fault detection and classification on transmission overhead line using bppn and wavelet ...

UTMUNIVERSITI TEKNOLOGI MALAYSIA

  FAULT DETECTION AND CLASSIFICATION

ON TRANSMISSION OVERHEAD LINE USING BPPN AND WAVELET TRANSFORMATION

BASED ON CLARKE’S TRANSFORMATION By

MAKMUR SAINISUPERVISED BY

PROF.IR.DR.ABDULLAH ASUHAIMI BIN MOHD ZINCO SUPERVISOR BY

PROF.IR.DR.MOHD WAZIR BIN MUSTAFA

Abstract

The transmission overhead line is one of the vital elements in the power system for transmitting the electrical energy. In the transmission, the disturbances are often occurred. In the conventional algorithm, alpha and beta (mode) currents generated by Clarke’s transformation are utilized to convert the signal of Discrete Wavelet Transform (DWT) to obtain the Wavelet Transform Coefficient (WTC) and the Wavelet Coefficient Energy (WCE). This study introduces a new algorithm, called Modified Clarke for fault detection and classification using DWT and Back-Propagation Neural Network (BPNN) based on Clarke’s transformation on transmission overhead line by adding gamma current in the system. Daubechies4 (Db4) is used as a mother wavelet to decompose the high frequency components of the signal error. Simulation is performed using PSCAD / EMTDC transmission system modeling and carried out at different locations along the transmission line with different types of fault, fault resistances, fault locations and fault of the initial angle on a given power system model

Abstract

The simulated fault types are in the study are the Single Line to Ground, the Line To Line, the Double Line to Ground and the Three Phases. There are four statistic methods utilized in the present study to determine the accuracy of detection and classification of faults. The result shows that the best and the worst structures of BPNN occurred on the configuration of 12-24-48-4 and 12-12-6-4, respectively. For instance, the error using Mean Square Error Method. The Error Of Clarke’s, Without Clarke’s and Modified Clarke’s are 0.05862, 0.05513 and 0.03721 which are the best, respectively, whereas, the worst are 0.06387, 0.0753 and 0.052, respectively. This indicates that the Modified Clarke’s result is in the lowest error. The method is successfully implement can be utilized in the detection and classification of fault in transmission line by utilities and power regulation in power system planning and operation.

Introduction

The proposed approach combines the decomposition of

electromagnetic wave propagation modes, using the Clarke’s

transformation of signal processing, given by the discrete

wavelet transformation based upon the maximum signal

amplitude (WTC) 2 to determine the time intrusion. We made

extensive use of simulation software PSCAD / EMTDC which

resulted in fault of the simulation of the transient signal

transmission line parallel with the number of data points. into a

two-phase signal.

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Introduction

For one kind of fault, this data is then transferred to MATLAB with the help of Clarke’s transformation to convert the three-phase signal.The signal is then transformed into Mother Wavelet. We manipulated several mothers wavelet such as DB4, Sym4, Coil4 and Db8 for comparison in terms of time and the distance estimation fault in parallel transmission line.

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. Clarke’s Transformation

Clarke's transformation, also referred to as (αβ) transformation, is a mathematical transformation to simplify the analysis of a series of three phases (a, b, c). It is a two-phase circuit (αβ0) stationery and conceptually very similar to the (dqo) transformation.

= =

Fault Characterization in Clarke’s Transformation

1. Single line to Ground Fault (AG) The egg line to ground fault (AG), assuming grounding resistance is zero. The instantaneous boundary

conditions are : = = 0 and = 0 then the boundary condition instantaneous are: = 2/3 ; = 0; and = 1/3

2 Line to line Fault (AB) The egg line to ground fault (AB), assuming grounding resistance is zero. The instantaneous boundary

conditions are : = 0 = - and = - then the boundary condition instantaneous are: = , = - and = 0

3 Line to line to Ground Fault (ABG) The egg line to ground fault (ABG), assuming grounding resistance is zero. The instantaneous boundary

conditions are : = 0 , = and = = 0 then the boundary condition instantaneous are:

= - - = - ; and = +

Characteristics of various different faults based on Clarke’s Transformation

Algorithm design proposed

Algorithm design proposed

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In this study, the simulations were performed using PSCAD, and the simulation results were obtained from the fault current signal.

The steps performed for this study were: Finding the input to the Clarke transformation and wavelet transform. The

signal flow of PSCAD was then converted into m. files (*. M) and then converted into mat. Files (*mat).with a sampling rate and frequency dependent 0.5 Hz – 1 MHz .

Determining the data stream interference, where the signal was transformed by using the Clarke transformation to convert the transient signals into the signal’s basic current (Mode).

Transforming the mode current signals again by using DWT and WTC, which were the generated coefficients, and then squared to be in order to obtain the maximum signal amplitude to determine the timing of the interruption.

Processing the ground mode and aerial mode and (WTC)2 using Bewley Lattice diagram of the initial wave to determine the fault location

Algorithm design proposed

Algorithm design proposed

Algorithm design proposed

Simulation Model and Results

The system was connected with the sources at each end, as shown in Fig. This system was simulated using PSCAD/EMTD. For the case study, the simulation was modeled on a 230 kV double circuit transmission line, which was 200 km in length. Transmission Line

Transmission data: Z1=Z2 = 0.03574 + j 0.5776 Zo = 0.36315 +j 1.32.647 Fault Starting = 0.22 second Duration in fault = 0.15 Second Fault resistance = 0.001 , 25, 50, 75 and 100 ohm Fault Inception Angle = 0 , 15, 30 , 45 ,60, 90 , 120 and 150 degree Source A and B Z1 = Z2 = Zo = 9.1859 + j 52.093 Ohm

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

Simulation Model and Results

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Fault resistance 0.001 and Fault inception angle 60 degrees,

The obtained result of different fault using DWT and BPNN ,with configuration (12-6-12-4)

The obtained result for different Resistance Fault using DWT and BPNN, with configuration (12-12-24-4)

The obtained result for different inception fault using DWT and BPNN with configuration (12-24-48-4)

The comparison result for model BPNN and PRN based on Clarke’s transformation with configuration (12-24-48-4)

The comparison SE for model BPNN and PRN based on Clarke’s transformation

VE comparison for model BPNN and PRN based on Clarke’s transformation

Comparison of MSE and MAE for Back Propagation Neural Network, Pattern Recognition Network and Fit Network Algorithm

This paper proposes a technique of using a combination of discrete wavelet transform (DWT) and back-propagation neural networks (BPPN) with and without Clarke’s transformation, in order to identify fault classification and detection on parallel circuit transmission lines. This technique applies Daubechies4 (Db4) as a mother wavelet. Various case studies have been studied, including variation distance, the initial angle and fault resistance. This study also includes comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke’s transformation will produce smaller MSE and MAE, compared to without Clarke’s transformation. Among the three structures, the Architects result was the best, which was 12-24-48-12. Four statistical methods are utilized in the present study to determine the accuracy of detection and classification faults, suggesting that the Back Propagation Neural Network results in the lowest error thus it is the best compared with Pattern Recognition Network and Fit Network.

Conclusion

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