Ahmed S. Musleh

In the evolving landscape of critical infrastructure security, the increasing sophistication of cyber threats poses challenges, particularly to the power grid’s new technology infrastructure. Despite traditionally being considered passive elements, power transformers, as critical components of the grid, are now equipped with smart technologies and intelligent monitoring systems. These enhancements, while beneficial for asset management, inadvertently increase their vulnerability to cyber threats. Existing literature predominantly focuses on the cybersecurity of smart grids, mainly utilizing model-based detection methods. Yet, there’s a noticeable gap in research on the cybersecurity of critical power components, notably power transformers, within the network. Our paper addresses this oversight by pioneering the examination of cybersecurity challenges presented by smart transformers. We introduce a comprehensive architecture and a well-formed taxonomy of cyber-physical attacks relevant to smart transformers. We present a detailed analysis of smart transformers’ topology, identifying vulnerable components and illustrating the ramifications of cybersecurity breaches. Finally, we discuss potential future cybersecurity challenges facing smart transformers. Our findings underscore the need for integrating advanced cybersecurity measures into the development of online condition monitoring systems, safeguarding power transformers against evolving cyber threats while paving the way for more resilient power grids.

The successful integration of renewable energy resources into the power grid hinges on the development of energy storage technologies that are both cost-effective and reliable. These storage technologies, capable of storing energy for durations longer than 10 hours, play a crucial role in mitigating the variability inherent in wind and solar-dominant power systems. To shed light on this matter, a transparent, least-cost macro energy model with user-defined constraints has been utilized for a case study of California. The model addresses all included technologies, solving for both hourly dispatch and installed capacities. Real-world historical demand and hourly weather data have been utilized to do this analysis. A novel approach has been introduced to assess the significance of long-duration energy storage technologies (LDS) in terms of their energy and power capacity. This method explores the contributions of pumped hydropower storage (PHS), compressed air energy storage (CAES), and power-to-gas-to-power (PGP) storage toward minimizing the overall balance of system cost. Historical electricity demand, hourly weather data, and current technology costs are used to investigate high-level implications for California’s power system options. Increasing the storage capacity of each technology from 1 to 10 hours results in 29.6%, 14.4%, and 7.5% cost reduction for PHS, CAES, and PGP cases respectively. However, in studied simulations, maximum availability (maximum) of pumped hydropower storage reduces the balance of system costs by 72.3% followed by CAES (60.6%) and PGP (48.6%) and suggests that pumped hydropower storage in combination with CAES/PGP could play an important role in California’s electricity system, provided that suitable sites can be identified and constructed at reasonable costs.

The increasing adoption of solar power globally presents an environmentally sustainable alternative to traditional energy sources. However, integrating solar distributed generation (DG) systems into the power grid introduces complexities that can impact the grid’s reliability and security. While information and communication technologies have significantly improved the management and control of solar DG systems, they concurrently introduce new cybersecurity vulnerabilities. This study explores the security and stability challenges introduced by solar DG systems, with a specific focus on the vulnerabilities in commercial solar inverters. Additionally, it assesses the operational and economic consequences of cybersecurity incidents directed at commercial solar inverters within the energy sector, with a particular emphasis on the Australian grid. Given the vulnerabilities and potential impacts identified, the paper proposes recommendations to enhance the cybersecurity posture of solar DG systems. The primary aim of this article is to enrich the understanding and mitigation strategies for cybersecurity risks associated with solar inverters, thereby ensuring the power grid’s reliable and secure operation. This contribution is critical for stakeholders across the energy sector as it underscores the need for robust cybersecurity measures in the face of increasing reliance on solar DG systems.

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs.

False data injection attack (FDIA) is a major threat in wide-area monitoring systems. Being able to differentiate FDIA from normal grid contingencies is a paramount necessity for a grid operator to decide the correct response on a critical prompt basis as well as reduce the overall FDIA's false alarms. Two FDIA's characterization algorithms are developed in this paper. The first is based on the principal component analysis (PCA) while the second is based on the canonical correlation analysis (CCA). Both algorithms are developed in an online platform to reduce the computational complexity. The various designed test cases demonstrate a promising FDIA characterization performance utilizing both algorithms. The testing results of three machine learning-based classifiers indicate that the proposed FDIA's characterization algorithms provide better classification models than conventional PCA-based characterization algorithm with CCA illustrating advanced characterization and detection results.

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.