Power System Automation Research Articles

Application of Data Security Protection in Smart Grid Based on Computer Electronic Technology

Abstract With the continuous improvement of the intelligence level of the State Grid, data security protection in power system automation has become an important challenge for the development of power enterprises. In order to solve this problem, this article is based on the research of power system structure, data types, and the current development status of security protection, combined with computer electronics technology, to conduct in-depth research on data security protection in power system automation, and establish a security protection model based on its theoretical foundation. In order to verify the effectiveness of the model, this paper tested it from three dimensions: data security protection degree, risk processing efficiency, and response time, and compared it with traditional security protection models. The test results show that under different test cases, the maximum protection degree of the data security protection model proposed in this article reaches 95.16%, and the average protection degree is about 91.88%. From the specific test results, it can be seen that the application of data security protection in power system automation based on computer electronic technology has strong operability.

Cyber resilience methods for smart grids against false data injection attacks: categorization, review and future directions

For a more efficient monitoring and control of electrical energy, the physical components of conventional power systems are continuously integrated with information and communication technologies, converting them into smart grids. However, energy digitalization exposes power systems into a wide range of digital risks. The term cyber resilience for electrical grids expands the conventional resilience of power systems, which mainly refers to extreme weather phenomena. Since this is a relatively new term, there is a need for the establishment of a solid conceptual framework. This paper analyzes and classifies the state-of-the-art research methodologies proposed for strengthening the cyber resilience of smart grids. To this end, the proposed work categorizes the cyberattacks against smart grids, identifies the vulnerable spots of power system automation and establishes a common ground about the cyber resilience. The paper concludes with a discussion about the limitations of the proposed methods in order to extract useful suggestions for future directions.

Novel IEC 61850-based off-site engineering and validation methodology for protection, automation, and control systems

The IEC 61850 standard has driven the development of power systems Protection, Automation, and Control Systems (PACS) over the past years. Although it is well-known for its communication protocols, several concepts and features are underused. This paper presents a new engineering and validation methodology applying the IEC61850 standards concepts to optimize the project life cycle by standardizing and enhancing engineering and testing. An electrical specification has been defined according to the IEC 61850 data modeling, reducing misinterpretations at the specification and configuration stage. The proposed top-down configuration approach establishes a user’s profile and product naming to eliminate the vendor’s dependability. The validations were performed using modern commercial tools. The results show that the proposed methodology makes it possible to have a reliable engineering process where the output of the previous stage provides all required information is independent of the next one. The project naming approach makes it possible to validate the system integration based on the agnostic system specification files in an early project stage. Thus, the protection devices and supervision systems can be validated independently, reducing integration issues in the later stages without affecting interoperability and making it possible to achieve interchangeability.

"Revolutionizing Lawn Care: AI-Driven Solar-Powered Humorless Grassland Mower With IoT Integration"

The primary objective of an AI-driven solar-powered humorless grassland mower with IoT integration is to provide an innovative, sustainable, and efficient solution for grassland maintenance, addressing the challenges of traditional lawn care methods while promoting environmental stewardship and technological advancement. Methods: Developing an AI-driven solar-powered humorless grassland mower with IoT integration involves integrating various technologies and methodologies such as AI Algorithm, Solar Power System Design, Robotics and Automation, IoT Integration, User Interface and Control System. Findings: It can vary depending on the specific objectives, implementation, and testing conducted. It can provide valuable insights into its performance, sustainability, reliability, and user acceptance, helping to inform further development and optimization efforts. Novelty: By introducing novel features and approaches in these areas, an AI-driven solar-powered grassland mower with IoT integration can offer unique capabilities and benefits that set it apart from conventional lawn care equipment and contribute to advancements in sustainable landscaping practices. This paper proposes an innovative AI-Driven Solar-Powered Humorless Grassland Mower with IoT Integration. Traditional manual lawn mowing not only demands labor but also contributes to environmental pollution through nonrenewable resource consumption. To address these challenges, our solution leverages advanced technologies to transform grassland upkeep. The mower is equipped with a range of IoT sensors including ultrasonic, proximity, GPS, and cameras. These sensors enable real-time data collection and decision-making, allowing the mower to adjust its mowing schedule based on forecasted data and navigate around obstacles. Furthermore, during periods of poor sunlight or low battery levels, the mower autonomously returns to its self-charging station for recharging. Data collected by the mower can be sent to the cloud for further analysis and storage. Users have the convenience of remotely controlling the mower through a smartphone app or web interface, enabling initiation, termination, or scheduling of mowing sessions from anywhere. Our AI-integrated IoT solution offers a sustainable and efficient approach to grassland maintenance, reducing labuor requirements and environmental impact while maximizing operational autonomy.

Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems

This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models.

Novel SMO-Based Detection and Isolation of False Data Injection Attacks Against Frequency Control Systems

The integration of modern power systems with information and communication technologies exposes them to various cyber threats. Load frequency control (LFC) is a communication-based automation in power systems that regulates the frequency of the grid. Its critical role makes it a highly attractive target for adversaries. This paper proposes a novel detection and isolation method of False Data Injection Attacks (FDIAs) against LFC. The defense method employs sliding mode observation techniques to detect FDIAs against LFC in real-time and discover which parts of the control loop have been compromised. Attacks are identified by comparing the generated residuals with a specific threshold that is designed in an adaptive manner. The proposed method is able to successfully distinguish the FDIAs from other system disturbances and is robust against uncertainties in power system parameters and noisy measurements. The effectiveness and scalability of the proposed defense method are confirmed on realistic power system models, considering nonlinearities, different topologies and diverse types of transmission links.

An Effective Dataset Preprocessing Method in Tilted Gear Defects Target Detection

Gear defect detection is a crucial component in power automation systems. Methods based on deep learning have exhibited excellent performance in detecting gears. However, the effect of defect detection for tilted gears is not as good. This is due to the traditional horizontal bounding box annotation method, which inevitably produces many overlapping areas in the annotated bounding box when used in tilted gear targets, and a large part of the areas do not belong to defects. In order to address the issue of low precision in the detection of defects in tilted gears based on deep learning, a dataset preprocessing scheme has been proposed. Initially, the images and annotation files of the training set and validation set are automatically rotated. Subsequently, the rotated data are utilized to train the defect detection model. Finally, the test set is subjected to the same image rotation method as the first step, resulting in the generation of high‐precision defect detection results, which are then input into the defect detection model. In order to facilitate comparison with the ground truth, the JSON file obtained from the detection result is rotated in reverse and the result is mapped to the original test set before rotation. In order to verify the effectiveness of the dataset optimization method proposed in this article, the same configuration file is used to train and evaluate the gear defect detection model with the dataset before and after optimization, respectively. The three detection models with the highest comprehensive indicators were employed to assess the dataset before and after optimization, with the average detection precision mAP serving as a quantitative comparison indicator. The findings demonstrate that the data optimization method proposed in this article has markedly enhanced mAP in tilted gear defect detection. Upon testing the proposed method on the PP‐YOLOE + model, mAP (0.5) exhibited an increase of 18.5%, while mAP (0.5 : 0.95) demonstrated a 29.1% improvement. When the method was tested on the RT‐DETR model, mAP (0.5) exhibited an increase of 24.7%, while mAP (0.5 : 0.95) demonstrated an 18.4% improvement. When tested on the few‐short model, mAP (0.5) increased by 23.3% and mAP (0.5 : 0.95) increased by 19.8%. This validates the effectiveness of the dataset processing method proposed in this article in the detection of tilted gear defects.

DEVICE FOR CONTROLLING PARAMETERS OF ACCUMULATOR BATTERIES AND THE CORRESPONDING DIRECT CURRENT NETWORK

Mobile devices for information, control and telemetry systems are powered by mobile generators through AC to DC converters, by batteries or, if possible, directly by solar panels. Solar panels typically work in a system that involves the usage of batteries to keep the systems running when the solar panels are not working or not providing enough power. The batteries can be recharged from the panels themselves if the solar panels have sufficient power or from an external direct current source. Also, for some mobile devices, power systems are used only with batteries, which are recharged from generators or, if possible, from standard electrical networks. All these options of power supply systems for mobile devices require operational control of battery parameters and corresponding direct current networks. The paper proposes a device for monitoring the following parameters: voltage of the direct current network from which the mobile device is powered or the batteries are charged; battery charge current control; control of insulation resistance of direct current circuits; control of the voltage of chargers; monitoring the status of chargers; protection of batteries from deep discharge; control of the state of switching nodes. The device is implemented as a two-processor system based on STM32F103 microcontrollers. A non-contact sensor of the LEM LA 100-P type is used to measure the direct current, which generates an analog signal proportional to the value of the direct current. This signal is passed to a 16- bit analog-to-digital converter. Given that these microcontrollers have integrated 12-bit analog-to- digital converters, an external 16-bit analog-to-digital converter of the ADS1115 type is used to ensure the necessary accuracy of direct current and voltage measurement, which transmits information to the basic processor via the I2C interface. The basic processor implements the main operating modes of the device, and the local processor provides information exchange with the general mobile power supply system through the RS-485 interface. The device is equipped with an indication system based on an LCD indicator of the VS1602A type and functionally programmable single LED indicators, a local keyboard for selecting control modes, a USB port for connecting additional modules and a SWD port for programming the Flash memory of microcontrollers and debugging programs in real time. During operation, parameters of non-standard events are stored in the device's non-volatile memory. The software of the basic and local processors has been developed, which ensures the functioning of the device in basic modes and performs periodic self-diagnosis of the device. The obtained results can be used in scientific research and in the design of real automated power systems for mobile information systems. Keywords: power systems of mobile information systems, batteries, battery charging, microcontroller, LCD indicator, I2C interface, USB interface, RS-485 interface, SWD interface.

Investigation on topology-optimized compressor piston by metal additive manufacturing technique: Analytical and numeric computational modeling using finite element analysis in ANSYS

Abstract Air compressors are widely used in factories to power automation systems and store energy. Several studies have been conducted on the performance of reciprocating and screw compressors. Advancements in design and manufacturing techniques, such as generative design and topology optimization, are leading to improved performance and turbomachinery growth. This work presents a methodology to design and manufacture air compressor pistons using topology optimization and metal additive manufacturing. The existing piston is converted to 3D CAD data and topology optimization is conducted to reduce material in stress concentration regions. Thermal and mechanical loads are considered in boundary conditions. The results show reduced material and improved efficiency, which is validated using ANSYS fluent. The optimized 3D model of the piston is too complex for conventional subtractive manufacturing, so laser sintering 3D printing is proposed. Honeycomb pattern infill patterns are used in 3D printing. This investigation is a step toward researching similar methods in other reciprocating compressor components such as cylinder, cylinder head, piston pins, crankshaft, and connecting rods, which will ultimately lead to improved compressor efficiency.

A novel hybrid cybersecurity scheme against false data injection attacks in automated power systems

The conventional power systems are evolving as smart grids. In recent times cyberattacks on smart grids have been increasing. Among different attacks, False Data Injection (FDI) is considered as an emerging threat that has significant impact. By exploiting the vulnerabilities of IEC 61850 Generic Object-Oriented Substation Events (GOOSE) and Sampled Values (SV) attackers can launch different FDI attacks. In this paper, a real-time set up capable of simulating FDI on GOOSE and SV protocols is developed to evaluate the impact of such attacks on power grid. IEC 62351 stipulates cybersecurity guidelines for GOOSE and SV, but only at communication or Information Technology (IT) level. Hence there is a need to develop a holistic security both at IT and Operation Technology (OT) level. In this regard, a novel sequence content resolver-based hybrid security scheme suitable to tackle FDI attacks on GOOSE and SV is proposed. Furthermore, the computational performance of the proposed hybrid security scheme is presented to demonstrate its applicability to the time critical GOOSE and SV protocols.

A Novel Method of Vector Shift Criterion Utilization in Power System Automation

The article describes the new application of a well-known protection criterion - Vector Shift (VS). In the paper, technical problems related to the utilization of this criterion have been described. Additionally, the authors conducted laboratory tests of a digital protection relay. The impact of different disturbances occurring in the power systems on the operation of the Vector Shift criterion has been examined. The obtained results have proved the validity of banning the Vector Shift criterion in some countries. Furthermore, a novel application of the Vector Shift criterion has been presented. This criterion could be utilized to determine the degree of balance of a separated island. This information may be helpful in novel power system protection devices used in Smart Grids or microgrids.

Automated Power System Fault Prediction and Precursor Discovery Using Multi-Modal Data

Electric power system operators monitor large multi-modal data streams from wide service areas. The current data setups stand to get more complex as utilities add more smart-grid sensors to collect additional data from power system substations and other in-situ locations. We propose a methodology to utilize multi-modal data for automated power system fault prediction, and precursor discovery that takes advantage of not only the utility owned measurements but also an abundance of data from other related databases such as weather observation systems. The process is automated to help operators analyze multi-modal data that may be impossible to process manually due to the size and variety. We automatically preprocess multi-source data and learn a joint latent representation from collocated streamed, sparse, and high-dimensional data collected from Phasor Measurement Units and external weather data. Then we utilize multi-instance learning to predict events and discover precursors simultaneously without relying on post-mortem studies of fault signatures. We apply the proposed methodology to provide early predictions of faults in the U.S. Western Interconnection. AU-ROC of 0.94 is achieved in predicting events by utilizing information 5 hours before event time using season-specific models. We show how precursors can be extracted from multi-modal data and interpreted for predicted events.

Towards Software-Defined Protection, Automation, and Control in Power Systems: Concepts, State of the Art, and Future Challenges

Nowadays, power systems’ Protection, Automation, and Control (PAC) functionalities are often deployed in different constrained devices (Intelligent Electronic Devices) following a coupled hardware/software design. However, with the increase in distributed energy resources, more customized controllers will be required. These devices have high operational and deployment costs with long development, testing, and complex upgrade cycles. Addressing these challenges requires that a ’revolution’ in power system PAC design takes place. Decoupling from hardware-dependent implementations by virtualizing the functionalities facilitates the transition from a traditional power grid into a software-defined smart grid. This article presents a survey of recent literature on software-defined PAC for power systems, covering the concepts, main academic works, industrial proof of concepts, and the latest standardization efforts in this rising area. Finally, we summarize the expected future technical, industrial, and standardization challenges and open research problems. It was observed that software-defined PAC systems have a promising potential that can be leveraged for future PAC and smart grid developments. Moreover, standardizations in virtual IED software development and deployments, configuration tools, performance benchmarking, and compliance testing using a dynamic, agile approach assuring interoperability are critical enablers.

Heterogeneous communication network planning based on ant colony algorithm in smart grid

With the development of smart grids, highly automated power systems increasingly rely on communication networks, and research on the reliability, safety, and economy of heterogeneous communication networks is particularly important. Traditional network planning methods do not take into account network load problems, resulting in serious network congestion and long delays. Aiming at the above problems, this paper proposes a heterogeneous communication network planning method for a smart grid based on an improved ant colony algorithm. This method improves the original ant colony algorithm by updating the pheromone concentration of the network node in real time and realizes the planning of heterogeneous communication networks in the smart grid. Simulation results show that the proposed method has the advantages of avoiding local network congestion, reducing the average delay, and improving network stability.

WSPRT Methods for Improving Power System Automation Devices in the Conditions of Distributed Generation Sources Operation

The trend towards the decentralization and decarbonization of the energy sector stimulates the adoption of generation facilities based on renewable energy sources (RES) and distributed generation (DG) facilities that utilize secondary energy resources. Operation features of DG facilities, such as a high speed of electromechanical transient processes and significant deviations of power quality indicators from standard values, require improvement and an increase in the speed of automation devices. Modern electroautomatic devices must determine the operating regions (normal and emergency) and adapt the operation algorithms to the conditions of the current mode. The study presented proposes methods developed to use the Wald Sequential Probability Ratio Test (WSPRT) to improve the reliability and efficiency of the power system automation devices. The paper provides examples of using WSPRT in the devices of automatic frequency load shedding, automatic transformer disconnection, and power quality control. The results of mathematical modeling confirm the high performance of WSPRT in power system automation devices owing to an increase in the reliability of operating regions identification and speed of response. For example, in the automatic frequency load shedding (AFLS) algorithm for a network with DG facilities at a sampling rate that meets the requirements of the IEC 61850 (80 samples per period), the acceptance time does not exceed 1 ms. The study substantiates the need to use WSPRT in the logic blocks of automation devices employed in active distribution networks.

Vulnerability Detection of ICS Protocols via Cross-State Fuzzing

Industrial control system (ICS) employs complex multistate protocols to realize high-reliability communication and intelligent control over automation equipment. ICS has been widely used in various embedded fields, such as autonomous vehicle systems, power automation systems, etc. However, in recent years, many attacks have been performed on ICS, especially its protocols, such as the hijacks over Jeep Uconnect and Tesla Autopilot autonomous systems, also the Stuxnet and DragonFly viruses over national infrastructures. It is important to guarantee the security of ICS protocols. In this article, we present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Charon</monospace> , an efficient fuzzing platform for the vulnerability detection of ICS protocol implementations. In <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Charon</monospace> , we propose an innovative fuzzing strategy that leverages state guidance to maximize cross-state code coverage instead of focusing on isolated states during the fuzzing of ICS protocols. Moreover, we devise a novel feedback collection method that employs program status inferring to avoid the restart of the ICS protocol at each iteration, allowing for continuous fuzzing. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Charon</monospace> on several popular ICS protocol implementations, including real-time publish subscribe, IEC61850-MMS, MQTT, etc. Compared with typical fuzzers, such as American fuzzy lop, Polar, AFLNET, Boofuzz, and Peach, it averagely improves branch coverage by 234.2%, 194.4%, 215.9%, 52.58%, and 35.18%, respectively. Moreover, it has already confirmed 21 previously unknown vulnerabilities (e.g., stack buffer overflow) among these ICS protocols, most of which are security critical and corresponding patches from vendors have been released accordingly.

Application and Design of Computer Technology in Power System Automation

Application and Design of Computer Technology in Power System Automation

Short-term power load forecasting based on gray relational analysis and support vector machine optimized by artificial bee colony algorithm.

Short-term power load forecasting is essential in ensuring the safe operation of power systems and a prerequisite in building automated power systems. Short-term power load demonstrates substantial volatility because of the effect of various factors, such as temperature and weather conditions. However, the traditional short-term power load forecasting method ignores the influence of various factors on the load and presents problems of limited nonlinear mapping ability and weak generalization ability to unknown data. Therefore, a short-term power load forecasting method based on GRA and ABC-SVM is proposed in this study. First, the Pearson correlation coefficient method is used to select critical influencing factors. Second, the gray relational analysis (GRA) method is utilized to screen similar days in the history, construct a rough set of similar days, perform K-means clustering on the rough sets of similar days, and further construct the set of similar days. The artificial bee colony (ABC) algorithm is then utilized to optimize penalty coefficient and kernel function parameters of the support vector machine (SVM). Finally, the above method is applied on the basis of actual load data in Nanjing for simulation verification, and the results show the effectiveness of the proposed method.

Big Data Application Technology and Prospect Analysis in Smart Distribution Network

Intelligent power distribution network system main application technology including the communications, computer and network, the modern electronic technology, etc., adapter power automation system main components for the master station, terminal automation monitoring system and communication system, has formed a complete collection, transmission and processing in the integration of information system, to realize the remote management of distribution network. The application of big data technology in smart distribution network makes it more intelligent and ADAPTS to the needs of social development. This paper analyzes the application technology and prospect of smart distribution network big data.

Application and Thinking of Artificial Intelligence in Electrical Automation

With the vigorous development of China’s market economy, a new generation of artificial intelligence technology has also developed rapidly, which has strongly promoted the optimization and improvement of China’s electrical automation control system. The use of artificial intelligence technology in power automation systems mainly has the following two advantages: it can reduce the manufacturing time of power products, thereby increasing the number of workers manufacturing power products and promoting enterprises to conduct larger-scale machine manufacturing, thereby improving the comprehensive economic benefits of enterprise machine manufacturing, but also improve the quality of manufactured products of electric power products. Since the machine control of artificial intelligence is more efficient than manual manufacturing, it reduces the manufacturing problems caused by the subjective factors of manufacturing workers to a certain extent and thus improves the qualification rate of machine production. Electrical engineering plays an important role in China’s urban development, and automatic control is an important part of electrical engineering, which can have a direct impact on the quality of electrical engineering. Therefore, in order to promote the healthy development of cities in China, this paper studies the application of artificial intelligence technology in electrical engineering automation by analyzing relevant data, in order to provide a reliable basis for relevant personnel to carry out their work.