Active islanding detection method for multi-inverter in power distribution system

This paper summarizes the literature on detection of islanding resulting from distributed generating capabilities in a power distribution system, with emphasis on the rural distribution systems. It is important to understand the legacy technology and equipment in the rural distribution electrical environment due to the growth of power electronics and the potential for adding the new generations of intelligent sensors. The survey identified four areas needing further research: 1. Robustness in the presence of distribution grid disturbances; 2. the future role of artificial intelligence in the islanding application; 3. more realistic standard tests for the emerging electrical environment; 4. smarter sensors. In addition, this paper presents a synchro-phasor-based islanding detection approach based on a wireless sensor network developed by the University of Texas at Austin. Initial test results in a control hardware-in-the-loop (CHIL) simulation environment suggest the effectiveness of the developed method.

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Comparative Study in Determining Features Extraction for Islanding Detection using Data Mining Technique: Correlation and Coefficient Analysis

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i3.pp1112-1124 ◽

2017 ◽

Vol 7 (3) ◽

pp. 1112 ◽

Cited By ~ 1

Author(s):

Aziah Khamis ◽

Yan Xu ◽

Azah Mohamed

Keyword(s):

Data Mining ◽

Power Distribution ◽

Distribution System ◽

Harmonic Distortion ◽

Rate Of Change ◽

Support Vector ◽

Islanding Detection ◽

Data Mining Technique ◽

Detection Scheme ◽

Power Distribution System

A comprehensive comparison study on the datamining based approaches for detecting islanding events in a power distribution system with inverter-based distributed generations is presented. The important features for each phase in the island detection scheme are investigated in detail. These features are extracted from the time-varying measurements of voltage, frequency and total harmonic distortion (THD) of current and voltage at the point of common coupling. Numerical studies were conducted on the IEEE 34-bus system considering various scenarios of islanding and non-islanding conditions. The features obtained are then used to train several data mining techniques such as decision tree, support vector machine, neural network, bagging and random forest (RF). The simulation results showed that the important feature parameters can be evaluated based on the correlation between the extracted features. From the results, the four important features that give accurate islanding detection are the fundamental voltage THD, fundamental current THD, rate of change of voltage magnitude and voltage deviation. Comparison studies demonstrated the effectiveness of the RF method in achieving high accuracy for islanding detection.

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Islanding detection based on asymmetric tripping of feeder circuit breaker in ungrounded power distribution system

ENERGYO ◽

10.1515/energyo.s40565-015-0162-7 ◽

2018 ◽

Author(s):

Yuwei SHANG ◽

Shenxing SHI ◽

Xinzhou DONG

Keyword(s):

Power Distribution ◽

Distribution System ◽

Circuit Breaker ◽

Islanding Detection ◽

Power Distribution System

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A Method of DC Arc Detection in All-Electric Aircraft

Energies ◽

10.3390/en13164190 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4190

Author(s):

Teng Li ◽

Zhijie Jiao ◽

Lina Wang ◽

Yong Mu

Keyword(s):

Fault Detection ◽

Real Time ◽

Power Distribution ◽

Distribution System ◽

Detection Method ◽

Discrete Wavelet ◽

Power Distribution System ◽

Time Frequency ◽

Intelligent Recognition ◽

Dc Arc

Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.

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