An Efficient Multifeature Model for Improving the Performance of Critical Energy Infrastructure

Energy infrastructure is well thought-out to be one of the complex infrastructures due to its convoluted configuration and automatic control among all of the systems. With such systems, various connections are made for the purpose of configurations. The energy system infrastructure aims to analytically evaluate each element of the system based on fundamental energy branches according to the customer demand. Developing a novel critical evaluation approach for complex energy infrastructure is pertinent to the evaluation of mixed energy system infrastructure. Considering the functional relationships between elements and their infrastructures, a system is needed to overcome the limitations of the current systems. By doing the efficient modeling of enhancing the performance infrastructure of critical energy infrastructure enable better quantitative evaluation of system. The purpose of the proposed study is to develop an evaluation approach for enhancing the performance of critical energy infrastructure. With the help of the proposed approach, efficient multifeature model for enhancing the performance of critical energy infrastructure was experimentally calculated. The experimental setup of the proposed study was done in the Super Decision tool for an efficient multifeature model for enhancing the performance. Results of the experiments reveal the effectiveness of the proposed research.

Cloud Simulation and Virtualization for Testing of Critical Energy Infrastructure Components

Cloud computing and cloud infrastructure are beginning to be applied in order to ensure effective functioning of management system of Critical Energy Infrastructure. This is improved informativeness of Supervisory Control and Data Acquisition system by the application of additional Wide-area Measurement and Control Systems (WAMCSs), since most of all processing and transition operations of data information streams of the WAMCSs may be performed by using on clouds. In light of these circumstances, it is importantly to get extensive modeling results considering wide area range of input data and latency time between critical energy infrastructure components and cloud infrastructure components. However, researchers were not, yet, had possibility to receive more adequate results based only mathematical modeling process. In this case, simulation models considering the cost time are more preferable than mathematical models. In the spirit development of this conception, authors are proposing to employ the simulation model based on virtual technology and Matlab Simulink toolkit in order to get modeling results for more important on-line mode of functioning of the WAMCSs.

Proactive Critical Energy Infrastructure Protection via Deep Feature Learning

Autonomous fault detection plays a major role in the Critical Energy Infrastructure (CEI) domain, since sensor faults cause irreparable damage and lead to incorrect results on the condition monitoring of Cyber-Physical (CP) systems. This paper focuses on the challenging application of wind turbine (WT) monitoring. Specifically, we propose the two challenging architectures based on learning deep features, namely—Long Short Term Memory-Stacked Autoencoders (LSTM-SAE), and Convolutional Neural Network (CNN-SAE), for semi-supervised fault detection in wind CPs. The internal learnt features will facilitate the classification task by assigning each upcoming measurement into its corresponding faulty/normal operation status. To illustrate the quality of our schemes, their performance is evaluated against real-world’s wind turbine data. From the experimental section we are able to validate that both LSTM-SAE and CNN-SAE schemes provide high classification scores, indicating the high detection rate of the fault level of the wind turbines. Additionally, slight modification on our architectures are able to be applied on different fault/anomaly detection categories on variant Cyber-Physical systems.