# Six Phase Transmission Line Boundary Fault Detection using Mathematical Morphology

DOI : 10.17577/IJERTV6IS120091

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#### Six Phase Transmission Line Boundary Fault Detection using Mathematical Morphology

Kriti Sharma and Shoyab Ali

Department of Electrical Engineering Vedant College of Engineering and Technology

Kota, India sharmakriti0012@gmail.com

Gaurav Kapoor

Department of Electrical Engineering Modi Institute of Technology

Kota, India gaurav.kapoor019@gmail.com

Abstract This paper presents a mathematical morphology based boundary protection scheme for the detection of close-in and remote-end faults that occur on six phase transmission line. A 400 kV, 50 Hz six phase transmission line of 200 km length has been simulated using MATLAB software. The proposed scheme makes use of six phase current measured at the relay location (bus-1) of a six phase transmission line. To assess the performance of the proposed method, various fault parameters are varied. Simulation results reveal the appropriateness of the proposed scheme.

Keywords Six Phase Transmission Line Protection; Mathematical Morphology; Boundary Protection; Fault Detection; Close-In And Remote-End Fault Detection.

1. INTRODUCTION

For the protection of six phase transmission line against the shunt and series faults, a protection scheme based on logic has been proposed by G. C. Sekhar and P. S. Subramanyam in [1]. A comparative study of electric field calculations beneath six phase and double circuit transmission lines has been described by R. M. Radwn and M. M. Samy in [2]. By the usage of charge simulation technique, calculation of electric field has been done for both double circuit and six phase transmission line at one meter above the earth level. Ebha Koley, Khushaboo Verma and Subhojit Ghosh [3] proposed hybrid WT and modular ANN based scheme for the protection of six phase transmission line which utilized the measured data of single end only. An algorithm for the over current protection of six phase transmission line with the help of numerical relay has been described by Shanker Warathe and R. N. Patel in [4] and based on test results, it was found that the numerical relay protects well six phase transmission line from over current problem. Classification of phase to phase faults on six phase transmission line by using Haar WT and ANN has been reported in [5]. F. Namdari and M. Salehi [6] proposed mathematical morphology and initial current travelling wave based high speed protection scheme for three phase transmission lines. Rapid discrimination of the fault direction and internal faults from the external faults has been done by comparing the arrival time and polarity of the initial current travelling wave captured from both the ends of a transmission

transmission line protection scheme against lightning strikes. Paulo A. H. Cavalcante et al. [10] proposed a scheme based on simplified multi-resolution morphological gradient (SMMG) for fault location on a three phase transmission line. The proposed scheme is dependent on sampled voltage signals collected from both the ends of a transmission line. Numerous advantages of six phase power transmission line over traditional three phase power transmission lines are: six phase transmission line generates less electric field, less necessity of right of way (ROW) and tower dimensions, increased line inductance and decreased line capacitance, preserved voltage stability, and increased reactive power limit at the far end voltage point [11-14].

In this paper, a mathematical morphology based fault identification scheme is proposed for the detection of six phase transmission line close-in and remote-end faults. At a variety of fault locations, the performance of scheme is discovered including faults at boundary locations. Distinguish explorations are done to analyze the impact of variation in fault parameters like fault type, fault location, fault resistance, ground resistance, and fault inception angle. Performance of the proposed scheme is tested from 1% of the line length up to 97.5% of the line length. Test results attained by the training of proposed technique validate the suitability of the proposed scheme under a diversity of fault circumstances.

2. SIMULATION OF SIX PHASE TRANSMISSION LINE Test system under consideration is comprised of 400 kV,

50 Hz, six phase transmission line of length 200 km as demonstrated in Fig.1. The six phase transmission line is connected to a load of 100 MW at the receiving end side. MATLAB software is used for the modeling and simulation of test system for numerous types of faults.

Six Phase Transmission Line

Generator

line under protection. Ashutosh Kumar Tiwari, Soumya Ranjan Mohanty and Ravindra Kumar Singh [7] proposed

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mathematical morphology based fault detection technique for the protection of DG penetrated electrical power distribution system. Vinayesh Sulochana, Anish Fransis and Andrew Tickle [8] proposed transmission line fault detection and classification scheme based on morphology and radon processed artificial neural network. Zehui Liang et al. [9] proposed mathematical morphology and integral method based

Fig.1. Single line diagram of six phase transmission line

3. MATHEMATICAL MORPHOLOGY

If f (p) is the signal [6] then its domain Df = {x0, x1,xp} and s (q) is the structuring element having domain Dq = {y1,

y2,…yq} and p > q, where p and q are the integers, then the dilation of f(p) by s (q), denoted by (fs) can be defined as: –

yd (p) = (fs)(p)=max{f(p-q)+s(q),0(p-q)p,q0}. (1)

The erosion of f (p) by s (q) denoted as (fs) can be defined as: –

ye (p) = (fs)(p)=min{f(p+q)-s(q), 0(p+q)p,q0}. (2)

4. PROPOSED FAULT DETECTION SCHEME Fig. 2 depicts proposed fault detection scheme [20].

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The proposed scheme is examined for close-in relay phase- AD-g fault occurring at 1% from the relay location with fault inception time of 0.0133 seconds having R f = 10, R g = 15. The six phase current for the period of phase-AD-g fault is shown in Fig. 3. The process of fault detection using mathematical morphological filter during phase- AD-g fault occurring at 1% from bus-1 at FIT = 0.0133 seconds with R f = 10 and R g = 15 can be seen from Fig. 4 to Fig. 5. Fig. 4 to Fig. 5 clarifies the magnitude of gradient-1, 2 and 3 of phase- A and D for the duration of phase-AD-g fault and from Fig. 4 and 5 it is clearly observed that the magnitude of gradients- 1, 2 and 3 of phase-A and D during phase-AD-g fault is higher than the magnitude of gradients-1, 2 and 3 of other phases. Table I summarizes the response of the proposed scheme for phase-AD-g fault occurring at 1% from relay location. As viewed from Table I, the magnitude of gradient-1, 2 and 3 of faulted phase (Phase-A and D) is more than the magnitude of gradient 1, 2 and 3 of un-faulted phase and this explains that the proposed mathematical morphological based fault detector in actual fact detects phase-AD-g fault occurred at 1% from the relay location.

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Fig. 3. Six phase current during phase-AD-g fault at 1% from bus-1 at FIT = 0.0133 seconds with R f = 10 and R g = 15

Fig. 2. Proposed fault detection scheme

Gradient-1 (grad1), gradient-2 (grad2) and gradient-3 (grad3) are the three types of mathematical morphological filter coefficients [10]. Following the calculation of these three coefficients, trip decision has been taken. Following six phase fault current decomposition using mathematical morphology filter, if magnitude of gradient-1, 2 or 3 of the faulted phase is

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found larger than the magnitude of gradient-1, 2 or 3 of a healthy phase, the relay detects the fault and issue trip

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command for the tripping of faulty phase (s). For numerous types of faults, the proposed scheme is tested with various

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5. TEST RESULTS AND DISCUSSIONS

To scrutinize the performance of mathematical morphological based fault detector, the proposed scheme is tested for various fault cases with variation in fault type, fault location, fault resistance, ground resistance and fault inception time.

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TABLE I. RELAY OUTPUT FOR PHASE-AD-G FAULT AT 1% FROM BUS-1 AT

FIT = 0.0133 SECONDS WITH R F = 10 AND R G = 15

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Fig. 7. Gradients-1, 2, 3 of phase-A during phase-ABEF-g fault

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Fig. 8. Gradients-1, 2, 3 of phase-B during phase-ABEF-g fault

 10^4 827 10^3 10^4 665 10^3 Erd 1.2712* 853.9 960.7 1.2732* 767.0 1.0743* 10^4 529 028 10^4 688 10^3 Grad1 1.1307* 1.5519* 1.4875* 7.7755* 1.4875* 1.3429* 10^4 10^3 10^3 10^3 10^3 10^3 7.8811* 1.2593* 1.4458* 6.3013* 1.4448* 1.0005* Grad2 10^3 10^3 10^3 10^3 10^3 10^3 Grad3 7.8811* 1.2593* 1.4458* 6.3013* 1.4448* 1.0005* 10^3 10^3 10^3 10^3 10^3 10^3

1. Phase-ABEF-g remote-end fault

The proposed scheme is tested for remote-end phase- ABEF-g low resistance fault occurring at 95% from the relay location with fault inception time of 0.02833 seconds having R f = 0.5, R g = 1. The six phase current for the duration of phase-ABEF-g fault is shown in Fig. 6. The process of fault detection using mathematical morphological filter during phase- ABEF-g fault occurring at 95% from bus-1 at FIT =

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0.02833 seconds with R f = 0.5 and R g = 1 can be seen from Fig. 7 to Fig. 10. Fig. 7 to Fig. 10 describes the

Fig. 9. Gradients-1, 2, 3 of phase-E during phase-ABEF-g fault

magnitude of gradient-1, 2 and 3 of six phases for the duration of phase-ABEF-g fault and from Fig. 7 to Fig. 10 it is clearly observed that the magnitude of gradients-1, 2 and 3 of phase- A, B, E and F during phase-ABEF-g fault is higher than the magnitude of gradients-1, 2 and 3 of other phases. Table II highlights the response of the proposed scheme for phase- ABEF-g fault occurring at 95% from relay location. As viewed from Table II, the magnitude of gradient-1, 2 and 3 of faulted phase is higher than the magnitude of gradient 1, 2 and

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3 of un-faulted phase and this explains that the proposed mathematical morphological based fault detector in point of fact detects phase-ABEF-g fault occurred at 95% from the relay location which is mainly a remote-end low resistance fault.

Fig. 10. Gradients-1, 2, 3 of phase-F during phase-ABEF-g fault

TABLE II. RELAY OUTPUT FOR PHASE-ABEF-G FAULT AT 95% FROM BUS-1

 Phase Outputs A B C D E F Dil 3.1552* 7.8973* 1.5010* 1.2483* 4.5779* 6.6614* 10^3 10^3 10^3 10^3 10^3 10^3 Erd 3.0995* 7.7891* 969.8 966.1 4.3562* 5.9316* 10^3 10^3 007 331 10^3 10^3 Grad1 9.5591* 7.7628* 2.3589* 1.9644* 9.7774* 8.4613* 10^3 10^3 10^3 10^3 10^3 10^3 Grad2 5.5240* 4.7516* 1.8681* 1.3764* 6.5126* 5.7584* 10^3 10^3 10^3 10^3 10^3 10^3 Grad3 5.5240* 4.7516* 1.8681* 1.3764* 6.5126* 5.7584* 10^3 10^3 10^3 10^3 10^3 10^3

AT FIT = 0.02833 SECONDS WITH R F = 0.5 AND R G = 1

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Fig. 6. Six phase current during phase ABEF-g fault at 95% from bus-1 at FIT = 0.02833 seconds with R f = 0.5 and R g = 1

2. Phase-ABCDEF-g remote-end fault

The proposed scheme is examined for remote-end phase- ABCDEF-g low resistance fault occurring at 85% from the relay location with fault inception time of 0.03166 seconds having R f = 10, R g = 15. The six phase current for the period of phase-ABCDEF-g fault is shown in Fig. 11. The procedure of fault detection using mathematical morphological filter for the period of phase- ABCDEF-g fault happening at

85% from bus-1 at FIT = 0.03166 seconds with R f = 10 and

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Fig. 14. Gradients-1, 2, 3 of phase-C during phase-ABCDEF-g fault

17 illustrates the magnitude of gradient-1, 2 and 3 of six phases for the period of phase-ABCDEF-g fault and from Fig. 12 to Fig. 17 it is noticeably observed that the magnitude of gradients-1, 2 and 3 of all six phases during phase- ABCDEF-g fault increases. Table III summarizes the response of the proposed scheme for phase-ABCDEF-g fault occurring at 85% from relay location. As inspected from Table

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III, the magnitude of gradient-1, 2 and 3 of all six faulted phase raises and this clarifies that the proposed mathematical

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morphological based fault detector in point of fact detects

Fig. 15. Gradients-1, 2, 3 of phase-D during phase-ABCDEF-g fault

phase-ABCDEF-g fault occurred at 85% from the relay location which is essentially a remote-end low resistance fault.

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Fig. 16. Gradients-1, 2, 3 of phase-E during phase-ABCDEF-g fault

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Fig. 11. Six phase current during phase ABCDEF-g fault at 85% from bus-1 at FIT = 0.03166 seconds with R f = 10 and R g = 15

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 6000 6000 Mag 6000 Mag 4000 4000 4000 2000 0 2000 0 2000 0/p> TABLE III. RELAY OUTPUT FOR PHASE-ABCDEF-G FAULT AT 85% FROM BUS-1 AT FIT = 0.03166 SECONDS WITH R F = 10 AND R G = 15

Fig. 17. Gradients-1, 2, 3 of phase-F during phase-ABCDEF-g fault

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 Phase Outputs A B C D E F 5.4351* 5.7897* 8.1342* 6.0585* 5.4672* 8.3995* Dil 10^3 10^3 10^3 10^3 10^3 10^3 5.0707* 5.3053* 7.5893* 5.4177* 5.0268* 7.9739* Erd 10^3 10^3 10^3 10^3 10^3 10^3 Grad1 1.2881* 1.1848* 1.2776* 1.1848* 1.2776* 1.2881* 10^4 10^4 10^4 10^4 10^4 10^4 Grad2 1.2881* 1.1743* 1.2776* 1.1743* 1.2776* 1.2881* 10^4 10^4 10^4 10^4 10^4 10^4 Grad3 1.2881* 1.1743* 1.2776* 1.1743* 1.2776* 1.2881* 10^4 10^4 10^4 10^4 10^4 10^4

Fig. 12. Gradients-1, 2, 3 of phase-A during phase-ABCDEF-g fault

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Fig. 13. Gradients-1, 2, 3 of phase-B during phase-ABCDEF-g fault

6. CONCLUSION

In this paper, a mathematical morphology based boundary protection scheme is proposed for six phase transmission line. The proposed scheme exploits dilation and erosion coefficients of six phase fault current measured at the relay location (Bus- 1). The proposed scheme effectively detects both close-in and remote-end faults that occur on six phase transmission line. The proposed scheme is tested for numerous categories of boundary faults with fault parameters variation. Simulation results show that both close-in and remote-end faults are correctly detected by mathematical morphology based fault detection scheme.

REFERENCES

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2. R. M. Radwn and M. M. Samy, Calculation of Electric Fields Underneath Six Phase Transmission Lines, JES, vol.-12, no.-4, pp. 839- 851, October 2016.

3. Ebha Koley, Khushaboo Verma and Subhojit Ghosh, An Improved Fault Detection, Classification and Location Scheme Based on Wavelet Transform and Artificial Neural Network for Six Phase Transmission Line using Single end Data Only, Springer Plus, pp. 1-22, September 2015.

4. Shanker Warathe and R. N. Patel, Six Phase Transmission Line Over Current Protection by Numerical Relay, ICACCS-IEEE, Janurary 2015.

5. Ravi Kumar, Ebha Koley, Anamika Yadav and A.S. Thoke, Fault Classification of Phase to Phase Fault in Six Phase Transmission Line using HAAR Wavelet and ANN, IEEE-SPIN, pp. 5-8, March 2014.

6. F. Namdari and M. Salehi, A High-Speed Protection Scheme Based on Initial Current Travelling Wave for Tranamission Lines Employing Mathematical Morphlogy, IEEE Transactions on Power Delivery, vol.- 32, no.-1, pp. 1-8, February 2017.

7. Ashutosh Kumar Tiwari, Soumya Ranjan Mohanty and Ravindra Kumar Singh, A Novel Fault Detection Technique in Distribution System with the Penetration of DG using Mathematical Morphology, IJST, vol.-9, no.-44, pp. 1-5, November 2016.

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9. Zehui Liang, Yongli Zhu, Lili Dai and Qingfeng Wen, Identification of Lightining Strikes on Transmission Lines Based on Mathematical Morphology and Integral Method, IEEE-IET-RPGC, September 2013.

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19. A. A. Hajjar, M. M. Mansour, H. E. A Tallat and S. O. Faried, Distance Protection for Six Phase Transmission Lines Based on Fault Induced High Frequency Transients and Wavelets, IEEE-CCECE, pp. 7-11, August 2002.

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