India is one of the leading nations in the world for the development and utilization of wind energy. The country’s wind power potential at 100 m above ground level is 302 GW. The Indian wind industry is on track to achieve the government’s 60 GW wind capacity target ahead of the 2022 deadline as it has already crossed 34 GW. Wind energy sector contributes to the country by generating employment, reducing the adverse effects of greenhouse gases and increasing the size of gross domestic product. To date, the Indian wind sector has developed by private sector investment. The government, on its part, supporting the wind industry in the country via a range of financial incentives and innovative schemes. That ensures scaling up of wind energy to reach the national energy demand in a socially, economically and ecologically sustainable manner. Attempts have been made in this review to frame a clear picture of the current status of India in the onshore and offshore sectors. Various guidelines for the development of wind power projects, policies and programmes by the ministry and its growing concern for the country’s energy security are discussed. This paper also highlights the importance of wind energy tariff and also explores the cost and economics of wind energy production. Current wind energy-related research studies are summarized. Concerns that are adhered in developing wind energy power plants including social, environmental and techno-economic impacts are addressed.

Bifacial Photovoltaics has gained significant traction in recent years due to a combination of superior radiation capture capabilities and reducing costs. This study builds on a prior 1.07 MW (DC) solar system analysis for Effat University Campus in Jeddah, Saudi Arabia, by adding a Bifacial system. The paper describes a modeling methodology focusing on critical parameters that affect bifacial gains, such as the solar system's tilt angle, surface albedo, and shading. The results have been summarized as sensitivities to changes in input variables such as the surface albedo with ceteris paribus assumption. This case study showed a change in surface albedo to increase the specific production from 1771 kWh/kW to 1829 kWh/kW suggesting an increase in bifacial gain of more than 3%.

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In an electrical power system, most of the faults occurs in overhead transmission lines because of most of the conductor exposure to the atmosphere. Therefore, Insulated Overhead Conductors (IOCs) are widely used. To overcome this, a robust real-time PD fault analysis system is required. To analyze and classify the raw voltage signal for detection of PD's in IOC's a Convolutional Neural Network (CNN) based fault classification algorithm is proposed in this paper. The CNN is implemented using popular pre-trained CNN architectures such as AlexNet, VGG16 & ResNet are applied to the voltage signals in the dataset. From the values of Precision, Recall & F1-Score it is observed that ResNet architecture provides the best prediction and classification results.