Abstract
This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated fault detection methods are also described to predict and by pass failing modules. Finally, the fully connected solar array, which is fitted with multiple sensors, is operated as an Internet of things network. Integrated with each module are sensors and radio electronics communicating all data to a fusion center. Gathering data at the fusion center to compute and transmit analytics requires secure low power communication solutions. To optimize the resources and power consumption, we describe a method to integrate fully distributed algorithms designed for a wireless sensor network in this CPS system.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 217-222 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538665312 |
| DOIs | |
| State | Published - Jun 15 2018 |
| Event | 1st IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2018 - Saint Petersburg, Russian Federation Duration: May 15 2018 → May 18 2018 |
Other
| Other | 1st IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2018 |
|---|---|
| Country | Russian Federation |
| City | Saint Petersburg |
| Period | 5/15/18 → 5/18/18 |
Fingerprint
Keywords
- Distributed average consensus
- Fault detection
- IoT Energy
- PV
- Shading
- Solar power analytics
ASJC Scopus subject areas
- Artificial Intelligence
- Hardware and Architecture
- Control and Optimization
- Industrial and Manufacturing Engineering
Cite this
Shading prediction, fault detection, and consensus estimation for solar array control. / Katoch, Sameeksha; Muniraju, Gowtham; Rao, Sunil; Spanias, Andreas; Turaga, Pavan; Tepedelenlioglu, Cihan; Banavar, Mahesh; Srinivasan, Devarajan.
Proceedings - 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 217-222.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Shading prediction, fault detection, and consensus estimation for solar array control
AU - Katoch, Sameeksha
AU - Muniraju, Gowtham
AU - Rao, Sunil
AU - Spanias, Andreas
AU - Turaga, Pavan
AU - Tepedelenlioglu, Cihan
AU - Banavar, Mahesh
AU - Srinivasan, Devarajan
PY - 2018/6/15
Y1 - 2018/6/15
N2 - This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated fault detection methods are also described to predict and by pass failing modules. Finally, the fully connected solar array, which is fitted with multiple sensors, is operated as an Internet of things network. Integrated with each module are sensors and radio electronics communicating all data to a fusion center. Gathering data at the fusion center to compute and transmit analytics requires secure low power communication solutions. To optimize the resources and power consumption, we describe a method to integrate fully distributed algorithms designed for a wireless sensor network in this CPS system.
AB - This paper describes three methods used in the development of a utility-scale solar cyber-physical system. The study describes remote fault detection using machine learning approaches, power output optimization using cloud movement prediction and consensus-based solar array parameter estimation. Dynamic cloud movement, shading and soiling, lead to fluctuations in power output and loss of efficiency. For optimization of output power, a cloud movement prediction algorithm is proposed. Integrated fault detection methods are also described to predict and by pass failing modules. Finally, the fully connected solar array, which is fitted with multiple sensors, is operated as an Internet of things network. Integrated with each module are sensors and radio electronics communicating all data to a fusion center. Gathering data at the fusion center to compute and transmit analytics requires secure low power communication solutions. To optimize the resources and power consumption, we describe a method to integrate fully distributed algorithms designed for a wireless sensor network in this CPS system.
KW - Distributed average consensus
KW - Fault detection
KW - IoT Energy
KW - PV
KW - Shading
KW - Solar power analytics
UR - http://www.scopus.com/inward/record.url?scp=85050151806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050151806&partnerID=8YFLogxK
U2 - 10.1109/ICPHYS.2018.8387662
DO - 10.1109/ICPHYS.2018.8387662
M3 - Conference contribution
SP - 217
EP - 222
BT - Proceedings - 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -