Anomaly Detection Techniques Due to Cyber Attack on The Smart Grid and Microgrid Integrated Utility

  Coimbatore Campus

Research Topic - Anomaly Detection Techniques Due to Cyber Attack on The Smart Grid and Microgrid Integrated Utility

In economic terms, electricity is a commodity capable of being bought, sold, and traded. Traditionally, electricity markets are developed and operated within strictly regulated frameworks. Today, countries rely on competitive markets to maintain efficient operations in the short term, either through bilateral physical contracts, power exchanges, or coordinated spot markets. Day ahead forecast in hand will help the system operator to schedule generation to meet anticipated demand and also a certain level of unexpected demand or loss of generation at optimum electricity price. System operators are responsible for their own electric grids. With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. False data injection attack (FDIA) is a kind of cyber-attack that can make severely secure and economic impacts on the power system. Machine learning detection methods are used for the countermeasures of FDI attacks which are the future focus of the power grid vital prominent safe operation aspect.

Department & Campus

Department of Electrical and Electronics Engineering, Coimbatore

Skillsets Preferred from Applicants

Handling with Machine learning and artificial intelligence techniques to be known.

Faculty Profile

Dr. K.R.M. Vijaya Chandrakala

Associate Professor (Sr. Gr.), Electrical and Electronics Engineering, School of Engineering, Coimbatore.


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