Search Results (177)

The operational modes and fault characteristics of distribution networks incorporating distributed generation are becoming increasingly complex. This complexity increases the difficulty of predicting switch control action times and leads to scattered samples with data scarcity. Consequently, it imposes higher demands on rapid fault isolation and load transfer control following system failures. To address this issue, this paper proposes a switch action time prediction and synchronous load transfer control method based on Bayesian optimization of bidirectional long short-term memory (Bo-BiLSTM) networks. A distribution network simulation model incorporating distributed generation was constructed using MATLAB/Simulink (R2023a). Three-phase voltage and current at the Point of Common Coupling (PCC) were extracted as feature parameters to establish a switch operation timing database. Bayesian optimization was employed to tune the BiLSTM hyperparameters, constructing the Bo-BiLSTM prediction model to achieve high-precision forecasting of switch operation times under fault conditions. Subsequently, a load-synchronized transfer control strategy was proposed based on the prediction results. A dynamic delay mechanism was designed to achieve “open first and then close” sequential coordinated control. Physical experiments verified that the time difference between opening and closing was controlled within 2–12 milliseconds (ms), meeting the engineering requirement of less than 20 ms. The results demonstrate that the proposed control method enhances switch operation time prediction accuracy while effectively supporting rapid fault isolation and seamless load transfer in distribution networks, thereby improving system reliability and control precision. Full article

Ultra-high-voltage direct current (UHVDC) transmission serves as a vital method for long-distance transmission of renewable energy in China. Commutation failure represents a common fault type in UHVDC transmission systems, causing the sending-end bus voltage to exhibit a “low-to-high” characteristic. This phenomenon poses a high-voltage disconnection risk for renewable energy units at the sending end. The high-voltage ride-through criteria for renewable energy incorporate both time and voltage peak factors. However, existing research relies solely on the voltage peak metric to assess disconnection risks for renewable units, failing to determine the specific stability level of the voltage. Therefore, this paper considers the cumulative effect of voltage transients over time, constructing a mathematical model of transient voltage during the entire fault process of a UHVDC transmission system at the sending end under commutation failure. Subsequently, a transient voltage rise stability margin metric based on a multi-binary table is proposed to evaluate the system’s transient voltage rise stability margin from both time and voltage peak dimensions. Finally, the accuracy of the proposed mathematical model and evaluation metric is validated using the PSCAD/EMTDC simulation platform. Results indicate that following a commutation failure in a UHVDC system, under the scenario of overvoltage instability alone, a higher short-circuit ratio (SCR) correlates with a lower system rated voltage. This configuration enhances the voltage stability margin of the sending end grid, improves its transient voltage stability, and helps mitigate the risk of renewable energy units disconnecting from the sending end grid. Full article

Runout is a common failure of brake discs. The detection of this fault usually depends on the driver, as there is a vibration in the car and on the brake pedal. As Advanced Driver Assistant Systems are implemented and autonomous driving modes are available, braking is carried out by the car instead. Brake disc runout can cause longer braking distance, so it is essential to recognize and repair it. NVH measurements have been validated to be one of the solutions to detect the fault immediately without disassembling the brake unit. In this article, the previous vibration measurements are extended with other methods that can also be used for fault detection. Brake fluid pressure measurement and integration of the disc rotation angle sensor enable the detection of faults without additional sensors. The aim of the research is to design a measurement method that can be compared with previously validated measurements. Full article

Bearing faults are the most common type of failure in induction motors, given their long operating times and mechanical loads. Because induction motors in industrial environments operate under various load conditions, effective methods for diagnosing bearing faults across these conditions have become increasingly important. Here, different load conditions were implemented with a powder clutch and a tension controller, and vibration data were acquired under both normal and faulty bearing conditions. To ensure diagnostic accuracy while improving time efficiency, a model bank-based fault diagnosis classifier is proposed, which utilizes independent classifiers trained for each load condition. For comparison, a single model-based classifier trained on all load conditions is also implemented. Both approaches are validated with three classifiers: support vector machine (SVM), multilayer neural network (MNN), and random forest (RF), with three input types: raw time-series signals, six statistical features, and three t-test–selected statistical features. Experimental results reveal that the model bank-based fault diagnosis classifier utilizing three statistical features selected by t-test maintained 98–100% accuracy while reducing operating time compared with Method 1 by 60.0, 71.2, and 60.0% for SVM, MNN, and RF, respectively. These results confirm that the proposed Method 2 utilizing time-domain analysis provides reliable and time-efficient performance for bearing fault diagnosis under variable load conditions. Full article

Ensuring the safe operation of large hydro-generators is essential for energy supply and economic development. Stator-winding single-phase grounding faults are among the most common failures in such generators. Conventional protection methods—such as fundamental voltage protection, third-harmonic voltage saturation, and low-frequency injection—lack fault location capability and cannot assess the fault severity. This paper proposes a stepwise variable-frequency pulse injection method for fault diagnosis and location in large hydro-generator stator windings. A finite element model of a salient-pole hydro-generator is established to analyze magnetic flux density and electromotive force distributions under normal and fault conditions, from which fault characteristics are derived. Equivalent circuit models suitable for low- and high-frequency pulse injection are developed. A bidirectional pulse injection circuit and algorithm are designed to identify the fault phase via terminal current vector characteristics, diagnose the faulty branch based on leakage loop equivalent inductance, and locate the fault point using voltage–current signal slopes. Simulation results validate the effectiveness of the proposed diagnostic approach. Full article

This paper reviews recent progress in fault detection, reliability analysis, and predictive maintenance methods for grid-connected solar photovoltaic (PV) systems. With the rising adoption of solar power globally, maintaining system reliability and performance is vital for a sustainable energy supply. Common faults discussed include panel degradation, electrical issues, inverter failures, and grid disturbances, all of which affect system efficiency and safety. While traditional diagnostics like thermal imaging and V-I curve analysis offer valuable insights, they mostly detect issues reactively. New approaches using Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) enable real-time monitoring and predictive diagnostics, significantly enhancing accuracy and reliability. This study represents the introduction of a consolidated decision framework and taxonomy that systematically integrates and evaluates the fault types, symptoms, signals, diagnostics, and field-readiness across both plant types and voltage levels. Moreover, this study provides quantitative benchmarks of performance metrics, energy losses, and diagnostic accuracies of 95% confidence intervals. Adopting these advanced techniques promotes proactive management, reducing operational risks and downtime, thus reinforcing the resilience and sustainability of solar power infrastructure. Full article

It is well known that power converters have the highest failure rate in the energy conversion chain in different industrial applications. This could definitely affect the reliability of the system. The reliability of converters in power conversion systems is crucial, as failures can lead to critical consequences and damage other system components. Therefore, it is important to predict and detect failures and take corrective actions to prevent them. One of the most common types of failure in power converters is semiconductor failure, which can manifest as an open circuit or a short circuit. This paper focuses on single and double open-circuit switch failures in a 5-level cascaded H-bridge inverter, for which a fast, precise method is required. A data-driven approach is employed here, using the output voltage and voltages across each H-bridge as diagnostic signals. A 1D-CNN LSTM neural network is trained to accurately detect and localize open-circuit faults, providing a reliable, practical solution. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

With rapid urbanization, deep foundation pit clusters (DFPCs) have become increasingly common, introducing complex and significant construction risks. To improve risk evaluation under such complexity and uncertainty, this study proposes a hierarchical assessment framework. First, fault tree analysis is used to systematically identify and decompose DFPC-related risks. Second, a Bayesian network (BN) is constructed based on the fault tree to model interactions among risks, and structural learning techniques are applied to optimize the BN structure. An analytic hierarchy process (AHP) is then used to assign prior probabilities, enabling the identification of critical risk factors. To validate the framework, numerical simulations are used to analyze the impact of support failures on pit stability. The results show that mid-span support failures have the greatest influence. Two DFPC layouts are simulated to assess the effects of failure location and pit spacing. When the spacing is 0.10H (H = excavation depth), failures in a subpit’s mid-support cause the most severe impact on adjacent pits. These results confirm the framework’s effectiveness in evaluating DFPC risk. Full article

Induction motors are a common component of electromechanical equipment in mining operations, yet they are susceptible to failures resulting from frequent start–stops, overloading, wear and tear, and component failure. It is evident that such failures can result in severe ramifications, encompassing industrial accidents and economic losses. The present paper proposes a detailed study of engine fault diagnosis technology. It has been demonstrated that prevailing intelligent engine diagnosis algorithms exhibit a limited diagnostic efficacy under variable operating conditions, and the reliability of diagnostic outcomes based on individual signals is questionable. The present paper puts forward the proposition of an investigation into a fault diagnosis algorithm for induction motors. This investigation utilized a range of analytical methods, including signal analysis, deep learning, transfer learning, and information fusion. Currently, the methods employed for fault diagnosis based on traditional machine learning are reliant on the selection of statistical features by those with expertise in the field, resulting in outcomes that are significantly influenced by human factors. This paper is the first to integrate a multi-branch ResNet strategy combining three-phase and single-phase currents. A range of three-phase current input strategies were developed, and a deep learning-based motor fault diagnosis model with adaptive feature extraction was established. This enables the deep residual network to extract fault depth features from the motor current signal more effectively. The experimental findings demonstrate that deep learning possesses the capacity to automatically extract depth features, thereby exceeding the capabilities of conventional machine learning algorithms with regard to the accuracy of motor fault diagnosis. Full article

This research investigates the use of a fiber optic sensor for detecting hydrogen gas during a thermal runaway of lithium-ion batteries (LIBs). Timely detection of thermal runaway in LIBs, particularly in storage and logistics, is crucial for effective safety management and preventing the escalation of incidents to adjacent cells. The sensors employed in this study utilize fiber Bragg grating (FBG) technology. The FBG sensors are coated with palladium nanoparticles, enabling the detection of hydrogen concentrations up to 5%. In abuse tests, the sensors successfully identified hydrogen emissions. Cross-sensitivity effects were observed during a secondary test and were thoroughly investigated. These interferences were found to be primarily caused by carbon monoxide (CO), a common byproduct of battery venting. While the presence of CO can interfere with hydrogen detection, both signals remain independently valuable as indicators of cell malfunction. This dual-response behavior enhances the robustness of fault detection under real-world battery failure scenarios. Full article

Enhancing the failure tolerance ability of networks is crucial, as node or link failures are common occurrences on-site. The current fault tolerance schemes are divided into reactive and proactive schemes. The reactive scheme requires detection and repair after the failure occurs, which may lead to long-term network interruptions. The proactive scheme can reduce recovery time through preset backup paths, but requires additional resources. Aiming at the problems of long recovery time or high overhead of the current failure tolerance schemes, the Polymorphic Network adopts field-definable network baseline technology, which can support diversified addressing and routing capabilities, making it possible to implement a more complex and efficient failure tolerance scheme. Inspired by this, we propose an efficient Multi-topology Failure Tolerance mechanism in Polymorphic Network (MFT-PN). The MFT-PN embeds a failure recovery function into the packet processing logic by leveraging the full programmable characteristics of the network element, improving failure recovery efficiency. The backup path information is pushed into the header of the failed packet to reduce the flow table storage overhead. Meanwhile, MFT-PN introduces the concept of multi-topology routing by constructing multiple logical topologies, with each topology adopting different failure recovery strategies. Then, we design a multi-topology loop-free link backup algorithm to calculate the backup path for each topology, providing extensive coverage for different failure scenarios. Experimental results show that compared with the existing strategies, MFT-PN can reduce resource overhead by over 72% and the packet loss rate by over 59%, as well as effectively cope with multiple failure scenarios. Full article

This article addresses the monitoring and diagnosis of high-speed spindles (HSM) used in CNC grinding machines, emphasizing the importance of the real-time evaluation of their dynamic behavior during commissioning. Due to the complexity of these dynamic phenomena, especially at high speeds (up to 150,000 RPM), common faults such as bearing wear, imbalance, or misalignment can lead to catastrophic failures and high repair costs. An original method is proposed, based on synchronous envelope vibration analysis (SEVA) using the Hilbert transform, to detect mechanical defects in both low-frequency domains (imbalance, mechanical looseness) and high-frequency domains (bearing faults). The system includes vibration, temperature, and speed sensors, and the experimental protocol involves step-by-step monitoring from 10,000 to 90,000 RPM. Through synchronous FFT analysis and IFFT, critical frequencies and their impacts on machining quality are identified. The method enables the accurate fault diagnosis of new or refurbished spindles under real industrial conditions, reducing downtime and production losses. The method involves both local and remote real-time monitoring and diagnosis using a remote data center protocol. Full article

Rolling bearing failures in rotating machinery essential to energy systems (e.g., motors, generators, or turbines) can cause downtime, energy inefficiency, and safety hazards—especially under variable speed conditions common in traction drives. Traditional diagnosis methods struggle with nonstationary signals from speed variations. In response, there is a growing trend toward unsupervised and adaptive signal processing techniques, which offer better generalization in complex operating scenarios. This paper proposes an intelligent fault diagnosis framework combining Adaptive Chirp Mode Decomposition (ACMD)-based order tracking with a novel Shortest Paths Density Peak Search (SP-DPS) clustering algorithm. ACMD is chosen for its proven ability to extract instantaneous speed profiles from nonstationary signals, enabling angular domain resampling and quasi-stationary signal representation. SP-DPS enhances clustering robustness by incorporating global structure awareness into the analysis of statistical features in both the time and frequency domains. The method is validated using both a public bearing dataset and a custom-built metro traction motor test bench, representative of electric traction systems. The results show over 96% diagnostic accuracy under significant speed fluctuations, outperforming several state-of-the-art clustering approaches. This study presents a scalable and accurate unsupervised solution for bearing fault diagnosis, with strong potential to improve reliability, reduce maintenance costs, and prevent energy losses in critical energy conversion machinery. Full article

Smart meters play a significant role in power systems, but their condition assessment faces challenges such as inconsistent evaluation criteria and inaccurate assessment results. This paper proposes feature engineering including feature construction and feature selection for smart meter group failure rate prediction. First, the basic structure and common fault types of smart meters are introduced. Smart meters are grouped by batch and distribution area. Next, 25 condition features are constructed based on failure mechanisms and technical specifications. Then, an evolutionary multi-objective feature selection algorithm combining NSGA-II, Jaccard similarity, and XGBoost is developed, where feature subsets are encoded as binary individuals optimized for three objectives: MSE, 1 − R², and the number of features. The experimental results demonstrate that the proposed method not only reduces the number of features (25→7) but also improves the prediction accuracy (MSE: 0.0049 → 0.0042, R²: 0.6638 → 0.7228) of smart meter group failure rates. Comparative studies with other feature selection methods further confirm the superiority of our approach. The optimized features enhance interpretability and computational efficiency, providing a practical solution for large-scale smart meter condition assessment in power systems. Full article