Technological and deployment challenges and user-response to uninterrupted DC (UDC) deployment in Indian homes

Jhunjhunwala, Ashok; Vasudevan, Krishna; Narasamma, Lakshmi; Ramamurthi, Bhaskar

doi:10.1109/icdcm.2015.7152006

Cited by 7 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This power is directly fed to the DC loads which are far more energy efficient as compared to the conventional AC loads [2]. The designed features of OGH make this system more energy efficient and economical as compared to the existing solutions.…”

Section: Introductionmentioning

confidence: 99%

Solar-DC deployment experience in off-grid and near off-grid homes: Economics, technology and policy analysis

Kaur

Jain²,

Jhunjhunwala

2015

2015 IEEE First International Conference on DC Microgrids (ICDCM)

Self Cite

View full text Add to dashboard Cite

India is a power deficit country and one third of its homes are off grid or near off grid. This paper presents an efficient and affordable Solar DC solution for powering such homes. Though several solutions have emerged in the past for powering these homes, those have been expensive and energy inefficient. These solutions rely on several DC to AC and AC to DC conversions, to feed the widely used AC home loads, thus, wasting a large chunk of the expensive power. The proposed Solar DC solution for off-grid homes (OGH) is developed to use the generated PV power efficiently. With this solution, the panel and battery size is reduced by 2 to 2.5 times and the cost to power a house is reduced to nearly half the cost of the existing solutions. The paper also presents a techno-economic comparison between the proposed OGH solution with some existing solar systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Solar-DC deployment experience in off-grid and near off-grid homes: Economics, technology and policy analysis

Kaur

Jain²,

Jhunjhunwala

2015

2015 IEEE First International Conference on DC Microgrids (ICDCM)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, to effectively utilize the power from DERs by reducing the number of conversion stages, an intentional islanding algorithm is proposed for a low voltage DC (LVDC) distribution system. The LVDC distribution system is designed based on the standards proposed in the literature [15][16][17][18][19].…”

mentioning

confidence: 99%

Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Kumar

Laxmi

2021

IEEE Access

View full text Add to dashboard Cite

Currently, research work is primarily dependent on the collection of large sets of data from systems and making predictions based on the knowledge obtained from the data, which is generally termed as 'data mining'. These data mining algorithms are of great importance in improving the performance of different applications. In this regard, Machine Learning (ML) algorithms have been demonstrated to be excellent tools to cope with difficult problems. In this paper, a classification learner based supervised ML algorithm is proposed for intentional islanding of DERs based on the live data collected from supervisory control and data acquisition (SCADA) system in post disaster situations. Literature presents various islanding detection techniques and also intentional islanding algorithms to address different problems in AC networks. These algorithms majorly work based on the control of current source or voltage source inverters. On the other hand, a low voltage DC distribution system allowing the removal of inverter is proposed, which is supposed to be more advantageous by reducing losses and is also economical when working with DERs. In this paper, ML based intentional islanding algorithm for DERs based low voltage DC distribution system is proposed by considering the effects of natural disasters. The learner models trained are fine tree, linear SVM, quadratic SVM and Gaussian SVM. The training of fine tree model is achieved with higher accuracy of 99.8%. The main objective of this work is to achieve a faster and accurate decision making. The performance of the ML based intentional islanding algorithm is compared with the earlier proposed artificial intelligence (AI) based intentional islanding algorithms. The AI algorithms proposed earlier are fuzzy inference systems (FIS), artificial neural networks (ANN) and adaptive network based fuzzy inference system (ANFIS). The comparison shows that, the decision making with ML based intentional islanding algorithm is faster and accurate than all other algorithms.

show abstract

Energy poverty: Electrification and well-being

Sharma

Chan

2016

Nat Energy

View full text Add to dashboard Cite

Technological and deployment challenges and user-response to uninterrupted DC (UDC) deployment in Indian homes

Cited by 7 publications

References 3 publications

Solar-DC deployment experience in off-grid and near off-grid homes: Economics, technology and policy analysis

Solar-DC deployment experience in off-grid and near off-grid homes: Economics, technology and policy analysis

Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management

Energy poverty: Electrification and well-being

Contact Info

Product

Resources

About