Enhancement of fault ride-through capability of grid-connected wind farm

Bhadane, Kishor V.; Ballal, Makarand Sudhakar; Moharil, R. M.; Kulkarni, Hemant Ramesh; Prasad, Hanuman

doi:10.1504/ijpelec.2021.114467

Cited by 3 publications

(2 citation statements)

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“…FRT of PV system can be divided into two groups modified control type and adding external devices as shown in Figure 2. [1333] [8,9] and super capacitor energy storage system (SCESS) [10] brake chopper circuits (BCC) [11], flexible alternating current transmission system (FACTS) devices [12], in addition to some other methods such as fault current limiters (FCLs) [13] and series dynamic breaking resistor (SDBR) [14], are the FRT capability control strategies using external devices. The modified inverter controllers (MIC) [15,16] and Computational methods [17,18] are the improved controller strategies without additional devices.…”

Section: Frt Methods Classificationmentioning

confidence: 99%

Low Voltage Ride Through Capability of Grid Connected Pv System

2023

IRJMETS

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PQ is at high concern for utility and grid operators since renewable energy is at large penetration having intermittent and stochastic behaviour. The biggest concern while designing and controlling grid feeding PV inverters is to meet the grid requirements. International grid requirements demand Low Voltage Ride Through (LVRT) capability and maintaining grid functionality during fault conditions. In order to maintain power flow from PV panel also at the condition fault at AC side a robust PV voltage-regulation must be designed. The controller build the linkage between the MPPT function and the bounded-voltage power flow control. In this paper, the behavior of a PV system is studied to present a detail survey on the various LVRT scheme presented in literature. A brief overview of the researchers is also presented.

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Section: Frt Methods Classificationmentioning

confidence: 99%

Low Voltage Ride Through Capability of Grid Connected Pv System

2023

IRJMETS

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“…e modernization of the power system accelerates the utilization of EVs in terms of V to G technology. In an innovative grid environment, EVs become a possible solution to balance the power fluctuations due to the intermittent nature of RES [115][116][117][118][119].…”

Section: Autonomous Electric Vehicle (Aev) Driving Subsystemmentioning

confidence: 99%

Autonomous Vehicles and Intelligent Automation: Applications, Challenges, and Opportunities

Bathla

Bhadane²,

Singh

et al. 2022

Mobile Information Systems

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Intelligent Automation (IA) in automobiles combines robotic process automation and artificial intelligence, allowing digital transformation in autonomous vehicles. IA can completely replace humans with automation with better safety and intelligent movement of vehicles. This work surveys those recent methodologies and their comparative analysis, which use artificial intelligence, machine learning, and IoT in autonomous vehicles. With the shift from manual to automation, there is a need to understand risk mitigation technologies. Thus, this work surveys the safety standards and challenges associated with autonomous vehicles in context of object detection, cybersecurity, and V2X privacy. Additionally, the conceptual autonomous technology risks and benefits are listed to study the consideration of artificial intelligence as an essential factor in handling futuristic vehicles. Researchers and organizations are innovating efficient tools and frameworks for autonomous vehicles. In this survey, in-depth analysis of design techniques of intelligent tools and frameworks for AI and IoT-based autonomous vehicles was conducted. Furthermore, autonomous electric vehicle functionality is also covered with its applications. The real-life applications of autonomous truck, bus, car, shuttle, helicopter, rover, and underground vehicles in various countries and organizations are elaborated. Furthermore, the applications of autonomous vehicles in the supply chain management and manufacturing industry are included in this survey. The advancements in autonomous vehicles technology using machine learning, deep learning, reinforcement learning, statistical techniques, and IoT are presented with comparative analysis. The important future directions are offered in order to indicate areas of potential study that may be carried out in order to enhance autonomous cars in the future.

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