Spatio-temporal information based protection scheme for PV integrated microgrid under solar irradiance intermittency using deep convolutional neural network

Manohar, Murli; Koley, Ebha; Ghosh, Subhojit; Mohanta, Dusmanta Kumar; Bansal, Ramesh C.

doi:10.1016/j.ijepes.2019.105576

Cited by 50 publications

(25 citation statements)

References 27 publications

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“…For example, architectures based on LSTM, 90 ENN, 85,86 GRU, 94 BRNN, 95 and TCN 100 have been used to predict electricity demand consumption. LSTM 89 and CNN 99 have also been used to forecast photo-voltaic energy load. A GRU has been used to forecast soot emission in diesel engines in Ref.…”

Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have proved to be powerful and are achieving high accuracy in many application fields. For these reasons, they are one of the most widely used methods of machine learning to solve problems dealing with big data nowadays. In this work, the time series forecasting problem is initially formulated along with its mathematical fundamentals. Then, the most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages and limitations. Particular attention is given to feed forward networks, recurrent neural networks (including Elman, long-short term memory, gated recurrent units, and bidirectional networks), and convolutional neural networks. Practical aspects, such as the setting of values for hyperparameters and the choice of the most suitable frameworks, for the successful application of deep learning to time series are also provided and discussed. Several fruitful research fields in which the architectures analyzed have obtained a good performance are reviewed. As a result, research gaps have been identified in the literature for several domains of application, thus expecting to inspire new and better forms of knowledge.

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Section: Hardware Performancementioning

confidence: 99%

Deep Learning for Time Series Forecasting: A Survey

et al. 2021

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“…However, the reliability of both the approaches have not been investigated during the presence of harmonics and weather intermittency of renewable DERs. A protection strategy based on the convolutional neural network for islanded microgrid [16] has considered the weather intermittency of PV-based DER. Another protection approach based on the probabilistic modelling of weather intermittency in renewable DERs has been validated for high resistance faults [17].…”

Section: Introductionmentioning

confidence: 99%

Microgrid protection against high impedance faults with robustness to harmonic intrusion and weather intermittency

Manohar

Koley

Ghosh

2021

IET Renewable Power Gen

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The high impedance faults (HIF) in microgrids are characterized by low magnitude fault current with inherent non-linearity and random behavior. The possible weather intermittency in PV integrated microgrids and the intrusion of harmonics due to power converters and non-linear loading, further reduce the fault current during HIF. The insignificant fault current does not allow detection of HIF by conventional overcurrent relays. In this regard, a reliable and robust protection scheme based on the combined framework of probabilistic modelling, least square and Adaline algorithm, sine cosine algorithm (SCA) and support vector machine (SVM), has been proposed to perform the tasks of mode detection, fault detection/classification and section identification during HIF in microgrid. The algorithm involves online extraction of harmonic components and stochastic modelling of intermittency in solar irradiance. The performance of the proposed scheme has been analysed in terms of robustness against harmonics intrusion and weather intermittency during HIF. The performance comparison of the proposed scheme with the decision tree (DT) and classical SVM based techniques reflects its effectiveness with improved immunity against harmonic intrusion and weather intermittency. Further, the proposed scheme has been validated using OPAL-RT digital simulator for evaluating its appropriateness in real-time settings.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…This protection scheme is capable of providing protection in the presence of noise using multiscale representation. In [7], the authors proposed a protection scheme for a solar PV-integrated micro-grid using a deep convolution neural network (CNN) which is effective under the intermittent nature of solar radiation. This has the merit of identifying the discriminatory features with a reduced computational cost.…”

Section: Introductionmentioning

confidence: 99%

A Protection Scheme for a Power System with Solar Energy Penetration

et al. 2020

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As renewable energy (RE) penetration has a continuously increasing trend, the protection of RE integrated power systems is a critical issue. Recently, power networks developed for grid integration of solar energy (SE) have been designed with the help of multi-tapped lines to integrate small- and medium-sized SE plants and simultaneously supplying power to the loads. These tapped lines create protection challenges. This paper introduces an algorithm for the recognition of faults in the grid to which a solar photovoltaic (PV) system is integrated. A fault index (FI) was introduced to identify faults. This FI was calculated by multiplying the Wigner distribution (WD) index and Alienation (ALN) index. The WD-index was based on the energy density of the current signal evaluated using Wigner distribution function. The ALN-index was evaluated using sample-based alienation coefficients of the current signal. The performance of the algorithm was validated for various scenarios with different fault types at various locations, different fault incident angles, fault impedances, sampling frequencies, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, different types of transformer windings and the presence of noise. Two phase faults with and without the involvement of ground were differentiated using the ground fault index based on the zero sequence current. This study was performed on the IEEE-13 nodes test network to which a solar PV plant with a capacity of 1 MW was integrated. The performance of the algorithm was also tested on the western part of utility grid in the Rajasthan State in India where solar PV energy integration is high. The performance of the algorithm was effectively established by comparing it with the discrete Wavelet transform (DWT), Wavelet packet transform (WPT) and Stockwell transform-based methods.

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Spatio-temporal information based protection scheme for PV integrated microgrid under solar irradiance intermittency using deep convolutional neural network

Cited by 50 publications

References 27 publications

Deep Learning for Time Series Forecasting: A Survey

Deep Learning for Time Series Forecasting: A Survey

Microgrid protection against high impedance faults with robustness to harmonic intrusion and weather intermittency

A Protection Scheme for a Power System with Solar Energy Penetration

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