کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
5001483 1368417 2017 9 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله
Transient stability assessment via decision trees and multivariate adaptive regression splines
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Transient stability assessment via decision trees and multivariate adaptive regression splines
چکیده انگلیسی


- A reliable method is proposed for real-time transient stability assessment.
- CART and MARS are used as the classification and regression methods, respectively.
- The method is tested on the model of BC Hydro power system with limited number of PMUs.
- The sensitivity of the models to data fed earlier or later sampling times is uncovered.
- The sensitivity of the models to noisy data is shown and discussed.

This paper focuses on the practical implementation of online transient stability assessment (TSA) tools that employ, in conjunction with high-speed synchronized phasor measurements obtained from phasor measurement units (PMUs), classification and regression trees (CART) and multivariate adaptive regression splines (MARS) models. To build CART and MARS models that are amenable to real-time applications, pertinent transient stability-related system characteristics are identified; these include voltage and current phasors, deviations from the centre-of-inertia angle and speed, and potential- and kinetic-energy related quantities. These characteristic quantities are evaluated using PMU measurements and then leveraged to train CART and MARS models for the full Western Electricity Coordinating Council (WECC) system. The resultant models are tested and validated with the full WECC system using credible contingency scenarios in the BC Hydro subsystem. High prediction accuracy rates are observed for both CART and MARS methods, making them attractive options for real-time TSA.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electric Power Systems Research - Volume 142, January 2017, Pages 320-328
نویسندگان
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