A Novel Current Based Faulted Phase Selection and Fault Detection Technique for EHV Transmission Systems with some Penetration of Wind Generation

Jianyi Chen

Research output: ThesisDoctoral Thesis

Abstract

In recent years, the capacity of electrical power systems (EPS) has been growing in order to match an increasing demand for electrical power. The expanding power source especially the penetration of the renewable energy makes the systems more difficult to manage and operate. Thus the task of protecting these systems especially for the extra-high-voltage (EHV) transmission line can no longer be handled using the traditional protection schemes, which were designed for simple power system configurations and therefore are not suitable for modern power systems. Also, the protection of EHV transmission line should take into account the increasing penetration of renewable energy generation such as wind farms and the effects of such generations on protection schemes.
This thesis describes a novel phase selection and fault detection scheme using current signal data from one end only of a typical UK 400kV transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the artificial neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance the performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for phase selection and detection is accurate and robust.
For testing the robustness of the scheme and also as this research project is a UK-China jointly funded EPSRC project, this designed scheme is also applied to a typical 500kV Chinese transmission system with only traditional power generations and with both traditional and renewable generations (wind farms). The effect of the penetration of wind farms on the performance of the proposed protection scheme is thus investigated. For both systems, promising results from the new protection scheme for the phase selection and fault detection are achieved.
LanguageEnglish
QualificationPh.D.
Awarding Institution
  • University of Bath
Supervisors/Advisors
  • Aggarwal, Raj, Supervisor
Award date21 Oct 2015
StatusUnpublished - 17 Oct 2015

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EHV power transmission
Fault detection
Farms
Electric lines
Neural networks
Wavelet transforms
Power generation
Feature extraction
Testing

Cite this

@phdthesis{9873a2a48f394d03a5e409800d76e16c,
title = "A Novel Current Based Faulted Phase Selection and Fault Detection Technique for EHV Transmission Systems with some Penetration of Wind Generation",
abstract = "In recent years, the capacity of electrical power systems (EPS) has been growing in order to match an increasing demand for electrical power. The expanding power source especially the penetration of the renewable energy makes the systems more difficult to manage and operate. Thus the task of protecting these systems especially for the extra-high-voltage (EHV) transmission line can no longer be handled using the traditional protection schemes, which were designed for simple power system configurations and therefore are not suitable for modern power systems. Also, the protection of EHV transmission line should take into account the increasing penetration of renewable energy generation such as wind farms and the effects of such generations on protection schemes.This thesis describes a novel phase selection and fault detection scheme using current signal data from one end only of a typical UK 400kV transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the artificial neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance the performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for phase selection and detection is accurate and robust.For testing the robustness of the scheme and also as this research project is a UK-China jointly funded EPSRC project, this designed scheme is also applied to a typical 500kV Chinese transmission system with only traditional power generations and with both traditional and renewable generations (wind farms). The effect of the penetration of wind farms on the performance of the proposed protection scheme is thus investigated. For both systems, promising results from the new protection scheme for the phase selection and fault detection are achieved.",
author = "Jianyi Chen",
year = "2015",
month = "10",
day = "17",
language = "English",
school = "University of Bath",

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TY - THES

T1 - A Novel Current Based Faulted Phase Selection and Fault Detection Technique for EHV Transmission Systems with some Penetration of Wind Generation

AU - Chen,Jianyi

PY - 2015/10/17

Y1 - 2015/10/17

N2 - In recent years, the capacity of electrical power systems (EPS) has been growing in order to match an increasing demand for electrical power. The expanding power source especially the penetration of the renewable energy makes the systems more difficult to manage and operate. Thus the task of protecting these systems especially for the extra-high-voltage (EHV) transmission line can no longer be handled using the traditional protection schemes, which were designed for simple power system configurations and therefore are not suitable for modern power systems. Also, the protection of EHV transmission line should take into account the increasing penetration of renewable energy generation such as wind farms and the effects of such generations on protection schemes.This thesis describes a novel phase selection and fault detection scheme using current signal data from one end only of a typical UK 400kV transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the artificial neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance the performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for phase selection and detection is accurate and robust.For testing the robustness of the scheme and also as this research project is a UK-China jointly funded EPSRC project, this designed scheme is also applied to a typical 500kV Chinese transmission system with only traditional power generations and with both traditional and renewable generations (wind farms). The effect of the penetration of wind farms on the performance of the proposed protection scheme is thus investigated. For both systems, promising results from the new protection scheme for the phase selection and fault detection are achieved.

AB - In recent years, the capacity of electrical power systems (EPS) has been growing in order to match an increasing demand for electrical power. The expanding power source especially the penetration of the renewable energy makes the systems more difficult to manage and operate. Thus the task of protecting these systems especially for the extra-high-voltage (EHV) transmission line can no longer be handled using the traditional protection schemes, which were designed for simple power system configurations and therefore are not suitable for modern power systems. Also, the protection of EHV transmission line should take into account the increasing penetration of renewable energy generation such as wind farms and the effects of such generations on protection schemes.This thesis describes a novel phase selection and fault detection scheme using current signal data from one end only of a typical UK 400kV transmission system. Firstly, the measured current signals are decomposed using the wavelet transform to obtain the necessary frequency details and then the spectral energy for a chosen number of wavelet coefficients are calculated using a moving short time window; this forms the feature extraction stage, which in turn, defines the inputs for the artificial neural network which is used for classifying the types of fault. After the fault type is identified, the proposed scheme selects the specific neural network of the fault type to distinguish between internal and external faults by utilizing the same patterns features extracted from the previous stage. The input features comprise both the high and low frequency components to enhance the performance of the scheme. An extensive series of studies for a whole variety of different system and fault conditions clearly show that the performance of the scheme both for phase selection and detection is accurate and robust.For testing the robustness of the scheme and also as this research project is a UK-China jointly funded EPSRC project, this designed scheme is also applied to a typical 500kV Chinese transmission system with only traditional power generations and with both traditional and renewable generations (wind farms). The effect of the penetration of wind farms on the performance of the proposed protection scheme is thus investigated. For both systems, promising results from the new protection scheme for the phase selection and fault detection are achieved.

M3 - Doctoral Thesis

ER -