.
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