Sathyajith Mathew

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2023). Direct and indirect short-term aggregated turbine- and farm-level wind power forecasts integrating several NWP sources. Heliyon. ISSN 2405-8440. 9(11). doi: 10.1016/j.heliyon.2023.e21479. Fulltekst i vitenarkiv
• Rajendran Pillai, Vipin Raj; Rajasekharan Nair Valsala, Rohit; Raj, Veena; Petra, Muhammed Iskandar; Krishnan Nair, Satheesh Kumar & Mathew, Sathyajith (2023). Exploring the Potential of Microgrids in the Effective Utilisation of Renewable Energy: A Comprehensive Analysis of Evolving Themes and Future Priorities Using Main Path Analysis. Designs. ISSN 2411-9660. 7(3). doi: 10.3390/designs7030058. Fulltekst i vitenarkiv Vis sammendrag

  Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research.

• Kunjuramakurup, Lijin; Sulthan, Sheik Mohammed; Ponparakkal, Muhammed Shanir; Raj, Veena & Mathew, Sathyajith (2023). A High-Power Solar PV-fed TISO DC-DC Converter for Electric Vehicle Charging Applications. Energies. ISSN 1996-1073. 16(5). doi: 10.3390/en16052186. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, a two-input, single-output (TISO) DC-DC converter for electric vehicle charging applications with solar photovoltaic (PV) as one of the sources is discussed. A novel, simple, and effective control strategy with maximum power point tracking (MPPT) to maximize power from PV while maintaining a constant bus voltage is proposed. A 100 V, 100 W TISO DC-DC converter is designed and comprehensive simulations are conducted under different conditions. The system characteristics are validated by comparing the results with a conventional P&O algorithm and multiple step size P&O algorithm. The proposed control strategy can precisely generate control signals to track MPPT while maintaining the bus voltage by controlling the output of a fixed DC source according to the changes in PV generation. The overall efficiency of the proposed approach is 98.67%, and the average output voltage is 99.09 V under selected conditions. The overall average output voltage ripple is 0.22% with the proposed approach, while it is 2.91% and 1.55% with the conventional P&O and multiple step size P&O MPPT techniques, respectively. Further, a 500 V, 17 kW high-power converter is designed and a simulation is carried out. The high-power converter provides an average output voltage of 496.92 V with an overall efficiency of 98.72%, and the average output voltage ripple is 0.16%. The results obtained from the simulation under the selected conditions lead to the conclusion that the converter has better efficiency and less variation in the output voltage at higher power levels with the proposed control technique.

• Veena, Raj; Dotse, Sam-Quarcoo; Mathew, Sathyajith; Petra, M. I. & Yassin, Hayati (2023). Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems. Energies. ISSN 1996-1073. 16(2). doi: 10.3390/en16020671. Fulltekst i vitenarkiv Vis sammendrag

  In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, João Gouveia Aparício Bento (2022). Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models. Energy. ISSN 0360-5442. 263(D). doi: 10.1016/j.energy.2022.125893. Fulltekst i vitenarkiv Vis sammendrag

  Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the proposed forecasting models. The turbine level power forecasts using different approaches are then combined into a single forecast using a weighting method based on recent forecast errors. These are then aggregated for the wind farm level power estimates. The proposed forecasting schemes are implemented with data from a Norwegian wind farm. We found that in both the direct and indirect forecasting approaches, the forecasting errors could be reduced between 8% and 22%, while inputs from several NWP sources are used together. The wind downscaling model, which is used in the indirect forecasting approach, could significantly contribute to the model's accuracy. The performance of both the direct and indirect forecasting schemes is comparable for the studied wind farm.

• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2022). Power production forecast for distributed wind energy systems using support vector regression. Energy Science & Engineering. ISSN 2050-0505. s. 1–12. doi: 10.1002/ese3.1295. Fulltekst i vitenarkiv Vis sammendrag

  Due to the inherent intermittency in wind power production, reliable short-term wind power production forecasting has become essential for the efficient grid and market integration of wind energy. The current wind power production forecasting schemes are predominantly developed for wind farms. With the rapid growth in the microgrid sector and the increasing number of wind turbines integrated with these local grids, power production forecasting schemes are becoming essential for distributed wind energy systems as well. This paper proposes a power production forecasting scheme developed explicitly for distributed wind energy projects. The proposed system integrates two submodels based on support vector regression: one for downscaling the wind speed predictions to the hub coordinates of the turbine and the other for predicting the site-specific performance of the turbine under this wind condition. The forecasting horizons considered are the hour ahead (t + 1) and the day ahead (t + 36), which align with the Nord pool's energy market requirements. For the day-ahead scheme, a multistep recursive approach is adopted. The accuracy and adaptability of the proposed forecasting scheme are demonstrated in the case of a distributed wind turbine.

• Femin, V.; Petra, I.; Mathew, Sathyajith & Hazra, J (2020). Econo- Environmental Dispatch Solutions for Power Systems Integrated with Renewable Energy Resources, 2020 International Conference and Utility Exhibition on Energy, Environment and Climate Change (ICUE) . IEEE conference proceedings. ISSN 978-1-7281-8334-3. doi: 10.1109/ICUE49301.2020.9307047.
• Yakoub, Ghali; Mathew, Sathyajith & Leal, J B (2020). Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1618. doi: 10.1088/1742-6596/1618/6/062034. Fulltekst i vitenarkiv
• Veena, R.; Mathew, Sathyajith & Petra, M.I. (2020). Artificially intelligent models for the site-specific performance of wind turbines. International Journal of Energy and Environmental Engineering. ISSN 2008-9163. 11, s. 289–297. doi: 10.1007/s40095-020-00352-2.
• Veena, R.; Manuel, S. M.; Mathew, Sathyajith & Petra, I. (2020). Parametric Models for Predicting the Performance of Wind Turbines. Materials Today: Proceedings. ISSN 2214-7853. 24(3). doi: 10.1016/j.matpr.2020.03.604.
• Mathew, Sathyajith (2020). Wake Induced Power Losses in Wind Farms . International Journal of Engineering and Advanced Technology. ISSN 2249-8958. 9(3), s. 2175–2180. doi: 10.35940/ijeat.C5611.029320.
• Mathew, Sathyajith (2020). Sustainable energy towards air pollution and climate change mitigation. Journal of Environmental Management. ISSN 0301-4797. 260(109978), s. 1–11. doi: 10.1016/j.jenvman.2019.109978. Vis sammendrag

  This is an evidence from a high-income economy in Southeast Asia and a support for scientific planning of the energy sector in ensuring air pollution and climate change mitigation. A comparative analysis of the energy options for electricity generation in the nation was made considering availability, cost and greenhouse gases emission – CO2, N2O and CH4, using a two-stage method comprising multi-objective optimization and TOPSIS. The renewable (RE) and non-renewable energy (NRE) options available were assessed through the lifecycle approach to determine the lifecycle greenhouse gas emission (LCGHG) and levelized cost of energy (LCOE) per MWh of electricity. Considering historical electricity consumption, annual GDP and population growth from 1965, energy consumption for the year 2035 was forecasted using support vector machine regressor in Weka. Future plans in energy diversification pathways were examined through various scenario multi-objective optimizations with a constraint on resource availability and energy target using genetic algorithm in MATLAB. The outputs were ranked using TOPSIS method. Results showed that greenhouse gases emission could be reduced by 10.3 percent compared to business as usual scenario while the energy mix could attain 10 percent renewable energy in the grid at a relatively lower generation cost.

• Mathew, Sathyajith (2018). EnergyPlus models for the benchmarking of residential buildings in Brunei Darussalam. Energy and Buildings. ISSN 0378-7788. doi: 10.1016/j.enbuild.2016.03.039.
• Mathew, Sathyajith (2015). Numerical simulation of flow over an airfoil for small wind turbines using the γ−Reθ model. International Journal of Energy and Environmental Engineering. ISSN 2008-9163. doi: 10.1007/s40095-015-0188-7.
• Mathew, Sathyajith (2014). A Novel Low Reynolds Number Airfoil Design for Small Horizontal Axis Wind Turbines. Wind Engineering : The International Journal of Wind Power. ISSN 0309-524X. doi: 10.1260/0309-524x.38.4.377.
• Mathew, Sathyajith (2003). A High-Power Solar PV-fed TISO DC-DC Converter for Electric Vehicle Charging Applications. Energies. ISSN 1996-1073.

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• Mathew, Sathyajith (2011). Advances in Wind Energy Conversion Technology. Springer. ISBN 978-3-540-88258-9. 216 s.
• Mathew, Sathyajith (2006). Wind Energy. Springer. ISBN 978-3-540-30906-2. 252 s.

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• Mian, Haris Hameed; Siddiqui, Muhammad Salman; Keprate, Arvind & Mathew, Sathyajith (2024). Neural network-based motion prtediction of semi-submercible floating platform for offshore wind.
• Mathew, Sathyajith (2023). Wind power forecasting.
• Mathew, Sathyajith (2023). An Introduction to Wind Power Forecasting.
• Mathew, Sathyajith (2020). Econo-Environmental Dispatch Solutions for Power Systems Integrated with Renewable Energy Resources.
• Mathew, Sathyajith (2020). Low Reynolds number airfoil for a wind turbine blade and method thereof.
• Mathew, Sathyajith (2020). Determining intermittent renewable energy penetration limits in a grid.
• Mathew, Sathyajith (2020). Wind Energy Conversion.
• Mathew, Sathyajith (2019). Basics of Wind Energy Conversion .

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