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Fast discrete s-transform and extreme learning machine based approach to islanding detection in grid-connected distributed generation

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Abstract

This paper presents an approach for islanding detection in distributed generation systems (DGs) using fast discrete s-transform (FDST) algorithm and bidirectional extreme learning machine (BELM) classifier. The system undertaken for this study comprises of two different kinds of DGs such as hydro turbine governor system and wind turbine generator. The analysis starts with extracting the non-stationary negative sequence voltage and current signals at DG end and then the instantaneous amplitude and frequency information are extracted through FDST algorithm. From this information, different distinguishing features are computed such as energy and standard deviation of amplitude to track the islanded event from different non-islanded events. The obtained features are examined through an extreme learning machine classifier to discriminate islanding and non-islanding events, under various operating conditions of distribution system. The accuracy of the proposed method is compared with other recently published techniques by various researchers to justify its improved performance for islanding detection.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Siksha O Anusandhan University, Bhubaneswar, Odisha, 751030, India

    Manohar Mishra & Pravat Kumar Rout

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  1. Manohar Mishra
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Correspondence to Manohar Mishra.

Appendix A

Appendix A

A.1 The parameter rating of studied model as shown in Fig. 2 for the proposed IDMs is listed below:

  1. 1.

    Grid data: 1000 MVA; 79 kV; 60 Hz.

  2. 2.

    Transformer data: (79/13) KV (Dyn1) for TR1, (13/13) KV (Dyn1) for TR2; (0.4/13) KV (Dyn1) for TR3; 10 MVA, R1 = R2 = 0.00375pu, 60 Hz; L1 = L2 = 0.1pu, Xm = 50pu.

  3. 3.

    Distribution generation:

    1. (a)

      DG1: HTG with “simplified synchronous machine”: 10 MVA, 13 kV, Inertia Const = 3.2 s, Internal resistance = 0.01466pu; Reactance = 0.22pu.

    2. (b)

      DG2: Wind Turbine with “asynchronous machine”: Rated MVA: 1.5; Rated KV: 0.4; Inertia Constant = 0.48pu; 60 Hz, Rs = 0.016pu, Rr = 0.015pu,Ls = 0.017pu; Lr = 0.156pu; Lm = 3.5pu.

  4. 4.

    Transmission line data: R0 = 0.0424 Ω/km, R1 = 0.0135 Ω/km, X0 = 1.39e-4 H/km, X1 = 4.9869e-5H/km, Co = 5.01e-9 F/km, C1 = 11.33e-9 F/km;

Distance: TL1:20 km; TL2:30 km; TL3:30 km.

A.2 The parameter rating of PV model represented as DG3 in Fig. 11.

1. Power rating

1 MW

2. Number of parallel and series cells, Np, Ns

100,33

3. Cell’s open circuit voltage, Voc

64.2 V

4. Cell’s Vmpp at STC

54.7 V

5. Cell’s short circuit current, Isc

5.96A

6. Cell’s Imp at STC

5.58 A

7. Charge of an electron, q

1.602 × 10−19C

8. Boltzmann’s constant, k

1.38x10−23J/K

9. Ideality factor, a

1.25

10. Cell’s reference temperature, Tref

300 K

11.Coefficient of cell, ki

0.0017

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Mishra, M., Rout, P.K. Fast discrete s-transform and extreme learning machine based approach to islanding detection in grid-connected distributed generation. Energy Syst 10, 757–789 (2019). https://doi.org/10.1007/s12667-018-0285-9

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