Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory
- Resource Type
- Conference
- Authors
- Rajanarayan Prusty, B; Mohan Krishna, S; Bingi, Kishore; Gupta, Neeraj
- Source
- 2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1) Artificial Intelligence and Applications (ICAIA), 2023 International Conference on, Technology Conference (ATCON-1), Alliance. :1-5 Apr, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Robotics and Control Systems
Transportation
Measurement
Temperature distribution
Renewable energy sources
Systematics
Stochastic processes
Machine learning
Reliability theory
Over-limit probability
photovoltaic (PV) generation
power system reliability
risk assessment
severity
- Language
Risk-based reliability assessment is prevalent for modern power systems under higher penetration of renewable generations. This paper highlights the importance of machine learning and probabilistic approaches for risk-based reliability assessment during power system operation and planning. A set of metrics for realistic risk-based reliability assessment considering over-limit probabilities and corresponding severities is suggested. Probabilistic load flow using Monte-Carlo simulation is used to estimate the over-limit probabilities of power system variables. A detailed presentation of steps for the generation of random samples of a set of correlated random variables, development of realistic risk metrics, and portrayal of their significances via critical result analyses for different cases is expected to serve as a reference text for novice researchers in the field of risk-based reliability assessment of modern power systems integrated with photovoltaic generations.