Abstract
In practical power system operation, knowing the voltage stability limits of the system is important. This paper proposes using a decision tree (DT) to extract guidelines through offline study results for assessing system voltage stability status online. Firstly, a sample set of DTs is determined offline by active power injection and bus voltage magnitude (P-V) curve analysis. Secondly, participation factor (PF) analysis and the Relief-F algorithm are used successively for attribute selection, which takes both the physical significance and the classification capabilities into consideration. Finally, the C4.5 algorithm is used to build the DT because it is more suitable for handling continuous variables. A practical power system is implemented to verify the feasibility of the proposed online voltage stability margin (VSM) assessment framework. Study results indicate that the operating guidelines extracted from the DT can help power system operators assess real time VSM effectively.
Highlights
Voltage stability refers to the capability of maintaining voltage within safe or acceptable limits, even in the case of credible contingencies [1,2,3,4]
This paper considers the actual demands of online voltage stability margin (VSM) assessment and presents the use of a decision tree (DT) to extract guidelines to help operators to evaluate power system state
This paper proposes constructing a DT using the C4.5 algorithm to extract operating guidelines for power system operators, and has two innovations
Summary
Voltage stability refers to the capability of maintaining voltage within safe or acceptable limits, even in the case of credible contingencies [1,2,3,4]. Algorithms, and various improved versions, to online voltage stability assessment [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] These attempts use black box models, which hide the inner data mining process and cannot extract underlying valuable information [30]. Reference [35] applied a DT to assess voltage stability and discussed the influence of the DT growing method on classification accuracy Each of these methods constructed DTs with the CART algorithm, which computes the Gini index to determine the most suitable attribute for purifying a sample set and builds a binary tree from top to bottom [37].
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