Bagged Decision Trees Based Scheme of Microgrid Protection Using Windowed Fast Fourier and Wavelet Transforms

Abstract

Abstract:Microgrids of varying size and applications are regarded as a key feature of modernizing the power system. The protection of those systems, however, has become a major challenge and a popular research topic because it involves greater complexity than traditional distribution systems. This paper addresses this issue through a novel approach which utilizes detailed analysis of current and voltage waveforms through windowed fast Fourier and wavelet transforms. The fault detection scheme involves bagged decision trees which use input features extracted from the signal processing stage which are selected by correlation analysis. The technique was tested on a microgrid model developed using PSCAD/EMTDS, which is inspired from an operational microgrid in Goldwind Science Technology Co. Ltd., in Beijing, China. The results showed a great level of effectiveness to accurately identify faults from other non-fault disturbances, precisely locating the fault and trigger opening of the right circuit breaker/s under different operation modes, fault resistances, and other system disturbances.

Protection for 330 kV transmission line and recommendation for Nigerian transmission system: a review

“…In this section, we will discuss some of the approaches used to protect transmission line which includes: i) distance relay approach [12], [13]; ii) wavelet approach [14], [15]; iii) the artificial neural network (ANN) approach [16]- [22], iv) fuzzy logic approach [23], [24], and v) mobile robot approach [25]- [28]. Other techniques are the hybrid method, machine learning and deep learning technique: i) neurofuzzy technique [24], [29]- [33], ii) wavelet and ANN technique [34]- [36], iii) wavelet and fuzzy-logic technique [37], [38], iv) wavelet and neuro-fuzzy technique [39], [40], v) machine learning approach [15], [41], [42], vi) support vector machine (SVM) [43], vii) k-nearest neighbours (KNN) and decision tree (DT) [44], and viii) principal component analysis (PCA) [45].…”

“…The main noticeable observation is the inability of most of the papers to explain extensively fault localization, thereby making it difficult to isolate or take on significant fault repairs within the shortest possible time. Also, in discrete wavelet transform (DWT) and decision tree (DT) [44]. It has limited time resolution capability and low performance for high-performance fault.…”

Improving the recognition of operating modes in intelligent electrical networks based on machine learning methods

“…It is known to use methods of processing redundant relay protection information and, in particular, voting schemes to improve reliability [10][11]. Technical solutions for RPA using artificial intelligence and machine learning, including algorithms for recognizing the modes of an electric network based on artificial neural networks (ANN) and a decision tree (DT), were discussed in [12][13][14][15][16][17]. However, for the organization of a logical part that unites several triggering elements of multi-dimensional relay protection, artificial intelligence methods were not used.…”

A PLL-Based Online Estimation of Induction Motor Consumption Without Electrical Measurement

“…Electrical power can then be calculated from motor models. Traditionally, the signal spectrum is obtained using an FFT algorithm [1][2][3] or Time-frequency methods when signals are non-stationnary [4]. As it will be seen in the following, the motor models require a high precision on the frequency measurement.…”