Search Results (55)

Power transformers are critical components in electrical power systems, where failures can cause significant outages and economic losses. Traditional maintenance strategies, typically based on offline inspections, are increasingly insufficient to meet the reliability requirements of modern digital substations. This work presents an integrated multi-sensor monitoring framework that combines online frequency response analysis (OnFRA^® 4.0), capacitive tap-based monitoring (FRACTIVE^® 4.0), dissolved gas analysis, and temperature measurements. All data streams are synchronized and managed within a SCADA system that supports real-time visualization and historical traceability. To enable automated fault diagnosis, a Random Forest classifier was trained using simulated datasets derived from laboratory experiments that emulate typical transformer and bushing degradation scenarios. Principal Component Analysis was employed for dimensionality reduction, improving model interpretability and computational efficiency. The proposed model achieved perfect classification metrics on the simulated data, demonstrating the feasibility of combining high-fidelity monitoring hardware with machine learning techniques for anomaly detection. Although no in-service failures have been recorded to date, the monitoring infrastructure is already tested and validated through laboratory conditions, enabling continuous data acquisition. Full article

With the widespread adoption of intelligent electronic devices (IEDs) in smart substations, the real-time data transmission and interoperability features of the IEC 61850 communication standard play a crucial role in ensuring seamless automation system integration. This paper presents a hardware-in-the-loop (HIL) platform experiment analysis based on a simulated IED and man-in-the-middle (MITM) attack, leveraging built-in IEC 61850 protocol software to replicate an existing substation communication architecture in cyber physical systems. This study investigates the framework performance and protocol robustness of this approach. First, the physical network infrastructure of smart grids is analyzed in detail, followed by the development of an HIL testing platform tailored for discrete communication network scenarios. Next, virtual models of intelligent electrical equipment and MITM attacks are created, along with their corresponding communication layer architectures, enabling comprehensive simulation analysis. Finally, in the 24-h stability operation test and the test of three typical fault scenarios, the simulated IED can achieve 100% of the protocol consistency passing rate, which is completely consistent with the protection action decision of the physical IED, the end-to-end delay is less than 4 ms, and the measurement accuracy matches the accuracy level of the physical IED, which verifies that the proposed test platform can effectively guide the commissioning of smart substations. Full article

This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments. Full article

This paper presents a novel method for assessing the technical condition of devices in the high-voltage (HV) switching bay of a substation, focusing on circuit breakers, disconnectors, and instrument transformers. These devices are typically maintained using a condition-based maintenance approach. The proposed method integrates data from individual maintenance tasks into a comprehensive assessment of each device’s technical condition. Traditionally, the technical condition and health index assessments rely solely on additive criteria. This study introduces an advanced assessment method that incorporates both additive and multiplicative criteria to enhance the prioritization of maintenance tasks. A data model is developed to extract the maintenance task data from device maintenance databases, enabling an automated assessment process. The proposed approach facilitates the generation of a c-curve throughout a device’s operational life. A comparison using real transmission system operator maintenance data demonstrates that the proposed method, which assesses device conditions using both additive and multiplicative criteria, outperforms the conventional approach that relies solely on additive criteria. Full article

For modern power plants to be dependable, safe, sustainable, and provide the highest operational efficiency (i.e., enhance dynamic load distribution with a faster response time at reduced reactive losses), there must be an intelligent dynamic load management system based on modern computational techniques to prevent overloading of power devices (i.e., alternators, transformers, etc.) in grid systems. In this paper, a co-simulation framework (Panda-SDN Load Balancer) is designed to achieve maximum operational efficiency from the power grid with the prime objective of real-time intelligent load balancing of operational power devices (i.e., power transformers, etc.). This framework is based on the integration of two tools: (a) PandaPower (an open-source Python tool) used for real-time power data (voltage; current; real power, P_Real; apparent power, P_Apparent; reactive power, P_Reactive; power factor, PF; etc.) load flow analysis; (b) Mininet used for the designing of a Software-Defined Network (SDN) with a POX controller for managing the load patterns on power transformers after load flow analysis obtained through PandaPower via the synchronization tool Message Queuing Telemetry Transport (MQTT) and Intelligent Electrical Devices (IEDs). In this research article, the simulation is performed in three scenarios: (a) normal flow, (b) loaded flow without the proposed framework, and (c) loaded flow with the proposed framework. As per simulation results, the proposed framework offered intelligent substation automation with (a) balanced utilization of a transformer, (b) enhanced system power factor in extreme load conditions, and (c) significant gain in system operational efficiency as compared to legacy load management methods. Full article

Swift urbanization and technical progress in Cambodia, specifically in Phnom Penh, require underground transmission lines (UGTL) as a viable substitute for overhead transmission lines (OHTL). However, the substantial cost of UGTL has prevented its extensive integration. In this respect, we identified the most cost-effective technological route for an underground transmission line between substations. Using geographic information system (GIS) data, we generated algorithms to define the optimal route for the installation of a UGTL and minimize the costs of the material and labor required. The research results presented an automated tool for route optimization which simplifies the planning of energy projects and partially relieves the financial burden of UGTL integration. The proposed method radically changes the planning of urban energy infrastructure, as it provides a technology-based, cost-efficient, and environmentally favorable decision for UGTL routing. It also fosters the development of sustainable and resilient urban energy systems in similar urban locations. Full article

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate. Full article

The evolution of Protection and Control (P&C) systems has developed though analogue and digital generations, and is presently advancing towards the utilization of Virtualization of Protection, Automation and Control environments (VPAC). This article focuses on redefining the features of traditional and modern P&C systems, Centralized Protection Automation and Control (CPAC), and VPAC, focusing on the integration of Intelligent Electronic Devices (IEDs) with secure communication that is time-effective in the centralized distribution of power and prevention of network vulnerability. Though standards such as IEC 61850-9-2 LE have been adopted, the actualization of full interoperability between diverse IED manufacturers remains elusive. With the digitization of technologies, P&C systems are naturally transitioning to virtual environments, with timing precision, redundancy and security being imperative. Latency and resource management and allocation in VPAC systems are considerable global issues. This paper discusses the issues of maintaining low operational performance in virtual substation environments while satisfying the requirements for performance in real time. The impacts of large volumes of data and artificial intelligence on the management of the grid are studied, and AI-based analytics that predict system failures and automatically change load flows are shown, as they have the potential to increase the flexibility and stability of the grid. The use of big data enables electric power utilities to enhance their protection systems, anticipate disturbances and improve energy management methods. The paper presents a comparative analysis between traditional P&C and its virtualized counterparts, with strong emphasis placed on the flexibility and scaling of VPAC resources. Full article

Towards the decarbonisation of the power system, digital substations have gradually increased in smart grids, where Ethernet cables have replaced large quantities of copper wires. With this transition, the standardised communication protocols through the LAN network play a central role in exchanging information and data between the physical power system and the control centres. One of the well-known protocols in the digital substations is IEC 61850 GOOSE (Generic Object-Oriented Substation Event), which is used to share time-critical information related to protection, automation, and control. The transmission time of this protocol affects power system operation and raises various issues, such as communication latencies and incorrect information. Therefore, it is necessary to consider the protocol transmission time for further protection and control mechanisms to ensure the stability and efficiency of the power system. For this purpose, this paper contributes the implementation of a cyber–physical power system (CPPS) testbed to measure the transfer time of IEC 61850 GOOSE under the real-time domain using the real-time simulator, Typhoon HIL, and its toolchains. This paper can benefit scholars and researchers in the relevant domains in implementing a CPPS testbed and an approach for transfer time measurement of communication protocols within the laboratory, eliminating the need for real-world substation devices. Full article

Smart substations are an important trend in substation construction. With increasing data traffic, it is difficult for the traditional Ethernet network to meet the real-time requirements of control information in smart substations. Hence, in this paper, a deterministic network architecture for substations based on time-sensitive networks (TSN) has been developed in order to realize the domain-wide time synchronization and efficient real-time communication of the “three-layer and two-network” model in smart substations. Furthermore, a design scheme for substation automation equipment based on TSN is proposed. The proposed device realizes the timely transmission of real-time control information packets by utilizing the Earliest TxTime First (ETF) Qdisc technology of Linux and the timing sending capability of Intel 210 NIC. Furthermore, it collaborates with the time-aware shaper (TAS) traffic scheduling mechanism of TSN switches to ensure the end-to-end deterministic delay of time-sensitive traffic. As a result, it provides efficient real-time communication services with low latency and jitter for smart substation automation systems. Full article

Substations, as critical nodes for power transmission and distribution, play a pivotal role in ensuring the stability and security of the entire power grid. With the ever-increasing demand for electricity and the growing complexity of grid structures, traditional manual inspection methods for substations can no longer meet the requirements for efficient and safe operation and maintenance. The advent of automated inspection systems has brought revolutionary changes to the power industry. These systems utilize advanced sensor technology, image processing techniques, and artificial intelligence algorithms to achieve real-time monitoring and fault diagnosis of substation equipment. Among these, the recognition of cable-to-terminal connection relationships is a key task for automated inspection systems, and its accuracy directly impacts the system’s diagnostic capabilities and fault prevention levels. However, traditional methods face numerous limitations when dealing with complex power environments, such as inadequate recognition performance under conditions of significant perspective angles and geometric distortions. This paper proposes a cable-to-terminal connection relationship recognition method based on the YOLOv8-pose model. The YOLOv8-pose model combines object detection and pose estimation techniques, significantly improving detection accuracy and real-time performance in environments with small targets and dense occlusions through optimized feature extraction algorithms and enhanced receptive fields. The model achieves an average inference time of 74 milliseconds on the test set, with an accuracy of 92.8%, a recall rate of 91.5%, and an average precision mean of 90.2%. Experimental results demonstrate that the YOLOv8-pose model performs excellently under different angles and complex backgrounds, accurately identifying the connection relationships between terminals and cables, providing reliable technical support for automated substation inspection systems. This research offers an innovative solution for automated substation inspection systems, with significant application prospects. Full article

This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency. Full article

This paper presents an investigation into the virtualization of substation protection IED functions using a sophisticated co-simulation environment that integrates virtual intelligent electronic devices (vIEDs) with a real-time power grid simulation. Anchored by the IEC 61850 protocol, this study constructs a virtualized IED framework, emphasizing the encapsulation of protection schemes using the example of different types of overcurrent protection within a containerized vIED. Using open-source software, this study aims to replicate the communication and functional aspects of physical IEDs. This study uses a co-simulation environment that couples virtualized network components with a real-time power grid simulation to validate the vIEDs against real substation hardware. Simulation results from induced short-circuit events confirm the operational congruence of the vIEDs with their physical counterparts, demonstrating their potential to serve as cost-effective and adaptable testbeds for substation automation. This paper concludes that virtualized IEDs represent a cost-effective, flexible alternative for substation automation testing, with future research directed towards increasing the functional complexity and real-world applicability of these virtual systems. Full article

The basic requirements for the design of a switchgear for main 35–220 kV step-down substations are reliability and efficiency. Switchgear circuits are chosen depending on the number of supply and transit lines and transformers, the substation’s place in the power supply system, and the area that can be allocated under the substation construction. The substation switchgear’s reliability depends on the circuit type and the equipment to be installed. When designing, as a rule, standard substation switchgear circuit solutions are chosen with often unreasonably overstated reliability indicators and, as a consequence, capital costs. This paper explores the issues of automated choice of the best option for the 35–220 kV substation switchgear circuit, considering its structural reliability and additional costs for the area allocated under the electrical unit. A distinctive feature of the work is a comprehensive accounting of the reliability indicators of the substation and the influence of the layout of switchgear circuits as a whole on the performance indicators of future electrical installations. The developed technique is used at the pre-design stages and allows for reducing further capital, maintenance, and repair costs for electrical units by up to 14.5%. The developed approach is implemented in the original TER CAD software product. Full article

The matching of schematic diagrams and physical information of terminal blocks in substation secondary screen cabinets plays a crucial role in the operation and maintenance of substations. To enhance the automation level of this task and reduce labor costs, a method for identifying and matching information of terminal blocks in substation secondary screen cabinets based on artificial intelligence is investigated in this paper. Initially, multi-layer object detection networks, tailored to the characteristics of both the schematic diagrams and the physical entities in substation secondary screen cabinets, are designed for the precise extraction of information. Subsequently, network topologies for both the schematic and physical systems are established using the Neo4j database, which allows for the digital storage of information in the substation secondary screen cabinet systems. Finally, the branch-and-bound method, improved by the application of a multi-modular graph convolutional network (MGCN) and deep Q-network (DQN), is employed to solve the maximum common subgraph (MCS) problem, resulting in the rapid and efficient matching of schematic and physical data. Full article