Classification Of Faults In Distribution System Biology Essay

Published:

Electric Power Distribution System is a complex network of electrical power system. Also, large number of lines on a distribution system experiences regular faults which lead to high value of current. In the transmission system which involves a simple connection, distribution system has a very complicated structure for making it to design the network for computational analysis. In this paper, the authors have simulated IEEE 13- node distribution system using PSCAD which is an unbalanced system and current and voltage samples are generated at the substation end as well as other nodes or branches. These different different faulted current and voltages samples show the fault in the distribution system.

Keywords:- IEEE 13 �node feeder, PSCAD, Faults, Distribution, Simulation.

INTRODUCTION

D

istribution systems being the largest portion of whole network, with loads distributed continually over the feeders, diagnosis of faults becomes a challenging task. The large number of lines in a distribution system experience regular faults, which are caused by storms, lightning, snowfall, rains, insulation breakdown and short circuits caused by birds and other external objects. In most of these cases, electrical faults manifest into damage of equipments and/or interruption of supply to the consumers. The system restoration can be expedited if the location of fault is known or can be estimated to some accuracy. Hence, faults in a distribution system have to be detected instantaneously, irrespective of whether they are of permanent or temporary nature, to isolate only faulty section. The fault location, forming part of fault diagnosis, helps in identifying the faulty section more precisely so that maintenance and repair time can be minimized. A fault or a disturbance, which leads to high values of line currents, is generally detected by the protective devices and faulty section is isolated using re-closures and/or circuit breakers. However, the identification of the fault and classification of the fault are normally not known. In the last few decades, considerable amount of work has been done in the area of fault diagnosis particularly to the radial distribution system. Many standard techniques are based on algorithmic approaches but some latest techniques involve the use of Artificial Intelligent. The Multi-Resolution Analysis (MRA) is one of the most active technique, the MRA provides an effective way to examine the features of a signal at different frequency bands. These features may be essential for pattern recognition. Hence, it is well suited for the fault identification and classification in the power systems.

Lady using a tablet
Lady using a tablet

Professional

Essay Writers

Lady Using Tablet

Get your grade
or your money back

using our Essay Writing Service!

Essay Writing Service

In this paper, a fault identification and fault classification technique, for radial distribution systems, using wavelet multi resolution and a rule based approach is proposed. The fault identification and classification schemes have been discussed and tested on 13-node test feeder systems.

PROBLEM FORMULATION

The IEEE 13 node radial feeder shown in Figure. 1 is considered as reference for making a simulation model on PSCAD the data and configurations are available which is mentioned in the Distribution System Analysis Subcommittee Report for generation of current and voltage samples at the substation end as well as other branches. The purpose behind publishing IEEE 13 node feeder data is to make available a common set of data that can be used by program developers and users so that the appropriateness of their solutions can be verified. Though the feeder is very small yet it displays some very interesting characteristics such as it is short and relatively highly loaded for a 4.16 kV feeder has one substation voltage regulator consisting of three single-phase units connected in wye, overhead and underground lines are also present with variety of phasing. It is further equipped with shunt capacitor banks, transformer and unbalanced spot and distributed loads. It is considered as very unbalanced system. Loads can be connected at a node (spot load) or assumed to be uniformly distributed along a line section (distributed load). Loads can be three-phase (balanced or unbalanced) or single-phase. Three-phase loads can be connected in wye or delta while single-phase loads can be connected line-to-ground or line-to-line. All loads can be modeled as constant kW and kVAr (PQ), constant impedance (Z) or constant current (I). the model is made by using the below configurations and data such as line data, Transformer data, Distributed load data, spot load data. This is shown below.

Lady using a tablet
Lady using a tablet

Comprehensive

Writing Services

Lady Using Tablet

Plagiarism-free
Always on Time

Marked to Standard

Order Now

Overhead Line Configuration Data:

Config. Phasing Phase Neutral Spacing

ACSR ACSR ID

601 B A C N 556,500 26/7 4/0 6/1 500

602 C A B N 4/0 6/1 4/0 6/1 500

603 C B N 1/0 1/0 505

604 A C N 1/0 1/0 505

605 C N 1/0 1/0 510

Underground Line Configuration Data:

Config. Phasing Cable Neutral Space ID

606 A B C N 250,000 AA, CN None 515

607 A N 1/0 AA, TS 1/0 Cu 520

Line Segment Data:

Node A Node B Length(ft.) Config.

632 645 500 603

632 633 500 602

633 634 0 XFM-1

645 646 300 603

650 632 2000 601

684 652 800 607

632 671 2000 601

671 684 300 604

671 680 1000 601

671 692 0 Switch

684 611 300 605

692 675 500 606

Transformer Data:

kVA kV-high kV-low R - % X - %

Substation: 5,000 115 - D 4.16 Gr. Y 1 8

XFM -1 500 4.16 � Gr.W 0.48 � Gr.W 1.1 2

Capacitor Data:

Node Ph-A Ph-B Ph-C

kVAr kVAr kVAr

675 200 200 200

611 100

Total 200 200 300

Regulator Data:

Regulator ID: 1

Line Segment: 650 - 632

Location: 50

Phases: A - B -C

Connection: 3-Ph,LG

Monitoring Phase: A-B-C

Bandwidth: 2.0 volts

PT Ratio: 20

Primary CT Rating: 700

Compensator Settings: Ph-A Ph-B Ph-C

R - Setting: 3 3 3

X - Setting: 9 9 9

Voltage Level: 122 122 122

Spot Load Data:

Node Load Ph-1 Ph-1 Ph-2 Ph-2 Ph-3 Ph-3

Model kW kVAr kW kVAr kW kVAr

634 Y-PQ 160 110 120 90 120 90

645 Y-PQ 0 0 170 125 0 0

646 D-Z 0 0 230 132 0 0

652 Y-Z 128 86 0 0 0 0

671 D-PQ 385 220 385 220 385 220

675 Y-PQ 485 190 68 60 290 212

692 D-I 0 0 0 0 170 151

611 Y-I 0 0 0 0 170 80

TOTAL 1158 606 973 627 1135 753

Distributed Load Data:

Node A Node B Load Ph-1 Ph-1 Ph-2 Ph-2 Ph-3 Ph-3

Model kW kVAr kW kVAr kW kVAr

632 671 Y-PQ 17 10 66 38 117 68

After using these above values and configuration for making the simulation model, the IEEE 13 �node simulation model which is shown in figure 2. Three � Phase current and voltage samples are obtained at substation end after extensive simulation by creating a fault and to study and analyze the current and voltages samples of different branches. The faulted and un-faulted current and voltages samples are generated when compile/run the simulated model at the of substation end as well as other branches/node of the model which is shown below in figure 3.

Figure 1: IEEE 13- node test feeder

Figure 2: Simulation of IEEE 13- node feeder in PSCAD

As per the above simulation model of IEEE 13 node feeder. The fault occurred in the distribution transformer between the node no. 632-645 across the phase B. so the generated samples of current(Ifault) and voltage(vfault) across phase B is distorted The simulation has the duration of run for 1 second; with the first breaker operation 0.38 sec.and second breaker operation at 0.41 sec.

Duration of fault being 0.1 sec. The peak absolute values of the current samples are being considered. The rms values of the current samples are obtained using PSCAD. The generated samples of current and voltages which is shown below in figure no. 3 across the different nodes. The distorted sample shows that the fault occurred in the system.

Figure 3 : Generated samples of current and voltages

The feeder shown in Figure 1 is designed and simulated in PSCAD for external fault apply on the nodes. The graphical interface of the software makes it very easy to build the circuit and observe the results. For the purpose of modeling certain assumptions were considered such as the elimination of voltage regulator, purging of cable, and distributed load being replaced as spot load at the end of the segment. The Frequency Dependent (Phase) Model is considered since it is numerically accurate and robust transmission line model available. Three � Phase current samples is obtained at substation end after extensive simulation by creating all ten types of faults (single � line to ground fault, double � line to ground fault, line �line fault) respectively at various locations i. e between nodes 632 � 633, 632 � 671 at different fault resistance value ranging from 0 ohm to 30 ohm. The simulation has the duration of run for 1 second; with fault occurring at 0.34165 sec and duration of fault being 0.1 sec. the peak absolute values of the current samples are being considered. The simulation model of IEEE 13 node feeder for external fault apply is shown below in figure 4 and the generated faulted samples of current and voltage is shown in figure no. 5 there are different different samples is generated at different different faults.

Figure 4: Simulation of IEEE 13- node feeder in PSCAD for external fault

Lady using a tablet
Lady using a tablet

This Essay is

a Student's Work

Lady Using Tablet

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Examples of our work

Figure 3: Generated samples of current and

Voltages

Conclusion:

In this paper, to identify and classify the faults in the radial distribution system and is tested on 13 node feeder. A Wavelet based approach is found to be very effective for identifying and classifying various types of fault (LG, LL, LLG, and LLLG). The method utilizes a set of quite simple and simulation based approach for identification and classification of faults. Hence, the method is quite simple to adopt and extremely fast for fault identification and classification.