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Cybersecurity in Critical Infrastructures, especially Digital Substations, has garnered significant attention from both the industrial and academic sectors. A commonly adopted approach to support research in this area involves the use of datasets, which consist of network traffic samples gathered during the operation of an infrastructure. However, creating such datasets from real-world electrical systems presents some challenges: (i) These datasets are often generated under controlled or idealized conditions, potentially overlooking the complexities of real-world operations within a digital substation; (ii) the captured data frequently contain sensitive information, making it difficult to share openly within the research community. This paper presents the creation of a new dataset aimed at advancing cybersecurity research, specifically focused on GOOSE spoofing attacks, given the crucial role of the GOOSE protocol in managing operational and control tasks within Digital Substations. The dataset highlights the real-world impacts of these attacks, demonstrating the execution of unintended operations under different operational scenarios, including both stable conditions and situations involving system failures. The data were collected from a laboratory testbed that replicates the actual functioning of a real digital substation with two bays. The experiments provided insights into key characteristics of GOOSE protocol traffic and the vulnerability of DS infrastructure to Spoofing Attacks. [ABSTRACT FROM AUTHOR]

This paper proposes a correction technique for bad data and high-precision analysis based on micro-phasor measurement unit (μPMU) data for a stable and reliable smart substation. First, a high-precision wide-area monitoring system (WAMS) with 35 μPMUs installed at Korea's Yeonggwang substation, which is connected to renewable energy sources (RESs), is introduced. Time-synchronized μPMU data are collected through the phasor data concentrator (PDC). A pre-processing program is implemented and utilized to integrate the raw data of each μPMU into a single comma-separated values (CSV) snapshot file based on the Timetag. After presenting the technique for identification and correction of event, duplicate, and spike bad data of μPMU, causal relationships are confirmed through the voltage and current fluctuations for a total of five states, such as T/L fault, tap-up, tap-down, generation, and generation shutdown. Additionally, the difference in active power between the T/L and the secondary side of the M.Tr is compared, and the fault ride through (FRT) regulations, when the fault in wind power generation (WP), etc., occurred, is analyzed. Finally, a statistical analysis, such as boxplot and kernel density, based on the instantaneous voltage fluctuation rate (IVFR) is conducted. As a result of the simulation evaluation, the proposed correction technique and precise analysis can accurately identify various phenomena in substations and reliably estimate causal relationships. [ABSTRACT FROM AUTHOR]

The idea of a decentralized electric grid has shifted from being a concept to a reality. The growing integration of distributed energy resources (DERs) has transformed the traditional centralized electric grid into a decentralized one. However, while most efforts to manage and optimize this decentralization focus on the electrical infrastructure layer, the operational and control layer, as well as the data management layer, have received less attention. Current electric grids rely on centralized control centers (CCCs) that serve as the electric grid's brain, where operators monitor, control, and manage the entire grid infrastructure. Hence, any disruption caused by a cyberattack or a natural event, disconnecting the CCC, could have numerous negative effects on grid operations, including socioeconomic impacts, equipment damage, market repercussions, and blackouts. This article introduces the idea of a fully decentralized electric grid that leverages autonomous smart substations and blockchain integration for decentralized data management and control. The aim is to propose a blockchain-enabled decentralized electric grid model and its potential impact on energy markets, sustainability, and resilience. The model presented underlines the transformative potential of decentralized autonomous grids in revolutionizing energy systems for better operability, management, and flexibility. [ABSTRACT FROM AUTHOR]

The inability to locate device faults quickly and accurately has become prominent due to the large number of communication devices and the complex structure of secondary circuit networks in smart substations. Traditional methods are less efficient when diagnosing secondary equipment faults in smart substations, and deep learning methods have poor portability, high learning sample costs, and often require retraining a model. Therefore, a secondary equipment fault diagnosis method based on a graph attention network is proposed in this paper. All fault events are automatically represented as graph-structured data based on the K-nearest neighbors (KNNs) algorithm in terms of the feature information exhibited by the corresponding detection nodes when equipment faults occur. Then, a fault diagnosis model is established based on the graph attention network. Finally, partial intervals of a 220 kV intelligent substation are taken as an example to compare the fault localization effect of different methods. The results show that the method proposed in this paper has the advantages of higher localization accuracy, lower learning cost, and better robustness than the traditional machine learning and deep learning methods. [ABSTRACT FROM AUTHOR]

A substation is the portion of a power grid that forms a link between the cyber system and the physical system. Reliability evaluation of smart substations based on a time-varying probabilistic hybrid attack graph (TVPHAG) is studied in this paper. First, the topology network of the smart substation is established, whose attributes are represented by probability. Then, in order to solve the problem of asynchrony in the cyber-physical system and the hybrid caused by heterogeneity, time-varying state equation in topology and cuts in algebra are introduced to TVPHAG. Based on TVPHAG, the evaluation of the reliability of cyber-physical systems with multiple equipment and multiple timescales is established. On this basis, the influences of physical conditions, cyberattacks, physical attacks, and cyber-physical attacks on substations are analyzed, respectively. Finally, the simulation shows that the method is effective in evaluating the reliability of smart substations, providing a new method for the evaluation of reliability. [ABSTRACT FROM AUTHOR]

In order to expand the simulation scale of smart substation, a simulation model of the inlet-outlet line unit, as described by the 0/108 S conductance for multivalued coefficients pre-storage is presented in this paper. As a part of orders decomposition, the address conversion circuit for multivalued coefficients can reduce the computational burden of processing elements. For the convenience of the generalization of the real-time digital solver based on FPGA (FRTDS, FPGA: Field-Programmable Gate Array), the address conversion circuit for multivalued coefficients is matched with the guide word, the formation module of sampled value (SV) packet, and the resolution module of generic object oriented substation event (GOOSE) packet are associated with the application identification (APPID) in the Ethernet frame. The address conversion circuit for multivalued coefficients, the formation module of SV packet, and the resolution module of GOOSE packet are reconstructed via the orders. A hardware-in-the-loop real-time simulation platform for smart substation is built based on the novel FRTDS. A case is given to demonstrate the simulation calculating capability and the hardware-in-the-loop ability of the novel FRTDS. [ABSTRACT FROM AUTHOR]

Being one of the fastest-growing economies in the world, Bangladesh needs to upgrade its electrical network and aim to reduce dependency on fossil fuel-based energy. For the aging and ever-expanding power network, it is necessary to have a smart substation in order to provide reliable, affordable, and sustainable electrical power. As Bangladesh is looking to integrate Distributed Generation (DG) in the power system, it is high time to think about integrating a smart distribution substation into its power network. In this paper, an investigation of the current power generation structure of Bangladesh was conducted and is described. The major focus was given to the upgradation of the existing substation and distribution setup of Bangladesh by providing suitable architectures, technologies, and communication protocols. Detailed studies of Bangladesh's prospects to incorporate the new technology and renewable energy into its power network are discussed. ETAP was used to simulate the prospective system to show the feasibility of the prospective smart distribution substation in Bangladesh's power network. [ABSTRACT FROM AUTHOR]