Power System Security Research Articles

Power Transformer On-Load Capacity-Regulating Control and Optimization Based on Load Forecasting and Hesitant Fuzzy Control

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer.

Power Network Vulnerability Assessment Based on Electric Power System Cyber Security Simulation Platform

Abstract This paper introduces a modeling approach for the integration of electric power and information networks, along with the creation of an electric power system network simulation platform. The system, achieved through the deep integration of computation, communication, and control technologies, offers new insights into power grid intelligence. The paper discusses the concept of power system vulnerability and presents a modeling scheme that effectively simulates and analyzes power systems. Using the Analytic Hierarchy Process (AHP), the impact of cyber attacks under different load conditions is evaluated, revealing that attacks have more severe consequences under heavy loads. In conclusion, this research contributes to enhancing the security and stability of power systems.

Preface

With the continuous growth of energy demand and the increasing awareness of environmental protection in countries around the world, it is urgent and imperative to establish a green energy innovation research and development, as well as promotion and application system, and electricity will play a huge role in the development of green energy. Focusing on the latest research development and progress in energy and power, 2024 3rd International Conference on Green Energy and Power Systems (ICGEPS 2024) was held via hybrid form in Harbin, China from January 19th to 21st, 2024. ICGEPS 2024 was created to build a high-quality academic forum to exchange and share successful experiences of innovation in the fields of green energy and power systems, and establish an effective cooperation platform for researchers and scholars from universities, research institutes and enterprises all over the world. Focusing on such topics as Clean Energy Technology, Energy Storage Technology, Renewable Energy Development Technology, Power System Reliability and Security, Inverter and Converter Technology, Intelligent Systems and Methods, etc., this Conference invited scientists, engineers, practitioners and students worldwide to exchange their current technical application experience and research results, so as to promote the development of relevant fields and industries. The Conference took place for three days, among which day two could be deemed the most critical. First is keynote speech part performed by renown experts and next oral presentation mainly by young scholars. Poster presentation was also held for researchers to present their research achievements. Discussion and Question and Answer happened throughout the meeting. In the Conference, several professors acting as keynote speakers shared their research experiences and insights with us. The speakers include Prof. Dr. Mingfei Ban (Northeast Forestry University, China), Prof. Dr. Mohd Shakir Md Saat (Universiti Teknikal Malaysia Melaka, Malaysia), Prof. Dr. Zhongkai Yi (Harbin Institute of Technology, China), and Prof. Dr. M. Prabhakar (Vellore Institute of Technology, Chennai, India). They analyzed the technical principles, operational characteristics, optimization methods, as well as challenges and solutions in practical applications of green energy and power systems from different perspectives. These studies not only bear theoretical depth, but also closely integrate with practical applications, which is of great significance for promoting the development of green energy and power systems. We would like to thank all the authors for their hard work and wonderful contributions, and all the experts and scholars for their attention and support to this Conference. We believe that with our joint efforts, research and application in the fields of green energy and power systems will achieve more remarkable results and make greater contributions to the cause of sustainable development of mankind. Finally, we look forward to meeting again in the next International Conference on Green Energy and Power Systems to discuss the future development path of green energy and power systems. The Committee of ICGEPS 2024 List of Committee Member is available in this pdf.

Security Event-Trigger-Based Distributed Energy Management Of Cyber-Physical Isolated Power System With Considering Nonsmooth Effects.

Due to cyber-physical fusion and nonsmooth characteristics of energy management, this article proposes a security event-trigger-based distributed approach to address these issues with developed smoothing technique. To tackle with nonconvex and nondifferentiable issue, a randomized gradient-free-based successive convex approximation is developed to smooth economic objective function. Due to resilience ability against security issue, a security event-triggered mechanism-based distributed energy management is proposed to optimize social welfare, which coordinately controls both power generators and load demand. The security event-triggered mechanism is designed to reduce power system security risks, and relieve communication burden caused by smoothing calculation, the convergence of proposed distributed algorithm is also properly proved. According to those obtained results on both IEEE 9-bus and IEEE 39-bus systems, it reveals that the proposed approach can achieve good convergence performance and have less security risks than other alternatives, which also proves that the proposed approach can be a viable and promising way for tackling with energy management issue of cyber-physical isolated power system.

Voltage stability index: a review based on analytical method, formulation and comparison in renewable dominated power system

In an interconnected complex power system network voltage stability evaluation is indispensable to guarantee secure power system operation. To further increase the system performance and to make the system safer assessment, the voltage stability improvement is obligatory. In the various literature different voltage security assessment method using the voltage stability index has been presented. Different lists proposed in literature can be utilized to realize the weak buses and weak transmission lines to enacting the countermeasures against issues of voltage insecurity. Additionally, the arrangement and measuring of inexhaustible assets, on the web and disconnected observing of force framework and the measure of load to be shed at whatever point essential. This paper shows a survey on the different voltage stability index from various perspectives. The audit results on various record gives a far and wide logical to perceive the impending works in this field and to choose the best index for variety of applications such as voltage security assessment, renewable energy integration, distributed generation (DG) placement and sizing, online monitoring of the power system, and shedding of load.

Automated federated learning‐based adversarial attack and defence in industrial control systems

AbstractWith the development of deep learning and federated learning (FL), federated intrusion detection systems (IDSs) based on deep learning have played a significant role in securing industrial control systems (ICSs). However, adversarial attacks on ICSs may compromise the ability of deep learning‐based IDSs to accurately detect cyberattacks, leading to serious consequences. Moreover, in the process of generating adversarial samples, the selection of replacement models lacks an effective method, which may not fully expose the vulnerabilities of the models. The authors first propose an automated FL‐based method to generate adversarial samples in ICSs, called AFL‐GAS, which uses the principle of transfer attack and fully considers the importance of replacement models during the process of adversarial sample generation. In the proposed AFL‐GAS method, a lightweight neural architecture search method is developed to find the optimised replacement model composed of a combination of four lightweight basic blocks. Then, to enhance the adversarial robustness, the authors propose a multi‐objective neural architecture search‐based IDS method against adversarial attacks in ICSs, called MoNAS‐IDSAA, by considering both classification performance on regular samples and adversarial robustness simultaneously. The experimental results on three widely used intrusion detection datasets in ICSs, such as secure water treatment (SWaT), Water Distribution, and Power System Attack, demonstrate that the proposed AFL‐GAS method has obvious advantages in evasion rate and lightweight compared with other four methods. Besides, the proposed MoNAS‐IDSAA method not only has a better classification performance, but also has obvious advantages in model adversarial robustness compared with one manually designed federated adversarial learning‐based IDS method.

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

Dual-layer optimization planning for offshore wind farms considering power system security

The vigorous development of offshore wind farms (OWFs) has further promoted the utilization of wind energy. Owing to the demand for low-carbon environmental protection, the increase in the scale of OWF constructions has also had a significant impact on the power system, which cannot be ignored. Specifically, the uncertainty and volatility of wind-power generation should be considered in power system optimization planning schemes. Therefore, this study proposes a probabilistic power-flow analysis index construction method that employs the static-security region theory to optimize the OWFs–grid-connection topology, including the number, location, and capacity of the public connection points connected to the onshore power grid. This method can be used to calculate the probability indicators of power grid insecurity for OWFs–grid-connected systems with different network topologies. Furthermore, a dual-layer multi-objective programming model that considers the economics and stability for integrated planning of electrical collector and multiterminal direct current transmission systems is established. The proposed model comprises two inner-layer models and one outer-layer model. The inner-layer models are used to construct the collection and transmission topologies of the OWFs–grid-connected system that minimize the probability of grid insecurity and the planning cost of the outer-layer model. Finally, the effectiveness of the proposed OWFs planning model was verified using an improved IEEE 24-bus system that can be used for topology planning of electrical collector and transmission systems while ensuring grid stability.

ENHANCING POWER SYSTEM SECURITY THROUGH RESILIENT FREQUENCY REGULATION IN THE FACE OF HYBRID CYBER-ATTACKS

The growing reliance on modern communication technologies in power systems for Frequency Regulation (FR) introduces vulnerabilities to cyberattacks, posing significant threats to system stability and reliability. These attacks can disrupt the coordination among various components, such as sensors, control centers, and actuators, thereby compromising the integrity of FR analysis. In response, this article proposes a resilient solution in the form of a deep-learning-based Attack Detection and Mitigation system. By integrating advanced AI techniques, this system aims to fortify the security of FR operations within the cyber-physical framework, swiftly identifying and neutralizing cyber threats. Ultimately, this approach ensures the continuous and reliable operation of power systems, mitigating the risks posed by hybrid cyberattacks and safeguarding critical infrastructure. The proposed system represents a proactive approach to mitigating the escalating risks associated with cyberattacks targeting FR in power systems. Through its deep-learning algorithms, the system can dynamically adapt to emerging threats, enhancing the resilience of FR analysis against malicious intrusions. By bolstering security measures within the cyber-physical model, the system minimizes the potential impact of cyberattacks on power system stability and reliability. Moreover, its ability to detect and mitigate threats in real-time ensures uninterrupted operation, thereby safeguarding the functionality of power systems even amidst the evolving landscape of hybrid cyber threats. This resilient solution represents a crucial step towards fortifying power system security and maintaining essential services in the face of adversarial cyber activities. Keywords: Attack detection and mitigation (ADM) system, Frequency Regulation(FR), hybrid power system, Renewable Energy Sources (RESs), resiliency.

A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.

Power Load Forecast Based on CS-LSTM Neural Network

Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM) neural network. Load data are specific data with time series characteristics and periodicity, and the LSTM algorithm can control the information added or discarded through the forgetting gate, so as to realize the function of forgetting or memorizing. Therefore, the use of the LSTM algorithm for load forecast is more effective. The CS algorithm can perform global search better and does not easily fall into local optima. The CS-LSTM forecasting model, where CS algorithm is used to optimize the hyper-parameters of the LSTM model, has a better forecasting effect and is more feasible. Simulation results show that the CS-LSTM model has higher forecasting accuracy than the standard LSTM model, the PSO-LSTM model, and the GA-LSTM model.

A Resilient Frequency Regulation for Enhancing Power System Security Against Hybrid Cyber-Attacks

A Resilient Frequency Regulation for Enhancing Power System Security Against Hybrid Cyber-Attacks

Critical Infrastructures at Risk Addressing Information Security in Power Systems

According to the power system network security risks and problems, this paper presents the work of information security and the general principles of information security from a technical and safety management put forward the idea of addressing security issues and methods of analysis of local area network security management related to issues, and according to their own experience of a relatively effective way of thinking.

A Survey of MPSoC Management toward Self-Awareness.

Managing Multi-Processor Systems-on-Chip (MPSoCs) is becoming increasingly complex as demands for advanced capabilities rise. This complexity is due to the involvement of more processing elements and resources, leading to a higher degree of heterogeneity throughout the system. Over time, management schemes have evolved from simple to autonomous systems with continuous control and monitoring of various parameters such as power distribution, thermal events, fault tolerance, and system security. Autonomous management integrates self-awareness into the system, making it aware of its environment, behavior, and objectives. Self-Aware Cyber-Physical Systems-on-Chip (SA-CPSoCs) have emerged as a concept to achieve highly autonomous management. Communication infrastructure is also vital to SoCs, and Software-Defined Networks-on-Chip (SDNoCs) can serve as a base structure for self-aware systems-on-chip. This paper presents a survey of the evolution of MPSoC management over the last two decades, categorizing research works according to their objectives and improvements. It also discusses the characteristics and properties of SA-CPSoCs and explains why SDNoCs are crucial for these systems.

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the Graph Attention Estimation Network (GAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing Graph Convolutional Neural Networks (GCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

Distribution Network Reconfiguration in Effective Presence of DGs, EVCSs and DRP: A Novel Multi-Objective Approach

By determining the ideal combination of switch on/off status, distribution network reconfiguration (DNR), a critical operational problem in distribution systems, has been employed to increase system efficiency. Additionally, benefits such as improved voltage profiles and loss reduction through reactive power compensation are provided by placing capacitors in the distribution network optimally. Thus, this study formulates multi-objective DNR with optimal allocation of distributed generators (DGs), electric vehicles charging stations (EVCSs) and capacitors. Additionally, the demand response programme (DRP) is employed to enhance the distribution system’s operation performance. Losses and voltage variation, which are significant objectives for conventional distribution systems, are typically the focus of the DNR problem. Modern distribution systems’ security concerns caused by DGs that could jeopardize power system security have been all but ignored in terms of the issue of power system operation. As a result, the primary objective of this study is to address the DNR issue in order to increase reliability. This study specifies the energy not provided (ENS) in order to do this. This work uses a modified shuffled frog leaping algorithm (MSFLA), which benefits from a novel mutation technique, to reduce processing time and improve solution quality, in particular to avoid becoming stuck in local optima. To evaluate the efficiency and adaptability of the suggested method, it is applied to three distribution systems, comprising 33, 69, and 86-node test networks. After that, the outcomes are contrasted with those of various approaches. Power loss and ENS are reduced by around 45% and 5.5%, respectively, by executing the suggested strategy on a 33-node test system compared to the initial value before the DNR.

Effects of impulse on prescribed-time synchronization of switching complex networks

The specified convergence time, designated by the user, is highly attractive for many high-demand applications such as industrial robot control, missile guidance, and autonomous vehicles. For the application of neural networks in the field of secure communication and power systems, the importance of prescribed-time synchronization(PTs) and stable performance of the system is more prominent. This paper introduces a prescribed-time controller without the fractional power function and sign function, which can reach synchronization at a prescribed time and greatly reduce the chattering phenomenon of neural networks. Additionally, by constructing synchronizing/desynchronizing impulse sequences, the PTs of switching complex networks(SCN) is achieved with impulse effects, where the time sequences of switching and impulse occurrences in the networks are constrained by the average dwell time. This approach effectively reduces the impact of frequent mode switching on network synchronization, and the synchronization time can be flexibly adjusted within any physically allowable range to accommodate different application requirements. Finally, the effectiveness of the proposed control strategy is demonstrated by two examples.

Low-Frequency and Sub-Synchronous Power Oscillation Suppression and Analysis of Hybrid Line Commutated Converter for HVDC Grid

Building high-proportion renewable energy grid requires economical and reliable High Voltage Direct Current (HVDC) converters. Line Commutated Converter (LCC) is widely used because of its long transmission distance, low power loss, low manufacturing cost, and large transmission capacity, which modular multilevel converter (MMC) cannot achieve, but has commutation failure (CF) problems. Low-frequency and sub-synchronous power oscillation caused by CF will affect the safety of bulk power systems. This paper theoretically analyzes the power oscillation during CF and CF recovery, and then proposes a novel hybrid line commutated converter (H-LCC) based on integrated gate commutated thyristor (IGCT), which can effectively mitigate CF and suppress power oscillation. The principle of H-LCC suppressing power oscillation is analyzed. On this basis, the performance of LCC, MMC and H-LCC under the same fault conditions is compared by a simulation study, which proves that H-LCC can improve the security and stability of high-proportion renewable energy power systems.

A decentralized framework for enhancing security in power systems through blockchain technology and trading system

ABSTRACT The primary objective of implementing secure smart power networks is to reduce the risk of data privacy breaches, including adversarial data poisoning and inference attacks. This study presents a novel approach, namely the Blockchain-based Power System Security Model (BC-PSSM), to augment security measures within power systems. The proposed method utilizes blockchain technology to enhance the security of data storage and verification and the mechanisms for data signing and authentication. It employs decentralized detection to improve the effectiveness and precision of intrusion detection with a Support Vector Machine (SVM). The BC-PSSM method exhibits promising outcomes in multiple facets of power system security detection, as evidenced by thorough simulations and evaluations. The simulation results demonstrate a significant decrease in computational burden and energy consumption by maintaining a high energy output level. The BC-PSSM algorithm performs remarkably in detection and false positive rates, resulting in heightened security and enhanced efficiency. The results also illustrate the positive impact of BC-PSSM in enhancing the security of power systems, offering robust protection against cybersecurity threats. BC-PSSM aims to enhance the security of power systems, thereby promoting a more robust and dependable energy infrastructure. This initiative plays a crucial role in protecting essential assets and mitigating the potential threats posed by cyber-attacks. The BC-PSSM algorithm enhanced efficiency to 92.15% and boosted overall security to 96.34%. It achieved this by achieving an amazing identification rate of 96.75% and an extraordinarily low false positive rate of 1.82%.

Enhancing cybersecurity in smart grids: Deep black box adversarial attacks and quantum voting ensemble models for blockchain privacy-preserving storage

Smart grids are getting important in today’s power management, so with that, smart grid technologies are increasingly important too. There have been a lot of concerns about smart grid technologies being hacked, and as a result, some deep black box adversarial attacks have been conducted and presented. We propose a new experimental methodology for benchmarking smart grid security with black box attacks. Additionally, concerning the type of smart grids, Smart Power Grids, deep black box adversarial attacks which can be crafted using virtually no knowledge about the target due to the inherent complexity of content available in cryptographic libraries like SecLib or Bouncy Castle how it affects security of cyber-physical power systems. We identify potential impacts of deep black box attacks on Smart Power Grids as implemented by the Department of Energy in 1996, we evaluate existing protection methods, and we find out the pitfalls thereof. With the aim of overcoming the aforementioned drawbacks, we initiate a study on deep black box adversarial attacks against Smart Power Grids showing that statistically significant effects against a national Smart Power Grid are achievable with absolute security. We also probe detection of cyber security attacks on Smart Power Grids. We illustrate landscape of smart grids with numerous cyber threats and demonstrate the limitations of traditional security practices. We show the importance of machine learning to detect attacks and the unlikelihood of identification of dependable and efficient detection schemes. We describe quantum voting ensemble models as one of the most powerful techniques in the detection of cyber security attacks. Finally, we propose an experimental setup and evaluation criteria to detect cyber security attacks in smart grids using quantum voting ensemble models. Then, we talk about private data storage in blockchain based smart grid infrastructure. We give an introduction of block chain and its essentiality in smart grids. We discuss privacy issues in block chain based smart grids. We acknowledge the strength of privacy safeguards, but on the same wavelength, we realize their weaknesses. Next, we propose a quantum resistant encryption technique that enhances the privacy of smart grids. We propose quantum voting ensemble models as one of the most promising techniques to address the issue of private data storage in block chains. As a result, we provide a comparison between the proposed models and traditional approaches to privacy protection in smart grids based on an experimental performance review. Then, we propose a unified strategy to improve smart grid cyber security by incorporating deep black box attacks with quantum voting ensemble models. Finally, we disclose several benefits of such integration and perform an experimental evaluation to investigate the effectiveness of the unified approach. The results of our study identify security gaps in smart grids and propose state-of-the-art mechanisms to address them. The challenges of smart grids system require the amalgamation of blockchain, quantum voting ensemble models and deep black box adversarial attacks. We achieve this objective proposing a unified strategy. The results of this study will equally be helpful for future research and smart grid cyber security implementations.