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This work demonstrates a techno-economical assessment of wind energy potential for four passes of Tamil Nadu (Aralvaimozhi, Shencottah, Palghat, and Cumbum) with uncertainty factors. First, a potential assessment was carried out with time-series data, and the Weibull parameters, such as c (scale) and k (shape), were determined using the modern-era...
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... other states of Western Ghats, Tamilnadu plays a major role in wind energy production [13], which stands top in the country due to its abundant wind energy availability and multiple potential sites by nature. Consolidating all these inferences, this work targets the potential assessment of four key wind passes of Tamilnadu, namely the Palghat gap (S1), Cumbum (S2), Shencottah (S3), and Aralvaimozhi (S4), as described in Figure 5 [33]. The wind speeds of all passes are commendable, as illustrated in the figure with a maximum rate of 9.75 m/s. ...
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... This research effort primarily concentrated on assessing the techno-economical benefit of wind energy potential attainable in the primary four wind passages of Aralvaimozhi, Shencottah, Palghat, and Cumbum in order to appreciate the nature of wind flow and its potential in the Tamil Nadu area [21], [22]. In this research study, authors had taken twenty years of (from the year 2000 to 2019) time-series data of historical, wind speed and direction data of 4 wind passes. ...
Due to its inherent geographical potential for wind/solar power and other renewable energy sources (RES) generation, Tamil Nadu is one of the pioneering Indian states in the early development and utilisation of RES. Tamil Nadu accounts for roughly 25% of India's total wind energy capacity. Increased penetration of RES in a power system indicates that RESs have replaced traditional power plants that have historically managed and stabilised the power system, resulting in novel power flow situations. Grid stability must be enhanced in light of the integration of large-scale RES into the grid. The purpose of this research is to analyse and evaluate the impact of large-scale integration of RES by examining mitigating methods from the most recent academic articles and technical reports in this field. This will aid in identifying the key risks affecting the network's stability margin as a result of the widespread integration of RES generation, allowing for a reasonable increase in the network's stability margin. The study's findings will also help to guide the selection of appropriate mitigation strategies. As the stability margin improves, the future fraction of RES additions to the Tamil Nadu extra high voltage (EHV) grid will be increased significantly.
... Its goal is to provide authorities with immediately deployable, inexpensive choices for satisfying climate objectives and decreasing global temperature rise. [2]. The projected energy revolution can potentially lower net costs while providing significant societal benefits, such as increased economic growth, job creation, and general well-being increases. ...
... These sources can provide historical wind speed information, allowing you to assess and evaluate the wind speed conditions in Aralvaimozhi. However, it is crucial to verify the reliability and accuracy of the data by crossreferencing multiple sources [2]. ...
Wind velocity and potential are frequently estimated using probability density functions (PDFs) such as the Inverse Weibull and Rayleigh. Determining whether they are suitable for modelling observed distributions is crucial. Many wind energy systems require a thorough understanding of wind speed probability distributions. This research presents an extensive mathematical approach for wind speed evaluation using the inverse Weibull distribution (IWD) and a modified maximum likelihood function. IWD is a valuable tool for modelling wind speed characteristics, providing insights into the PDF and cumulative distribution function (CDF) of wind speeds. The suggested modified maximum likelihood (MML) function includes tweaks and improvements to increase parameter estimate accuracy in the IWD. The IWD is computed using the MML function, which considers data features, outliers, and unique research demands. Model calibration techniques , including goodness-of-fit assessment, sensitivity analysis, and error analysis, are employed to refine and assess the model's accuracy. The developed mathematical model based on the inverse Weibull distribution and modified maximum likelihood function offers a robust approach for wind speed evaluation and analysis, contributing to renewable energy resource assessment, wind farm design, and structural engineering applications. Ó 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
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... Notably, distributed generations (DGs) are becoming a prominent option for incorporating energy distribution due to their economic, environmental, and technical advantages [8] that could help to advance renewable-based EV charging stations. Fossil-fuel-based energy production causes a surge in the release of greenhouse gases that exacerbate ecological circumstances due to effluence elements [9][10][11]. Many research articles have been published related to distributed energy, DC/DC converters, disturbance systems, and DC motor drives. ...
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... The Weibull distribution is positively skewed for this reason therefore this distribution is suitable to model the wind speed data. In recent years, a large number of studies have been published and suitability of the Weibull distribution to model wind speed data has been discussed [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. ...
In the present paper, the Weibull distribution is used to analyse the wind speed data of Liptovský Mikuláš-Ondrašová (49°05′52″ N, 19°35′32″ E), situated in northern Slovakia. Analysed wind speed data were collected over the 11-year period (2005-2015) and they were recorded three times a day. The results show that the seasonal values of the shape parameter k range from 1.474 to 1.607, with yearly value of 1.546 while the seasonal values of the scale parameter c range from 2.488 to 3.010 m/s , with yearly value of 2.726 m/s . We find out that according to the coefficient of determination and root mean square error, the Weibull distribution performs well in fitting the wind speed data.
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... The VAWT model is constructed using wind power Equations (1)-(3) given below and the complete virtual Simulink model, shown in Figure 3a, comprises five sections designated as wind area calculation, turbine speed calculation, power coefficient (C p ) calculating subsystem, and energy calculation unit [37]. ...
This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.
