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Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process.

Despite being man-made, cyber-power systems and the associated cyber-attacks evolve in tandem with human expertise, which matures over time. This dynamic mirrors the evolutionary processes observed in infectious disease ecology within microbial and human host systems. In the past, researchers have explored several bio-inspired, immunity-based approaches for cyber-attack defense, but these are not directly applicable to cyber-power systems. Although a few studies have applied bio-inspired methods in cyber-power systems, these were not focused on cybersecurity. Given the increasing integration of automation, communication, and computing technologies in power systems, it is essential to understand how bio-inspired, immunity-based approaches can be incorporated to build cyber-immunity in the cyber-power systems. In this work, we first explore the parallels between cyber-power systems and infectious disease ecology. Building on the analysis of similarities between different aspects of cyber-power security and infectious diseases, this work aims to develop a bio-inspired, resilient cyber defense system that can identify, protect, detect, respond to, and recover from complex, well-targeted cyber attacks in power systems. The proposed framework has two key features: 1) it draws upon principles and insights from both engineered and natural defense mechanisms used in combating infectious diseases, and 2) it evolves in response to cyber attacks by transmitting iterative feedback upon detecting specific attack stages. This iterative learning process enables the framework to defend against similar future attacks like the adaptive immune system of the human body. A realistic cyber-attack scenario has been developed using a real-time cyber-power substation automation testbed to validate the proposed bio-inspired framework. Our use case demonstrates that the framework can help defending against the initial steps of a cyber-attack and, through iterative learning, effectively counter all subsequent stages of similar attacks. The implementation of this bio-inspired framework will enable cyber-power systems to handle randomness, ambiguity, and rapidly evolving cyber threats while integrating digital substation automation technologies.

Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs ( $\mu $ -PMUs), and smart meters. However, large amount of data collected by these devices bring several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications.

Bidirectional wireless communication is employed in various smart grid components such as smart meters and control and monitoring applications where security is vital. The Trusted Third Party (TTP) and wireless connectivity between the smart meter and the third party in the key management-based encryption techniques for the smart grid are expected to be totally trustworthy and dependable. In a wired/wireless medium, however, a man-in-the-middle may seek to disrupt, monitor and manipulate the network, or simply execute a replay attack, revealing its vulnerability. Recognizing this, this study presents a novel authentication management (model) comprised of two layer security schema. The first layer implements an efficient novel encryption method for secure data exchange between meters and control center with the help of two partially trusted simple servers (constitutes the TTP). In this setting, one server handles the data encryption between the meter and control center/central database, and the other server administers the random sequence of data transmission. The second layer monitors and verifies exchanged data packets among smart meters. It detects abnormal packets from suspicious sources. To implement this node-to-node authentication, One class support vector machine algorithm is proposed which takes advantages of the location information as well as the data transmission history (node identification, packet size, and data transmission frequency). This schema secures data communication, and imposes a comprehensive privacy throughout the system without considerably extending the complexity of the conventional key management scheme.

The power grid is evolving with the increased integration of Distributed Energy Resources (DERs) and the growing need for bidirectional communication to manage DERs. As this evolution unfolds, the communication system supporting the power grid operation demands thorough analysis. Traditionally sparse wired mediums cannot support increasing number of DERs connection. The continuous advancement of alternative wireless technologies provides possibility of efficient and economical communication methods. This paper explores the multifaceted area of alternative communication mediums tailored for DER-centric power distribution networks (PDN). A meticulous examination of the performance of diverse alternative mediums supported by experimental analysis and measured against a set of performance metrics, are presented. A comprehensive comparative analysis and guidelines considering existing protocols, to ensure a resilient power distribution system with DERs are also provided, highlighting the role of communication systems for a distributed energy landscape.

The power system network is moving towards a smarter grid with increasing deployment of distributed generators, and prosumers, embedded with distributed control. An evolving active distribution network will require a distribution system operator (DSO) which will utilize information and communication technology to perform optimization and control of distributed energy resources (DERs). There are two major challenges in performing simulation and analysis of a distribution system. The first one is the detailed modelling and integration of a large number of distributed generators and the second one is the integration of communication and power layers in real-time simulation. In this paper, a co-simulation framework is proposed, which facilitates the performance monitoring of both layers simultaneously. A CIGRE benchmark system is used to investigate the performance of the communication layer along with the detailed modelling of distributed generators. A network topology with different network scenarios is used to analyze the impact of the network performance. This analysis helps in determining network scenarios for the optimal operation of the distribution system. The simulation study has utilized Real-Time Digital Simulator (RTDS), Typhoon, and OpalRT real-time simulators for the power layer, and NetSim for designing a communication layer between all the simulators and emulating the actual communication.

Electric distribution systems are going through a major upgrade with increasing penetration of distributed energy resources (DERs) and impacting transmission system operational analysis. Traditionally, the transmission system’s voltage stability margin is computed by performing a continuation power flow (CPF) analysis. In transmission-CPF, loads in distribution systems are aggregated at the transmission-distribution (T&D) interface buses, and the distribution system’s electrical characteristics, like feeder length, increasing penetration of DERs, and associated control, are not considered. The traditional CPF thus considers the distribution network as a purely passive network without active network management and distributed energy resources. In this paper, two integrated T&D CPF methods are implemented using a co-simulation framework that uses individual transmission and distribution solvers. The first method is iteratively-coupled T&D CPF (IC-TDCPF), where the iterative coupling concept forces convergence of boundary variables at T&D PCC (Point of common coupling) at every CPF step. The second method, Loosely Coupled T&D CPF (LC-TDCPF), relaxes the boundary convergence constraints at PCC to achieve faster calculation speed. Moreover, a reduced distribution network equivalent is also developed to enable faster computation of VSM by reducing the number of integrated T&D buses. The developed T&D CPF co-simulation framework uses an open-source platform MATPOWER for solving transmission system and open-source software OPENDSS for solving distribution system. Three large integrated T&D systems with 4196, 10587, and 12295 buses are developed to demonstrate the impact of DERs, voltage-dependent load models, and distribution network topology on integrated voltage stability margin using the proposed T&D CPF and co-simulation.

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise algorithmic classification and suitable metrics. It has been identified that classical controller definitions along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for algorithmic performance evaluation; while providing the taxonomy. Furthermore, a majority of these control algorithms operate in multiple time scales, and therefore, algorithmic time decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements while focusing on the architectural subtleties of algorithms is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in the presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Finally, the detailed discussion provided in this paper identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test-bed.

For past several years the resiliency of the power grid is severely challenged by extreme events. Black start and restoration scheme (BS&RS) is critical to enhance distribution grid resiliency. Variability in the power supply capacity of distributed energy resources (DERs) and load demand stand against the efficiency of sequential black start restoration of an unbalanced distribution system. An efficient black start restoration scheme must consider forecasted generation and demand profile to assess resource participation and generate valid switching sequence during sequential restoration of distribution grid. This work proposes a novel model predictive control (MPC) based efficient BS&RS strategy for reconfigurable distribution grid with DERs. The proposed algorithm generates optimal switching sequence by coordinating smart switches, black start DERs (BS-DERs) and energy storage systems (ESSs). Active power-frequency droop, reactive power-voltage droop and ramp rate constraints are assigned to BS-DERs for practical purposes. The non-convexity of sequential service restoration problem due to operational constraints of distribution grid, line switches and dynamics of ESS are linearized and solved as a mixed integer linear programming problem (MILP). The proposed scheme is also extended to incorporate errors associated with forecasting, DER capacity and load demand. Results of IEEE 123-bus and 1069-bus systems indicate effectiveness of the proposed approach.

With increasing concerns about climate change, there is a transition from high-carbon-emitting fuels to green energy resources in various applications including household, commercial, transportation, and electric grid applications. Even though renewable energy resources are receiving traction for being carbon-neutral, their availability is intermittent. To address this issue to achieve extensive application, the integration of energy storage systems in conjunction with these resources is becoming a recommended practice. Additionally, in the transportation sector, the increased demand for EVs requires the development of energy storage systems that can deliver energy for rigorous driving cycles, with lithium-ion-based batteries emerging as the superior choice for energy storage due to their high power and energy densities, length of their life cycle, low self-discharge rates, and reasonable cost. As a result, battery energy storage systems (BESSs) are becoming a primary energy storage system. The high-performance demand on these BESS can have severe negative effects on their internal operations such as heating and catching on fire when operating in overcharge or undercharge states. Reduced efficiency and poor charge storage result in the battery operating at higher temperatures. To mitigate early battery degradation, battery management systems (BMSs) have been devised to enhance battery life and ensure normal operation under safe operating conditions. Some BMSs are capable of determining precise state estimations to ensure safe battery operation and reduce hazards. Precise estimation of battery health is computed by evaluating several metrics and is a central factor in effective battery management systems. In this scenario, the accurate estimation of the health indicators (HIs) of the battery becomes even more important within the framework of a BMS. This paper provides a comprehensive review and discussion of battery management systems and different health indicators for BESSs, with suitable classification based on key characteristics.

This paper develops a novel pseudo-measurement construct termed a Correlational Measurement (CM) to improve observability and state estimation accuracy. CMs utilize the correlation between similar classes of loads and DERs. The modifications required to accommodate CMs in traditional node voltage or branch current based state estimators are derived. Additionally, an observability analysis framework for distribution systems with DERs is presented to seamlessly integrate CMs. The framework assembles an observability test and an observable island identification procedure from the literature along with a novel observability metric. Finally, an on-line CM parameter estimation procedure is presented. Simulations on the IEEE 123 bus test system and real field data from Pecan Street demonstrate the utility of the proposed CMs and the observability analysis framework in improving observability and state estimation accuracy.

Recent advancements in prediction technologies have motivated the power distribution utilities to actively utilize forecasts to minimize the impact of high-impact low frequency (HILF) events. Accurate forecasting coupled with past operational experiences potentially enables pre-event control, aiming to reduce the impact on the system in a network with finitely large numbers of automatic and manually operated control devices. Impacts to the critical infrastructure facilities, also known as critical loads (CLs), are minimized when a Section of the network is appropriately deenergized before the event strikes. However, predictions are not perfect, and given limited accuracy with ever-changing weather forecasts, control actions are required to be constantly updated until the event has passed. Once the events are predicted with high enough confidence and the necessary reconfiguration strategy has been identified, the operator must configure the manually operable switches significantly ahead of event landfall to ensure the operational crews’ safe return from potentially hazardous zones. Given limited resource availability and requisite manual switching operations, some of the loads would also have to be deenergized. Overall resiliency of the system will be improved if the loads remain connected through remotely operable switches until minutes before the event makes landfall. The proposed two-stage control framework with a necessary mathematical formulation facilitates the same. Furthermore, the proposed framework enables continuous corrective action based on available lead time. The developed proactive control framework is demonstrated using a modified IEEE 123-Node system model and a real-world isolated $\mu $ -grid based on the Cordova Electric Cooperative system with superior performance results.

Short-term voltage stability of power systems is governed by load dynamics, especially the proportion of small induction motors prevalent in residential air-conditioners. It is essential to efficiently monitor short-term voltage stability in real-time by detailed data analytics on voltage measurements acquired from phasor measurement units (PMUs). It is likewise critical to identify the location of faults resulting in short-term voltage stability issues for effective remedial actions. This paper proposes a time-series deep learning framework using 1D-convolutional neural networks (1D-CNN) for real-time short-term voltage stability assessment (STVSA), which relies on a limited number of phasor measurement units (PMU) voltage samples. A two-stage STVSA application is proposed. The first stage comprises a 1D-CNN-based fast voltage collapse detector. The second stage comprises of 1D-CNN-based regressor to quantify the severity of the short-term voltage stability event. Two novel indices are presented, and their predicted future values are used to quantify the severity of short-term voltage stability events. This work also considers DB-SCAN clustering-based fault detection and physics-based fault localization for effective short-term voltage stability assessment and remedial actions by identifying the most critical PMUs. A bad data pre-processing technique is also included to mitigate the impact of missing data and outliers on short-term voltage stability assessment accuracy. The proposed framework is validated using the standard IEEE test systems and compared against other machine learning models to demonstrate the superiority of 1D-CNN-based time-series deep learning for short-term voltage stability assessment.

To guarantee the reliable power supply, the expected operation of all the components in the power system is critical. Distance protection system is primarily responsible of isolating the faulty section from the healthy part of the grid. Failure in protection devices can result in multiple conflicting alarms at the power grid operation center and complex events analysis to manually find the root cause of the observed system state. If not handled in time, it may lead to the propagation of the faults/failures to the adjacent transmission lines and components. With availability of the synchronized measurements from phasor measurement units (PMUs), real-time system monitoring and automated failure diagnosis is feasible. With multiple adverse events and possible data anomalies, the complexity of the problem will be escalated. In this paper, a PMU based algorithm is presented and discussed to detect the root cause of the failure in transmission protection system based on the observed state, e.g. multiple line tripping, breaker failures. The failure diagnosis algorithm is further enhanced to come up with the fully functional version of the failure diagnosis tool, which is tailored for the cases in which the PMU anomalies are present. In the developed algorithm the validity of the PMU data is critical; however, such causes as communication errors or cyber-attacks might lead to the PMU data anomalies. This issue is well-addressed in this paper and some major types of anomaly detection methods suitable for PMU data are discussed. Results show that the ensemble approach has some distinct advantages in data anomaly detection compared to the previously used standalone algorithms. Additionally, the enhanced failure diagnosis method is developed to clean the inaccurate data in case of the anomaly in measured voltage magnitudes. Finally, both original and enhanced versions of the tool are tested on 96-bus test system using the real-time OPAL-RT simulator. The results show the accuracy of the enhanced tool and its advantages over the primary version of the tool.

Abstract A key focus recently has been in assessing the risk of a coordinated cyber-physical attack and minimizing the impact of a successful attack. Most of the cyber-attackers will have limited system information and conventional power grid N − 1 security analysis cannot be extended to assess the risk. Centrality measures are widely used in the network science and an attacker with incomplete information can use it to identify power system vulnerabilities by defining the system as a complex network but without real-time system measurements. This paper presents a graph theory based centrality indices for vulnerability assessment of the power system due to various bus and branch contingencies using limited system information and provides a preliminary defense mechanism to prevent such an attack. Proposed work answers the fundamental question of possible attack scenarios by balancing risk (limited information with low risk to get caught or high risk attack to access more system information) and impact (identifying contingencies with maximal impact on system operation). Statistical comparisons are made between the graph theory measures compared to the corresponding DC power flow based N − X linear sensitivity measures. A unified N − X centrality based performance index is proposed and validated against the AC power flow based performance index by doing the real-time simulations of an N − 3 attack scenario. Defensive mechanisms using topology-based performance indices are also presented.

With the push for higher efficiency and reliability, an increasing number of intelligent electronic devices (IEDs) and associated information and communication technology (ICT) are integrated into the Internet of Things (IoT)-enabled smart grid. These advanced technologies and IEDs also bring potential vulnerabilities to the intelligent cyber–physical smart grid. State estimation, as a primary step of system monitoring and situational awareness, is a potential target for attackers. A number of other smart grid applications, such as voltage stability assessment and contingency screening, utilize state estimation results as input data. False data injection (FDI) is a specific way to attack state estimation by manipulating input data. Existing research mainly focuses on the mathematical analysis of FDI attacks; however, in these methods, discussions of reachability requirements to compromise measurements considering cyberinfrastructure are limited. Reachability is defined as a measure that estimates the number of hosts to compromise for the possible FDI. Most of the existing FDI attack methods require the simultaneous manipulation on multiple measurement devices in different substations, in order to bypass the bad data detection, which may be impractical. In this paper, a new type of reachability-based FDI attack considering the cybernetwork with a practical attack is proposed and validated on two IEEE test systems. The corresponding defence mechanisms are (a) decentralized state estimation (DSE), (b) DSE with additional backup computational nodes, (c) communication network rerouting, and (d) intrusion detection system, and they were developed and presented with validation for two IEEE test systems with superior performance for an IoT-enabled intelligent smart grid system.

Distribution system resilience is an emerging topic of interest given an increasing number of extreme events and adverse impacts on the power grid (e.g. Hurricane Maria and Ukraine cyber-attack). The concept of resilience poses serious challenges to the power system research community given varied definitions and multivariate factors affecting resilience. The ability of nature or malicious actors to disrupt critical services is a real threat to the life of our citizens, national assets and the security of a nation. Many examples of such events have been documented over the years. Promising research in this area has been in progress focused on the quantification and in enabling resilience of the distribution system. The objective of this study is to provide a detailed overview of distribution system resilience, the classification, assessment, metrics for measuring resilience, possible methods for enabling resilience, and the associated challenges. A new multi-dimensional and multi-temporal resilience assessment framework is introduced along with a research roadmap outlining the future of resilience to help the reader conceptualise the theories and research gaps in the area of distribution system cyber-physical resilience.