Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG

doi:10.1007/s11708-014-0337-3

Front. Energy

2015, Vol. 9

Issue (1) : 75-90 https://doi.org/10.1007/s11708-014-0337-3

RESEARCH ARTICLE

Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG

Mehrdad TARAFDAR HAGH¹, Homayoun EBRAHIMIAN², Noradin GHADIMI²(

)

¹. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 0411, Iran
². Department of Electrical Engineering, Ardabil Branch, Islamic Azad University, Ardabil 045, Iran

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Abstract

In this paper, a passive neuro-wavelet based islanding detection technique for grid-connected inverter-based distributed generation was developed. The weight parameters of the neural network were optimized by intelligent water drop (IWD) to improve the capability of the proposed technique in the proposed problem. The proposed method utilizes and combines wavelet analysis and artificial neural network (ANN) to detect islanding. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem-free. Unintentional islanding is one of the encountered problems. Discrete wavelet transform (DWT) is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. In passive schemes with a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulation results from Matlab/Simulink shows that the proposed method has a small non-detection zone, and is capable of detecting islanding accurately within the minimum standard time.

Keywords islanding detection neuro-wavelet intelligent water drop (IWD) non-detection zone (NDZ) distributed generation (DG)

Corresponding Author(s): Noradin GHADIMI

Just Accepted Date: 03 November 2014 Online First Date: 17 December 2014 Issue Date: 02 March 2015

Cite this article:

Mehrdad TARAFDAR HAGH,Homayoun EBRAHIMIAN,Noradin GHADIMI. Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG[J]. Front. Energy, 2015, 9(1): 75-90.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-014-0337-3
https://academic.hep.com.cn/fie/EN/Y2015/V9/I1/75

Fig.1 Schematic diagram of a grid-interfaced DG unit

Fig.2 Classification of islanding detection schemes

Fig.3 Step response of system in islanding mode

Fig.4 NDZ for the constant current interface controls for DG

Fig.5 Flowchart of the proposed IWD algorithm

Fig.6 Neuron

Fig.7 One layer network of R input elements and S neurons

Fig.8 Three layer network of R input elements and S neurons with IWD

Fig.9 db1 mother wavelet

Tab.1 Frequency band information for the different levels of wavelet analysis

Fig.10 Flowchart of proposed technique for islanding detection

Fig.11 Schematic diagram of a grid-interfaced DG unit

Fig.12 Effective voltage waveform of the common coupling point for islanding mode

Fig.13 Wavelet detail D-3 of voltage waveform in case of

Fig.14 Wavelet detail D-4 of voltage waveform in case of

Fig.15 Wavelet detail D-6 of voltage waveform in case of

Tab.2 Performance matrix of DG

Tab.3 Voltage relay responses

Tab.4 Various condition for voltage deviation test

Fig.16 Three phase voltage waveform of the common coupling point for voltage deviation mode

Fig.17 Rate of change of active power for voltage deviation mode

Fig.18 Output of detection method for voltage deviation mode

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