Multi criteria series arc fault detection based on supervised feature selection
نویسندگان
چکیده
منابع مشابه
Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis
Arc fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of arc faults in low-voltage circuits, it is difficult for general methods to protect all loads from series arc faults. From the analysis of many series arc faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaus...
متن کاملMulti-task Feature Selection based Anomaly Detection
Network anomaly detection is still a vibrant research area. As the fast growth of network bandwidth and the tremendous traffic on the network, there arises an extremely challengeable question: How to efficiently and accurately detect the anomaly on multiple traffic? In multi-task learning, the traffic consisting of flows at different time periods is considered as a task. Multiple tasks at diffe...
متن کاملResearch on Low-voltage Series Arc Fault Detection Method Based on Least Squares Support Vector Machine
Arc fault is one of the important reasons of electrical fires. In virtue of cross talk, randomness and weakness of series arc faults in low-voltage circuits, very few of techniques have been well used to protect loads from series arc faults. Thus, a novel detection method based on support vector machine is developed in this paper. If series arc fault occurs, high frequency signal energy in circ...
متن کاملEnsemble-based Feature Selection Criteria
Recursive Feature Elimination (RFE) combined with feature ranking is an effective technique for eliminating irrelevant features when the feature dimension is large, but it is difficult to distinguish between relevant and redundant features. The usual method of determining when to stop eliminating features is based on either a validation set or cross-validation techniques. In this paper, we pres...
متن کاملMulti-Objective Semi-Supervised Feature Selection and Model Selection Based on Pearson's Correlation Coefficient
This paper presents a Semi-Supervised Feature Selection Method based on a univariate relevance measure applied to a multiobjective approach of the problem. Along the process of decision of the optimal solution within Pareto-optimal set, atempting to maximize the relevance indexes of each feature, it is possible to determine a minimum set of relevant features and, at the same time, to determine ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Electrical Power & Energy Systems
سال: 2019
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2019.05.012