Biblio

The accelerated move toward adopting the Smart Grid paradigm has resulted in numerous drawbacks as far as security is concerned. Traditional power grids are becoming more vulnerable to cyberattacks as all the control decisions are generated based on the data the Smart Grid generates during its operation. This data can be tampered with or attacked in communication lines to mislead the control room in decision-making. The false data injection attack (FDIA) is one of the most severe cyberattacks on today’s cyber-physical power system, as it has the potential to cause significant physical and financial damage. However, detecting cyberattacks are incredibly challenging since they have no known patterns. In this paper, we launch a random FDIA on IEEE-39 bus system. Later, we propose a Bagging MLP-based autoencoder to detect the FDIAs in the power system and compare the result with a single ML model. The Bagging MLP-based autoencoder outperforms the Isolation forest while detecting FDIAs.

The electrical grid connects all the generating stations to supply uninterruptible power to the consumers. With the advent of technology, smart sensors and communication are integrated with the existing grid to behave like a smart system. This smart grid is a two-way communication that connects the consumers and producers. It is a connected smart network that integrates electricity generation, transmission, substation, distribution, etc. In this smart grid, clean, reliable power with a high-efficiency rate of transmission is available. In this paper, a highly efficient smart management system of a smart grid with overall protection is proposed. This management system checks and monitors the parameters periodically. This future technology also develops a smart transformer with ac and dc compatibility, for self-protection and for the healing process.