Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

Protection of Active Distribution Networks and Their Cyber Physical Infrastructure

Today’s Smart Grid constitutes several smaller interconnected microgrids. However, the integration of converter-interfaced distributed generation (DG) in microgrids has raised several issues such as the fact that fault currents in these systems in islanded mode are way less than those in grid connected microgrids. Therefore, microgrid protection schemes require a fast, reliable and robust communication system, with backup, to automatically adjust relay settings for the appropriate current levels according to the microgrid’s operation mode. However, risks of communication link failures, cyber security threats and the high cost involved to avoid them are major challenges for the implementation of an economic adaptive protection scheme. This dissertation proposes an adaptive protection scheme for AC microgrids which is capable of surviving communication failures. The contribution is the use of an energy storage system as the main contributor to fault currents in the microgrid’s islanded mode when the communication link fails to detect the shift to the islanded mode. The design of an autonomous control algorithm for the energy storage’s AC/DC converter capable of operating when the microgrid is in both grid-connected and islanded mode. Utilizing a single mode of operation for the converter will eliminate the reliance on communicated control command signals to shift the controller between different modes. Also, the ability of the overall system to keep stable voltage and frequency levels during extreme cases such as the occurrence of a fault during a peak pulse load period. The results of the proposed protection scheme showed that the energy storage -inverter system is able to contribute enough fault current for a sufficient duration to cause the system protection devices to clear the fault in the event of communication loss. The proposed method was investigated under different fault types and showed excellent results of the proposed protection scheme. In addition, it was demonstrated in a case study that, whenever possible, the temporary disconnection of the pulse load during the fault period will allow the utilization of smaller energy storage device capacity to feed fault currents and thus reduce the overall expenditures. Also, in this dissertation we proposed a hybrid hardware-software co-simulation platform capable of modeling the relation between the cyber and physical parts to provide a protection scheme for the microgrid. The microgrid was simulated on MATLAB/Simulink SimPowerSystems to model the physical system dynamics, whereas all control logic was implemented on embedded microcontrollers communicating over a real network. This work suggested a protection methodology utilizing contemporary communication technologies between multi-agents to protect the microgrid