Qatar fault detection in smart grid

Can machine learning detect faults of smart grids?

In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes.

How to classify faults in a smart grid?

A classification technique based-on the conventional K-NN algorithm is proposed to detect and classify different types of fault in a smart grid. In the proposed technique, the PCA method is used to decrease the dataset size while LDA provides online classification before applying the K-NN.

Can computational intelligence detect islanding phenomenon in smart distributed grids?

The importance of computational intelligence to detect islanding phenomenon in smart distributed grids , , , . Those works present a probabilistic Neural Network (NN) and Support Vector Machine (SVM) as powerful self-adapted machine learning techniques for fault detection.

Can LDA be used to classify faults in a smart grid?

Table 3 LDA base-line: performance of the KNN classification algorithm when LDA is the only instrument for feature reduction A classification technique based-on the conventional K-NN algorithm is proposed to detect and classify different types of fault in a smart grid.

Can KNN detect faults in a smart grid?

In this paper, the KNN technique augmented with principal component analysis (PCA) and linear discriminant analysis (LDA) is used to detect and classify different faults in a smart grid.

Which method is used in fault detection & diagnosis of power grids?

Fuzzy logic (FL) and genetic algorithm (GA) are two widely used methods used in fault detection and diagnosis of the power grids . Fuzzification in different membership function has a determinative role to increase the precision of the FL controller.