Abstract
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.
Highlights
Power system security assessment is an effective tool for checking the security of power systems, which aims to determine whether, and to what extent, a power system is reasonably safe from serious interference to its operation [2,3]
In terms of fast and accurate contingency screening and ranking, an online static security assessment module based on a multi-step adaptive least absolute shrinkage and selection operator (Lasso) regression algorithm is proposed in this paper
The proposed approach is examined on the IEEE 14-bus, 118-bus, and 300-bus test systems, and the results indicate that this approach manages to handle this issue with reduced time, and is suitable for online application
Summary
The security of a system has always been an important issue, which is related to the ability to continue normal operation in post-contingency conditions [1]. Static security assessment is concerned with factors related to the insecurity situation [4,5], such as overload, overvoltage, and so on, via the load flow calculation of the power system in post-contingency conditions. Dynamic security assessment mainly analyzes the transient stability of the power system after the fault of the power system according to the real-time data [6,7,8]. To predict the transient stability status by machine learning algorithms in the post-fault condition, the real-time data are respectively obtained from phasor measurement units (PMUs) in [6] and the simulation results in [7,8].
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