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) |
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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 |
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Country | Russian Federation |
City | Saint Petersburg |
Period | 5/15/18 → 5/18/18 |
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