Reliable Operation Research Articles

Application of monarch butterfly optimization algorithm for solving optimal power flow

This paper proposes a highly flexible, robust, and efficient constraint-handling approach for the solution of the optimal power flow (OPF) problem and this solution lies in the ability to solve the power system problem and avoid the mathematical traps. Centralized control of the power system has become inevitable, in the interest of secure, reliable, and economic operation of the system. In this work, OPF is solved by considering the three distinct objectives, generation cost minimization, power loss minimization, and enhancement of voltage stability index. These three objectives are solved separately by considering the evolutionary-based monarch butterfly optimization (MBO) algorithm. This MBO algorithm is validated on the IEEE 30 bus network and the obtained results are compared with differential evolution, particle swarm optimization, genetic algorithm, and Jaya algorithm. The obtained results reveal that among the various optimization algorithms considered in this work, the MBO evolves as the best algorithm for all three case studies.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

99.6% efficiency DC-DC coupling for green hydrogen production using PEM electrolyzer, photovoltaic generation and battery storage operating in an off-grid area

This paper presents a novel connection and control strategy for a hydrogen generation system using a proton exchange membrane electrolyzer powered by solar energy in an off-grid area without network backup. Given that, the proposed architecture is based on the indirect control of the Photovoltaic plant (achieved by removing a power-converter), the presented solution is more efficient and more reliable than the traditional scheme. Furthermore, with this innovation one can reach the same hydrogen production with smaller electrolyzers.The methodology includes detailed models of the photovoltaic panel and the electrolyzer, along with a control strategy that considers the degradation mechanisms of the electrolyzer to ensure reliable and prolonged operation. The results show that the proposed strategy keeps the operating power of the electrolyzer constant, even under variations in irradiance, thanks to energy storage in batteries. It is demonstrated that the proposed system offers efficiency above 99.6% during the analyzed period, with a 100% utilization rate of the electrolyzer, avoiding periods of inactivity and high current peaks. The study also includes simulations and experimental tests that confirm the feasibility and effectiveness of the presented solution, highlighting its advantages in terms of efficiency and investment costs compared to direct connections and other existing methods.

Optimal Control Strategies for Reliable Operation of Electric Vehicle Charging Stations under Fault Tolerant Scenarios

Optimal Control Strategies for Reliable Operation of Electric Vehicle Charging Stations under Fault Tolerant Scenarios

Generalized composite multiscale improved diversity entropy and its application to diesel injector fault feature extraction and diagnosis

The diesel injector is the core component of marine diesel engines, and its health monitoring is one of the keys to ensuring the efficient and reliable operation of diesel engines and even ships. However, extracting effective feature information from nonlinear dynamic signals is the difficulty in realizing intelligent fault diagnosis of diesel fuel injectors. The existing diversity entropy (DE) is an advanced algorithm for extracting fault features. Still, it has the problem of failing to obtain sufficiently effective fault features, thus affecting diagnostic accuracy due to its inherent defects. To solve the above problem, a new generalized composite multiscale improved diversity entropy (GCMIDE) is first proposed, and the comparative advantages of the proposed entropy in terms of consistency, robustness, and computational efficiency are verified with common noise-simulated signals. GCMIDE is further utilized as an excellent means of feature extraction, and a new injector fault diagnosis method, GCMIDE-Relieff-SSA-BP, is proposed to meet the needs of efficiency and practicality in fault diagnosis. Finally, the method is adopted for the diagnostic experiments of diesel fuel injectors with different fault types and different fault degrees for full validation. The experimental results all indicate that, in comparison with six commonly used entropy-based fault diagnosis methods, the proposed method has the best feature extraction performance and fault diagnosis accuracy under a different number of features or different training sample ratios. This provides an effective tool for fault diagnosis of injectors and even intelligent operation and maintenance of diesel engines.

Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

Vibration characteristics of rolling bearing cage under speed variation conditions

Speed variation conditions will deteriorate the stress state of the rolling bearing internal components and aggravate the vibration of the cage, thereby affecting the service life and reliability of rolling bearings. In this paper, a nonlinear dynamic analysis model of the cylindrical roller bearing is established considering the rubbing effect and hydrodynamic contact between the cage and the guiding ring, as well as the sliding between the cage and other rolling elements. The vibration characteristics of the cage is discussed under the start-stop conditions and different fluctuations of rotation rate. The results show that the vibration of the cage mainly occurs at the middle and late stages of the acceleration process, and the impact mainly occurs at the middle stage of the deceleration process under the start-stop conditions. Moreover, the fluctuation of inner ring speed will enhance the unstable motion of the cage, among which the vibration and impact of the cage are most severe under the rectangular fluctuation of speed, followed by simple harmonic and triangular fluctuations. The findings can provide a theoretical guidance for improving the performance of rolling bearings and ensuring their safe and reliable operation.

Fault diagnosis of rolling bearings driven by multi-channel data fusion and feature fusion under time-varying speed conditions

Abstract Bearings, as the core component for power transmission, are crucial in ensuring the safe and reliable operation of equipment. However, the fault information contained in a single-channel vibration signal is inherently limited. Additionally, under time-varying speed conditions, features are prone to drift, and the cross-domain diagnostic performance of most traditional domain adaptation (DA) models may drop dramatically. To solve the above problems and enhance the ability of DA models in extracting domain invariant features, this paper introduces a Multi-channel data fusion and Attention-guided Multi-feature Fusion-driven Center-aligned Network (MAMC). Initially, a multi-channel time-frequency information fusion strategy based on wavelet transform is constructed to achieve a comprehensive fusion of multi-channel data, thereby obtaining richer fault feature representations. Subsequently, a multi-branch feature fusion network, integrated with an attention mechanism, is devised to capture significant features across various dimensions and scales, resulting in more comprehensive and representative fault features. Finally, a novel Center-Aligned Domain Adaptation method (CADA) is proposed based on domain adversarial methods and center loss. By minimizing the distance between deep domain invariant features and trainable common class centers, the issue of domain shift between data is effectively alleviated, and the cross-domain diagnostic performance of DA models under the time-varying speed conditions is improved. The experimental results indicate that the MAMC method exhibits superior performance on both bearing datasets and is a promising approach for cross-domain intelligent fault diagnosis.

Comparison of inelasticity creep failure evaluation codes for elevated-temperature non-nuclear pressure equipment

PurposeTo ensure the reliable and safe operation of elevated-temperature pipes and equipment in the long term, it is essential to thoroughly assess the creep rupture life. Nevertheless, there is currently no design code that specifies a creep rupture life evaluation method for non-nuclear elevated-temperature equipment. The paper aims to discuss this issue.Design/methodology/approachAn analysis was conducted to compare the differences and conservativeness in calculating creep strain using three major codes (ASME-CC-2843, API-579 and BS-7910) based on the results of the 316H creep constitutive model and creep strain prediction. In addition, the creep resistances of 316H, 304H and 347H were compared. Subsequently, the ANSYS Usercreep subroutine was developed to compare the discrepancies between different codes under multiaxial stress conditions using numerical simulations.FindingsBS-7910 employs the Norton creep model with calculation parameters for the average creep strain rate, which is not applicable for the engineering design stage. ASME-CC2843 code primarily focuses on the primary and secondary creep stages, making it more suitable for non-nuclear pipeline and equipment design. For 316H, the creep strain curves predicted by ASME-CC2843 and API-579 typically intersect at a specific point. By combining the creep strain predicted by ASME-CC2843 and API-579, 347H exhibits superior predicted creep resistance compared to 316H, whereas 316H exhibited better predicted creep resistance than 304H.Originality/valueThis study provides a guide for future evaluation methods and material choices for non-nuclear equipment and pipelines operating at elevated temperatures.

Reliability analysis of DDV and SPM in BOP-based Weibull model and expand fault dataset using deep learning

The hydraulic systems of deepwater Blowout Preventers (BOPs) are crucial for ensuring the reliability and safety of offshore oil and gas extraction operations. A functional failure can lead to uncontrolled blowouts, resulting in casualties and significant economic losses on the rig. The Direct Drive Valve (DDV) and the Subsea Plated Mounted (SPM) valve are key components that help maintain the proper functioning of the hydraulic system in deepwater BOPs. This study begins by utilizing the Weibull analysis method to assess the reliability of the DDV and SPM valves using limited fault data samples. To enhance the accuracy of predictions, Weibull parameters are estimated through various methods, including Maximum Likelihood Estimation (MLE), Least Squares Estimation (LSE), and a combination of Correlation Coefficient Optimization with Support Vector Regression (CCO + SVR).Given the challenges in gathering extensive fault data for DDV and SPM valves—due to complex subsea environments, cost constraints, time limitations, and other factors—this study proposes a method employing a Back Propagation Neural Network (BPNN) model to augment the limited fault data samples. To ensure the reliable operation of the DDV and SPM valves, preventive maintenance cycles are established at 2840 and 7550 operations, respectively. At the same reliability level, as the number of operational cycles increases, the remaining service life of the valves gradually decreases, leading to a higher probability of failure over a shorter timeframe. The Mean Remaining Life (MRL) of the DDV and SPM valves, corresponding to different operational times, is analyzed, providing essential reference points for their usage and maintenance. When the extended data sample is utilized for reliability evaluation, the reliability characteristics of the small fault data samples are effectively reflected, and the parameter prediction error remains low. This indicates that the extended data sample is more suitable for reliability evaluation.

Design, analysis, and demonstration of the COAST guidewire robot with middle tube rotation for endovascular interventions.

Minimally invasive procedures for endovascular interventions involve manual navigation of a guidewire. Endovascular interventions encompassing highly tortuous vessels would benefit from guidewires which exhibit higher dexterity. This paper introduces a version of the COAST (COaxially Aligned STeerable) guidewire system capable of exhibiting higher dexterity. The system presented in this paper consists of three coaxially aligned tubes with a tendon to actuate the middle tube. Furthermore, it is possible to independently rotate the middle tube with respect to the outer tube. This variation enables the guidewire to achieve curvature in different planes while avoiding rotation of the entire structure. We also present the simulated stability of the guidewire with different outer tube geometries and experimentally validate the model. Experimental analysis and modeling of the kinematic behavior of the system is presented. A model to calculate the curvature vs. tendon stroke relationship for the optimal notch geometry is presented with an average RMSE of 0.16mm. A control strategy addressing the snapping instabilities to ensure reliable operation is discussed. A custom phantom vessel and an aortic arch phantom model were used to demonstrate the ability of the system to safely navigate through tortuous pathways without exhibiting these elastic instabilities.

Real-time Actuator Fault Detection and Isolation for Quadrotor UAV via Directional Residuals

Abstract Fault diagnosis is critical for the safe and reliable operation of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce its negative impact. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying (LPV) model is established to describe quadrotor UAV dynamics, which considers measurement noise and external disturbance. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and Η_/L∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L∞ performance condition, and actuator fault is detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residuals and the defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

SAFETY ASSURANCE TRIPPING OPERATIONS IN OIL AND GAS PRODUCTION

The article discusses the issues of industrial safety at the enterprises of the oil and gas industry, in particular during the performance of tripping operations. The relevance of the research topic is justified, based on the fact that this industry in our country (oil production) occupies one of the leading positions in the world, and all processes are accompanied by tripping operations, therefore, the issue of ensuring industrial safety remains relevant.The problems of the oil and gas sector and their impact on the drilling process, in particular, high wear of equipment and the human factor, were considered.The study focuses on non-compliance with occupational health and safety standards, as well as shortcomings in the risk management system that contributed to the occurrence of these emergencies. Rostekhnadzor data related to the distribution of industrial injuries were analyzed. Cases of violation of industrial safety regulations during tripping operations were considered, which led to severe traumatic injuries and fatal outcomes.Based on the analysis of regulatory requirements in the field of production safety of tripping operations, shortcomings were found, and recommendations were made for making changes based on the existing safety status at the facilities of such problems as equipment deterioration, the relevance of existing protective equipment.The second block is aimed at considering the technical features of the receiving walkways and identifying design imperfections during their operation.The article proposes a shock-resistant protective limiting device of its own design, which will contribute to improving the production safety of tripping operations when drilling wells. The use of this device will minimize the risks of damage to equipment and reduce the likelihood of emergencies. Due to its simplicity and the use of available materials, the device has high strength and durability, which ensures its reliable operation in conditions of increased loads and difficult climatic conditions. In addition, the introduction of such devices in production processes helps reduce the cost of repair and maintenance of equipment, as well as increase the overall efficiency of drilling operations by reducing downtime and unplanned shutdowns associated with incidents.

ASSESSMENT OF THE PROBABILITY OF OCCURRENCE OF EMERGENCY SITUATIONS DURING OPERATION OF THE PYROLYSIS PLANT

The article discusses the issues of reliability and safety of operation of a pyrolysis plant for the disposal of used tires, which, in the context of an increasing volume of waste and the need for their effective disposal, is an urgent topic. The problem statement is related to the identification of factors affecting the reliability of the installation and the development of methods to prevent emergencies.The purpose of the study is to analyz the reliability of the pyrolysis plant and develop recommendations for its improvement. To achieve this goal, the causes and probabilities of failures of system elements are investigated, an analysis using the failure tree method is carried out. This method made it possible to identify the weaknesses of the system and assess the probability of their failures, which is critical to prevent emergencies.An assessment of the reliability of the installation showed that the greatest contribution to the probability of accidents is made by a malfunction of the gas separation system. This element is the most vulnerable due to its complexity and importance in the pyrolysis process, as it is responsible for the safe and effective separation of pyrolysis products. Any malfunction of the gas separation system can lead to the accumulation of dangerous gases, increasing the risk of explosions and fires. The identified emergencies require special attention to the development of effective measures to minimize them.The article proposes methods to improve the reliability of the system, the main of which is the redundancy of the most vulnerable elements. This will significantly reduce the probability of failures and increase the overall reliability of the installation. In addition, it is recommended to carry out regular inspections and maintenance of all elements of the system. These measures will help to identify and eliminate potential malfunctions in a timely manner, which will significantly reduce the likelihood of failures that can lead to emergencies.Thus, ensuring the reliability and safety of a pyrolysis plant for the disposal of used tires requires an integrated approach. Enabling redundancy of vulnerable elements and regular maintenance will minimize the likelihood of failures that can lead to emergencies and guarantee stable and safe operation of the system. These measures will not only increase the efficiency of disposal, but also ensure reliable and long-term operation of the installation.This article focuses on the importance of identifying vulnerable elements of the system and conducting regular maintenance to improve the reliability and safety of operation of the pyrolysis plant.

Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

Estimation of the Remaining Useful Life of Axially Loaded Members Through Nested Least-Square Support Vector Regression

ABSTRACT Prognosis and Health Management (PHM) is crucial for ensuring the reliable operation of structural components, such as bracing members. Despite this, limited study exists for the RUL estimation of civil engineering components. This paper presents a nested framework utilizing the Least Square Support Vector Regression (LS-SVR) for accurately predicting the Remaining Useful Life (RUL) of axially loaded members. The framework addresses the challenges of sparse data and multidimensional damage-sensitive features (DSFs) by employing a two-stage process. In the first stage, DSFs are forecasted using one-dimensional mapping, providing essential inputs for the second stage, where axial stiffness degradation is predicted using LS-SVR. The proposed framework is validated through an experimental study on axially loaded members subjected to low-cycle fatigue. To enhance model performance, simulated data generated from an OpenSees model is combined with the experimental data. The nested LS-SVR framework demonstrates superior performance compared to traditional methods, achieving high accuracy in RUL estimation as evidenced by the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The framework’s ability to forecast axial stiffness degradation, a critical indicator of structural health, makes it a valuable tool for real-time assessment of structural component RUL. By integrating this framework with real-time monitoring systems, proactive maintenance strategies can be implemented, leading to safer and more efficient structural health management.

Reliability optimization through soft digital twins

This paper presents a framework for developing a “soft digital twin” (SDT) for reliability optimization through modeling and simulation. The focus is on demonstrating the effectiveness of the SDT in modeling maintenance task processes within mission-critical facilities, which is particularly essential for reliable operations in critical infrastructure such as nuclear facilities. The need for efficient completion of maintenance tasks (MTs) to ensure high reliability and minimal downtime underscores the challenge faced by managers, who desire a predictive tool akin to a “crystal ball” for optimal resource configuration in the face of uncertainties from equipment failures, staffing issues, and supply chain disruptions. The proposed SDT functions as this predictive tool, leveraging the simulation’s versatility to provide insights into resource configurations and staff planning. This work extends the prior research by incorporating additional model validation, sensitivity testing, and analysis of the impact of various resource changes on the system. It employs a combination of data-driven frameworks and stochastic modeling methods to construct an adaptive SDT capable of accommodating changes in system behavior. The work provides a framework for the construction of SDT, testing and validation of its results, and its application to a case study of a mission-critical facility focused on replication and then optimizing of two key performance indicators based on human resource mixtures. Initial findings suggest that the SDT yields reliable results comparable to retrospective test datasets, offering the potential to minimize MTs’ time in the system while maximizing throughput within specified time frames through scenario experimentation and optimization.

ANALYSIS OF THE CAUSES OF ACCIDENTS IN THE OIL AND GAS INDUSTRY

In the modern world, the oil and gas industry plays a key role in ensuring the energy security of countries. An important problem of this industry is the high level of accidents on main pipelines. The importance of issues of ensuring the safety of production processes and working conditions of personnel is obvious: accidents that occur at enterprises can paralyze work for a long time, attracting the attention of the media and regulatory authorities, causing not only financial but also significant image damage. In connection with the above, the analysis and statistical study of accidents in this area have become relevant in recent years. Occupational safety measures include risk assessment at workplaces, briefings, medical examinations, provision of personnel with personal protective equipment, special assessment of workplaces, occupational safety days, etc. In order to restore the technical and economic indicators of the oil and gas industry, increase reliability and trouble-free operation, complexes of measures are carried out to repair, reconstruct and modernize the components of the main gas pipeline. Ensuring industrial safety during repair work depends on both the reliability of the process equipment and the reliability of the personnel, since a significant number of accidents and incidents occur due to reasons related to the influence of the human factor. Many studies have been devoted to the problem of management in the field of labor protection and ensuring safety at work. Despite the significant number of works, a practical lack of developments on the issues of managing the processes of ensuring safety and labor protection at enterprises in the oil and gas industry has been discovered, which indicates the need for a more in-depth study of this topic.

Fault-tolerant graph embeddings in Archimedean networks

Fault tolerance refers to the ability of a system to continue functioning even if some of its components fail. This concept is fundamental in several domains because reliability and continuous operation are essential in carrying out their functions. Graph embedding means mapping a graph into some other structure that preserves some of the properties or relations held by the original graph. Applications in machine learning, data mining, and network analysis make fault-tolerant embedding of graphs a crucial topic. In this paper, fault-tolerant embeddings are proposed in Archimedean tessellations. The path in the embeddings does not intersect or overlap.