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Multi-variable Dependent Forecast Algorithm for Predicting Secondary Arc
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DOI：10.17775/CSEEJPES.2019.00200.png)
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Abstract
En 中文
Hybrid reactive power compensation (HRPC) combines step-controlled shunt reactors and series compensation, and will be employed in ultra-high-voltage (UHV) power systems. The single-phase auto-reclosure characteristics of secondary arcs in systems with HRPC require further investigation. In this paper, both the arc-recalling voltage and subsidiary variations in arc current are investigated with and without HRPC. The frequency components of the secondary arc current and variations in arcing time are analyzed for various influential factors, such as the neutral reactor, arc resistance, fault location, degrees of compensation of HRPC, and the length of the transmission line. The non-dominated sorting genetic algorithm II (NSGA-II) and support vector machine regression are combined to create a multi-variable dependent forecasting algorithm to predict the characteristics of the secondary arc in UHV systems with HRPC. This paper provides a theoretical reference for optimizing the parameters of HRPC, and for developing adaptive auto-reclosure schemes and protection equipment.
Keywords:
Forecasting algorithm
hybrid reactive power compensation
low-frequency component
secondary arc characteristics
support vector machine regression
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5.9
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