Open Fault Research Articles

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot’s operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.

A secure fault detection for digital microfluidic biochips

Abstract Among all the modern technological advances, digital microfluidic biochip has been extending a salient solution to healthcare and bio-laboratories with the pledge of high sensitivity and reconfigurability. Such biochip devices fulfill the requirement of a faster testing kit for the detection of different novel diseases, which is indispensable in the market due to the tremendously disrupted scenario of healthcare systems. To eliminate erroneous testing, the current scope of digital microfluidic biochips is widened as a viable testing method using various bioprotocols with a reduced cost in developing countries. This paper addresses the existing security challenges and operational faults in the identification mechanism of proteins such as severe acute respiratory syndrome coronavirus 2 spike protein in state-of-the-art digital microfluidic biochips. We are the first to propose a safety detection solution along with a fault identification algorithm using an inductive transfer learning model. Experimental results of the proposed model register a threshold accuracy of 98% while applying the own dataset. This work will provide a better security-enabled fault-free safety assurance framework against attack and fault identification with better accuracy in digital microfluidic biochips for the detection of different diseases and many other healthcare diagnoses, without any overhead of completion time for bioprotocols.

Heat transfer study on a stator-permanent magnet electric motor: A hybrid estimation model for real-time temperature monitoring and predictive maintenance

When electrical machines operate without a specific cooling system, the surrounding environment plays a crucial role in the rise of temperature and the duty cycle of operation. More clearly, a natural convection cooling system with a low value of heat transfer coefficient carries the risk of thermal breakdown, insufficient safety, and reliability. This paper studies the heat transfer aspects of a low-power flux switching permanent magnet (FSPM) motor under natural convection cooling to implement a novel real-time sensor-less temperature monitoring system. Thermal and electromagnetic experiments are carried out to create foundations for transient and steady-state numerical models. A data-driven, deep learning algorithm estimates the core and permanent magnet (PM) eddy current losses in real-time, besides the already available copper and friction losses. Subsequently, a two-node thermal equivalent circuit in a hybrid model with a feed-forward neural network estimates the dynamic temperature profile of windings and PMs. It is indicated that the worst-case estimation error is below 7.5%, and the configuration is applicable under a wide range of operation states and environmental conditions. Lastly, the system, including the power source, FSPM motor, and hybrid temperature estimation unit, will be implemented in MATLAB/Simulink to investigate the fault prediction and operation management capabilities.

A fault location method for DC transmission line based on the distributed parameter model considering line parameter uncertainty

Addressing the impact of whether line parameters can be accurately obtained and the errors due to environmental influences on the accuracy of fault location for DC transmission line, this paper proposes a fault location method based on the distributed parameter model that accounts for uncertainty in line parameter. To address the challenge of accurately obtaining DC transmission line parameters, a high-order derivative-based parameter estimation method is introduced to determine unknown equivalent parameters of the DC transmission line. Additionally, an optimization algorithm is incorporated to dynamically correct parameter errors, thereby enhancing parameter stability and constructing an accurate line model. Based on this, theoretical derivations confirm the applicability of the Tellegen's quasi-power theorem in the normal operation and internal fault equivalent models of DC transmission line. A fault location function is established based on the relationship between the line's Tellegen quasi-power characteristic and fault distance. The proposed method's effectiveness and accuracy are validated using a DC system model built on the PSCAD/EMTDC simulation platform. Simulation results indicate that the proposed method can locate faults under different fault conditions.

Torque dynamic performance enhancement of five‐phase permanent magnet synchronous motor with open‐circuit fault

AbstractMultiphase permanent magnet synchronous motors (PMSMs) have attracted much more attention for their high efficiency, low torque ripple and good fault tolerance. However, open‐circuit fault results in asymmetrical motor behaviour, and deteriorates torque performance, especially dynamic response. This paper proposes a novel fault‐tolerant direct torque and flux control (DTFC) strategy to restrain fluctuating torque and enhance torque dynamic performance of a five‐phase PMSM with open‐circuit fault. The novelty of the proposed strategy is the development of DTFC based on stator flux orientation under the open‐circuit fault condition and the third harmonic current suppression with carrier‐based pulse width modulation technology. Consequently, the decoupling control of torque and stator flux can be achieved under the open‐circuit fault condition, and the third harmonic current can be restrained without additional control. Combined with deadbeat control, the heavy computation burden can be reduced. Thus, the proposed strategy not only can enhance torque dynamic performance under open‐circuit fault operation, but also keep smooth torque with circular stator flux trajectory before and after open‐circuit fault. The effectiveness of the proposed strategy is verified by the simulation and experimental results.

Kajian Yuridis terhadap Kerugian yang ditimbulkan Akibat Kecelakaan Perkeretaapian Berbasis Nilai Keadilan

Railway transportation is one of the mass transportation modes widely used by the Indonesian people. However, train accidents often occur and cause losses to the victims. Therefore, the value of justice becomes very important as a basis for examining the issue of losses due to train accidents. This research aims to analyze the regulation of liability and compensation related to railway accidents in Indonesian law and evaluate whether these regulations are based on the value of justice. This research is a normative legal research that analyzes the relevant laws and regulations using a statutory and conceptual approach. The type of data used is secondary data in the form of primary legal materials such as laws and government regulations, as well as secondary legal materials such as books and journals, using analytical-prescriptive descriptive qualitative analysis methods. The research results explain that the regulation of liability and compensation related to railway accidents in Indonesia is regulated in several laws and regulations such as the Railway Law, Government Regulations, and related ministerial regulations. In general, these regulations require railway facility operators to provide compensation to service users who suffer material and immaterial losses due to accidents caused by the operator's fault. The amount of compensation is determined based on the calculation of losses suffered by the victim, considering factors such as medical expenses, loss of property, and physical or psychological suffering. Although it attempts to accommodate the values of justice, there are still shortcomings in terms of unclear criteria, types, and amounts of compensation that must be given, as well as the potential for differing interpretations or inconsistent implementation, which could lead to injustice for accident victims.

Deep Learning-Driven Thermal Imaging Hotspot Detection in Solar Photovoltaic Arrays Using YOLOv10

The effective management of solar photovoltaic (PV) arrays is vital to maximising energy generation and ensuring long-term performance reliability. The presence of faults, or partial shading, can give rise to hotspots in PV arrays, making their detection critical for timely maintenance and avoiding further impacts on their performance. This paper describes a new approach to the hotspot detection issue in thermal images of solar PV arrays by leveraging deep learning. This study employs the YOLOv10 (You Only Look Once, version 10) model to deliver very high accuracy and speed in identifying and localising hotspots under defined regions of interest (ROIs). The proposed method begins with taking thermal images from multiple solar PV installations while capturing a range of operational conditions and fault types. These images are annotated with hotspot regions to create a robust training dataset. Given YOLO's capability for real-time object detection with high precision and mean average precision (mAP), the YOLOv10 model is then implemented to train on this dataset. Multiple changes were made to the YOLOv10 hyperparameters to optimise them for detecting hotspots in thermal images. The experimental scenario of this paper demonstrates that this approach indeed performs significantly better than standard image processing methods as well as prior deep learning models for detecting both hotspot accuracy as well as speed of processing the images. The YOLOv10 method demonstrated the highest available classification performance with a mAP at intersection over union (IoU) of 0.5 accuracy of 0.91 with inference time suitable for real-time applications. The sample results demonstrated the model's continuous ability to detect hotspots and provide location data under various conditions, such as crossing through different times of day and weather. The results of this study demonstrate that YOLOv10 can be used for improved detection of hotspots in the explicably thermal imagery of solar PV arrays.

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Abstract Gas turbine engines are complex mechanical marvels widely employed in diverse applications such as marine vessels, aircraft, power generation, and pumping facilities. However, their intricate nature renders them susceptible to numerous operational faults, significantly compromising their performance and leading to excessive emissions, consequently incurring stringent penalties from environmental regulatory bodies. Moreover, the deterioration of gas turbine performance is exacerbated by variations in working conditions based on operational settings and environmental conditions. Past studies have focused on certain working conditions that limit effectiveness in real-world applications where operational settings and environmental conditions vary during operations. The influence of these working conditions on the performance of gas turbines also needs to be assessed, as they can lead to different fault patterns resulting in unplanned maintenance, unnecessary maintenance costs, unsafe conditions and stringent penalties. This study uses the gas turbine simulation program to simulate a high-bypass turbofan engine inspired by Pratt & Whitney PW-4056, analysing the combined effects of operational settings and environmental conditions on engine performance while also incorporating simulations of common gas turbine faults like fouling and erosion in various locations and severities along the gas path. The model’s accuracy is confirmed by low mean absolute percentage errors of 0.004% of thrust at the cycle reference point and 0.15% and 0.28% at 2 km and 7 km altitudes, respectively, demonstrating the model’s robustness across varying operational scenarios. In conclusion, this research highlights the significant effects of operational settings and environmental factors on gas turbine performance, particularly impacting specific fuel consumption and thrust. The study reveals that operational settings and environmental factors significantly impact fuel consumption and thrust. Specifically, compressor fouling and low-pressure turbine erosion increase nitrogen oxide (NOx) emissions by 4.5% and 11.1%, while fouling of nozzle guide vanes and high-pressure turbine erosion raise unburned hydrocarbon by 10.0% and 20.2%, and carbon monoxide (CO) by 3.2% and 5.2%, respectively, compared to a healthy engine. These insights highlight the importance of component-specific degradation in influencing gas turbine performance and emissions.

Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

Real-time energy management simulation for enhanced integration of renewable energy resources in DC microgrids

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

Ironmaking process under artificial intelligence technology: A review

The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.

A smooth switching strategy for steady-state operation control and fault transient control of doubly fed induction generator

Abstract As the proportion of wind power generation systems in power systems continues to rise, their dynamic response during system malfunctions has gained significant importance. As the grid short-circuit ratio continues to decrease, traditional fault ride-through algorithms for wind power generators may no longer be applicable. This is evidenced by voltage oscillations under significant disturbances and overvoltage issues during low-voltage ride-through, which are further aggravated during asymmetric fault conditions. Therefore, this paper focuses on the low-voltage ride-through issues of doubly fed induction wind power generators in weak power grids. It investigates the steady-state operation and transient control schemes and proposes a smooth transition strategy for steady-state and fault transient operations of doubly fed wind power generators. This approach guarantees the synchronization of active and reactive power, mitigates steady-state reactive power imbalance, and prevents voltage fluctuations triggered by recurring low-voltage ride-through occurrences. Furthermore, during fault recovery, a ramped active power restoration approach is employed to mitigate the coupling of active and reactive power introduced by the phase-locked loop, enabling rapid and stable restoration of active and reactive power outputs. As a result, superior dynamic performance is achieved during both symmetric and asymmetric fault processes. Finally, the superiority of the proposed smooth transition strategy under different fault scenarios is validated through simulations with the RTLAB platform.

Voltage optimal control model of regional distribution network after reconfiguration based on multi-energy conversion

In the event of a significant operational fault in the regional distribution network connected to the distributed new energy generator set, it may be necessary to quickly reconfigured certain areas of the system. This process must ensure minimal impact on the voltage of the regional distribution network. To address this issue, a voltage optimal control strategy for regional distribution network based on multi-energy conversion is proposed. Firstly, the energy conversion and adjustable characteristics of electricity, heat, and gas energy conversion equipment in the regional distribution network are analyzed in this paper. The steady-state operation model of the regional distribution network with multi-energy conversion is established. Then, the voltage optimal control model for rapid reconfiguration of the multi-energy distribution network is established by considering the two optimization objectives of power recovery for the important load and system voltage fluctuation. The model is solved based on the greedy algorithm. Finally, an equivalent model of the regional distribution network with multi-energy conversion equipment access is built based on the operation data of the distribution network in a certain area of Northeast China. The feasibility and effectiveness of the model are analyzed and verified.

Modified third‐order sliding mode control with adaptive gains for a multiphase PMSG wind turbine under double‐phase open fault

Modified third‐order sliding mode control with adaptive gains for a multiphase PMSG wind turbine under double‐phase open fault

A Novel Method for Bearing Fault Diagnosis Based on Novel Feature Sets with Machine Learning Technique

Abstract Bearings often experience small and medium raceway damage due to operating and loading conditions, which induces abnormal dynamic behavior. In this study, a dynamic model of the bearing system with various conditions and bearing faults is developed based on experimental investigations, Extreme Machine Learning (EML), and supervised machine learning K-Nearest Neighbors (KNN). The effects of defects on system dynamic response and the damage vibration of the bearing are investigated through simulation. Experiments verify the typical dynamic characteristics. The fundamental bearing characteristics frequencies and statistical features withdrawn from a vibration response are utilized for fault identification using a machine learning algorithm. Bearing characteristics, frequencies, and statistical features were applied to both proposed approaches and compared regarding their prediction quality. The result shows that the EML performs better than the KNN in terms of precision of fault recognition by up to 99%. This work provides valuable insights for operation, maintenance, and early fault warning related to bearings.

Safety considerations for cryogenic hydrogen circulation system in China spallation neutron source phase II project

Cryogenic hydrogen circulation system is a critical infrastructure of the China Spallation Neutron Source phase II project. Safety considerations related to hydrogen are paramount for the stable operation of the system. This study focuses on the cryogenic hydrogen circulation system within the context of the China Spallation Neutron Source phase II project and conducts a detailed investigation of hydrogen safety issues throughout the process. The operational modes and fault occurrence mitigation strategies of the cryogenic hydrogen circulation system are analyzed and discussed. Additionally, employing centralized layout, zoning management, and multiple barrier layers as guiding concepts for hydrogen safety design, the study addresses physical and interlocking control designs concerning hydrogen distribution, hydrogen venting, vacuum, hydrogen circulation pressure control, and hydrogen leakage. Drawing upon the successful experiences of the first-phase project, the hydrogen safety design of the cryogenic hydrogen circulation system in the China Spallation Neutron Source phase II project provides valuable insights for the development and optimization of hydrogen safety designs across various industrial domains.

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

A hybrid method for the intelligent effectiveness management of production operators

AbstractThe paper presents a hybrid method for the effective management of human resources when executing production processes. The method was proposed for managing production operators in systems of relatively high complexity—with the possibility of multiple alternative ways of designating operators to production stations. The method combines a novel approach of assessing the effectiveness of production operators, the use of an intelligent algorithm and the risk assessment method. The concept of effectiveness was used in the sense of a praxeological approach. Availability, performance and quality were defined as its components, and were evaluated through the prism of losses occurring in production processes. The aim of the study is to develop a hybrid method in order to delegate production operators to tasks, not only on the basis of a competence metric, but also on the basis of an individual effectiveness indicator and the risk assessment of production process stages. For this purpose, a novel method of assessing the effectiveness of the use of available resources when implementing production processes (based on a set of quantitative indicators and taking into account the categorization of production losses) was juxtaposed using the Tabu Search algorithm and the FMEA method. The method enables a near-optimal solution to be generated—one in which operators are assigned to production tasks in the most effective way possible, which in turn minimizes both the risk of production delays and quality defects due to operator fault.

Effects of reservoir mechanical properties on induced seismicity during subsurface hydrogen storage.

The intermittent storage of hydrogen in subsurface porous media such as depleted gas fields could be pivotal to a successful energy transition. Numerical simulations investigate the intermittent storage of hydrogen in a porous, depleted subsurface reservoir. Various parametric studies are performed to assess the effect of mechanical properties of the reservoir (i.e. Young's modulus, Poisson's ratio, Biot coefficient and permeability) on the induced fault slip of a single through-going fault that transverses the entire reservoir. Simulations are run using a three-dimensional, finite element, fully coupled hydromechanical code with explicit representations of layers and faults. The effect of the domain mesh refinement and fault mesh refinement on the fault slip versus operation time solution is investigated. The fault is observed to slip in two distinct events, one during the second injection period and one in the third injection period. The fault is not observed to slip during the storage or withdrawal periods. It is found that in order to minimize seismic risk, a reservoir rock with high Young's modulus (>40 GPa), high Poisson's ratio (>0.30) and high Biot coefficient (>0.65) would be preferable for hydrogen storage. Reservoir rocks of low Young's modulus (10-30 GPa), intermediate Poisson's ratio (0.00-0.30) and low-to-intermediate Biot coefficient (0.25-0.65), at high injection rates, were found to have higher potential of inducing large seismic events.This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'.