Computational Research Progress in Applied Science & Engineering

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Volume 05,Issue 04
   Volume & Issue: Volume 05,Issue 04

A Hybrid Method for Fault Location on VSC-HVDC System Using ANFIS with New Training Algorithm and Hilbert-Huang Transform

  Volume 05,Issue 04


Author :

Mohammad Shirmohammadli


Abstract :

High-voltage direct current (HVDC) system is a highly efficient alternative for transmitting large amounts of electricity over long distances and for special purpose applications. As a key enabler in the future energy system based on renewables, HVDC is truly shaping the grid of the future. Designing an accurate and fast fault location method in HVDC system is necessary to maintain uninterrupted power delivery and protect sensitive devices of these systems. This paper presents a hybrid method for locating fault on voltage source converter HVDC (VSC-HVDC) transmission line using one terminal current data. The proposed method includes three main modules: the feature extraction module, the estimator module and training module. In the feature extraction module, Hilbert-Huang Transform (HHT) is used to frequency, time and energy domain feature extraction. The extracted features are time delay, characteristic frequency, energy attenuation and high-frequency energy. In the second module, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent estimator. In ANFIS, antecedent and conclusion parameters have vital role on ANFIS performance. Therefor in training module, Adaptive and Hybrid Artificial Bee Colony (aABC) algorithm are used to train the ANFIS. The proposed method is tested on 250 km VSC-HVDC transmission line and the obtained results have shown that a combination of new features and optimized ANFIS has high accuracy in fault location in HVDC systems.


Keywords :

VSC-HVDC, Feature extraction, Fault location, ANFIS, aABC algorithm.

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