Cyber-physical systems approach for wide area control applications

Cyber Physical Systems (CPS), widely used in pervasive computing, integrate computation in the cyber world with control of physical processes. Developing CPS is challenging because interactions between physical and cyber components are complex and often unpredictable. Traditional debugging techniques can detect bugs in the cyber world, but bugs introduced from physical components and induced by limitations in the software interface to physical hardware are still difficult to detect. My research will capture the state of art and the state of the practice in verification and validation of CPS. Based on this, I will design middleware that combines models of the physical world with programming-language based assertions to help developers to design, develop, and debug robust CPS applications with ease.

Naval forces rely heavily on Cyber-Physical Systems (CPS) to conduct operations and achieve mission success. As CPS infrastructure becomes exposed to the contested world through networks, CPS security becomes much more important. In a CPS, the cyber components manage the physical components. ONR proposes that the overall goal for CPS resiliency is to have the physical systems behave properly regardless of fault and disruption. Our approach to CPS resiliency focuses on the physical components. We observed that the inertia of the physical components provides a natural but limited resilience, and is capable of tolerating short-term disruption without affecting the health and safety of the CPS. This and the fact that a CPS has a large difference between physical and cyber time scales, enables a unique approach to CPS resiliency. We present Byzantine Fault Tolerant++ (BFT++), a cyber resilient architecture that engineers the cyber components to be brittle against attack, which consequently forces cyber attacks and related disruptions to be short-lived and within tolerance of the physical system’s inertia.

Modern microgrids are networked systems comprising physical and cyber components for networking, computation, and monitoring. These cyber components make microgrids more reliable but increase the system complexity. Therefore, risk assessment methods are an imperative technology for cyber-physical systems to ensure safe and secure operations. The authors propose a reliability approach that utilizes the failure modes of power and cyber network key components to perform the risk analysis in microgrids. In this work, the authors have used the Failure Mode and Effect Analysis (FMEA) approach for risk assessment of microgrid systems and determine the influence of various failure modes on their performance. FMEA is one of the most effective methods to assess the risk involving the cyber components. It is the method of examining components, modules, and subsystems to determine failure modes in a system and their causes and consequences. A novel approach is proposed to calculate risk priority numbers based on factors like severity, occurrence, and detection. A risk matrix is calculated from the proposed FMEA worksheet, which acts as a graphical representation for evaluating components risk based on risk priority number or criticality metrics.

Cyber-physical systems (CPSs) are engineered solutions obtained through the seamless integration of computational algorithms and physical components. Nowadays, they are entitled to drive innovation and competition in different fields such as agriculture, building design and automation, energy, healthcare, transportation and, above all, industrial manufacturing. In fact, CPS technology is going to transform the way people interact with engineered systems — just as the Internet has transformed the way people interact with information. Among CPS technologies, measurement and networking ones play a fundamental role, being enrolled as the natural bridge between the cyber and physical components of a CPS. A number of interconnected sensors and transducers are claimed to give inner and outer, local measurement data that, once processed through suitable algorithms and analytics, have to provide reliable information about the effectiveness of the whole CPS and its interaction with the external world, as well as its health status for maintenance and troubleshooting purposes. So, great attention has to be paid to network models and architectures, to sensor forms and functions, in order to make CPSs properly integrate the continuous and the discrete, compounded by the uncertainty of open environments. The paper aims at opening a preliminary discussion on the role and features of measurement and networking technologies in CPS application fields, with special regard to industrial manufacturing, within a scientific community traditionally close to instrumentation and measurement (IM) issues, in order to stimulate and encourage useful and fruitful cross-fertilization. After some details concerning the general structure of a CPS, its typical architectural model and main features, a special focus is put on CPSs involved in industrial manufacturing along with measurement and networking facets. Some considerations about major measurement challenges that the CPS self-awareness feature poses in maintenance and troubleshooting operations are then drawn. Concluding remarks are finally reported.

The increasing integration of information technology with operational technology leads to the formation of Cyber-Physical Systems (CPSs) that intertwine physical and cyber components and connect to each other. This interconnection enables the offering of functionality beyond the combined offering of each individual component, but at the same time increases the cyber risk of the overall system, as such risk propagates between and aggregates at component systems. The complexity of the resulting systems in many cases leads to difficulty in analyzing cyber risk. Additionally, the selection of cybersecurity controls that will effectively and efficiently treat the cyber risk is commonly performed manually, or at best with limited automated decision support. In this paper, we extend our previous work in [1] to analyze attack paths between CPSs on one hand, and we improve the method proposed therein for selecting a set of security controls that minimizes both the residual risk and the cost of implementation. We use the DELTA demand-response management platform for the energy market stakeholders such as Aggregators and Retailers [2] as a use case to illustrate the workings of the proposed approaches. The results are sets of cybersecurity controls applied to those components of the overall system that have been identified to lie in those attack paths that have been identified as most critical among all the identified attack paths. KeywordsAttack pathsCyber risk aggregationCyber security controlsPower grid

The microgrid is a typical cyber-physical microgrid system (CPMS). The physical unconventional distributed generators (DGs) are intermittent and inverter-interfaced which makes them very different to control. The cyber components, such as the embedded computer and communication network, are equipped with DGs, to process and transmit the necessary information for the controllers. In order to ensure system-wide observability, controllability and stabilization for the microgrid, the cyber and physical component need to be integrated. For the physical component of CPMS, the droop-control method is popular as it can be applied in both modes of operation to improve the grid transient performance. Traditional droop control methods have the drawback of the inherent trade-off between power sharing and voltage and frequency regulation. In this paper, the global information (such as the average voltage and the output active power of the microgrid and so on) are acquired distributedly based on multi-agent system (MAS). Based on the global information from cyber components of CPMS, automatic generation control (AGC) and automatic voltage control (AVC) are proposed to deal with the drawback of traditional droop control. Simulation studies in PSCAD demonstrate the effectiveness of the proposed control methods.

A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. Such a system has wide applications in the scenarios of traffic control, battlefield surveillance, environmental monitoring, and so on. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments integrated with many types of cyber-space resources. The potential of this integration is unbounded. To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time. Therefore, CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber. The various properties of the physical world must be addressed in information management and knowledge discovery. This paper discusses the problems of mining sensor data in CPS: With a large number of wireless sensors deployed in a designated area, the task is real time detection of intruders that enter the area based on noisy sensor data. The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data. IntruMine first analyzes the trustworthiness of sensor data, then detects the intruders' locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.

The rapid evolution of intelligent transportation systems has accelerated the integration of cyber–physical systems (CPS) within modern vehicles. Intelligent car systems rely on a tight coupling between physical components, such as sensors and actuators, and cyber components, including data processing, control algorithms, and communication networks. This paper presents an integrated cyber–physical approach for sensor-enabled intelligent car systems that enhances situational awareness, driving safety, and operational efficiency. The proposed framework combines multi-sensor data acquisition, real-time analytics, decision-making algorithms, and adaptive control mechanisms within a unified CPS architecture. By enabling continuous interaction between the physical vehicle environment and cyber intelligence, the system supports advanced driver assistance and autonomous functionalities. Experimental analysis and system-level evaluation demonstrate the effectiveness of the proposed approach in improving responsiveness, reliability, and scalability of intelligent car systems.

In Cyber-Physical Systems (CPSs), physical processes, computational resources and communi- cation capabilities are tightly interconnected. Traditionally, the physical components of a CPS are described by means of differential or difference equations, while the cyber components are modeled by means of discrete dynamics. Therefore, hybrid systems, that are heterogeneous dynamical sys- tems characterized by the interaction of continuous and discrete dynamics, are a powerful modeling framework to deal with CPSs. Motivated by the great importance of security issues for CPSs, we characterize the observability and diagnosability properties for hybrid systems in the general case where the available information may be corrupted by an external attacker. Then, as CPSs are found in a wide range of applications, we demonstrate how to estimate the continuous state by simulating two scenarios: the control of a Direct Current (DC) Microgrid, and the control of a network of Unmanned Aerial Vehicles (UAVs) cooperatively transporting a payload.

The increasingly witnessed integration of information technology with operational technology leads to the formation of Cyber-Physical Systems (CPSs) that intertwine physical and cyber components and connect to each other to form systems-of-systems. This interconnection enables the offering of functionality beyond the combined offering of each individual component, but at the same time increases the cyber risk of the overall system, as such risk propagates between and aggregates at component systems. The complexity of the resulting systems-of-systems in many cases leads to difficulty in analyzing cyber risk. Additionally, the selection of cybersecurity controls that will effectively and efficiently treat the cyber risk is commonly performed manually, or at best with limited automated decision support. In this work, we propose a method for analyzing risk propagation and aggregation in complex CPSs utilizing the results of risk assessments of their individual constituents. Additionally, we propose a method employing evolutionary programming for automating the selection of an optimal set of cybersecurity controls out of a list of available controls, that will minimize the residual risk and the cost associated with the implementation of these measures. We illustrate the workings of the proposed methods by applying them to the navigational systems of two variants of the Cyber-Enabled Ship (C-ES), namely the autonomous ship and the remotely controlled ship. The results are sets of cybersecurity controls applied to those components of the overall system that have been identified in previous studies as the most vulnerable ones; such controls minimize the residual risk, while also minimizing the cost of implementation.

This paper firstly reviews the development history of power system protection, especially the wide area protection and control trending to integration in smart grid. It also descripts the features of adaptability and coordination in the integrated wide area protection control system. This multi-levels system is composed of the local intelligent electronic device, the high-speed communication network, the integrated wide area protection and control. One of the key element is the data center at the wide area level. The data center is an information platform for receiving and storing and sharing the power grid information. Based on the IEC 61850 standards, the paper mainly descripts a scheme of object-oriented data center. The scheme is about the object-oriented dynamic data stream layout by using the model files. For the latency requirements about off-line analysis process and real-time data serving, the data center is designed as a multi-levels storage system. It achieves the following goals: wide applicability, easy expansion and high performance to support information platform for integrated wide area protection and control system.

This paper presents a layered modeling framework for cyber-physical power systems (CPPS) to enhance reliability assessment. The proliferation of cyber-specific hazards in modern power grids necessitates a comprehensive evaluation of interdependent system components. Existing CPPS testbeds suffer from significant limitations, including prohibitive costs, scalability constraints, and insufficient capability to address the inclusion of cyber-security concerns across the entire grid infrastructure. This work proposes a three-layer CPPS architectural model comprising physical, communication, and cyber components. The methodology systematically analyzes individual component failure impacts, enabling the aggregation of elements with similar failure characteristics into discrete sub-layers. A real-time simulation testbed is utilized to evaluate the impact of the failure of CPPS components. Furthermore, insights from existing studies are leveraged to develop a robust model that captures the dynamics of system behavior under various operating conditions. This approach effectively captures system interdependencies while reducing computational complexity, thereby facilitating accurate modeling of CPPS dynamics and enabling robust reliability assessments for modern power grid infrastructures.

Guest Editorial

High-sensitivity and miniaturized measurement of electric fields is important for applications in power grid such as lightning waveform capturing and electrical equipment monitoring. In addition, the demand of wide-area information measurement in smart grid also necessitates the development of miniaturized and noncontact sensors. So far, there have already been a variety of electric-field sensing devices. However, these approaches meet the challenges of large size and low sensitivity. To solve the problem, this paper proposed a new electric-field sensor based on piezoelectric effect. The piezoelectric effect enables the conversion of electric fields into piezoelectric material deformations and then the air-gap capacitances in the device. The results indicate that the electric-field micro sensor can reach a high sensitivity of 30 V/m. The side length of the sensor chip can be as small as a few millimeters. Owing to the feasibility in mass production of the proposed sensor, it can potentially be extended to the industrial applications in power grid.

Precision timing and communication networking experiments in a real-time power grid hardware-in-the-loop laboratory