scholarly journals Analysis of stochastic system status of electrificated railways based on machine learning

2021 ◽  
Vol 2131 (2) ◽  
pp. 022092
Author(s):  
P A Bodrov ◽  
N A Popova ◽  
A L Ganashek
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Power System ◽  
Stochastic System ◽  
Stochastic Systems ◽  
Energy System ◽  
Monitoring Tools ◽  
Predicted Values ◽  
And Control ◽  
Efficiency And Reliability

Abstract Stochastic systems are the systems in which changes are random, the predicted values depend on the probability distribution. An example of a stochastic system is a power system, the operation of which is influenced by many random factors, their analysis and control will give an opportunity to control the safe cycle as well as operation reliability. Improving the efficiency and reliability of the energy system is impossible without the development of special monitoring tools and predicting their state.

Machine learning for power system protection and control

2021 ◽  
Vol 34 (1) ◽  
pp. 106881
Author(s):  
Hanyu Yang ◽  
Xubin Liu ◽  
Di Zhang ◽  
Tao Chen ◽  
Canbing Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power System ◽  
Power System Protection ◽  
System Protection ◽  
Protection And Control ◽  
And Control

scholarly journals Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 826
Author(s):  
Taha Selim Ustun ◽  
S. M. Suhail Hussain ◽  
Ahsen Ulutas ◽  
Ahmet Onen ◽  
Muhammad M. Roomi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Power System ◽  
Smart Grids ◽  
Detection System ◽  
Object Oriented ◽  
Security Systems ◽  
Iec 61850 ◽  
Communication Traffic ◽  
And Control

Increased connectivity is required to implement novel coordination and control schemes. IEC 61850-based communication solutions have become popular due to many reasons—object-oriented modeling capability, interoperable connectivity and strong communication protocols, to name a few. However, communication infrastructure is not well-equipped with cybersecurity mechanisms for secure operation. Unlike online banking systems that have been running such security systems for decades, smart grid cybersecurity is an emerging field. To achieve security at all levels, operational technology-based security is also needed. To address this need, this paper develops an intrusion detection system for smart grids utilizing IEC 61850’s Generic Object-Oriented Substation Event (GOOSE) messages. The system is developed with machine learning and is able to monitor the communication traffic of a given power system and distinguish normal events from abnormal ones, i.e., attacks. The designed system is implemented and tested with a realistic IEC 61850 GOOSE message dataset under symmetric and asymmetric fault conditions in the power system. The results show that the proposed system can successfully distinguish normal power system events from cyberattacks with high accuracy. This ensures that smart grids have intrusion detection in addition to cybersecurity features attached to exchanged messages.

Complex stochastic systems modelling and control via iterative machine learning

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2685-2692 ◽  
Author(s):  
Aiping Wang ◽  
Puya Afshar ◽  
Hong Wang
Keyword(s):  
Machine Learning ◽  
Stochastic Systems ◽  
Systems Modelling ◽  
And Control

Optimal approaches to manage power system decarbonation

2020 ◽  
Vol 64 (1-4) ◽  
pp. 1447-1452
Author(s):  
Vincent Mazauric ◽  
Ariane Millot ◽  
Claude Le Pape-Gardeux ◽  
Nadia Maïzi
Keyword(s):  
Free Energy ◽  
Linear Programming ◽  
Phase Transitions ◽  
Power System ◽  
Energy System ◽  
Energy Sources ◽  
Low Carbon ◽  
Faraday’S Law ◽  
Optimal Description ◽  
Low Carbon Technologies

To overcome the negative environemental impact of the actual power system, an optimal description of quasi-static electromagnetics relying on a reversible interpretation of the Faraday’s law is given. Due to the overabundance of carbon-free energy sources, this description makes it possible to consider an evolution towards an energy system favoring low-carbon technologies. The management for changing is then explored through a simplified linear-programming problem and an analogy with phase transitions in physics is drawn.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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