Abstract
Dynamic security assessment is widely used in dispatching operation systems, and calculation speed is one of its most important performance indices. In this study, an improved k-nearest neighbour (k-NN) method is proposed aiming to predict the stability indicators of power system, for example, critical clearing time. The method is much faster than the simulation and suitable for online analysis. Firstly, a simulation sample database is constructed based on historical online data and a logistic regression model with least absolute shrinkage and selection operator is trained to pick the stability features, which are chosen from static quantities like running state and active power of electric elements. While a new operation mode needs to be evaluated, a weighted k-NN is implemented to obtain the most familiar samples in the database using the chosen features; the final result will be determined comprehensively by the familiar samples. The validity of the proposed method is verified by simulation using online data of State Grid Corp of China and different key faults. It is proved that the method meets the requirements for speed and accuracy of online analysis system.
Highlights
Along with the development of ultra-high voltage (UHV) technology, the characteristic of the power system is facing profound changes
In China, dynamic security assessment (DSA) has been widely applied in dispatching systems above the provincial level, which is unknown as online security and stability analysis
A comprehensive security analysis will be made by DSA every 15 min, which includes more than 1000 transient stability simulation of pre-defined faults, and needs extremely large calculation
Summary
Along with the development of ultra-high voltage (UHV) technology, the characteristic of the power system is facing profound changes. There are many regularities and experiences contained in the historical data, which could be applied in quick judgment of online stability analysis to improve the calculation speed and validity. With the consideration that it is easy to find similar samples in the latest historical online data, the logistic regression with least absolute shrinkage and selection operator (LASSO) and improved k-NN method are introduced to extract the stability features and make the quick judgment. The rest of the paper is organised as follows: Section 2 introduces the LASSO method for extracting features and the improved k-NN for quick judgment; Section 3 describes the main ideas and analysis steps of the method; results are illustrated and evaluated in Section 4 using actual data; and Section 5 concludes the paper
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