Grid Security Research Articles

Detection of False Data Injection Attacks on Smart Grids Based on A-BiTG Approach

A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection of FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based methods do not fully exploit the short-term local characteristics and long-term dependencies of power grid data and have poor correlation with past and future time series information, resulting in a lack of credibility in the detection results. In view of this, an FDIA detection model combining a bidirectional temporal convolutional network and bidirectional gated recurrent unit with an attention mechanism (A-BiTG) was proposed. The proposed model utilizes a bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) to consider past and future temporal information in the grid. This enhances the ability of the model to capture long-term dependencies and extract features, while also solving the model’s problem of exploding and vanishing gradients. In addition, an attention mechanism (AM) was added to dynamically assign weights to the extracted feature information and retain the most valuable features to improve the detection accuracy of the model. Finally, the proposed method was compared with existing methods on the IEEE 14-bus and IEEE 118-bus test systems. The results show that the proposed detection model is more robust and superior under different noise environments and FDIA signals with different intensities.

An improved lightweight and privacy preserving authentication scheme for smart grid communication

Smart grid is an efficient and reliable technical framework for controlling computers and automation equipment, and how to ensure the communication security in smart grid is an important issue. Cryptographic authentication scheme is a feasible solution, and the existing authentication schemes for smart grid seek to ensure better performance. Some existing authentication schemes lack comprehensive security considerations and have security or privacy vulnerabilities, which makes them vulnerable to specific attacks. The paper reviews a recent scheme ISG-SLAS (Yu and Park, 2022) and analyzes its potential insecure aspects in detail, including unable to resist ESL attack, cannot provide un-traceability and etc. To this end, the paper designs an improved authentication scheme for smart grid based on symmetric cryptography. Through informal security analyses and formal security analyses with real-or-random (ROR) model and Scyther platform, the security of the proposed scheme is demonstrated. From the perspective of performance, the proposed scheme is compared with ten advanced authentication schemes for smart grid, and the results show that the proposed scheme excels other recent schemes in computational overhead, communication overhead and storage overhead, reduced by 10.1%, 30.8% and 36.1% of ISG-SLAS and 58.9%, 64.1% and 24.3% of the average value of all alternatives respectively.

Novel risk index integrating practical operation limits enhances probabilistic contingency ranking for large-scale photovoltaic plant planning

This research addresses the pressing need for heightened grid security amid increasing uncertainties in photovoltaic PV generation. The research problem lies in the limitations of conventional contingency analysis metrics, failing to adequately consider both contingency occurrences and uncertainties inherent in PV generation. In response, a comprehensive algorithm is proposed that introduces a novel severity function framework, enhancing traditional contingency ranking metrics. This approach incorporates grid remedial actions and refines line and bus voltage classification by considering available correction time, aiming to offer a more robust security assessment. Motivated by the imperative to address uncertainty in PV generation, the proposed work builds on established analysis tools. A probabilistic load flow algorithm manages PV generation uncertainties, utilizing historical data for contingency incidence uncertainty. Additionally, a probabilistic model for PV plants integrates historical solar data, deriving hourly probability density functions to meet grid code requirements, including reactive power considerations. The justification for this work lies in the algorithm's demonstrated efficacy, validated on the IEEE 14-bus network. Results highlight its ability to identify risks associated with line overloading and bus voltage breaches. Comparative evaluations underscore proper coupling buses for security, favoring distributed capacity to mitigate line overloading risks. The study's key results emphasize voltage risk amplification with reactive power omission, stressing the significance of compensation strategies. This research addresses a critical problem, presenting a comprehensive algorithmic solution to enhance grid security amidst uncertainties in PV integration. Findings offer valuable insights for strategically interaction between large scale PV plants and electrical grid, contributing to an improved grid security paradigm in a dynamic and uncertain energy model.

Fault prevention strategy for coupled networks of power grids and transportation with the participation of electric vehicles

The increasing number of electric vehicles (EVs) that are connected to the power grid has raised concerns about the security of both transportation and power grids. This paper presents a fault prevention strategy that involves the use of social networks to improve the performance of electric vehicle charging stations. Firstly, the goal of this strategy is to improve the electric vehicle charging station’s performance by developing an evolutionary game model that takes into account the various factors that affect the electric vehicle’s response and travel time. Secondly, a number of charging station sample data is generated and analyzed using a convolutional neural framework. The capacity of the stations is then calculated by taking into account the different incentive measures offered by the power grid.. Furthermore, an optimal power flow model considering fault risk constraints and charging and discharging capacity constraints is established in the power grid or power grid subsystem to adjust the generator output and charging station power to realize the safe dispatching of the power grid. The simulation example shows that this method can fully consider the influence of traffic networks in the participation of EVs in power grid fault prevention, which is more reasonable.

Probabilistic Sizing of Energy Storage Systems for Reliability and Frequency Security in Wind-Rich Power Grids

Probabilistic Sizing of Energy Storage Systems for Reliability and Frequency Security in Wind-Rich Power Grids

Impact of renewable energies on the electromechanical phenomena of electricity grids

At present, as in any industrial process, the generation of electricity is obliged to reduce polluting emissions into the environment. Therefore, the vast majority of governments are promoting the use of renewable generation sources mainly based on solar and wind energy. However, due to the low inertia characteristic of this type of electricity generation, the security of the grids is compromised, since in the presence of a severe disturbance there can be uncontrolled disconnections of generation plants, consumption centers and eventually the total collapse of the electrical system (blackout). Therefore, the present research work presents simulations in the time domain to graphically analyze the degradation of the security of electricity grids before the incorporation of renewable generation plants. To achieve this objective, a 3-generator, 9-node test system was used from the literature.

A Method for Identifying External Short-Circuit Faults in Power Transformers Based on Support Vector Machines

Being a vital component of electrical power systems, transformers significantly influence the system stability and reliability of power supplies. Damage to transformers may lead to significant economic losses. The efficient identification of transformer faults holds paramount importance for the stability and security of power grids. The existing methods for identifying transformer faults include oil chromatography analysis, temperature assessment, frequency response analysis, vibration characteristic examination, and leakage magnetic field analysis. These methods suffer from limitations such as limited sensitivity, complexity in operation, and a high demand for specialized skills. In this paper, we propose a method to identify external short-circuit faults of power transformers based on fault recording data on short-circuit currents. It involves analyzing the current signals of various windings during faults, extracting appropriate features, and utilizing a classification algorithm based on a support vector machine (SVM) to determine fault types and locations. The influence of different kernel functions on the classification accuracy of SVM is discussed. The results indicate that this method can proficiently identify the type and location of external short-circuit faults in transformers, achieving an accuracy rate of 98.3%.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Research on non-intrusive load decomposition model based on parallel multi-scale attention mechanism and its application in smart grid

This paper presents the MUSENILM model, a non-intrusive load decomposition model incorporating a parallel multi-scale attention mechanism to enhance energy monitoring and management in smart grids. The core innovation of the proposed model is its ability to extract multi-scale features, enhancing the model's understanding of time series data and achieving significant performance improvements on the UK-DALE and REDD public datasets. Specifically, when MUSENILM identifies the fridge electricity consumption pattern on the UK-DALE public dataset, compared to previous models, the accuracy improves from 88% to 91%, and the F1 score increases from 87% to 90%; on the load decomposition tasks of the remaining four appliances, the F1 scores are all improved, while the mean absolute error (MAE) and cumulative absolute error (SAE) for the five appliances are also reduced. Additionally, it shows better results compared to previous models on the REDD dataset. Moreover, when the MUSENILM model is transplanted to embedded devices and applied in smart grids, it can effectively identify illegal lithium battery charging events of electric bicycles in different scenarios, which is crucial for ensuring grid security and optimizing energy distribution. This research not only provides an efficient method for the field of NILM but also offers practical solutions for violation monitoring and management in smart grids.

Evolution and role of virtual power plants: Market strategy with integration of renewable based microgrids

The use of distributed energy resources (DER) programs, which include separate renewable energy (RE) programs, has expanded in residential as well as commercial structures as an outcome of recent technological developments. The virtual power plant (VPP) may improve the security and reliability of an electricity grid's operations through including energy storage, changeable loads, and distributed energy resources (DER), among other characteristics. Consequently, a growing number of scholars tend to focus on VPP and providing recommendations for its improvement. In order to facilitate the incorporation of distributed generation into electricity distribution systems, cutting-edge management ideas for facilities have emerged in the last few years. As a result, it is now more crucial than ever to improve management abilities for the combining of dispersed energy output and consumption using various virtual power plants (VPPs). To increase operational profits, it's crucial to make use of their capacity to play a role in power markets. This work provides a classification scheme for, and an in-depth analysis of, recent studies that provide VPP models featuring interactions with various forms of electricity markets. The objective of this article is to find out which VPP strategy will yield the greatest possible return in each legislative scenario. These consist of the model's design, strategies for resolving complex mathematical problems, involvement in various marketplaces, and the application of the suggested models to real-world case studies. The research has been evaluated, and it has been concluded that contemporary models are more comprehensive and realistic. This article additionally elaborates on and emphasizes the VPP concept from other researchers' findings. Moreover, a number of prevalent VPP projects from throughout the globe are included. The VPP research also addresses some potential challenges and recommendations for prospective growth.

Smart grid security based on blockchain and smart contract

Smart grid security based on blockchain and smart contract

A BPSO based collaborative management platform for multi-source data security in power grids

By designing a digital power grid multi-source data security collaborative management platform, the system configuration problem of the OMS system and the power grid management platform for the main distribution network of the power grid is solved. A design method for the digital power grid multi-source data security collaborative management platform based on discrete particle swarm optimization algorithm is proposed. Based on the design concept of SOA, realize the overall design framework of the platform according to the design method of multi-layer technical system in the business presentation layer, business process and composition layer, service layer, component layer and resource layer, realize the basic layer design of the system management platform through the basic application platform design scheme of XML configuration, implement the query, processing and output representation of the grid’s multivariate data using B/S architecture protocol, and use the Spring Framework The platform software architecture is implemented using J2EE technology and multi module component design scheme. The discrete particle swarm optimization algorithm is used for the fusion and scheduling of multi-source data in the digital power grid. The interface design and functional construction of the power grid management platform are implemented in the OMS system of the power grid main distribution network, and the logical model of the transformation project is constructed to achieve platform optimization and construction. Tests have shown that the designed digital power grid multi-source data security collaborative management platform has good human-machine interaction, strong data fusion scheduling ability, reduced resource and subsystem coupling, and supports the flexibility of physical deployment and maintenance.

Smart Grids Secured by Dynamic Watermarking: How Secure?

Unconditional security for smart grids is defined. Cryptanalyses of the watermarked security of smart grids indicate that watermarking cannot guarantee unconditional security unless the communication within the grid system is unconditionally secure. The successful attack against the dynamically watermarked smart grid remains valid even with the presence of internal noise from the grid. An open question arises if unconditionally authenticated secure communications within the grid, together with tamper resistance of the critical elements, are satisfactory conditions to provide unconditional security for the grid operation.

A novel temporal–spatial graph neural network for wind power forecasting considering blockage effects

Wind Power Forecasting is crucial for the operational security, stability, and economic efficiency of the power grid, yet it faces significant accuracy challenges due to the variable nature of wind energy and complex interactions within wind farms. This study introduces a novel neural network model specifically designed for Wind Power Forecasting, incorporating both a gated dilated inception network and a graph neural network. This innovative approach enables the concurrent analysis of temporal and spatial features of wind energy, significantly enhancing the forecasting accuracy. A pivotal feature of this model is its unique mechanism to compute the mutual influence between wind turbines, with a particular focus on the blockage effect, a key factor in turbine interactions. The model’s efficacy is validated using a real-world dataset, targeting a 48-hour prediction horizon. The experimental outcomes demonstrate that this model achieves superior performance compared to state-of-the-art methods, with a notable improvement of 6.87% in Root Mean Square Error and 8.77% in Mean Absolute Error. This study not only highlights the model’s enhanced forecasting capabilities but also emphasizes the importance of integrating spatial and temporal dynamics in wind farms for improving Wind Power Forecasting accuracy.

A dynamic mathematical model used for controller design of a supercritical once-through boiler-turbine unit in all load conditions

Recently, widespread adoption of renewable energy significantly impacts the security and stability of power grid. Utilizing coal-fired units in real-time load figuration remains a primary method to ensure grid stability. Therefore, supercritical Once-Through Boiler-Turbine (OTBT) units continuously enhance flexibility to meet the evolving needs of the grid. To broaden the load boundary and achieve fully automated control from unit start-up to rated load, establishing a dynamic mathematical model used for controller design of OTBT units in all load conditions is a necessary prerequisite. This model should accurately predict model outputs in both once-through mode (OTM) and recirculation mode (RCM) while also reflecting the dynamic transition processes between modes. In this study, we first conducted a detailed analysis of the characteristics of recirculation system. Mass and energy conservation equations are established separately for economizer-waterwall system and superheater system, including a level model for storage tank in RCM. Subsequently, through reasonable assumptions and analysis of the transition process, the modified steam dryness is identified as a crucial indicator to distinguish between RCM and OTM. This approach unified the model structures in RCM and OTM, avoiding issues related to model switching during mode transitions. Following this, model parameters were identified based on an improved dynamic search fireworks algorithm. Finally, multiple sets of comparative simulation experiments demonstrated that the established model exhibits high accuracy throughout the unit operation. Results from open-loop simulations indicated that this model can serve as a foundation for controller design of coordinated control system in all load conditions.

Strategy of Flywheel–Battery Hybrid Energy Storage Based on Optimized Variational Mode Decomposition for Wind Power Suppression

The fluctuation and intermittency of wind power generation seriously affect the stability and security of power grids. Aiming at smoothing wind power fluctuations, this paper proposes a flywheel–battery hybrid energy storage system (HESS) based on optimal variational mode decomposition (VMD). Firstly, the grid-connected power and charging–discharging power of the HESS are determined based on the sliding average algorithm. Secondly, the VMD algorithm, optimized using long short-term memory (LSTM), is used to decompose the hybrid energy storage power (HESP) into a series of sub-modes with frequencies from low to high. Then, the state of charge of the battery energy storage system and the speed of the flywheel energy storage system are monitored in real time, and the primary power of the HESS is modified according to the rules formulated by fuzzy control. Finally, through a simulation example, it is concluded that the method meets the requirements of smoothing wind power fluctuations and gives full play to the characteristics of energy storage battery and flywheel energy storage to ensure the stable operation of the energy storage system. The method presented in this paper can provide a reference for HESP configuration and control operation strategy formulation.

Short-term Wind Power Forecasts based on VMD-KPCA and CFSBOA-BiLSTM

Accurate prediction of wind power is of great significance to reduce the impact of large-scale wind power connection and improve the security and stability of power grid. A short-term wind power forecasting method based on VMD-KPCA and CFSBOA-BiLSTM is proposed. Firstly, the key environmental factors restricting the volatility of wind power are fully considered, and the variational mode decomposition method is used to decompose the wind power and meteorological factors. Secondly, the kernel principal component analysis method is used to reduce the dimension and construct the sub-sequence characteristic data set. Finally, the multi-strategy improved butterfly algorithm is used to optimize the BiLSTM network hyperparameters and establish the CFSBOA-BiLSTM prediction model. The simulation analysis uses the measured data of a wind farm in northwest China. The experimental results show that the combined forecasting model can fully mine the hidden information of the data and improve the accuracy of short-term wind power forecasting. Compared with the BiLSTM model, RMSE, MAE and MAPE decreased by 60.37%, 66.6% and 67.59%, respectively.

Energy Policies and Interconnection Standards for Energy generation Distributed: Global Status, Development Trends and Perspectives in Peru

Energy policies and interconnection standards play a key role in promoting and regulating distributed energy generation globally. The work should examine the current state of global interconnection policies and standards, emerging trends and their impact on clean energy adoption. Globally, energy policies for distributed generation typically focus on marketing renewable energy sources and improving energy efficiency. Many countries implement tax incentives, subsidies and support programs to stimulate the adoption of distributed generation technologies, such as solar panels and small wind systems. The rules of Interconnections often focus on establishing technical standards to ensure the security, stability and reliability of electrical grids, as well as facilitating the connection and seamless integration of distributed generation systems

A Risk Assessment Framework for Cyber-Physical Security in Distribution Grids with Grid-Edge DERs

Integration of inverter-based distributed energy resources (DERs) is reshaping the landscape of distribution grids to fulfill the socioeconomic, environmental, and sustainability goals. Addressing the technological challenges of DER grid integration requires an adaptive communication layer for efficient DER management and control. This transition has given rise to a cyberphysical system (CPS) architecture within the distribution system, causing new vulnerabilities for cyberphysical attacks. To better address potential threats, this paper presents a comprehensive risk assessment framework for cyberphysical security in distribution grids with grid-edge DERs. The framework incorporates a detailed CPS model accounting for dynamic DER characteristics within the distribution grid. It identifies vulnerabilities in DER communication systems, models attack scenarios, and addresses communication latency crucial for inverter control timescales. Subsequently, the quantification of attack impacts employs an attack probability model including both the vulnerability and criticality of cyber components. The proposed risk assessment framework was validated through testing on the modified IEEE 13-node and 123-node test feeders.

Implementation of AI for forecasting of wind speed and solar irradiation using different Machine Learning techniques

High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very import ant. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describe the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Indore has been highlighted in this research.