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... Some of the key features of smart grids are agile reconfigurability and dynamic optimization of grid operations, rapid detection and response to faults in the system, integration of renewable power sources with conventional fossil fuels, and providing of pervasive monitoring facilities for power systems. An important step in Industrial Revolution 4.0 is the digitization of Industry 3.0 and bringing together the Information and Communication Technology (ICT) and Operational Technology (OT) for controlling the physical processes, their monitoring, and maintenance [1]- [3]. In the case of power systems, smart grids are the emerging point of Industrial hand, OT comprises Programmable Logic Controllers (PLCs), Remote Terminal Units (RTUs), Intelligent Electronic Devices (IEDs), Phasor Measurement Units (PMUs), relays, Human Machine Interfaces (HMIs), etc. [3]. ...
Increased connectivity and remote reprogrammability/reconfigurability features of embedded devices in current-day power systems (including interconnections between information technology -- IT -- and operational technology -- OT -- networks) enable greater agility, reduced operator workload, and enhanced power system performance and capabilities. However, these features also expose a wider cyber-attack surface, underscoring need for robust real-time monitoring and anomaly detection in power systems, and more generally in Cyber-Physical Systems (CPS). The increasingly complex, diverse, and potentially untrustworthy software and hardware supply chains also make need for robust security tools more stringent. We propose a novel framework for real-time monitoring and anomaly detection in CPS, specifically smart grid substations and SCADA systems. The proposed method enables real-time signal temporal logic condition-based anomaly monitoring by processing raw captured packets from the communication network through a hierarchical semantic extraction and tag processing pipeline into time series of semantic events and observations, that are then evaluated against expected temporal properties to detect and localize anomalies. We demonstrate efficacy of our methodology on a hardware in the loop testbed, including multiple physical power equipment (real-time automation controllers and relays) and simulated devices (Phasor Measurement Units -- PMUs, relays, Phasor Data Concentrators -- PDCs), interfaced to a dynamic power system simulator. The performance and accuracy of the proposed system is evaluated on multiple attack scenarios on our testbed.
... I-IoT is committed to using IoT to link anything, anywhere, and at any time in the context of manufacturing systems in order to improve productivity, e ciency, safety, and intelligence. [13] According to [14], Industry 4.0, or the fourth industrial revolution, has prepared the path for systematically deploying modernized power grids (PGs) to handle rising energy demand by incorporating renewable energy sources. Smart grid (SG) in the context of Industry 4.0 allows energy utilities to monitor and control power generation, transmission, and distribution processes in a more e cient, exible, reliable, sustainable, decentralized, secure, and economical manner by utilizing advanced Information and Communication Technology (ICT), intelligent information processing (IIP), and futureoriented techniques. ...
The invention of new technologies has enabled the development of the manufacturing industry, from mechanical systems to highly automated assembly lines. The Industry 4.0 Concept requires self-sufficient factories, self-prediction, self-comparison, self-reconfiguration, and self-maintenance. Service innovation and industrial big data are receiving more attention from academia and industry. This article discusses managing manufacturing transformation services in the era of the Industrial Revolution 4.0 in the big data environment and the readiness of intelligent predictive informatics tools. This paper focuses on the basic concepts of Industry 4.0 and the current state of the manufacturing system. It also identifies research gaps between existing systems and Industry 4.0 requirements. The main contribution is the Industry 4.0 implementation structure, which consists of a multi-layered framework that can help people understand and achieve the requirements of Industry 4.0. The most cited members in this cluster are ten life cycles, eight construction industries, and eight learning systems. This study found that the number of publications on Big Data and Industry has increased overall in the last six years, with China being the most powerful country. It also found several other sub-topics related to Big Data and Industry, such as big data Analytics, Risk Management, and Industrial Revolution.
... Therefore, establishing an effective smart grid enables load management, significantly reduces system losses and energy wastage, provides accurate data monitoring, and ensures flexibility in expansion and integration within the power system network. Similarly, the electricity grid, with limited sensing devices, manual control, and maintenance, offers customers limited participation options [10][11][12][13]. In this context, the smart grid focuses on maintaining intergenerational diversity with updated processes, enhanced efficiency, and active power control. ...
This article addresses the integration of renewable energy power plants into smart grids and active power control. Renewable energy sources contribute to environmentally friendly and sustainable energy production, but the fluctuations inherent in these sources pose a challenge for energy grids. The article examines various technologies that can be used to overcome this challenge and make energy grids more reliable. Smart grids aim to improve energy grids by optimizing energy production, transmission, and distribution using data analytics, automation, and communication technologies. The integration of renewable energy power plants into these smart grids offers significant advantages, including the ability to predict energy production, integrate with energy storage systems, and manage energy demand. The article also emphasizes the importance of active power control. Active power control is used to manage energy production steadily, thereby maintaining grid stability. Balancing energy fluctuations from renewable energy sources and storing excess energy when needed enhances grid stability. In conclusion, this article discusses the crucial role of integrating renewable energy power plants into smart grids and implementing active power control in the energy sector. These integration and control methods are important steps in making energy grids more sustainable, efficient, and reliable.
... It is similar to traditional data or image augmentation techniques such as introducing noise or applying blurring [79]. Autoencoder neural networks' capability has been demonstrated in areas such as image reconstruction [80], feature extraction [81], augmenting data for anomaly detection [82], noise reduction in medical images [83]. It consists of a pair of an encoder and a decoder, where the encoder is able to generate the compact representation for the whole dataset, which is then passed to the decoder to reconstruct the original data from this simplified representation with high fidelity [84]. ...
Magnetic flux leakage (MFL), a widely used Nondestructive Evaluation (NDE) method, for inspecting pipelines to prevent potential long-term failures. During field testing, uncertainties can affect the accuracy of the inspection and the decision-making process regarding damage conditions. Therefore, it is essential to identify and quantify these uncertainties to ensure the reliability of the inspection. This study focuses on the uncertainties that arise during the inverse NDE process due to the dynamic magnetization process, which is affected by the relative motion of the MFL sensor and the material being tested. Specifically, the study investigates the uncertainties caused by sensing liftoff, which can affect the output signal of the sensing system. Due to the complexity of describing the forward uncertainty propagation process, this study compared two typical machine learning-based approximate Bayesian inference methods, Convolutional Neural Network (CNN) and Deep Ensemble (DE), to address the input uncertainty from the MFL response data. Besides, an Autoencoder method is applied to tackle the lack of experimental data for the training model by augmenting the dataset, which is constructed with the pre-trained model based on transfer learning. Prior knowledge learned from large simulated MFL signals can fine-tune the Autoencoder model which enhances the subsequent learning process on experimental MFL data with faster generalization. The augmented data from the fine-tuned Autoencoder is further applied for machine learning-based defect size classification. This study conducted prediction accuracy and uncertainty analysis with calibration, which can evaluate the prediction performance and reveal the relation between the liftoff uncertainty and prediction accuracy. Further, to strengthen the trustworthiness of the prediction results, the decision-making process guided by uncertainty is applied to provide valuable insights into the reliability of the final prediction results. Overall, the proposed framework for uncertainty quantification offers valuable insights into the assessment of reliability in MFL-based decision-making and inverse problems.
... Smart grid technology have additional benefits like improved demand response, cost savings, better customer involvement, lower CO 2 emissions, integration of renewable energy technologies and electric vehicles [2] [3]. The electricity grid is an ideal illustration of a cyber-physical system (CPS) due to its integration of information and communication technology (ICT) [4]. Now that they are linked to the Internet, critical smart grid components including distribution management systems (DMS) and advanced metering infrastructure (AMI) are open to cyberattacks. ...
Cybersecurity is important in the realization of various smart grid technologies. Several studies have been conducted to discuss different types of cyberattacks and provide their countermeasures. The False Command Injection Attack (FCIA) is considered one of the most critical attacks that have been studied. Various techniques have been proposed in the literature to detect FCIAs on different components of smart grids. The predominant focus of current surveys lies on FCIAs and detection techniques for such attacks. This paper presents a survey of existing works on FCIAs and classifies FCIAs in smart grids according to the targeted component. The impacts of FCIAs on smart grids are also discussed. Subsequently, this paper provides an extensive review of detection studies, categorizing them based on the type of detection technique employed.
... In addition, the SG can offer reliable energy distribution, live monitoring of energy consumption, two-way energy flow, better resource allocations, outages prediction and prevention, ideal real-time balance between energy demand and supply, as well as incorporation of micro energy generators such as solar power into the electricity grid. Authors in [5] point out that the SG's advanced automation and distributed intelligence can provide fault detection, recovery as well as DR management. ...
... For instance, message Log Req , Auth 1 , Auth 2 and Auth3 all incorporate random parameters. Here, Log Req = {A 5 4 , D 3 = R 3 *�R 5 * and D 4 = h (UID i *||ɸ S ||R 4 ||R 5 *). Whereas R 3 and ñ are generated by the user U i , R 4 is generated at the SGS i . ...
... In the proposed protocol, 4 messages are exchanged during the login and AKA procedures. The four messages include Log Req = {A 5 . However, this requires correct guessing of the random nonces R 3 , ñ, R 4 and R 5 . ...
The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols.
... Human body recognition in an enclosure space[2]. ...
With the rapid advancement of wireless networks and the widespread use of WiFi technology, there is a growing interest in utilizing WiFi information for identification purposes. These emerging identification technologies in the new generation have had a profound impact on various aspects of modern life, such as smart furniture research, intelligent security systems, and human-computer interaction. This paper delves into the research and application exploration of WiFi-based identification technology within the context of the new generation. It introduces the knowledge and working principles of Channel State Information (CSI), and discusses the fundamental technologies of Multiple-Input Multiple-Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) in detail. Additionally, it explores current applications and presents a promising future for identification technology from a developmental perspective. By examining the advantages and challenges associated with WiFi-based identification technology, this essay sheds light on its potential impact in various domains. Understanding and exploring this technology are crucial as it has the potential to enhance user experiences, optimize resource allocation, and facilitate intelligent and adaptive systems in the new generation.
... The growing demand for reliable and uninterrupted electricity supply has overloaded the existing energy ecosystem and power grids all around the world [1]. The non-stop escalating level of energy demands call for an urgent integration of micro renewable energy resources, such as wind, solar, biomass, geothermal, hydroelectric, nuclear, and fuel/gas to the power grid [2][3][4]. However, the integration of Distributed energy resources (DERs) in existing power grids systems faces various challenges, such as poor event monitoring, control, and cybersecurity threats due to lack of reliable, efficient, and secure information and communication technologies. ...
Energy is a crucial need in today's world for powering homes, businesses, transportation, and industrial processes. Fossil fuels, such as oil, coal, and natural gas, have been the primary sources of energy for decades. However, there is growing recognition of the negative environmental impact of fossil fuels and the need to transition to cleaner and more sustainable sources of energy. Distributed Energy Resources (DERs)$( {\mathcal{D}\mathcal{E}{\mathcal{R}}_{\mathcal{s}}} )$, such as wind and solar offer several benefits including, reducing energy costs, increasing resiliency, and decreasing carbon emissions. However, the integration of (DERs$\mathcal{D}\mathcal{E}{\mathcal{R}}_{\mathcal{s}}$) into the grid requires advanced communication and secure control strategies to ensure a stable and reliable grid operations. In this regard, a blockchain‐based industrial wireless sensor network (BCWSN)$( {\mathcal{B}\mathcal{C}\mathcal{W}\mathcal{S}\mathcal{N}} )$ can provide secure and resilience data transmission to facilitate intelligent integration, monitoring, and control of DERs$\mathcal{D}\mathcal{E}{\mathcal{R}}_{\mathcal{s}}$ in the smart grid. In this research, a smart contracts framework in Solana BCWSN$\mathcal{B}\mathcal{C}\mathcal{W}\mathcal{S}\mathcal{N}$ called Advanced Solana Blockchain (ABC$\mathcal{A}\mathcal{B}\mathcal{C}$) is proposed for DERs$\mathcal{D}\mathcal{E}{\mathcal{R}}_{\mathcal{s}}$ in the smart grid. The proposed ABC$\mathcal{A}\mathcal{B}\mathcal{C}$ scheme enables resilient and secure real‐time control and monitoring of DERs$\mathcal{D}\mathcal{E}{\mathcal{R}}_{\mathcal{s}}$ in smart grids. The performance evaluations and security analysis illustrated that this ABC$\mathcal{A}\mathcal{B}\mathcal{C}$ scheme is secure, reliable, and suitable in terms of lightweight data sharing between DERs$\mathcal{D}\mathcal{E}{\mathcal{R}}_{\mathcal{s}}$ in smart grids.
... Understanding the importance of creating secure software is crucial in today's world, as we heavily rely on software systems for a wide range of daily activities [21], [22]. Therefore, it is necessary to ensure several security aspects [23], such as the proper functioning of software even under malicious attacks [24] and preventing unauthorized access to confidential data. ...
Incorporating security into software development in small and medium-sized enterprises (SMEs) is an increasingly relevant challenge and a crucial necessity, especially in an uncertain and fast-paced environment like that of an agile setting. Given the growing threat of cyberattacks, it is imperative to address this issue. This article examines and subsequently analyzes existing strategies in the literature regarding secure software development in the context of SMEs employing agile methodologies. The study initiates a systematic literature review to identify strategies employed in this context. The findings reveal that 57.9% of the studies present strategies to tackle security in agile software development, with 20.2% specifically focusing on SMEs. Subsequently, practices demonstrating success in integrating security measures into the software development lifecycle (SDLC) are analyzed and categorized. The results underscore the necessity of addressing security in the agile environment, as it remains a significant challenge in software development. Effective approaches are also required for small businesses to ensure application protection and long-term sustainability.
... The limited capacity of 9 manufacturers and the potential for high costs make maintenance and replacement difficult. This is a hot topic for researchers and power producers [19]. ...
... 18) and auxiliary equipment (Tab. 19). Choosing the correct and complete main and auxiliary equipment to meet the requirements in the SAPF equipment shed is an urgent requirement; the equipment needs to meet the criteria of cost, quality, and level of performance. ...
Industry 4.0 technology is growing rapidly in the manufacturing industry and other businesses. Devices used in Industry 4.0 are manufactured with high frequency switching functions and generate harmonics that negatively affect power quality. Choosing the direct or parallel connection method of the harmonic current absorber depends on the specific requirements of the system and the goal of improving power quality. Shunt Active Power Filter (SAPF) is the best device currently used to improve power quality. This study proposes to use the fuzzy-rough MARCOS method to make decisions on SAPF selection based on experts’ opinions to improve the quality of power sources at the source of smart manufacturing plants using Industry 4.0 devices. This study implements two decision-making methods in Multi-Criteria Decision-Making (MCDM). The first is the SWARA method (Stepwise Weight Assessment Ratio Analysis), and the second is the MARCOS method (Measurement Alternatives and Ranking According to Compromise Solution). The fuzzy-rough method is used to incorporate uncertain information into the results of decision-making and to use linguistic values. The analysis results of the fuzzy-rough SWARA method show that the price factor and power filter range have the greatest influence on the choice of SAPF for harmonic mitigation. Analysis results from the fuzzy-rough MARCOS method show that manufacturer Schneider Electric has the best features according to the evaluation results from decision makers. Sensitivity analysis methods were used to confirm the findings. The harmonic value THDi displayed in the field after installing the harmonic filter is, respectively, THDi1 = 5%, THDi2 = 6%, and THDi3 = 5%, it meets the regulations of Circular 30/2019/TT-BCT. According to this circular, the requirement for total harmonic value (THDi) is below 12%. With THDi1, THDi2, and THDi3 values all below 12%. In operating electrical systems in production and business environments, using SAPF filters for harmonic mitigation helps improve power quality. The fuzzy-rough method is applied, and the decision maker’s decisions are used to adjust the intention to use the SAPF set to suit the conditions.
