Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks
Published 2014 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Accurate Fault Classifier and Locator for EHV Transmission Lines Based on Artificial Neural Networks
Authors
Keywords
-
Journal
MATHEMATICAL PROBLEMS IN ENGINEERING
Volume 2014, Issue -, Pages 1-19
Publisher
Hindawi Limited
Online
2014-07-16
DOI
10.1155/2014/240565
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems
- (2014) Kuo-Nan Yu et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Synchronization control of memristor-based recurrent neural networks with perturbations
- (2014) Weiping Wang et al. NEURAL NETWORKS
- Particle Swarm Optimization Algorithm for Unrelated Parallel Machine Scheduling with Release Dates
- (2013) Yang-Kuei Lin MATHEMATICAL PROBLEMS IN ENGINEERING
- Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm
- (2013) Haipeng Peng et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- A Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection
- (2013) Erik Cuevas et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network
- (2013) Kai Hu et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting
- (2013) Wen-Yeau Chang Journal of Applied Mathematics
- Exponential Synchronization of Coupled Switched Neural Networks With Mode-Dependent Impulsive Effects
- (2013) Wenbing Zhang et al. IEEE Transactions on Neural Networks and Learning Systems
- Distributed Synchronization of Coupled Neural Networks via Randomly Occurring Control
- (2013) Yang Tang et al. IEEE Transactions on Neural Networks and Learning Systems
- Transmission Line Fault Classification and Location Using Wavelet Entropy and Neural Network
- (2012) Aritra Dasgupta et al. ELECTRIC POWER COMPONENTS AND SYSTEMS
- A New Hybrid Nelder-Mead Particle Swarm Optimization for Coordination Optimization of Directional Overcurrent Relays
- (2012) An Liu et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Stability of delayed neural networks with time-varying impulses
- (2012) Wenbing Zhang et al. NEURAL NETWORKS
- Accurate Transmission Line Fault Location Considering Shunt Capacitances Without Utilizing Line Parameters
- (2011) Wanjing Xiu et al. ELECTRIC POWER COMPONENTS AND SYSTEMS
- An alternative approach to fault location on power distribution feeders with embedded remote-end power generation using artificial neural networks
- (2011) Yilmaz Aslan ELECTRICAL ENGINEERING
- A Hybrid Framework for Fault Detection, Classification, and Location—Part I: Concept, Structure, and Methodology
- (2011) Joe-Air Jiang et al. IEEE TRANSACTIONS ON POWER DELIVERY
- Voltage Stability Control of Electrical Network Using Intelligent Load Shedding Strategy Based on Fuzzy Logic
- (2011) Houda Jouini et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Parameter estimation of dynamical systems via a chaotic ant swarm
- (2010) Haipeng Peng et al. PHYSICAL REVIEW E
- Estimation of Fault Location and Fault Resistance for Single Line-to-ground Faults in Multi-ring Distribution Network Using Artificial Neural Network
- (2009) Meshal Al-Shaher et al. ELECTRIC POWER COMPONENTS AND SYSTEMS
- A new and accurate fault location algorithm for combined transmission lines using Adaptive Network-Based Fuzzy Inference System
- (2009) Javad Sadeh et al. ELECTRIC POWER SYSTEMS RESEARCH
- Fault Classification and Faulted-Phase Selection Based on the Initial Current Traveling Wave
- (2008) Xinzhou Dong et al. IEEE TRANSACTIONS ON POWER DELIVERY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started