A hybrid energy harvesting approach for transmission lines based on triboelectric nanogenerator and micro thermoelectric generator.

To effectively detect faults in transmission lines, monitoring the operating status of these lines is imperative. However, providing power to monitoring devices for transmission line status presents a significant challenge. In this research, a hybrid energy harvesting approach based on micro thermoelectric generator (MTEG) and triboelectric nanogenerator (TENG) is proposed, and a theoretical model for MTEG-TENG hybrid energy harvesting is established. This study develops an integrated energy harvesting prototype, which incorporates oscillating-TENG (O-TENGs), MTEGs, and a power management control unit. This prototype not only harvests energy from the vibrations of transmission lines but also converts the lines thermal energy into electricity. The Experiment results show that the maximum open-circuit voltages of O-TENG and MTEG reach 80.3 V and 1.094 V, respectively. Compared to a single MTEG energy harvesting device, the prototype of the MTEG-TENG hybrid energy harvesting device demonstrates a 5.36% improvement in energy harvesting and battery charging performance. Consequently, this approach achieves self-powered monitoring with excellent stability and lower manufacturing costs. It provides an efficient and durable power approach for transmission line status monitoring devices.

Lightweight Hash-Based Authentication Protocol for Smart Grids.

Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or buildings to measure electricity usage and allow power suppliers or consumers to monitor and manage it in real time. However, SMs require a secure service to address malicious attacks during memory protection and communication processes and a lightweight communication protocol suitable for devices with computational and communication constraints. This paper proposes an authentication protocol based on a one-way hash function to address these issues. This protocol includes message authentication functions to address message tampering and uses a changing encryption key for secure communication during each transmission. The security and performance analysis of this protocol shows that it can address existing attacks and provides 105,281.67% better computational efficiency than previous methods.

Smart Grid Security: An Effective Hybrid CNN-Based Approach for Detecting Energy Theft Using Consumption Patterns.

In Internet of Things-based smart grids, smart meters record and report a massive number of power consumption data at certain intervals to the data center of the utility for load monitoring and energy management. Energy theft is a big problem for smart meters and causes non-technical losses. Energy theft attacks can be launched by malicious consumers by compromising the smart meters to report manipulated consumption data for less billing. It is a global issue causing technical and financial damage to governments and operators. Deep learning-based techniques can effectively identify consumers involved in energy theft through power consumption data. In this study, a hybrid convolutional neural network (CNN)-based energy-theft-detection system is proposed to detect data-tampering cyber-attack vectors. CNN is a commonly employed method that automates the extraction of features and the classification process. We employed CNN for feature extraction and traditional machine learning algorithms for classification. In this work, honest data were obtained from a real dataset. Six attack vectors causing data tampering were utilized. Tampered data were synthetically generated through these attack vectors. Six separate datasets were created for each attack vector to design a specialized detector tailored for that specific attack. Additionally, a dataset containing all attack vectors was also generated for the purpose of designing a general detector. Furthermore, the imbalanced dataset problem was addressed through the application of the generative adversarial network (GAN) method. GAN was chosen due to its ability to generate new data closely resembling real data, and its application in this field has not been extensively explored. The data generated with GAN ensured better training for the hybrid CNN-based detector on honest and malicious consumption patterns. Finally, the results indicate that the proposed general detector could classify both honest and malicious users with satisfactory accuracy.

Validation of Machine Learning-Aided and Power Line Communication-Based Cable Monitoring Using Measurement Data.

The implementation of power line communications (PLC) in smart electricity grids provides us with exciting opportunities for real-time cable monitoring. In particular, effective fault classification and estimation methods employing machine learning (ML) models have been proposed in the recent past. Often, the research works presenting PLC for ML-aided cable diagnostics are based on the study of synthetically generated channel data. In this work, we validate ML-aided diagnostics by integrating measured channels. Specifically, we consider the concatenation of clustering as a data pre-processing procedure and principal component analysis (PCA)-based dimension reduction for cable anomaly detection. Clustering and PCA are trained with measurement data when the PLC network is working under healthy conditions. A possible cable anomaly is then identified from the analysis of the PCA reconstruction error for a test sample. For the numerical evaluation of our scheme, we apply an experimental setup in which we introduce degradations to power cables. Our results show that the proposed anomaly detector is able to identify a cable degradation with high detection accuracy and low false alarm rate.

Energy Consumption Analysis for Continuous Phase Modulation in Smart-Grid Internet of Things of beyond 5G.

Wireless sensor network (WSN) underpinning the smart-grid Internet of Things (SG-IoT) has been a popular research topic in recent years due to its great potential for enabling a wide range of important applications. However, the energy consumption (EC) characteristic of sensor nodes is a key factor that affects the operational performance (e.g., lifetime of sensors) and the total cost of ownership of WSNs. In this paper, to find the modulation techniques suitable for WSNs, we investigate the EC characteristic of continuous phase modulation (CPM), which is an attractive modulation scheme candidate for WSNs because of its constant envelope property. We first develop an EC model for the sensor nodes of WSNs by considering the circuits and a typical communication protocol that relies on automatic repeat request (ARQ)-based retransmissions to ensure successful data delivery. Then, we use this model to analyze the EC characteristic of CPM under various configurations of modulation parameters. Furthermore, we compare the EC characteristic of CPM with that of other representative modulation schemes, such as offset quadrature phase-shift keying (OQPSK) and quadrature amplitude modulation (QAM), which are commonly used in communication protocols of WSNs. Our analysis and simulation results provide insights into the EC characteristics of multiple modulation schemes in the context of WSNs; thus, they are beneficial for designing energy-efficient SG-IoT in the beyond-5G (B5G) and the 6G era.

Enhancing Cybersecurity in Distributed Microgrids: A Review of Communication Protocols and Standards.

The effective operation of distributed energy sources relies significantly on the communication systems employed in microgrids. This article explores the fundamental communication requirements, structures, and protocols necessary to establish a secure connection in microgrids. This article examines the present difficulties facing, and progress in, smart microgrid communication technologies, including wired and wireless networks. Furthermore, it evaluates the incorporation of diverse security methods. This article showcases a case study that illustrates the implementation of a distributed cyber-security communication system in a microgrid setting. The study concludes by emphasizing the ongoing research endeavors and suggesting potential future research paths in the field of microgrid communications.

A New Semi-Quantum Two-Way Authentication Protocol between Control Centers and Neighborhood Gateways in Smart Grids.

To address the potential threat to the power grid industry posed by quantum computers and ensure the security of bidirectional communication in smart grids, it is imperative to develop quantum-safe authentication protocols. This paper proposes a semi-quantum bidirectional authentication protocol between a control center (CC) and a neighboring gateway (NG). This method uses single photons to facilitate communication between the CC and the NG. Security analysis demonstrates that the protocol can effectively resist common attack methods, including double CNOT attacks, impersonation attacks, interception-measurement-retransmission attacks, and entanglement-measurement attacks. Comparisons with other protocols reveal that this protocol has significant advantages, making it more appealing and practical for real-world applications. Finally, by simulating the protocol on the IBM quantum simulator, this protocol not only validates the theoretical framework but also confirms the practical feasibility of the protocol.

Security Baseline for Substation Automation Systems.

The use of information technology and the automation of control systems in the energy sector enables a more efficient transmission and distribution of electricity. However, in addition to the many benefits that the deployment of intelligent and largely autonomous systems brings, it also carries risks associated with information and cyber security breaches. Technology systems form a specific and critical communication infrastructure, in which powerful control elements integrating IoT principles and IED devices are present. It also contains intelligent access control systems such as RTU, IDE, HMI, and SCADA systems that provide communication with the data and control center on the outer perimeter. Therefore, the key question is how to comprehensively protect these specialized systems and how to approach security implementation projects in this area. To establish rules, procedures, and techniques to ensure the cyber security of smart grid control systems in the energy sector, it is necessary to understand the security threats and bring appropriate measures to ensure the security of energy distribution. Given the use of a wide range of information and industrial technologies, it is difficult to protect energy distribution systems using standard constraints to protect common IT technologies and business processes. Therefore, as part of a comprehensive approach to cyber security, specifics such as legislative framework, technological constraints, international standards, specialized protocols or company processes, and many others need to be considered. Therefore, the key question is how to comprehensively protect these specialized systems and how to approach security implementation projects in this area. In this article, a basic security concept for control systems of power stations, which are part of the power transmission and distribution system, is presented based on the Smart Grid domain model with emphasis on substation intelligence, according to the Purdue model. The main contribution of the paper is the comprehensive design of mitigation measures divided into mandatory and recommended implementation based on the standards defined within the MITRE ATT&CK matrix specified, concerning the specifications of intelligent distribution substations. The proposed and industry-tested solution is mapped to meet the international security standards ISO 27001 and national legislation reflecting the requirements of NIS2. This ensures that the security requirements will be met when implementing the proposed Security Baseline.

LACP-SG: Lightweight Authentication Protocol for Smart Grids.

Smart grid (SG) recently acquired considerable attention due to their utilization in sustaining demand response management in power systems. Smart meters (SMs) deployed in SG systems collect and transmit data to the server. Since all communications between SM and the server occur through a public communication channel, the transmitted data are exposed to adversary attacks. Therefore, security and privacy are essential requirements in the SG system for ensuring reliable communication. Additionally, an AuthentiCation (AC) protocol designed for secure communication should be lightweight so it can be applied in a resource-constrained environment. In this article, we devise a lightweight AC protocol for SG named LACP-SG. LACP-SG employs the hash function, "Esch256", and "authenticated encryption" to accomplish the AC phase. The proposed LACP-SG assures secure data exchange between SM and server by validating the authenticity of SM. For encrypted communication, LACP-SG enables SM and the server to establish a session key (SEK). We use the random oracle model to substantiate the security of the established SEK. Moreover, we ascertain that LACP-SG is guarded against different security vulnerabilities through Scyther-based security validation and informal security analysis. Furthermore, comparing LACP-SG with other related AC protocols demonstrates that LACP-SG is less resource-intensive while rendering better security characteristics.

Development a Low-Cost Wireless Smart Meter with Power Quality Measurement for Smart Grid Applications.

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.

Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks.

Wireless sensor networks (WSNs) have been utilized as communication infrastructure for smart grid applications. The primary requirement of WSNs for smart grid applications is to transmit delay-critical data from smart grid assets ether at the maximum rate or by reducing collision rates. Additionally, WSNs should utilize the limited resources of the network to provide the required long-term QoS. The achievement of these objectives requires a remarkable design of WSN protocols to satisfy the requirements of smart grid applications. In this study, a multi-channel cluster tree protocol is proposed to prevent collisions and increase network performance. In the proposed scheme, the cluster head serves to broadcast a beacon frame containing information on the allocated channels and time slots. This enables the new node to determine its channel and timeslot. A performance analysis reveals that the proposed scheme can achieve a low end-to-end delay and low collision rates compared with the well-known IEEE 802.15.4 MAC protocols widely used in the literature to provide QoS to smart-grid applications.

Energy Harvesting Device for Smart Monitoring of MV Overhead Power Lines-Theoretical Concept and Experimental Construction.

Modern technological advancements have opened avenues for innovative low-energy sources in construction, with electric field energy harvesting (EFEH) from overhead power lines serving as a prime candidate for empowering intelligent monitoring sensors and vital communication networks. This study delves into this concept, presenting a physical model of an energy harvester device. The prototype was meticulously designed, simulated, constructed, and tested, to validate its foundational mathematical model, with implications for future prototyping endeavors. The findings illustrate the potential of harnessing ample power from this device when deployed on medium-voltage (MV) overhead power lines, facilitating the monitoring of electric and meteorological parameters and their seamless communication through the Internet of Things (IoT) network. The study focused on the medium voltage applications of the harvester. Two dielectric materials were tested in the present experiments: air and polyurethane. The measurement results exhibited satisfactory alignment, particularly with the air dielectric. Nevertheless, deviations arose when employing polyurethane rubber as the dielectric, due to impurities and defects within the material. The feasibility of generating the requisite 0.84 mW output power to drive process electronics, sensors, and IoT communications was established. The novelty of this work rests in its comprehensive approach, cementing the theoretical concept through rigorous experimentation, and emphasizing its application in enhancing the efficacy of overhead power line monitoring.

A Lightweight Authentication and Key Agreement Protocol for IoT-Enabled Smart Grid System.

The IoT-enabled Smart Grid uses IoT smart devices to collect the private electricity data of consumers and send it to service providers over the public network, which leads to some new security problems. To ensure the communication security in a smart grid, many researches are focusing on using authentication and key agreement protocols to protect against cyber attacks. Unfortunately, most of them are vulnerable to various attacks. In this paper, we analyze the security of an existent protocol by introducing an insider attacker, and show that their scheme cannot guarantee the claimed security requirements under their adversary model. Then, we present an improved lightweight authentication and key agreement protocol, which aims to enhance the security of IoT-enabled smart grid systems. Furthermore, we proved the security of the scheme under the real-or-random oracle model. The result shown that the improved scheme is secure in the presence of both internal attackers and external attackers. Compared with the original protocol, the new protocol is more secure, while keeping the same computation efficiency. Both of them are 0.0552 ms. The communication of the new protocol is 236 bytes, which is acceptable in smart grids. In other words, with similar communication and computation cost, we proposed a more secure protocol for smart grids.

Impact, Vulnerabilities, and Mitigation Strategies for Cyber-Secure Critical Infrastructure.

Several critical infrastructures are integrating information technology into their operations, and as a result, the cyber attack surface extends over a broad range of these infrastructures. Cyber attacks have been a serious problem for industries since the early 2000s, causing significant interruptions to their ability to produce goods or offer services to their clients. The thriving cybercrime economy encompasses money laundering, black markets, and attacks on cyber-physical systems that result in service disruptions. Furthermore, extensive data breaches have compromised the personally identifiable information of millions of people. This paper aims to summarize some of the major cyber attacks that have occurred in the past 20 years against critical infrastructures. These data are gathered in order to analyze the types of cyber attacks, their consequences, vulnerabilities, as well as the victims and attackers. Cybersecurity standards and tools are tabulated in this paper in order to address this issue. This paper also provides an estimate of the number of major cyber attacks that will occur on critical infrastructure in the future. This estimate predicts a significant increase in such incidents worldwide over the next five years. Based on the study's findings, it is estimated that over the next 5 years, 1100 major cyber attacks will occur on critical infrastructures worldwide, each causing more than USD 1 million in damages.

Contributions to Power Grid System Analysis Based on Clustering Techniques.

The topic addressed in this article is part of the current concerns of modernizing power systems by promoting and implementing the concept of smart grid(s). The concepts of smart metering, a smart home, and an electric car are developing simultaneously with the idea of a smart city by developing high-performance electrical equipment and systems, telecommunications technologies, and computing and infrastructure based on artificial intelligence algorithms. The article presents contributions regarding the modeling of consumer classification and load profiling in electrical power networks and the efficiency of clustering techniques in their profiling as well as the simulation of the load of medium-voltage/low-voltage network distribution transformers to electricity meters.

Research on Data Poisoning Attack against Smart Grid Cyber-Physical System Based on Edge Computing.

Data poisoning attack is a well-known attack against machine learning models, where malicious attackers contaminate the training data to manipulate critical models and predictive outcomes by masquerading as terminal devices. As this type of attack can be fatal to the operation of a smart grid, addressing data poisoning is of utmost importance. However, this attack requires solving an expensive two-level optimization problem, which can be challenging to implement in resource-constrained edge environments of the smart grid. To mitigate this issue, it is crucial to enhance efficiency and reduce the costs of the attack. This paper proposes an online data poisoning attack framework based on the online regression task model. The framework achieves the goal of manipulating the model by polluting the sample data stream that arrives at the cache incrementally. Furthermore, a point selection strategy based on sample loss is proposed in this framework. Compared to the traditional random point selection strategy, this strategy makes the attack more targeted, thereby enhancing the attack's efficiency. Additionally, a batch-polluting strategy is proposed in this paper, which synchronously updates the poisoning points based on the direction of gradient ascent. This strategy reduces the number of iterations required for inner optimization and thus reduces the time overhead. Finally, multiple experiments are conducted to compare the proposed method with the baseline method, and the evaluation index of loss over time is proposed to demonstrate the effectiveness of the method. The results show that the proposed method outperforms the existing baseline method in both attack effectiveness and overhead.

A General-Purpose Distributed Analytic Platform Based on Edge Computing and Computational Intelligence Applied on Smart Grids.

Currently, in many data landscapes, the information is distributed across various sources and presented in diverse formats. This fragmentation can pose a significant challenge to the efficient application of analytical methods. In this sense, distributed data mining is mainly based on clustering or classification techniques, which are easier to implement in distributed environments. However, the solution to some problems is based on the usage of mathematical equations or stochastic models, which are more difficult to implement in distributed environments. Usually, these types of problems need to centralize the required information, and then a modelling technique is applied. In some environments, this centralization may cause an overloading of the communication channels due to massive data transmission and may also cause privacy issues when sending sensitive data. To mitigate this problem, this paper describes a general-purpose distributed analytic platform based on edge computing for distributed networks. Through the distributed analytical engine (DAE), the calculation process of the expressions (that requires data from diverse sources) is decomposed and distributed between the existing nodes, and this allows sending partial results without exchanging the original information. In this way, the master node ultimately obtains the result of the expressions. The proposed solution is examined using three different computational intelligence algorithms, i.e., genetic algorithm, genetic algorithm with evolution control, and particle swarm optimization, to decompose the expression to be calculated and to distribute the calculation tasks between the existing nodes. This engine has been successfully applied in a case study focused on the calculation of key performance indicators of a smart grid, achieving a reduction in the number of communication messages by more than 91% compared to the traditional approach.

SSA-VMD for UWB Radar Sensor Vital Sign Extraction.

The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) radar sensor is suitable for this application because of its strong penetration ability, high range resolution and low average power consumption. However, due to weak heartbeat amplitude and measurement noise, the accurate measurement of the target heart rate is a challenge. In this paper, singular spectrum analysis (SSA) is proposed to reconstruct the eigenvalues of noisy vital signs to eliminate noise peaks around the heartbeat rate; combined with the variational modal decomposition (VMD), the target vital signs can be extracted with high accuracy. The experiment confirmed that the target vital sign information can be extracted with high accuracy from ten subjects at different distances, which can play an important role in short distance human detection and vital sign monitoring.

Energy Aware Load Balancing Framework for Smart Grid Using Cloud and Fog Computing.

Data centers are producing a lot of data as cloud-based smart grids replace traditional grids. The number of automated systems has increased rapidly, which in turn necessitates the rise of cloud computing. Cloud computing helps enterprises offer services cheaply and efficiently. Despite the challenges of managing resources, longer response plus processing time, and higher energy consumption, more people are using cloud computing. Fog computing extends cloud computing. It adds cloud services that minimize traffic, increase security, and speed up processes. Cloud and fog computing help smart grids save energy by aggregating and distributing the submitted requests. The paper discusses a load-balancing approach in Smart Grid using Rock Hyrax Optimization (RHO) to optimize response time and energy consumption. The proposed algorithm assigns tasks to virtual machines for execution and shuts off unused virtual machines, reducing the energy consumed by virtual machines. The proposed model is implemented on the CloudAnalyst simulator, and the results demonstrate that the proposed method has a better and quicker response time with lower energy requirements as compared with both static and dynamic algorithms. The suggested algorithm reduces processing time by 26%, response time by 15%, energy consumption by 29%, cost by 6%, and delay by 14%.

Detection of False Data Injection Attacks in Smart Grids Based on Expectation Maximization.

The secure operation of smart grids is closely linked to state estimates that accurately reflect the physical characteristics of the grid. However, well-designed false data injection attacks (FDIAs) can manipulate the process of state estimation by injecting malicious data into the measurement data while bypassing the detection of the security system, ultimately causing the results of state estimation to deviate from secure values. Since FDIAs tampering with the measurement data of some buses will lead to error offset, this paper proposes an attack-detection algorithm based on statistical learning according to the different characteristic parameters of measurement error before and after tampering. In order to detect and classify false data from the measurement data, in this paper, we report the model establishment and estimation of error parameters for the tampered measurement data by combining the the k-means++ algorithm with the expectation maximization (EM) algorithm. At the same time, we located and recorded the bus that the attacker attempted to tamper with. In order to verify the feasibility of the algorithm proposed in this paper, the IEEE 5-bus standard test system and the IEEE 14-bus standard test system were used for simulation analysis. Numerical examples demonstrate that the combined use of the two algorithms can decrease the detection time to less than 0.011883 s and correctly locate the false data with a probability of more than 95%.