Wide Area Measurement Systems (WAMSs) are used in power networks to improve the situational awareness of the operator, as well as to facilitate real-time control and protection decisions. In WAMSs, Phasor Data Concentrators (PDCs) collect time-synchronized data of Phasor Measurement Units (PMUs) through the communication system, and direct it to the control center to be used in wide-area control and protection applications. Due to the dependence of WAMSs on information and communication technologies, cyber-attacks can target these systems and propagate through them, i.e., infect a greater number of components by accessing and controlling a few of them. On this basis, this paper initially develops a Learning-Based Framework (LBF) to estimate the required defense strategy to counter the propagation of cyber-attacks in WAMSs. Afterwards, through solving a linear Binary Integer Programming (BIP) problem, this paper develops a mitigation strategy to optimally reconfigure the communication network and reduce the contamination probability for critical PMUs and PDCs while maintaining the observability of the grid. The simulation results obtained from IEEE 14- and 30-bus test systems corroborate the effectiveness of the proposed LBF and communication network reconfiguration strategy in mitigating the propagation of cyber-attacks in WAMSs.

An engineering design case study and related pedagogical support are presented on the topic of sustainable structural product design. Case participants perform the roles of a sustainability product manager, structural engineer, and thermal engineer in the design of a 0.8 kWh vehicle battery assembly with the goal of jointly minimizing cost and carbon footprint while also satisfying stress and deflection constraints. At the frontier of education, the case is unique in providing a design canvas (similar to quad charts used in industry tollgate reviews) with design efficiency and performance measures relative to theoretical minima. In developing the design, industry and graduate student participants grapple with material selection, layout design, and wall thickness as dominating design decisions. Survey instruments were employed to gauge the case effectiveness. Assessed with a five-point Likert scale, all of the participants agreed or strongly agreed that this case study involved a useful problem$({\mathbf{M}}\boldsymbol{=4.83,}\ \mathbf{SE}\boldsymbol{=.090),}$enhanced their engineering skills$\mathbf{(Nt}\boldsymbol{=4.78,} \mathbf{SE} \boldsymbol{=.101),}$enhanced awareness of sustainability concerns$(\mathbf{M}\boldsymbol{=4.72,} \mathbf{SE}\boldsymbol{=.109)}$, and increased classroom participation$(\mathbf{M}\boldsymbol{=4.67,}\ \mathbf{SE}\boldsymbol{=.114)}$relative to other teaching methods. Validated educational outcomes included understanding that (1) the chosen layout design, material selection, and wall thickness determine a majority of the cost and carbon footprint, and (2) minimizing the cost and carbon footprint were not mutually exclusive objectives.

The U.S. Department of Homeland Security (DHS) has recently identified digital relays as targets vulnerable to cyber-attacks. The DHS has also noted that attacks to multiple relays can bring about cascading outages of transmission lines, leading to blackouts. As a result, making protective relays cyber-resilient is a prominent security issue in power networks. Line current differential relays (LCDRs) are among the potentially vulnerable digital relays that are increasingly deployed for protecting critical transmission lines. LCDRs, however, lack the required resiliency against cyber attacks, due to their high dependence on communication systems. This paper unveils that such susceptibilities can result in unwarranted trip signals through false data injection attacks (FDIAs), and so cause instability if several attacks are coordinated. It also presents a solution for detecting FDIAs and distinguishing them from real internal faults. To detect attacks, the proposed method compares the estimated and locally measured voltages at an LCDR's terminal for both the positive sequence (PS) and negative sequence (NS). To estimate the local voltage for each sequence, the proposed technique uses an unknown input observer (UIO), the state-space model of the faulty line, and remote and local measurements, all associated with that sequence. The difference between the measured and estimated local voltages for each sequence remains close to zero during real internal faults because, in this condition, the state-space model based on which the UIO operates correctly represents the line. Nevertheless, the state-space model mismatch during FDIAs leads to a large difference between measured and estimated values in both sequences. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus network.

Line current differential relays (LCDRs) detect faults accurately and promptly, by comparing all currents flowing into the line. This type of relay has been identified in the literature as a reliable protection for lines in DC microgrids (MGs). LCDRs, however, lack the required resiliency against cyber intrusions, such as false data injection attacks (FDIAs) and time synchronization attacks (TSAs), due to their high dependence on communication infrastructure and/or the Global Positioning System (GPS). This paper first introduces coordinated attacks-i.e., several almost-simultaneous FDIAs or TSAs that are carried out independently to achieve a specific objective-as potential threats for MGs. Then, through a case study, it shows how coordinated FDIAs and TSAs can initiate a sequence of events that result in instability of an entire MG. Afterwards, an approach is presented to detect FDIAs and TSAs, and to distinguish them from real faults. The proposed method is comprised of passive oscillator circuits (POCs) installed in series with each converter. During faults, the resultant RLC circuit causes the POCs to resonate and generate a damped sinusoidal component with a specific frequency, i.e., fd. However, fdis not generated during FDIAs and TSAs, since unlike faults, which are physical events that trigger the natural frequencies of a system, cyber-attacks happen in the cyber layer without provoking natural frequencies of the physical layer. Thus, an LCDR pickup without detecting fddenotes an FDIA or a TSA. Since fdis locally measured and analyzed by each LCDR, the proposed detection approach cannot be targeted by cyber-attacks. The proposed method is evaluated on a simulated ±2.5 kV DC MG. Numerical analysis confirms that the proposed method (i) is system-independent; (ii) detects FDIAs and TSAs in less than 1 ms; (iii) is sensitive to high-resistance faults; (iv) can determine fault types, and (v) reduces faults' peak currents.

The application of line current differential relays (LCDRs) to protect transmission lines has recently proliferated. However, the reliance of LCDRs on digital communication channels has raised growing cyber-security concerns. This paper investigates the impacts of false data injection attacks (FDIAs) on the performance of LCDRs. It also develops coordinated attacks that involve multiple components, including LCDRs, and can cause false line tripping. Additionally, this paper proposes a technique for detecting FDIAs against LCDRs and differentiating them from actual faults in two-terminal lines. In this method, when an LCDR detects a fault, instead of immediately tripping the line, it calculates and measures the superimposed voltage at its local terminal, using the proposed positive-sequence (PS) and negative-sequence (NS) submodules. To calculate this voltage, the LCDR models the protected line in detail and replaces the rest of the system with a Thevenin equivalent that produces accurate responses at the line terminals. Afterwards, remote current measurement is utilized by the PS and NS submodules to compute each sequence's superimposed voltage. A difference between the calculated and the measured superimposed voltages in any sequence reveals that the remote current measurements are not authentic. Thus, the LCDR's trip command is blocked. The effectiveness of the proposed method is corroborated using simulation results for the IEEE 39-bus test system. The performance of the proposed method is also tested using an OPAL real-time simulator.

False data injection attacks (FDIAs) against the automatic generation control (AGC) system can lead to unstable or non-optimal operation of the power grid. This paper introduces a method to detect FDIAs targeting the AGC system by developing a stochastic unknown input estimator (SUIE). The SUIE estimates the states of the load-frequency control system, which contains the AGC as a control loop. An increase in the SUIE's residual function (RF) beyond a defined threshold signifies an FDIA. The SUIE can be designed such that it works independently from some or all inputs to the system's state-space model. In addition, the effect of process and measurement noise on the estimated states is minimized through an optimal gain setting technique for the SUIE. Therefore, not only does the SUIE eliminate the need for information about real-time load changes throughout the grid, it also maximizes the state estimation accuracy. The combination of these features distinguishes the proposed method from existing FDIA detection techniques for the AGC system. This paper also develops a number of attack identification SUIEs (AISUIEs) to determine which measurements are compromised by an FDIA, thus facilitating FDIA mitigation strategies. The AISUIEs model FDIAs targeting each AGC measurement by an attack input. These inputs serve as the unknown inputs of different AISUIEs, whose RFs indicate the type of attack. The designed AISUIEs also differentiate between attacks and non-attack abnormalities such as faults. Simulation analysis of a three-area power system corroborates the effectiveness of the proposed method. In addition, the performance of the proposed method is tested using an OPAL real-time simulator, and is compared with another technique from the literature.