Biblio

The integration of distributed energy resources (DERs) and expansion of complex network in the distribution grid requires an advanced two-level state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper implements a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the implemented MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs), deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement this methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the "valley of death" and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex (CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time.

The following topics are dealt with: security of data; distributed power generation; power engineering computing; power grids; power system security; computer network security; voltage control; risk management; power system measurement; critical infrastructures.

The ability to react to a malicious attack starts with high fidelity recognition, and with that, an agile response to the attack. The current Operational Technology (OT) systems for a critical infrastructure include an intrusion detection system (IDS), but the ability to adapt to an intrusion is a human initiated response. Orchestrators, which are coming of age in the financial sector and allow for levels of automated response, are not prevalent in the OT space. To evolve to such responses in the OT space, a tradeoff analysis is first needed. This tradeoff analysis should evaluate the mitigation benefits of responses versus the physical affects that result. Providing an informed and automated response decision. This paper presents a formulation of a novel tradeoff analysis and its use in advancing a cyber-physical architecture for automated responses (CyPhAAR).

The ability to advance the state of the art in automated cybersecurity protections for industrial control systems (ICS) has as a prerequisite of understanding the trade-off space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the trade-offs of available mitigating actions. This network design can include a commercial ICS controller and simple plant or emulated plant to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable microgrid for cyber resilience analysis (SPEMMCRA).

Modern infrastructure is heavily reliant on systems with interconnected computational and physical resources, named Cyber-Physical Systems (CPSs). Hence, building resilient CPSs is a prime need and continuous monitoring of the CPS operational health is essential for improving resilience. This paper presents a framework for calculating and monitoring of health in CPSs using data driven techniques. The main advantages of this data driven methodology is that the ability of leveraging heterogeneous data streams that are available from the CPSs and the ability of performing the monitoring with minimal a priori domain knowledge. The main objective of the framework is to warn the operators of any degradation in cyber, physical or overall health of the CPS. The framework consists of four components: 1) Data acquisition and feature extraction, 2) state identification and real time state estimation, 3) cyber-physical health calculation and 4) operator warning generation. Further, this paper presents an initial implementation of the first three phases of the framework on a CPS testbed involving a Microgrid simulation and a cyber-network which connects the grid with its controller. The feature extraction method and the use of unsupervised learning algorithms are discussed. Experimental results are presented for the first two phases and the results showed that the data reflected different operating states and visualization techniques can be used to extract the relationships in data features.