Given that power systems are essential to modern life and electricity demand continues to rise, ensuring their reliable and secure operation has become a critical priority. Wireless networked control systems (WNCSs), which rely on wireless channels for communication between controllers, sensors, and actuators, are increasingly deployed in energy systems such as multi-area interconnected power systems to enhance flexibility and scalability. WNCSs are susceptible to deception attacks and time-varying communication delays that can compromise interconnection stability and deteriorate performance. This paper presents an observer-based secure control methodology that models deception via independent Bernoulli processes with unknown attack probabilities, while explicitly considering actuation and measurement delays. Using a Lyapunov stability framework, we established computationally feasible linear matrix inequality conditions enabling the co-design of the controller and observer with proven stability and disturbance rejection. A two-area interconnected powersystem case study validates the approach. The proposed method was tested with offline gains covering nine scenarios. Results indicate that the method sustains closed-loop performance across all nine combined attack/delay scenarios and recovers quickly even in worst-case conditions, supporting secure control of WNCSs in realistic adversarial environments.

Power systems are essential in the current lifestyle and the demand on the electricity is continuously increasing. To meet the demand load and to provide customers with the required electricity, power generation areas have been connected to maintain the supply even though one generation area fails. This study investigates the risk of cyber-attacks on power systems, which can result in instability and system failure. Specifically, the focus is on the stabilization of a multi-area interconnected power system (MAIPS) when subjected to a denial of service (DoS) attack that disrupts communication channels. First, we apply a controller with static feedback to stabilize the MAIPS under normal conditions. Next, we examine the system’s stability under DoS attack, while considering certain parameters such as the duration and frequency of the attack. Finally, we present an example of a three-area interconnected power system with multiple scenarios to show the efficacy of the presented method. The proposed method successfully maintains the MAIPS’ stability in the nominal situation and the presence of DoS attack. Moreover, the overshoot of the states during the transient response is small and the time needed to get the system stabilized is reasonable.

This paper proposed a load frequency control (LFC) to ensure power quality of a two-area interconnected hybrid power system (PS) involving integrated electric vehicles (EVs) and communication delay. The control framework consists of three optimization-based proportional-integralderivative (PID) controllers named genetic algorithm (GA-PID), particle swarm optimization (PSO-PID), and grey wolf optimization (GWO-PID) controllers, and fractional order proportional-integral-derivative (FOPID) for improving the system's stability and performance. The performance of the proposed controllers for handling the challenges arising from EV integration, communication delay, and renewable energy sources (RESs) intermittency has been assessed. A thorough performance analysis is subsequently carried out according to the simulation results and various error indices, including integral square error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE). The findings show that the proposed GA-PID controller performs better dynamically than the PSO-PID, GWO-PID, and FOPID controllers in terms of robustness and convergence rate. The results suggest the improvement of LFC in power systems through optimization algorithms.

With the intimate integration of power grids and cyber networks, limited bandwidth and packet delay have a rapidly expanding negative impact on power system performance. The presented multi-area interconnected power system consists of four areas, each including thermal and hydro-generation plants. This paper investigates the stability analysis problem for cyber-physical systems with a round-robin communication protocol under mixed cyberattacks and load changes. The objective is to stabilize a multi-area interconnected power system (MAIPS) using a static feedback controller while minimizing the defined performance function. Then, the stability of the MAIPS is characterized when the system is subjected to a transmission delay while considering predetermined limits for the duration and the frequency of the delay. Our findings indicate that time delays can influence system stability and that choosing an appropriate sampling interval is necessary to ensure the stability of the system. Finally, an illustrative example of three areas of interconnected power systems with several scenarios is presented to verify the effectiveness of the proposed method.

Modern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid with generation mix has routed the utilization of infrastructures involving phasor measurement units (PMUs). This is to have access to real-time grid information. However, the traffic of digital information and communication is potentially vulnerable to data-injection and cyber attacks. To address this issue, a median regression function (MRF)-based state estimation is presented in this paper. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous variable-driven representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revisited New England 39-Bus system with large scale photovoltaic (PV) power plant. The system was affected with multiple system disturbances and severe data-injection attacks. The results show the effectiveness of the proposed MRF method against the mainstream and regression methods. The proposed scheme can accurately estimate the states and evaluate the contaminated measurements while improving the situation awareness of wide area monitoring systems (WAMS) operations in modern power grids

The next-generation electric power systems (smart grid) are studied intensively as a promising solution to the energy crisis. One important feature of the smart grid is the integration of high-speed, reliable, and secure data communication networks to manage the complex power systems effectively and intelligently. We provide in this chapter a comprehensive survey of the communication architectures in the power systems, including the communication network compositions, technologies, functions, requirements, and research challenges. As these communication networks are responsible for delivering power system-related messages, we discuss specifically the network implementation considerations and challenges in the power system settings.

One important problem, especially in power systems, is the control of systems under cyberattacks. In this chapter, a secure state controller and observer-based controller for discrete-time CPS subject to both cyber- (denial-of-service (DoS) and deception) and physical attacks will be presented. The occurrences of the cyber- and physical attacks will be considered as Bernoulli distributed white sequences with variable conditional probabilities. A sufficient condition is first derived under which the observer-based controller is guaranteed to have the desired security level using the stochastic analysis techniques. Then, the controller will be designed by solving a linear matrix inequality (LMI) using Yalmip and Matlab. Finally, the feasibility of the proposed system is demonstrated by solving the known example of a two-area power system.

The Industry Revolution 4.0 pushes the industry to digitize all its operations. Cyberphysical Power Systems (CPPS), such as autonomous automobile systems and medical monitoring are examples of this revolution. However, as these systems are interconnected via the internet, they become more vulnerable to cyberattacks and in particular, stealthy attacks. Cyberattacks could affect the operations of CPPS and cause physical damages before any indication. So, there is a need to design a secure control system to withstand in these circumstances. In this chapter, an event-triggering control (ETC) scheme is designed for discrete time CPPSs contain random measurements and actuation delays and subject to simultaneous hybrid distributed denial-of-service (DDoS) and deception attacks. The cyberattacks are designed as Bernoulli distributed white sequences with variable conditional probabilities. Moreover, an event-triggered scheme is proposed to decrease the communication overhead in the system, where the measurement's signal is sent only under a certain triggering condition. An observer-based control is designed to maintain the stability of the CPPS under all possible scenarios of single or hybrid simultaneous attacks in the forward and/or backward communication. Linear matrix inequalities are used to represent the overall control scheme. At the end, an illustrative example is presented discussed to show the effectiveness of the proposed methodology.

A wireless networked control system (WNCS) consists of a dynamic system to be controlled, sensors, actuators, and a remote controller. A WNCS has two types of wireless transmissions, i.e., the sensor's measurement transmission to the controller and the controller's command transmission to the actuator. In this paper, we are surveying the literature on the communication networks in WNCSs and the challenges related to them, such as the communication standards, delay, Packet dropout, and delay jitter. Then, the control approaches in the design of a WNCS are presented, including the interactive design approaches and the joint design approaches. Also, several applications of WNCSs have been discussed in terms of their structure, functionality, and control design. These applications include Intra-Vehicle Wireless networks, Wireless Avionics Intra-Communication, Building Automation, and Water pumping. After that, security issues in WNCSs from a control engineering point of view are detailed while focusing on the major kinds of cyber attacks affecting WNCSs. Finally, future directions and conclusions are summarized at the end of the paper.

Industry Revolution 4.0 pushes the industry to digitize all its operations. Cyberphysical Systems (CPSs), such as autonomous automobile systems and medical monitoring are examples of this revolution. However, as these systems are interconnected via the Internet, they become more vulnerable to cyber-attacks and in particular, stealthy attacks. Cyber attacks could affect the operations of CPS and cause physical damages before any indication. So, there is a need to design a secure control system to withstand in these circumstances. In this article, an event-triggering control scheme is designed for discrete time CPSs contain random delays in measurements and actuation signals and subject to simultaneous hybrid distributed denial of service (DDoS) and deception attacks. The cyber attacks are designed as Bernoulli distributed white sequences with conditional probabilities that are variable. Moreover, An event-triggering control scheme is proposed for decreasing the communication overhead in the system, such that the measurements' signals are sent when a selected triggering condition is met. An observer based control is designed to maintain the stability of the CPS under all possible scenarios of single or hybrid simultaneous attacks in the forward and or backward communication. Linear matrix inequalities are used to represent the overall control scheme. At the end, two illustrative examples are presented and discussed to show the effectiveness of the presented scheme.

Modern power grid is a generation mix of conventional generation facilities and variable renewable energy resources (VRES). The complexity of such a power grid has urged the utilization of infrastructures involving phasor measurement units (PMUs) to have access to real-time grid information. However, the traffic of digital information and communication is prone to data-injection and cyber-attacks. To address this issue, a median regression function (MRF)-based state estimation is proposed. The algorithm was stationed at each monitoring node using interacting multiple model (IMM)-based fusion architecture. An exogenous function-based representation of the state is considered for the system. A mapping function-based initial regression analysis is made to depict the margins of state estimate in the presence of data-injection. A median regression function is built on top of it while generating and evaluating the residuals. The tests were conducted on a revised New England 39-Bus system with large scale PV power plant in the presence of harsh data-injection attacks and multiple system disturbances. Results show the proposed MRF method can accurately estimate the states and evaluate the contaminated measurements.

A teleoperation system is referred to as a plant that is controlled remotely, and it is often composed of a human operator, a local master manipulator, and a remote slave manipulator, all connected by a communication network. Bilateral teleoperation systems (BTOS) include transmissions in both the forward and backward directions between the master and slave. This paper discusses a class of (BTOS) focusing on the security of the system after modeling the master and slave robots mathematically. The false data injection attack is examined, where the attacker may inject false data into the states that are being exchanged between the master and slave robots. The vulnerability of BTOS, where the attack destabilizes the system, is presented. A deep learning-based detection technique is proposed to detect the presence of false data injection attacks. The deep learning model with convolution neural network structure is trained and tested with considering complex attacks where the attacker has full knowledge of the system and proficiency to emanate and control the target system. The proposed model achieves 96\% validation accuracy, and the efficacy of the proposed deep learning detector is demonstrated and tested into the BTOS.

In this chapter, an attack defense method is proposed to address the secure remote state-estimation problem caused by linear false-data injection (FDI) attacks on cyberphysical power systems (CPPS). The pseudorandom number as a watermarking to encrypt and decrypt the data transmitted through the wireless network is utilized. Via the proposed method, the transmitted data in the normal operation can be recovered. Since the data modified by the attacker can be marked with the watermarking, the χ2 detector is capable of detecting the attack. For three different attack scenarios, the evolution of the remote estimation-error covariances and the detection performance are analyzed, respectively. With the goal of minimizing the estimation-error covariance, the optimal parameter set of the watermarking is derived. Furthermore, the proposed method can even be extended to detect a replay attack. Finally, several examples of an IEEE system are provided to illustrate the derived results.

The backbone of this chapter of the book evolves from finite dimension vector spaces, mappings and convex analysis. Much of the material is standard linear algebra, with which the reader is assumed to have some familiarity and therefore our intention is to provide an overview of the key ideas and mathematical tools. In addition, this appendix provides a collection of basic analytical results organized to make the book self-contained and help the readers follow up the topics in a systematic and easy way. This includes a glimpse of graph theory, basic linear matrix inequalities and stability notions.

In this chapter, we alleviate a smart grid control and cybersecurity problem characterizing the vulnerabilities of electric power networks to false data attacks. The analysis problem is related to a constrained cardinality minimization problem. Based on a polyhedral combinatorics argument, it is shown that an exact optimal solution to this cardinality minimization problem can be attained by l 1 relaxation technique provided. The developed results are essentially different from well-known results based on mutual coherence and restricted isometric property. The results are demonstrated on benchmarks including the IEEE 118-bus, IEEE 300-bus and the Polish 2383-bus and 2736-bus systems.

Cyber Physical Systems (CPS) are defined over the integrations of computation, communication, and control to achieve the desired performance of modern physical processes. It turns out that security threats have a high possibility of affecting CPS and can be affected by several cyber attacks without providing any indication about failure. In this paper, we examine the stabilisation of distributed CPS affected by a denial of service (DoS) attack. First, a static output feedback controller will be designed to achieve the stability of a nominal distributed system. Then, a simple and typical scenario where communication sequence is purely Round-robin is considered and a bound of attack frequency and duration is calculated to ensure the stability of the distributed CPS. Finally, a numerical example is provided to demonstrate the feasibility of the proposed system.

The teleoperation system is often composed of a human operator, a local master manipulator, and a remote slave manipulator that are connected by a communication network. This paper proposes a survey on feedback control design for the bilateral teleoperation systems (BTSs) in nominal situations and in the presence of cyber-attacks. The main idea of the presented methods is to achieve the stability of a delayed bilateral teleoperation system in the presence of several kinds of cyber attacks. In this paper, a comprehensive survey on control systems for BTSs under cyber-attacks is discussed. Finally, we discuss the current and future problems in this field.

Cyber Physical Systems (CPSs) are defined as the integrations of computation, control, and communication to obtain a prespecified behaviour of the physical processes. Due to their nature, CPSs could be highly affected by security threats. In this research, a secure filter for discrete-time delayed nonlinear systems affected by the two major kinds of cyber attacks i.e. denial-of-service (DoS) and deception attacks is proposed. The cyber attacks are modelled as Bernoulli distributed white sequences with variable probabilities. First, a predefined level of security is guaranteed by setting a sufficient condition using the techniques of stochastic analysis. Second, we obtain the gains of the proposed filter by solving a linear matrix inequality using YALMIP and MATLAB/Simulink. Finally, a numerical example is solved to show the effectiveness of this method on CPSs.

Data-driven smart sensors are being widely used in industrial applications to estimate and evaluate the quality of critical variables. By using physical devices, most of the critical variables are being measured with difficulties. When process data is discarded, The quality variables sampling rate of majority of the smart sensors have been developed on labeled and labeled number of samples, small and large. The prediction accuracy enhancement quality will limit the large loss of information being generated from a measuring device. However, utilizing all available process data contained information, is a major and common issue of data-driven smart sensor. In this article, a new Multi-Layer Learning Machine (MLLM) approach is recommended for the applications of smart sensors Using the Extreme Learning Machine as a foundation (ELM). Initially, a semi-supervised auto-encoders deep network structure is being used as an extraction for unsupervised feature with the reference to all process samples. At that point, extreme learning machine is used for regression with the quality variable added. In the meantime, the manifold regularization technique is presented for MLLM.The new strategy can deeply separate, and information is extracted from the data contains, and get more data from the unlabeled samples. The proposed MLLM procedure is implemented in adebutanizer column application to assess the C4 contents. Finally, the simulation result verify that our approach enhancesboth of the expectation and prediction accuracy in comparison with the available methods.