Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 11, Issue 4, Pages 3606-3619Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2020.2991335
Keywords
Power system stability; Security; Power system dynamics; Correlation; Stability analysis; Feature extraction; Power system transients; Dynamic security assessment; partial mutual information; iterated random forest; spatial-temporal dynamic visualization
Categories
Funding
- University Applied Fundamental Research Project of Yichang City [A19-402-a15]
- Open Foundation of Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station [2019KJX11]
- National Natural Science Foundation of China [51607104, 51807109, TSG-008242019]
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With the continuous expansion of the power system scale and extensive application of phasor measurement units (PMUs), the secure operation of power systems has been increasingly concerned. In this paper, an integrated scheme for online dynamic security assessment (DSA) based on feature selection and regression prediction is proposed. First, partial mutual information (PMI) and the Pearson correlation coefficient (PCC) are used to select the key variables in the feature selection process. Second, an iterated random forest (IRF) is applied to predict the transient stability margin (TSM) based on the selected variables. Combining the feature selection process and regression prediction, a DSA model is constructed. Finally, a spatial-temporal dynamic visualization approach is proposed, which can intuitively provide real-time dynamic security information of power systems. The integrated scheme, which is tested on the IEEE 39-bus system and a practical 1648-bus system provided by the software PSS/E, exhibits desirable assessment accuracy and is suitable for online application. In the robustness test, some impact factors for power system operation are considered, such as topology change, variation of generator/load power distribution and variation of load characteristics. Moreover, the impacts of missing data and measurement noise are studied.
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