Keywords

1 Introduction

Because of environmental and energy concerns it is very difficult to construct new transmission lines and generation. So instead of constructing new systems, it is essential to increase power transfer capability using existing systems. In order to meet the needs of power transfer it is more important to control the power flow in transmission lines. In addition to this, FACTS devices play a major role in the transmission system. As they are utilized to control the power flow and to change power system parameters. The parameters like line impedances, bus voltages, and phase angles of the power system can be regulated by means of using FACTS devices such as STATCOM, SVC, SSSC, and UPFC. FACTS devices also have the ability to decrease the generation cost, increasing transmission capacities, and improve the stability and security of power systems. The transient and steady-state components of voltage and current signals are affected by compensating devices during fault conditions. These signals will create problems with relay functioning.

The identification and classification of transmission line faults with FACTS devices is a very difficult task. In [1], current and voltage signals are used to find the fault location. But the fault type and the phase in which fault occurs are not reported. In [2], an adaptive Kalman filtering approach is proposed for protecting uncompensated power distribution networks. In [3], an advanced series compensators for compensated transmission systems is employed. But the limitation Kalman filtering is the requirement of a number of different filters to complete the task and also the fault resistance cannot be modeled. Neural networks are applied in [3,4,5] for pattern recognition but they need large training time and large data and design of a new neural network are needed for each transmission line. In [1,2,3,4,5] different methods based on support vector machines fuzzy logic systems, TT transform, S transform, and wavelet transform are proposed. In these attempts the classification and identification of faulted section is done in a transmission line compensated by TCSC protected by metal-oxide varistor (MOV) or transmission line compensated by series capacitors protected by metal-oxide varistor (MOV) or compensated by both the above-mentioned approaches. The advantage of post fault current and voltage samples are taken from both ends of the line and build a recursive optimization algorithm to identify the fault distance in a transmission line with a series FACTS device. But there is no need of the FACTS device model in this algorithm and it can able to locate the fault without mentioning the type of fault.

According to [6] Power quality conditions and the impact of FACTS devices can be analyzed by using wavelet analysis more effectively. In [7] fault identification in the presence of FACTS devices is obtained by using fuzzy wavelet approach. In [8], wavelet-based entropy algorithm method is applied to find the fault in the presence of FACTS devices has been discussed, the effectiveness of the wavelet entropy algorithm has been checked. In [9], protective gear response is analyzed by using wavelets, which have been discussed.

The UPFC consists of both STATCOM and SSSC which are connected with a common DC link, which allows the bidirectional flow of real power between series output terminals of SSSC and the shunt terminals of the STATCOM. This work performed with UPFC. Protective schemes design in presence of multiterminal network with UPFC is more difficult nowadays. This paper uses Bior 1.5 as a mother wavelet to perform both fault identification and compensation evaluation in the presence of UPFC.

2 Wavelet Transform

A wavelet analysis is nothing but the expansion of functions by means of wavelets, which are created in the form of dilations and translations of a fixed function known as mother wavelet. A mother wavelet is an oscillatory function which has some finite energy and zero average value. It is possible to obtain time and frequency information of a signal using wavelet transform when compared with fourier transformation, which can give only frequency information.

Wavelet transform provides an effective time-frequency representation of signals. All basis functions are formed by shifting and scaling of “mother” wavelet function \( \psi (t) \in L^{2} \left( R \right) \)

$$ \psi_{m,n} \left( t \right) = 2^{{ - \frac{m}{2}}} \psi \left( {2^{ - m} t - n} \right)m,n \in Z $$
(1)

Signal \( f(t) \in L^{2} \left( R \right) \) can be then represented as

$$ f\left( t \right) = \sum\limits_{m} {\sum\limits_{n} {d_{m,n} } } \psi_{m,n} \left( t \right) $$
(2)

where \( d_{m,n} \) are spectral wavelet coefficients

$$ d_{m,n} = \left\langle {f\left( t \right),\psi_{m,n} \left( t \right)} \right\rangle $$
(3)

For discrete signals \( f(k) \in L^{2} \left( Z \right) \) gives similar results and its equivalent transform is called Discrete Wavelet Transform (DWT).

3 Flowchart for Fault Identification

3.1 Algorithm

  1. Step 1:

    Initiate Ia1, Ib1, Ic1 … Ia9, Ib9, Ic9 at all zones.

  2. Step 2:

    Obtain detailed coefficients at each bus number 1, 2, 3, 4, 5, 6, and 7.

  3. Step 3:

    Obtain fault index value at each Bus.

  4. Step 4:

    Highest values fault index in the zone indicate the fault in that Zone.

  5. Step 5:

    Highest Value in the particular phase will give the faulty phase where Fault occurs.

3.2 Flow Chart

Fig. 1
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Flow chart for fault identification

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4 Test System

Fig. 2
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One line diagram of a test system

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4.1 Test System Data and Its Associated Parameters

Table 1 Test system data
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Table 2 Wavelet information and its associated parameters
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5 Simulation Results and Analysis

The test system consists of nine zones and seven buses. The length of each zone is shown in the figure. It has five number of DG s and two number of utility grid sources connected. The proposed multiterminal system is operated with 220 kV, 50 Hz. The behaviour of the system is analyzed by using Bior 1.5 mother wavelet detailed coefficients has been calculated and then some of the detailed coefficients have been obtained. Fault analysis carried with the help of wavelet multiresolution analysis for the proposed system with and without UPFC. The performance of the system is studied during line to Ground (LG), Line-Line to Ground (LLG) and Line-Line-Line to Ground Fault (LLLG) at each zone. Coefficients are drawn and tabulated at each bus. Variation of fault index in each bus has analyzed. The analysis also made for different fault inception angles (FIA) and at different distances.

Network during LG Fault in Zone-2, as it has 25 km length, fault analysis has done at different distances and for different fault inception angles. From Table 3 it is evident that coefficients are high in Zone-2 in phase A. The impact of Fault in Zone-2 is high, coefficients are high for phase A, Hence the fault is of LG type.

Table 3 LG Fault on Transmission line in Zone-2 without UPFC
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From Fig. 3 it clearly represents that the Fault Index value is high for Zone-2 and for Phase A. The compensation of fault can be partly achieved by connecting UPFC in between Zone-2 and Zone-3. Wavelet multiresolution analysis is performed by connecting UPFC between zone-2 and zone-3. The coefficients are taken at all the zones and buses. Loads 1, 2, 3, 4 & 5 are connected at Primaries side of DG’s 1, 2, 3, and 4, which are not actually shown in one line diagram.

Fig. 3
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Variation of fault index in all the zones without UPFC and AG fault in zone-2

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The compensation of fault current at Zone-2 has been shown clearly. For example, the detailed coefficient vale for AG fault at Zone-2 is at 800 is 6243.144, whereas its value is compensated to 4539.152. Thus UPFC has a considerable effect on Zone-2. Similarly, the coefficients during LG fault at FIA of 20° and at a distance of 5 km are at phase A, phase B, Phase C is 5325.777, 602.9014, 609.057, which shows this value is highest in Phase A. Therefore, it is an LG Fault (Fig. 4).

Fig. 4
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Variation of fault index in different zones with UPFC and AG fault in zone-2

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Figure 5 represents three variations of fault index due to LG Fault in Zone-2 without connecting UPFC. For understanding purpose variation of index values at Bus-1, 2, 3, and 7 have been shown. One phase current got increased much, which can be observed from the diagram.

Fig. 5
figure 5

Variation of fault index at buses 1, 2, 3, 7 due to LG fault in zone-2 without UPFC

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For understanding purpose variation of index values at Bus-1, 2, 3, and 7 have been shown. Two-phase currents got increased much, which can be observed from Fig. 6 (Fig. 7).

Fig. 6
figure 6

Represents three variation of fault index due to LLG fault in zone-2 without connecting UPFC

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Fig. 7
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Variation of fault index at bus 3, 7 due to LLG fault in zone-2 with UPFC

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Figure 8 represents three the phase variations of fault index due to LLG Fault in Zone-2 with connecting UPFC. For understanding purpose variation of index values at Bus-1, 2, 3, and 7 have been shown. Two-phase currents index values variation at different buses has been analyzed, which can be observed from the diagram (Table 4).

Fig. 8
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Variation of effective coefficients of LG fault in zone-2 from terminal-6

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Table 4 LG Fault on Transmission line in Zone-2 with UPFC
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Table 5 represents with the sum of detailed coefficients with LLLG in Zone-6, which clearly shows the impact of three-phase fault in Zone-6, the coefficients got increased. As UPFC is connected between Zone-2 and Zone-3, it has an impact on all the zones. The impact of UPFC on Zone-6 due to LLLG fault on Zone-6 can be tabulated in Table 4 (Table 6).

Table 5 LLLG Fault on Transmission line in Zone-6 without UPFC
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Table 6 LLLG Fault on Transmission line in Zone-6 with UPFC
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The impact also can be seen for different angles i.e., Fault inception angles. For understanding purposes, only one zone has been shown. Figure 10 represents the impact of UPFC on Zone-6 and Zone-7. From the above figures and tables, It is evident that interconnected networks there is an impact of fault in any Zone reflects fault current on other zones also. At the initial stage by applying fault at each zone, analyzed the detailed coefficients. The fault impact is high in the zone where the fault occurs, whereas there is an impact on other zones. This paper uses UPFC as a compensating device. Even though UPFC is connected between Zone-2 and Zone-3. The fault currents are compensated up to a certain limit, therefore fault current has an impact on other zones too in presence of UPFC (Figs. 9 and 10).

Fig. 9
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Variation of fault index LLLG fault in zone-6 without UPFC

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Fig. 10
figure 10

Variation of fault index LLLG fault in zone-6 with UPFC

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6 Conclusions

The evaluation of dynamic behavior during transient conditions has been studied for multiterminal network. Wavelet analysis will give the entire system performance at any part of the system. This paper mainly concentrates on fault detection and its classification of multiterminal networks. It also gives a critical evaluation of nine zones of performance under different fault conditions. The performance of multiterminal transmission networks with and without UPFC under different fault conditions has been estimated. This algorithm successfully analyzed the different faults in all the zones. The proposed scheme is fast and accurate. Even it performs well at different fault inception angles. The effectiveness of the system is obtained by connecting UPFC between Zone-2 and Zone-3 has been evaluated more effectively. Wavelet-based multiresolution analysis is applied to multiterminal interconnected networks. Bior 1.5 is chosen as mother wavelet.