Search Results (702)

In this work, we present a neural network-based study capable of predicting faults in oil-insulated power transformers through the analysis of dissolved gases. The advantage of this study lies in using data already collected by electric power companies, which gather it to comply with international or regional standards; however, they sometimes act only after the equipment is already in a faulty condition. Therefore, the challenge in this work was data regularization, as collections typically occur at long intervals of 6 to 12 months. Furthermore, samples are often irregular, as data collection depends on factors such as weather and the availability of maintenance teams. As a result of this work, Multilayer Perceptron (MLP), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were used to predict failures with advanced forecasts ranging from 1 to 6 months, achieving accuracies of 97.5% and 85%, respectively. Thus, these models prove to be important tools for maintenance planning, enabling adequate predictability for organizing equipment shutdowns without the need for high investments in installing tools to capture this information online and adapting substations to send data to control rooms or other analysis centers. Full article

The rapid increase in the adoption of electric vehicles (EVs) in recent years is leading to a significant impact on the electric grid. To ensure sufficient power to these EVs, multiple electric vehicle charging stations (EVCSs) need to be deployed strategically in the electrical power network. Randomly adding these EVCSs can cause potential power quality problems and necessitate additional infrastructure like new distribution/transmission lines, transformers and sub-stations. This can be overcome by optimal deployment of EVCSs. Many existing optimization techniques suffer from premature convergence, sensitivity to initial parameters, the curse of dimensionality and not performing well on non-linear problems. This leads to suboptimal results. To address these drawbacks, a novel method, based on the Artificial Hummingbird Algorithm (AHA), has been developed to identify the optimal location of EVCSs. The novel method, the Modified Artificial Hummingbird Algorithm (MAHA), has been applied to the standard power network–IEEE-57 bus system to find the optimal placement of EVCSs. When compared to existing methods of AHA, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO), the results show that MAHA is more effective in determining the optimal placement of EVCSs. Full article

The study presents the results of research aimed at developing digital models for determining the operating parameters of railway power supply systems equipped with distributed generation plants based on renewable energy sources (RESs). RESs can be used in railway transport to increase the reliability of power supply to facilities located in areas with insufficiently developed power grids. This primarily applies to consumers, for whom a power failure can lead to significant damage, accidents, and a threat to human life. RES can serve as independent power sources for special-group consumers and can increase energy conversion efficiency. Furthermore, large-scale implementation of renewable energy sources can significantly reduce energy supply costs and improve power quality. The study employs phase-coordinate modeling, which is characterized by the following features: a systems approach, which implies determining operating conditions while considering the properties and characteristics of complex traction and supply networks; versatility, which enables modeling of power supply systems of various structures and designs; and comprehensiveness, which involves calculating normal, emergency, and special operating parameters—crucial for scenarios such as ice melting on catenary wires. The modeling results obtained using the Fazonord AC-DC software (ver. 5.3.5.2) show that RES-based distributed generation plants provide a variety of beneficial effects: reduction in electricity consumption from power system networks; decrease in voltage unbalance and harmonic distortion on the busbars of regional windings of traction substations; and stabilization of voltage levels on current collectors of electric locomotives. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

With the increasing adoption of unattended power distribution substations, accurate foreign object detection has become critical to ensure safe system operation. This study proposes a detection model tailored for substation monitoring, targeting hazards such as fire, water accumulation, and small animal intrusion, while accounting for varying on-site illumination conditions. First, an adaptive illumination normalization module is introduced to accommodate diverse lighting conditions, thereby enhancing its capability to capture foreign objects under complex illumination environments. Second, a multi-scale feature extraction and attention-based refinement structure is developed to effectively capture foreign objects with diverse sizes and textures, aligning with the specific detection requirements of substation scenarios. Third, a task-oriented loss function is constructed by incorporating illumination-adaptive weighting into the objectness component, thereby enhancing robustness under uneven illumination conditions. Experimental results demonstrate that the proposed method outperforms representative detection approaches, validating its effectiveness for foreign object detection in substation monitoring applications. Full article

This study proposes a rigorous methodology for the optimal placement of voltage regulators in the Guayacanes feeder of the Buena Fe substation, Ecuador, by integrating electrical feeder modeling, exhaustive search, and multi-criteria decision-making. The feeder was modeled in detail by incorporating its radial topology, nodal electrical parameters, and representative operating conditions under minimum- and maximum-load scenarios. Based on this model, a set of technical evaluation criteria was established to quantify the impact of regulator installation, including active power losses, reactive power losses, global voltage deviation, average voltage variation, and voltage imbalance. An exhaustive search strategy was then implemented to evaluate all feasible regulator-location alternatives over the candidate nodes, thereby ensuring a complete exploration of the solution space. The resulting alternatives were ranked using the Weighted Sum Method (WSM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), allowing the comparison of candidate locations from a multi-criteria perspective. The results indicate that node MTS 108932 provides the most technically favorable overall solution, achieving the greatest improvement in voltage profile quality and the most significant reduction in electrical losses. In addition, a sensitivity analysis was conducted by varying the weighting structure of the decision criteria, confirming the robustness of the selected alternative under different decision-maker preference scenarios. The proposed framework provides a technically sound decision-support methodology for voltage regulation planning in real radial distribution systems. Full article

High renewable penetration introduces stochastic variability in distribution-network operation, requiring probabilistic AC power-flow tools that remain accurate in the tails while avoiding the computational burden of large Monte Carlo simulation. This paper presents a fully reproducible non-intrusive polynomial chaos expansion (PCE) framework for uncertainty propagation through nonlinear Newton–Raphson AC power flow. The method uses sparse-grid quadrature to train PCE surrogates from deterministic power-flow evaluations and is benchmarked against high-fidelity Monte Carlo simulations. In the validation, the IEEE 33-bus feeder is evaluated using up to 50,000 Monte Carlo samples, 95% bootstrap confidence intervals, PCE orders 2–5, correlated uncertainty scenarios, realistic thermal-loading recalibration, reactive-power sensitivity of renewable injections, multi-feeder testing on IEEE 33-bus, CIGRE MV, CIGRE LV, and IEEE 118-bus networks, and a 365-snapshot full-year daily screening. For the base IEEE 33-bus case, third-order PCE required only 494 deterministic power-flow evaluations and reproduced the 50,000-sample Monte Carlo benchmark with relative mean errors of 0.014% for minimum voltage, 0.119% for active losses, and 0.113% for substation import. The corresponding wall-clock speed-up was 13.29×, while reducing deterministic evaluations by approximately 101×. Correlated load–PV uncertainty increased the upper tail of substation import from 6.06 MW to 6.30 MW, and realistic thermal recalibration revealed line-loading p99 values above 100% for the 60% target case, demonstrating the operational value of physically meaningful ampacity settings. The proposed workflow provides an open, scalable, and tail-aware basis for uncertainty-informed distribution-network planning under renewable variability. Full article

Accurate substation-level Short-Term Load Forecasting (STLF) is essential for secure day-ahead power-system operation, yet localized demand is often affected by meteorological variation and discrete calendar shifts such as statutory holidays and makeup workdays. At this spatial scale, end-to-end forecasting models may over-smooth abrupt local changes and fail to represent peaks and valleys accurately. To address this issue, this study proposes a Calendar-Aware Frequency-Decoupled Framework (CA-FDF) for 24 h ahead substation load forecasting. The load series is decomposed by the Discrete Wavelet Transform (DWT), and the low-frequency component is tracked by a regime-aware Recursive Least Squares (RLS) filter. The residuals are then refined through explicit calendar-state encoding and day-ahead weather forecasts. A Multi-Layer Perceptron (MLP) learns latent weather representations, while SHapley Additive exPlanations (SHAP) interpret calendar- and weather-related effects. Experiments on hourly operational data from 29 anonymized substations in East China show that CA-FDF achieves a Mean Absolute Percentage Error (MAPE) of 1.92% and outperforms representative baselines under the same day-ahead setting. The results indicate that frequency-decoupled residual refinement improves localized load forecasting, with calendar-aware correction contributing the largest gain. Full article

This research introduces a unique optimization framework centered on the Geese V-Formation Algorithm to enhance the technical planning of distributed energy resources in renewable microgrid-oriented radial distribution systems. The proposed methodology addresses the optimal placement and sizing of photovoltaic panels, wind turbines, battery energy storage systems, and capacitor banks to provide comprehensive voltage support, minimize active power losses, and refine overall grid functionality. Drawing inspiration from the aerodynamic efficiency of migratory geese, the Geese V-Formation Algorithm integrates dynamic leader-follower coordination, adaptive role rotation, and cooperative information exchange mechanisms. These features allow the algorithm to effectively balance global exploration and local exploitation, making it uniquely suited to address the complex, nonlinear, and multi-objective nature of modern microgrid design. The effectiveness of this approach was evaluated through rigorous simulations on the IEEE-33 and IEEE-69 bus distribution systems utilizing the Python programming language. The empirical results indicate that the Geese V-Formation Algorithm achieves substantial power loss reductions, reaching 91.62% and 92.45%, respectively, when integrating solar and wind resources with energy storage and reactive power compensation. Furthermore, the optimized configurations significantly improved bus voltage profiles and enhanced substation power factors, confirming the technical effectiveness of the framework under the considered benchmark constraints. By providing a technical decision-support approach for engineers and utility planners, this framework facilitates the deployment of reliable, decentralized renewable energy systems that align with global energy transition objectives and promote sustainable infrastructure development. Full article

This work proposes a vendor-independent integration method for International Electrotechnical Commission (IEC) 61850 Manufacturing Message Specification (MMS) communication protocol into Programmable Logic Controller (PLC) systems that support an open network communication interface available for the PLC program. IEC 61850 is globally well accepted for electrical substation control, and the protocol MMS is used for integrating the electrical substation bay level into the station level, where the PLC orchestrates the process level of the substation and parallel processes. This method was created because most PLCs lines do not natively support any protocol of IEC 61850, although it often needs to be used for the control of electrical substations. For the development of the prototype presented in this paper, PLCs from the Siemens AG families S7-1500 and S7-410, which support open communication over Transmission Control Protocol/Internet Protocol (TCP/IP) with external systems, were used for validation. Different results regarding network communication and PLC program performance are presented in this paper. The implemented solution presents a meaningful implementation of the MMS application layer into the PLC program and was successfully validated with real industrial, single and redundant PLC systems. Full article

The growing demand for energy-efficient urban rail transit has led to the increasing deployment of reversible substations (RS) in traction power supply systems. These substations, equipped with bidirectional converter devices (BCDs), involve high initial costs and complex parameter optimization challenges. This paper presents a coordinated optimization method for BCD-equipped RS using a two-layer model. In the upper layer, the model determines the siting of RS and the capacity of BCD to minimize life-cycle cost (LCC). In the lower layer, it adjusts the control parameters of BCDs to reduce annual operating cost. An improved salp swarm algorithm (ISSA), incorporating Tent chaotic mapping and Levy flight, is developed to solve the model. A case study based on an 18.2 km subway line shows that the optimized configuration reduces overall cost by 5.12% and electricity cost by 10.53% compared with a conventional rectifier system. Moreover, it achieves a 1.19% reduction in electricity cost over a system with fixed control parameters, while maintaining rail potential and catenary voltage within safe limits. These findings demonstrate that the proposed method strikes an effective balance between initial investment and long-term operational benefits, contributing to improved energy efficiency and economic performance. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

The global energy system is undergoing a significant transformation driven by rapid electrification, urbanization, and the emergence of new categories of electricity consumers. In particular, the increasing load density in low-voltage distribution networks within urban areas requires a reconsideration of conventional methodologies for the placement of transformer substations. Traditional planning approaches are often based on empirical service radii or static demand factors and therefore fail to adequately reflect the complexity of modern urban power systems. This study proposes a multi-objective optimization model for the optimal placement of transformer substations in 10/0.4 kV urban distribution networks. The proposed model simultaneously considers power losses, economic costs, and system reliability. In addition, the design load model is extended through the introduction of a comfort coefficient that captures additional electricity consumers typical of modern urban infrastructure, including HVAC systems, elevators, pumping systems, and electric vehicle charging stations. In contrast to traditional empirical approaches, the transformer service radius is modeled as a physical parameter determined by voltage drop limits, cable thermal constraints, and failure intensity. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Each candidate solution generated by the algorithm is validated through AC load-flow simulations performed in the DIgSILENT PowerFactory environment. The proposed methodology is evaluated using real data from a 0.48 km² urban area in the city of Tashkent. The results indicate that increasing the transformer service radius reduces capital investment costs but leads to higher power losses and longer interruption durations. According to the Pareto analysis, a service radius of approximately 300 m represents the optimal compromise between technical, economic, and reliability criteria for the studied area. The proposed methodology can serve as an effective tool for the scientifically grounded planning of urban power supply systems and for improving energy efficiency in modern distribution networks. Full article

Maintaining aging equipment is essential for ensuring the reliability and performance of power substations, which are critical components of electric power systems. Preventive maintenance (PM) policies are widely used in this context, but their definition is often based on fixed schedules that do not explicitly account for system degradation and failure dynamics. This paper proposes a structured decision-making framework for evaluating the extension of PM intervals in complex engineering systems. The approach integrates Reliability Block Diagram (RBD) modeling with an imperfect maintenance representation based on the Generalized Renewal Process (GRP) and a Kijima model, allowing the representation of cumulative degradation effects. A decision criterion combining system reliability and the expected number of failures is defined to assess the feasibility of maintenance strategies. The framework is applied to a power substation case study, with emphasis on the transmission line subsystem, considering different PM extension scenarios and parameter uncertainty through sensitivity analysis. The results show that maintenance interval extension must be treated as a constrained problem, in which reductions in maintenance effort must be balanced against increased degradation and failure risk. For the presented case study, only moderate extensions are found to be consistently feasible across all evaluated conditions. The proposed approach provides a systematic and practical method for supporting maintenance planning under uncertainty, enabling consistent and transparent evaluation of maintenance strategies. Full article

Ensuring safety compliance is paramount in substation operations. However, worker re-identification (Re-ID) remains highly challenging due to severe occlusions, uniform appearance similarity, and substantial illumination variations across shifts and environments. Moreover, the escalating cost of manual identity annotation in large-scale, multi-site surveillance systems necessitates annotation-free approaches for practical deployment. In this paper, we propose AdaInCV (Adaptive Intra-Class Variation Contrastive Learning), an unsupervised Re-ID framework tailored for substation worker safety monitoring. The proposed method quantitatively evaluates the model’s learning capacity for each pseudo-cluster by measuring intra-class feature variation after DBSCAN clustering, and adaptively selects training samples with appropriate difficulty throughout the learning process. To this end, two novel strategies are introduced. Adaptive Sample Mining (AdaSaM) progressively constructs reliable identity clusters while dynamically updating the memory dictionary. Adaptive Outlier Filtering (AdaOF) further exploits informative outlier samples—primarily caused by heavy occlusion or extreme illumination—as hard negatives to enhance contrastive representation learning. Extensive experiments on two widely used Re-ID benchmarks (Market-1501 and MSMT17), as well as an in-house Substation Worker Re-ID (SWRID) dataset, demonstrate that AdaInCV achieves state-of-the-art performance with significantly faster convergence than existing methods, establishing a practical foundation for intelligent safety supervision in power grid operations. Full article