Loss of Utility Detection and Seamless Operation of Distributed Generation System

Buduma, Parusharamulu; Pinto, Smitha Joyce; Panda, Gayadhar

doi:10.1109/tia.2020.2976800

Cited by 14 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They can meet end-users' demands for electricity quality, supply reliability and safety while addressing the challenge of providing power to remote areas and reducing reliance on traditional fossil fuels. However, distributed generation (DG) facilities are subject to significant fluctuations due to environmental factors, making capacity allocation and equipment selection critical to meet daily load demands [1].…”

Section: Introductionmentioning

confidence: 99%

Optimization of economic and technical aspects in solar-biogas hybrid rural microgrids based on multiple energy storage methods

Tan,

Su,

Zhang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

View full text Add to dashboard Cite

This paper tackled the issue of identifying the most suitable capacity setup for multi-energy complementary microgrids in rural regions. The microgrid model integrated solar and biogas storage within an AC/DC hybrid system. The use of biogas and solar power generation could meet the electricity load demand of users, as well as the waste heat generated by biogas power generation be used to meet the heat load demand. By integrating the two energy storage methods of electricity and heat storage, the synergistic utilization of electricity and heat energy was achieved to improve the flexibility and energy efficiency of the microgrid system. Through multi-objective optimization, the study aimed to maximize renewable energy utilization, minimize costs and reduce carbon emissions while ensuring power supply reliability. The results suggested that the optimal configuration for this rural microgrid includes a 2.6 kW biogas generator, 30.0 kW photovoltaic panels, a 2.6 kW battery pack and a 10.00 kW bidirectional inverter. This configuration minimized the total net cost and yielded favorable outcomes in terms of various evaluation indicators.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization of economic and technical aspects in solar-biogas hybrid rural microgrids based on multiple energy storage methods

Tan,

Su,

Zhang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

View full text Add to dashboard Cite

show abstract

“…Phaselet-based islanding detection that employs phaselet algorithm in conjunction with filtering window for phasor estimation from negative sequence components of voltage and current has been reported in [31]. Islanding detection based on a parametric time-frequency analysis together with an optimal probabilistic neural network is also reported in [32]. A new feedback-based PIDM for once cycle control photovoltaic (PV) inverter is also reported [33].…”

Section: Introductionmentioning

confidence: 99%

Islanding detection method based on image classification technique using histogram of oriented gradient features

Manikonda

Gaonkar

2020

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

Islanding power quality detection using lighting search optimization with deep learning model on distributed generation systems

Soumya

Raj²,

Deepanraj³

et al. 2022

Renewable Energy Focus

View full text Add to dashboard Cite

Loss of Utility Detection and Seamless Operation of Distributed Generation System

Cited by 14 publications

References 27 publications

Optimization of economic and technical aspects in solar-biogas hybrid rural microgrids based on multiple energy storage methods

Optimization of economic and technical aspects in solar-biogas hybrid rural microgrids based on multiple energy storage methods

Islanding detection method based on image classification technique using histogram of oriented gradient features

Islanding power quality detection using lighting search optimization with deep learning model on distributed generation systems

Contact Info

Product

Resources

About