Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network

Chao, Kuei‐Hsiang; Yang, Min-Sen; Hung, Chin-Pao

doi:10.3390/en6084152

Cited by 13 publications

(14 citation statements)

References 14 publications

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“…Several approaches based on intelligence techniques, such as wavelet analysis, and neuro-fuzzy and adaptive artificial neural networks (NNs), for effectively detecting and classifying islanding conditions have recently been developed [180,181]. Their signal-processing techniques facilitate the extraction of hidden characteristics of the measured signals from which features are used as input to an artificial intelligence for further processing to identify islanding and non-islanding.…”

Section: Challenges Of Microgridsmentioning

confidence: 99%

Evolution of Microgrids with Converter-Interfaced Generations: Challenges and Opportunities

Hossain¹,

Pota²,

Hossain³

et al. 2018

Preprint

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Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are revised according to their specific roles to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

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Section: Challenges Of Microgridsmentioning

confidence: 99%

Evolution of Microgrids with Converter-Interfaced Generations: Challenges and Opportunities

Hossain¹,

Pota²,

Hossain³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The results of the proposed method show that although as signal processing tool the wavelet transform has suitable time-frequency localization ability, it faces barriers, e.g., batch processing step, non-uniform frequency sub-bands, less flexibility and detection failure during noisy conditions [27]. Different methods based on the combination of artificial neural network and fuzzy logic are presented in [25,28,29]. A deep learning method with a hybrid wavelet transform and multi resolution singular spectrum entropy is done for a single phase photovoltaic system.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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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“…According to the International Energy Outlook 2009 (IEO 2009) report prepared by the Energy Information Administration of the USA [5], total world consumption of marketed energy is projected to increase by 44% from 2006 to 2030, as shown as Figure 1. This has driven the research efforts in areas such as photovoltaic (PV) cells [6]- [10], wind energy [11]- [14], fuel cells [15]- [19]. Photovoltaic technology is perhaps the best known of these alternatives, providing low-voltage DC output for small-scale distributed generation installations.…”

Section: Introductionmentioning

confidence: 99%

A High-Efficiency Single-Stage Low-Power Photovoltaic Inverter System with Maximum Power Point Tracking Control

Lai¹,

You²

2014

EPE

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This paper proposes a single-stage inverter system with maximum power point tracking control (MPPT) applicable in low-power photovoltaic (PV) energy conversion systems. The proposed system is successfully implemented using a single digital signal processor (DSP) TMS320F2808. The proposed single-stage inverter system has the following features: 1) the ability to harvest the maximum PV power using two simple and effective current sampling methods; 2) flexible topology based on the positioning of DC link capacitor on the outside of the inverter bridge circuits; 3) reduced volume and higher efficiency than the conventional two-stage inverters, and 4) MPPT accuracy of 99.3% with overall efficiency of 90% under the full-load condition.

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Islanding Detection Method of a Photovoltaic Power Generation System Based on a CMAC Neural Network

Cited by 13 publications

References 14 publications

Evolution of Microgrids with Converter-Interfaced Generations: Challenges and Opportunities

Evolution of Microgrids with Converter-Interfaced Generations: Challenges and Opportunities

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

A High-Efficiency Single-Stage Low-Power Photovoltaic Inverter System with Maximum Power Point Tracking Control

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