Transforming cyber security in modern power grids: the synergistic application of complex recurrent spectral network to address vulnerabilities and ensure resilience

With the increasing scale of modern cross regional interconnected power grid, a large number of renewable energy is connected to the grid, and the security and stability issues of the power grid is becoming more and more prominent. Meanwhile, the majority industrial users have higher and higher requirements for the reliability of power supply. So it is necessary to build a large-scale power stability control system to ensure the secure and stable operation of the power grid. Because the conventional communication technology of the power stability control system is difficult to meet the requirements of a large number of intelligent devices and high-speed data processing, The key technologies of Synchronous Transfer Module-l (STM-I) high-speed large capacity transmission, multi control terminals aggregation transmission, large-scale wireless access, communication between substations based on IEC 61850 are proposed, to solve the technical problems caused by the characteristics of large-scale power stability control system of modern power grid, such as huge amount of control stations, wide region for control and complexity of control objects.

Chapter 18 - Grid connected converters in power grids systems: a systematic review on impacts and challenges

The reliable and effective operation of modern power grids is highly dependent on accurately adjusting the control system parameters of power converters. Traditional approaches to parameter tuning often depend on analytical models and offline optimization, which may not fully describe the intricate dynamics and nonlinearities seen in real-world modern power grids. This paper presents an innovative method for intelligently adjusting the control system settings of converters in a modern power grid. The proposed approach utilizes machine learning methods, particularly robust artificial neural networks, to tune the converter control parameters and improve the overall modern power grid performance. This intelligent tuning system can obtain ideal parameters for stable, economical, and resilient modern power grid operation under different operating circumstances and disturbances by training the neural network models robustly using detailed simulation data and real-time measurements. This study provides a comprehensive description of the intricate structure of the intelligent tuning framework, including the neural network models and the robust methods. The proposed approach’s usefulness in enhancing the modern power grid frequency control, active power regulation, and transient response is validated via comprehensive case studies in comparison to existing parameter tuning approaches. The performed simulation and laboratory real-time experiments indicate that the smart tuning system is adaptable and resilient, making it a potential alternative for improving the stability and performance of modern power grids.

The synchronous condenser nowadays contributes an essential share of resources for generating reactive power to ensure voltage stability in modern power systems. Such resource has to be taken care of by owners of electricity infrastructures. In any case, real projects, experiments and simulation results have shown need to use the synchronous condenser for enhancing power quality of the grid. Hence, traditional generators are being retrofitted to synchronous condensers in order for them to serve a better purpose of voltage stabilization after they are retired and new synchronous condensers are installed by electricity utility managers to serve same purpose too. This paper presents the synchronous condenser technology. It discusses the experience and lessons learnt from the use of the synchronous condenser in real projects. It also provides an outlook on the development of the use of the technology in modern power grid using two simulation study scenarios. These developments include Scenario One: utilizing only the synchronous condenser for voltage regulation on a power grid. And Scenario Two: Installing the synchronous condensers with Type-3 wind farm for voltage support on an electricity network, such contextualization is towards voltage stability in modern power grids.

Modern power grid has a fundamental role in the operation of smart cities. However, high impact low probability extreme events bring severe challenges to the security of urban power grid. With an increasing focus on these threats, the resilience of urban power grid has become a prior topic for a modern smart city. A resilient power grid can resist, adapt to, and timely recover from disruptions. It has four characteristics, namely anticipation, absorption, adaptation, and recovery. This paper aims to systematically investigate the development of resilient power grid for smart city. Firstly, this paper makes a review on the high impact low probability extreme events categories that influence power grid, which can be divided into extreme weather and natural disaster, human-made malicious attacks, and social crisis. Then, resilience evaluation frameworks and quantification metrics are discussed. In addition, various existing resilience enhancement strategies, which are based on microgrids, active distribution networks, integrated and multi energy systems, distributed energy resources and flexible resources, cyber-physical systems, and some resilience enhancement methods, including probabilistic forecasting and analysis, artificial intelligence driven methods, and other cutting-edge technologies are summarized. Finally, this paper presents some further possible directions and developments for urban power grid resilience research, which focus on power-electronized urban distribution network, flexible distributed resource aggregation, cyber-physical-social systems, multi-energy systems, intelligent electrical transportation and artificial intelligence and Big Data technology.

Solar generation resources are being deployed at an unprecedented rate in modern power grids. The unpredictability of solar energy demands systematic investigation of impact of solar penetration on power system operation and planning. Modern power grids have tackled solar uncertainty by building solar farms in locations with consistent solar irradiance, and less successfully by solar forecasting. Solar forecasting has remained a challenging process in modern power grids. This paper aims to understand the impact that solar forecasting can have on power system operation and planning. A variety of probabilistic models that can estimate solar irradiance and identify their performance using real-world data are investigated. Results presented in this paper show that the heavily used normal or log-normal distributions are not suited for solar irradiance prediction. These probabilistic estimates are utilized to compute probabilistic power flows (PPF). PPF result show how sensitive calculated line flows are to forecast method, in particular where lines are closer to photovoltaic installations. Also by using the PPF of accurate forecast, we identify the conditions under which the modern power grid can operate reliability or in need of mitigations.

Cyber-physical security of the smart grid obtains increasing attention. Critical transmission lines have a major impact on large-scale cascading failures in modern power grids. In this paper, we proposed a predicting model of cascading failures based on the fault chain theory and Fuzzy C-Means. The development process of cascading failures in a power grid is described based on the fault chain and dynamic fault tree theories. Loading rate, coupling relationship of sequent tripped lines and power flow variation are considered in the proposed method to predictive the fault chains comprehensively. A novel branch vulnerability assessment method is proposed based on the fault chain and dynamic fault tree theories to identify critical branches which have a significant impact on cascading failure expansion. The effectiveness of the proposed assessment model is tested based on IEEE 39-bus system.

With the trend of constructing Internet protocol (IP)-based systems, modern power grids are involving into integrated networks made up of cyber and physical infrastructure with the goal of improving stability, reliability, and efficiency. Cyber technology is the backbone of modern power grid operation, yet vulnerabilities in the cyber network can introduce cyber-enabled disruption of physical components, which may lead catastrophic outcomes. Thus, cyber-physical equipment assessment is needed for modern power grids to better prepare against unexpected contingencies. In this paper, the digital relay is representative of cyber-physical equipment in power grids since it is a connector between the cyber network and the physical infrastructure. This paper presents two methods to evaluate cyber-physical risk of all digital relays in a power system. These methods are based on cyber-physical architecture and critical clearing time respectively. The analysis is conducted on an 8-substation model with its cyber network and cyber-physical architecture. The ranks of each digital relay provide useful information for situational awareness in modern power grids. An online framework that evaluates cyber-physical assets in power systems from these two perspectives is presented.

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

Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.

In the developing countries electrical energy is very important for its all-round improvement by saving thousands of dollars and investing them in other sector for development. For Growing needs of power existing hierarchical, centrally controlled grid of the 20th Century is not sufficient. To produce and utilize effective power supply for industries or people we should have Smarter Electrical grids that address the challenges of the existing power grid. The Smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure along with modern IT services, sensing and metering technologies, and modern energy management techniques based on the optimization of demand, energy and network availability and so on. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues like security, efficiency to communications layer field. In this paper we propose new model for security in Smart Grid Technology that contains Security Module(SM) along with DEM which will enhance security in Grid. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area.

The increasing complexity and demand for reliability in modern power systems necessitate advanced techniques for fault detection, classification, and location. This work presents a comprehensive study on the application of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault management in power systems. ANFIS, combining the benefits of neural networks and fuzzy logic, offers a robust framework for handling the non-linearities and uncertainties inherent in power system faults. The proposed method leverages historical fault data to train the ANFIS model, enabling it to accurately detect, classify, and locate various types of faults, including line-to-ground, line-to-line, and three-phase faults. The model’s performance is evaluated using a simulated power system environment, and its effectiveness is validated through extensive testing under different fault scenarios. Results demonstrate that the ANFIS-based approach achieves high accuracy in fault detection and classification, significantly reducing the response time. Additionally, the system exhibits a strong capability in pinpointing fault locations with minimal error margins. This research underscores the potential of ANFIS as a powerful tool for improving the consistency and competence of fault management in power systems. The findings suggest that integrating ANFIS into existing protection schemes can lead to improved operational efficiency (97–99%), whereas in case of ANN, the efficiency is (92–95%) resilience and reduced downtime. Future work will focus on real-time implementation and the incorporation of ANFIS with other smart grid technologies to further augment fault management capabilities.

This paper provides a comprehensive review of dynamic control strategies for FACTS (Flexible AC Transmission Systems) devices, aligning with the “Dynamic Control Strategies for FACTS Devices in Modern Power Grids.” It addresses the critical role of FACTS in managing modern power grids, focusing on their ability to enhance power quality, stabilize voltage, and improve energy efficiency. As power systems evolve to incorporate renewable and distributed energy sources, the challenges of ensuring reliable and stable grid operations become increasingly complex. The review discusses how FACTS technology and dynamic control strategies are vital components in addressing these challenges. By regulating voltage profiles and power system stability, FACTS devices contribute to the efficient integration of renewable energy sources, aligning with the overarching theme of the review article. In essence, this review sets the stage for a deeper exploration of the dynamic control strategies that underpin FACTS devices’ contributions to the modern power grid’s reliability and efficiency, emphasizing their relevance and significance in the evolving energy landscape.

Chapter five - Wide-area monitoring and anomaly analysis based on synchrophasor measurement

The integration of cybersecurity measures, specifically Intrusion Detection Systems (IDS), into grid modernization initiatives is essential to protect critical energy infrastructures from evolving cyber threats. This paper presents a conceptual model for embedding cybersecurity frameworks into the modernization of power grids, focusing on the seamless integration of IDS within the grid architecture. With the increasing digitization of energy systems, traditional grids face heightened vulnerabilities, making robust cybersecurity strategies a priority. Through an in-depth review of existing cybersecurity frameworks and IDS models, this paper identifies the gaps in current integration approaches and proposes a layered security architecture that integrates AI and real-time monitoring for proactive threat detection. The proposed model also emphasizes scalability, interoperability, and compliance with regulatory frameworks. In addition to offering a phased implementation roadmap, the paper discusses the strategic, regulatory, and institutional challenges associated with integrating advanced cybersecurity systems into grid infrastructure. Furthermore, it explores future research areas, including quantum-safe cryptography, autonomous threat responses, and the potential use of blockchain for secure grid transactions. By addressing both the theoretical foundations and practical aspects of cybersecurity in grid modernization, this paper contributes to the development of more secure, resilient, and future-proof energy grids.