Editorial: Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC)

Tomar, Anuradha; Malik, Hitendra K.; Kumr, P.; Iqbal, Atif

doi:10.1007/978-981-19-2828-4_1

Cited by 8 publications

(3 citation statements)

References 156 publications

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“…Modifications and new Z-source topologies have increased dramatically in quantity. Recently, it has also been suggested to enhance impedance networks by adding linked magnetism in order to improve voltage even more with a reduced shoot-through time [53][54][55][56].…”

Section: Modeling and Performance Of A 7-level Quasi Z-source Cascade...mentioning

confidence: 99%

Performance Evaluation of Solar PV-Based Z-Source Cascaded Multilevel Inverter with Optimized Switching Scheme

et al. 2022

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AC loads may demand a fixed or variable voltage at their input terminals. When using inverters to power such loads, the response of the inverter must be precisely controlled to suit the demands of the AC loads. Inverters with higher efficiency and sensitivity will play an increasingly essential role as the need for solar PV applications in prospective green technology grows. To increase power quality and provide a reliable power source, an inverter architecture with harmonic reduction approaches is proposed. The multilevel inverter (MLI), unlike conventional inverters, is developed by cascaded single inverter units and is often used to connect renewable energy sources. As a result, they can be utilized to efficiently reduce harmonics. Among the three topologies, the most widely used in industries is the neutral-point clamped MLI. When the levels are raised, however, they demand a larger number of diodes. When the level of the flying capacitor exceeds three, several capacitors are necessary. As a result, the optimum option for synthesizing the right output voltage from several DC sources is a cascaded multilevel inverter (CMLI). Each link in a CMLI is connected by a single DC source; therefore, there is no voltage imbalance. However, getting equal DC voltages at the input of each unit is once again a limitation. In this work, various existing multilevel inverter topologies including hybrid topologies with different switching strategies are investigated and reported. The performance of a solar PV-based seven-level quasi-Z-source cascaded H-Bridge MLI (qZS-CHBMLI) has been thoroughly examined with the best switching scheme and best topology of multilevel inverters using MATLAB/Simulink.

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Section: Modeling and Performance Of A 7-level Quasi Z-source Cascade...mentioning

confidence: 99%

Performance Evaluation of Solar PV-Based Z-Source Cascaded Multilevel Inverter with Optimized Switching Scheme

et al. 2022

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“…A variety of band selection algorithms have been used for plant disease detection, such as the instance-based Relief-F algorithm, genetic algorithms, partial least square, and random forest [ 24 ]. In the past several years, applications of machine learning (ML) methods in crop production systems have been increasing rapidly, especially for plant disease detection [ 25 , 26 , 27 ]. Machine learning refers to computational algorithms that can learn from data and perform classification or clustering tasks, which are suitable for finding the patterns and trends from hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Dhakal

Sivaramakrishnan

Zhang

et al. 2023

Sensors

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Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography–Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills.

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“…The foundation of this endeavor lies in the creation of tailored forecasting models, finely attuned to specific contexts over varying timeframes, enabling precise predictions of energy generation. However, the effectiveness of these models is inherently tied to the quality of the underlying data, which must be valid, accurate, reliable, consistent, and complete [2,3] . This paper places a strong emphasis on the pivotal role of artificial intelligence and machine learning in addressing the complexities of energy forecasting.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Forecasting of Solar Power Generation using Machine Learning

Andrianajaiana,

DavidRazafimahefa,

Cristian-Gyozo

et al. 2023

IFR

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Current photovoltaic systems are equipped with a monitoring system. The data is recorded with a predefined time base. The operator has a large amount of data. This article is a contribution to the analysis and understanding of this data. It proposes to estimate Generation from this data using machine learning. The study was conducted at the 1Mw generation site in Miroslava, Iasi, Romania. The visualization and interpretation of the generation data is presented in this article. Then, prediction techniques are presented to obtain an estimate of the plant’s generation. These techniques are: Simple Exponential Smoothing (SES), Autoregression (AR), Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMA), Holt Winter’s Exponential Smoothing (HWES), Long Short Term Memory neural (LSTM) and Convolutional Neural Network (CNN).

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Editorial: Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC)

Cited by 8 publications

References 156 publications

Performance Evaluation of Solar PV-Based Z-Source Cascaded Multilevel Inverter with Optimized Switching Scheme

Performance Evaluation of Solar PV-Based Z-Source Cascaded Multilevel Inverter with Optimized Switching Scheme

Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels

Analysis and Forecasting of Solar Power Generation using Machine Learning

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