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A Passive Islanding Detection Algorithm Based on Modal Current and Adaptive Boosting

  • Research Article-Electrical Engineering
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Abstract

An effective and timely detection of islanding is a task of paramount importance to nullify the potential threat to field personnel and equipment damage. A passive islanding detection method based on modal current and adaptive boosting is proposed in the present work. The three-phase currents of the target distributed generation unit are converted into modal signals reducing the dataset. These modal currents are subsequently decomposed into mono-frequency components by employing empirical mode decomposition tool. The authentic mono-frequency components identified using correlation are transformed with the help of Hilbert transform. Various features like entropy, skewness, power, kurtosis, signal-to-noise ratio, and total harmonic distortion are obtained from Hilbert transform. Subsequently, these features act as the input for the adaptive boosting technique to categorize islanding and non-islanding classes. The results obtained explicitly demonstrate that the proposed methodology is highly accurate with reduced non-detection zone.

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Abbreviations

\( _{{I_{r} }} \) :

Current in r phase

\( _{{I_{y} }} \) :

Current in y phase

\( _{{I_{b} }} \) :

Current in b phase

\( A \) :

Modal coefficient corresponding to phase r

\( B \) :

Modal coefficient corresponding to phase y

\( C \) :

Modal coefficient corresponding to phase b

\( h(t) \) :

Cauchy’s integral

\( f(t) \) :

Orthogonal component (HT) of sample signal

\( s(t) \) :

Sample signal

\( * \) :

Convolution operator

\( y(t) \) :

Analytical function

\( x(t) \) :

Amplitude of analytical signal

\( _{{V_{i} }} \) :

Correlation coefficient

\( E \) :

Original signal

\( F \) :

IMFs obtained from original signal

\( N \) :

Total count of samples

\( k \) :

No. of IMFs

\( \bar{E} \) :

Mean value of \( E \)

\( \bar{F} \) :

Mean value of \( F \)

\( \sigma_{E} \) :

Standard deviation of \( E \)

\( \sigma_{F} \) :

Standard deviation of \( F \)

\( _{\zeta } \) :

Highest ratio factor

\( {\text{NI}}_{\text{True}} \) :

Non-islanding cases classified as non-islanding

\( {\text{NI}}_{\text{False}} \) :

Non-islanding cases misclassified as islanding

\( I_{\text{True}} \) :

Islanding cases correctly classified as islanding

\( I_{\text{False}} \) :

Islanding cases misclassified as non-islanding

DG:

Distributed generation unit

DS:

Distribution system

PQ:

Power quality

CB:

Circuit breaker

NDZ:

Non detection zone

IDM:

Islanding detection methodology

AFD:

Active frequency drift

SMS:

Slip-mode frequency shift

SFS:

Sandia frequency shift

VS:

Voltage shift

ROCOV:

Rate of change of voltage

ROCOF:

Rate of change of voltage

UV:

Under-voltage

OV:

Over-voltage

UF:

Under-frequency

OF:

Over-frequency

AI:

Artificial intelligence

ML:

Machine learning

WT:

Wavelet transform

DT:

Decision tree

RBF:

Radial basis function

SVM:

Support vector machine

PNN:

Probabilistic neural network

IDF:

Islanding detection factor

Ada-Boost:

Adaptive boosting

SNR:

Signal-to-noise ratio

THD:

Total harmonic distortion

EMD:

Empirical mode decomposition

HT:

Hilbert transform

IMF:

Intrinsic mode function

MC:

Modal current

DFT:

Discrete Fourier transform

KNN:

K-nearest neighbor

LR:

Logistic regression

GNB:

Gaussian Naïve–Bayes

DWT:

Discrete wavelet transform

ANN:

Artificial neural network

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, 177005, India

    Nimish Bhatt & Ashwani Kumar

Authors
  1. Nimish Bhatt
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  2. Ashwani Kumar
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Correspondence to Nimish Bhatt.

Appendix

Appendix

See Tables 6 and 7.

Table 6 Test system parameters
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Table 7 Types of loads
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Bhatt, N., Kumar, A. A Passive Islanding Detection Algorithm Based on Modal Current and Adaptive Boosting. Arab J Sci Eng 45, 6791–6801 (2020). https://doi.org/10.1007/s13369-020-04709-x

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