Journal ArticleDOI
Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integral
TLDR
The diagnostic results demonstrate that this proposed method is efficient in identifying faults within local sub-networks as well as those on the tie lines with strong fault tolerance and high diagnostic accuracy.About:
This article is published in Electric Power Systems Research.The article was published on 2013-12-01. It has received 39 citations till now. The article focuses on the topics: Fault (power engineering) & Fuzzy logic.read more
Citations
More filters
Journal ArticleDOI
A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion
TL;DR: The diagnosis results show that the proposed method is able to reliably identify the different fault categories which include both single fault and compound faults, which has a better classification performance compared to any one of the individual classifiers.
Journal ArticleDOI
Application of NSGA-II Algorithm for fault diagnosis in power system
TL;DR: This work presents use of a non-dominated sorting in genetic algorithms-II (NSGA-II) to the fault diagnosis problem, and Pareto approach is employed to settle the model, which eliminates errors resulted in the weight setting for fault diagnosis.
Journal ArticleDOI
Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems
Jos Manuel Bernal de Lzaro,Alberto Prieto Moreno,Orestes Llanes Santiago,Antnio Jos da Silva Neto +3 more
TL;DR: A comparison between five performance measures in the adjustment of a Gaussian kernel used with the preprocessing techniques: Kernel Fisher Discriminant Analysis (KFDA) and Kernel Principal Component analysis (KPCA) is made.
Journal ArticleDOI
Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration
Dalibor Petković,Milan Gocic,Shahaboddin Shamshirband,Sultan Noman Qasem,Sultan Noman Qasem,Slavisa Trajkovic +5 more
TL;DR: In this article, the radial basis function network with particle swarm optimization (RBFN-PSO) and RBFN-BP were used to estimate the reference evapotranspiration (ET0) for the water resource planning and scheduling of irrigation systems.
Journal ArticleDOI
A binary coded brain storm optimization for fault section diagnosis of power systems
TL;DR: A novel variant of brain storm optimization (BSO) in objective space algorithm, referred to as BCBSO (binary coded BSO), is proposed in this paper, which comprehensively demonstrates the superiority ofBCBSO in terms of successful rate, diagnosis error, robustness, computation efficiency, convergence speed, and statistics.
References
More filters
Journal ArticleDOI
Orthogonal least squares learning algorithm for radial basis function networks
TL;DR: The authors propose an alternative learning procedure based on the orthogonal least-squares method, which provides a simple and efficient means for fitting radial basis function networks.
Journal ArticleDOI
Advantages of Radial Basis Function Networks for Dynamic System Design
TL;DR: The recently developed algorithm is introduced for designing compact RBF networks and performing efficient training process, including their generalization ability, tolerance to input noise, and online learning ability.
Journal ArticleDOI
A fast nonlinear model identification method
TL;DR: A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters, which solves the least-squares problem recursively over the model order without requiring matrix decomposition.
Journal ArticleDOI
Implementing Fuzzy Reasoning Petri-Nets for Fault Section Estimation
Xu Luo,Mladen Kezunovic +1 more
TL;DR: Several key issues in implementing fuzzy reasoning PNs for fault section estimation are addressed, which include optimal design of structure of diagnosis models to avoid large matrix size, utilization of fuzzy logic parameters to effectively handle uncertainties, realization of matrix execution algorithm to achieve parallel reasoning and adaptability, and integration of more reliable input data to enhance estimation accuracy.
Journal ArticleDOI
Load Forecasting Using Hybrid Models
TL;DR: Two hybrid neural networks derived from fuzzy neural networks (FNN) using the fuzzified wavelet features as the inputs to FNN and fuzzy neural network (FNCI) employing the Choquet integral as the outputs of FNN are presented.