A passive islanding detection technique for distributed generations

2017 ◽  
Author(s):  
Bhatraj Anudeep ◽  
Paresh Kumar Nayak
Keyword(s):  
Detection Technique ◽  
Islanding Detection ◽  
Distributed Generations

A Hybrid Multi-feature based Islanding Detection Technique for Grid Connected Distributed Generation

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Karan Sareen ◽  
Bhavesh R. Bhalja ◽  
Rudra Prakash Maheshwari
Keyword(s):  
Detection Technique ◽  
Standard Normal Distribution ◽  
Normal Distribution Function ◽  
Islanding Detection ◽  
Distributed Generations ◽  
R Ratio ◽  
Fault Parameters ◽  
Standard Normal ◽  
Feature Based ◽  
Nuisance Tripping

Abstract This paper presents a hybrid islanding detection technique for grid connected Distributed Generations (DGs). The proposed scheme is based on three different decisive factors which are derived from negative sequence voltage (NSV) by applying Hilbert transform, Wavelet transform and standard normal distribution function. Different types of non-islanding and islanding events have been simulated by modeling IEEE 34-bus system using PSCAD/EMTDC software package. The performance of the proposed scheme has been evaluated on large number of cases which are generated by varying load and fault parameters. The simulation results indicate that the proposed scheme is able to detect islanding situation accurately even under perfect power balance condition. Moreover, the proposed scheme provides better stability in case of specific type of non-islanding event i. e. fault on adjacent feeder during which most of the existing scheme issues nuisance tripping. Furthermore, it provides satisfactorily results during change in network topology and also at different values of X/R ratio of DG. At the end performance of the proposed technique is also comprehensively compared with the schemes currently reported in the literature. This comparison apparently demonstrates the advantages of the proposed technique over existing schemes.

scholarly journals An Islanding Detection Technique for Inverter-Based Distributed Generation in Microgrids

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 130
Author(s):  
Mazaher Karimi ◽  
Mohammad Farshad ◽  
Qiteng Hong ◽  
Hannu Laaksonen ◽  
Kimmo Kauhaniemi
Keyword(s):  
Distributed Generation ◽  
Phasor Measurement Units ◽  
Rate Of Change ◽  
Detection Technique ◽  
Islanding Detection ◽  
Simulation Studies ◽  
Local Measurements ◽  
Measurement Units ◽  
Utility Grid ◽  
Frequency Fluctuations

This article proposes a new passive islanding detection technique for inverter-based distributed generation (DG) in microgrids based on local synchrophasor measurements. The proposed method utilizes the voltage and current phasors measured at the DG connection point (point of connection, PoC). In this paper, the rate of change of voltages and the ratio of the voltage and current magnitudes (VoI index) at the PoC are monitored using micro-phasor measurement units. The developed local measurements based decentralized islanding detection technique is based on the VoI index in order to detect any kind of utility grid frequency fluctuations or oscillations and distinguishing them from islanding condition. The simulation studies confirm that the proposed scheme is accurate, robust, fast, and simple to implement for inverter-based DGs.

scholarly journals An Anti-Islanding Protection Technique Using a Wavelet Packet Transform and a Probabilistic Neural Network

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2701 ◽  
Author(s):  
Masoud Ahmadipour ◽  
Hashim Hizam ◽  
Mohammad Lutfi Othman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Neural Network ◽  
Shannon Entropy ◽  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Detection Technique ◽  
Islanding Detection ◽  
Energy Entropy ◽  
Cost Parameters ◽  
Logarithmic Energy

This paper proposes a new islanding detection technique based on the combination of a wavelet packet transform (WPT) and a probabilistic neural network (PNN) for grid-tied photovoltaic systems. The point of common coupling (PCC) voltage is measured and processed by the WPT to find the normalized Shannon entropy (NSE) and the normalized logarithmic energy entropy (NLEE). Subsequently, the yield feature vectors are fed to the PNN classifier to classify the disturbances. The PNN is trained with different spread factors to obtain better classification accuracy. For the best performance of the proposed method, the precise analysis is done for the selection of the type of input data for the PNN, the type of mother wavelet, and the required transform level which is based on the accuracy, simplicity, specificity, speed, and cost parameters. The results show that, by using normalized Shannon entropy and the normalized logarithmic energy entropy, not only it offers simplicity, specificity and reduced costs, it also has better accuracy compared to other smart and passive methods. Based on the results, the proposed islanding detection technique is highly accurate and does not mal-operate during islanding and non-islanding events.

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