h. seifi

This research investigates the performance of artificial neural network in solving contact problems, which are important due to their inherent complexity and prevalence in engineering systems. While analytical and numerical methods can solve some contact problems, they often struggle with complicated loading and geometries. This study specifically examines the neural network's capability to determine the extent of incomplete contact zones in symmetric geometries involving single and multiple contacts between frictionless surfaces. To achieve this, a multi-layer perceptron network is implemented using the Levenberg-Marquardt algorithm within MATLAB. The optimal network model for each problem is identified through a trial-and-error approach. Furthermore, the performance of this algorithm across all case studies is evaluated and compared with other methods. The findings demonstrate that the optimal neural network model can predict outcomes with over ninety percent accuracy when compared to analytical, semi-analytical, and numerical solutions. This accuracy is achieved for random inputs within the training data range and for one to three outputs. Such results underscore the potential of neural networks to deliver high precision across various applications.

Recent advances in power system monitoring and control require communication infrastructure to send and receive measurement data and control commands. These cyber-physical interactions, despite increasing efficiency and reliability, have exposed power systems to cyber attacks. The Automatic Generation Control (AGC) is one of the most important control systems in the power system, which requires communication infrastructure and has been highly regarded by cyber attackers. Since a successful attack on the AGC, not only has a direct impact on the system frequency, but can also affect the stability and economic performance of the power system. Therefore, understanding the impact of cyber attacks on AGC and developing strategies to defend against them have necessity and research importance. In most of the research in the field of attack-defense of AGC, the limitations of AGC in modeling such as governor dead band and communication network transmission delay have been ignored. On the other hand, considering two cyber attacks on the AGC and proposing a way to defend against them simultaneously, have not been considered. In this paper, while using the improved AGC model including governor dead band and communication network transmission delay, the effect of two attacks - data injection attack (FDI) and delay attack which are the most important cyber attacks on AGC - has been investigated. Also, the simultaneous effect of these two attacks is discussed as a combined cyber attack. The Kalman filter-based three-step defense method has been proposed to detect, estimate and mitigate the impact of the attacks and its effectiveness has been tested on the two-area AGC system.