An Assessment of Onshore and Offshore Wind Energy Potential in India Using Moth Flame Optimization

Krishnamoorthy R, Udhayakumar K, Kannadasan Raju, Rajvikram Madurai Elavarasan and Lucian Mihet-Popa
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Krishnamoorthy R: Department of Electrical and Electronics Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India
Udhayakumar K: Department of Electrical and Electronics Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India
Kannadasan Raju: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Tamil Nadu 602117, India
Rajvikram Madurai Elavarasan: Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Tamil Nadu 602117, India
Lucian Mihet-Popa: Faculty of Electrical Engineering, Ostfold University College, No-1757 Halden, Norway

Energies, 2020, vol. 13, issue 12, 1-41

Abstract: Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m 2 . The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

Keywords: bimodal; India; mixed; offshore; statistical analysis; Weibull; wind speed distribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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