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LAPSE:2023.30302v1

A Data-Driven Approach for Online Inter-Area Oscillatory Stability Assessment of Power Systems Based on Random Bits Forest Considering Feature Redundancy

Songkai Liu, Dan Mao, Tianliang Xue, Fei Tang, Xin Li, Lihuang Liu, Ruoyuan Shi, Siyang Liao, Menglin Zhang

April 14, 2023

Abstract
To utilize the rapidly refreshed operating data of power systems fully and effectively, an integrated scheme for inter-area oscillatory stability assessment (OSA) is proposed in this paper using a compositive feature selection unit and random bits forest (RBF) algorithm. This scheme consists of offline, update, and online stages, and it can provide fast and accurate estimation of the oscillatory stability margin (OSM) by using the real-time system operating data. In this scheme, a compositive feature selection unit is specially designed to realize efficient feature selection, which can significantly reduce the data dimensionality, effectively alleviate feature redundancy, and provide accurate correlation information to system operators. Then, the feature set consisting of the selected pivotal features is used for the RBF training to build the mapping relationships between the OSM and the system operating variables. Moreover, to enhance the robustness of the scheme in the face of variable operating conditions, an update stage is developed. The effectiveness of the integrated scheme is verified on the IEEE 39-bus system and a larger 1648-bus system. Tests of estimation accuracy, data processing speed, and the impact of missing data and noise data on this scheme are implemented. Comparisons with other methods reveal the superiority of the integrated scheme. In addition, the robustness of the scheme to variations in system topology, distribution among generators and loads, and peak and minimum load is studied.

Record ID

LAPSE:2023.30302v1

Keywords

data-driven, feature redundancy, inter-area oscillatory stability, random bits forest

Subject

Numerical Methods and Statistics

Suggested Citation

Liu S, Mao D, Xue T, Tang F, Li X, Liu L, Shi R, Liao S, Zhang M. A Data-Driven Approach for Online Inter-Area Oscillatory Stability Assessment of Power Systems Based on Random Bits Forest Considering Feature Redundancy. (2023). LAPSE:2023.30302v1

Author Affiliations

Liu S: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Mao D: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Xue T: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Tang F: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Li X: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Liu L: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China
Shi R: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang 443002, China [ORCID]
Liao S: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Zhang M: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Journal Name

Energies

Volume

Issue

First Page

1641

Year

2021

Publication Date

2021-03-15

DOI:

https://doi.org/10.3390/en14061641