Safe Operation Of System Research Articles

Grounding Grids Corrosion Evaluation of Electrified Railway Based on Two‐Stage Optimization Algorithm

AbstractDue to the complex soil environment and continuous traction ground return, the electrified railway grounding grids (ERGG) are easily susceptible to corrosion, which seriously threatens the safe operation of the traction power supply system (TPSS). In this paper, the corrosion evaluation model is presented based on electrical network theory. In order to solve the equations with multi‐variable and high‐dimension characteristics of the model, a two‐stage optimization (TSO) algorithm is proposed. In the first stage, the CPLEX solver (CPLEX optimizer produced by IBM) is adopted to obtain the resistance value of multiple branches, which provides the initial solver and search scope for the next step. In the second stage, the particle swarm optimization (PSO) algorithm is used to determine the optimal solution so as to minimize the node potential difference. Finally, a corrosion experimental system is designed and established to verify the effectiveness of the above algorithm. The results show that TSO algorithm can accurately diagnose the corrosion state of grounding conductors and adapt to the various scenarios of ERGG. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

Numerical study on characteristics of flow accelerated corrosion in a globe valve under different working conditions

Abstract As a part of pipeline systems, globe valves play an important role in cutting off and regulating fluid transmission in fields such as petrochemicals, coal chemicals, and metallurgy. During the transportation of corrosive fluid media, flow accelerated corrosion (FAC) caused by internal flow is the main form of valve failure, which seriously affects the safe and reliable operation of the entire transmission system. In this study, the effects of different valve openings and inlet velocities on characteristics of internal flows and corrosion were investigated by numerical simulations in globe valve. The distributions of velocity, pressure, and wall shear stress were obtained and discussed in detail. The corrosion rate of key components of globe valve was obtained and analyzed to reveal characteristics of FAC. Results show that the FAC is more serious with the increase of inlet velocity while it becomes the more serious at moderate valve opening degree. In addition, wall shear stress was verified to be able to describe FAC in globe valve. The obtained results were quite meaningful for guiding the structural design of globe valves in corrosion suppression, which extends the service life of globe valves and ensures the safety of conveying systems.

Evaluation of grounding grid corrosion extent based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning

As one of the most widely used forms of energy, the safety and stability of power systems are crucial to modern society. Grounding grids dissipate current and reduce touch and pace voltage during lightning strikes or fault currents, ensuring the safety of personnel and equipment. However, prolonged submersion in soil causes inevitable corrosion, compromising grounding efficacy by increasing resistance and reducing current dissipation. This deterioration can result in unsafe local potential differences. This study uses Laser-Induced Breakdown Spectroscopy (LIBS) to measure corrosion degrees in grounding grids. Spectral data from samples with varying corrosion extent were collected, with outliers removed using the Local Outlier Factor (LOF) algorithm. Principal Component Analysis (PCA) reduced data dimensionality, revealing clustering in spectral data corresponding to corrosion extent. Three machine learning models were compared: Adaptive Boosting - Backpropagation Neural Network (Adaboost-BP), Support Vector Machine (SVM), and Random Forest (RF). The RF model showed the highest accuracy in predicting corrosion degree (R²=0.9845, MSE=0.0296), outperforming Adaboost-BP and SVM, especially for intermediate corrosion extent. These findings validate the effectiveness and reliability of combining LIBS with machine learning for predicting grounding grid corrosion, providing a theoretical foundation for the safe operation of power systems.

Learning about tail risk: Machine learning and combination with regularization in market risk management

High-quality risk management is the key to ensuring the safe, efficient, and stable operation of the financial system. The current Basel Accord requires financial institutions to regularly calculate and disclose Value at Risk (VaR) and Expected Shortfall (ES) measures. However, the inaccuracy and instability of traditional risk models have reduced users' confidence. Therefore, we propose two new probabilistic deep learning frameworks for estimating VaR and ES. The trained first framework can output expectiles that are more sensitive to tail risks to map VaR and ES measures. In the second framework, we propose to approximate VaR and ES measures with spline quantile function and estimate the parameters by designing various deep learning architectures. To ensure the effectiveness of the proposed architectures, we derived the training loss and constraints for them. In addition, we solve the problem that existing machine learning risk models are difficult to estimate ES. In this way, combining various individual risk models has great potential for risk management. Therefore, we propose a regularization-based combination framework that adaptively selects and shrinks individual risk models. The developed individual methods and combinations outperform existing methods in backtesting, assisting financial institutions to allocate capital more effectively according to the Basel Capital Accord.

A novel guided wave testing method for identifying rail web cracks using optical fiber Bragg grating sensing and orthogonal matching pursuit

Ensuring safe operation of railway systems is essential. Detecting early-stage rail defects is crucial to prevent catastrophic incidents, such as derailments. Traditional manual inspection routines are often inefficient and lack robustness. This paper proposes a novel guided wave testing (GWT) method using optical fiber sensing combined with an orthogonal matching pursuit (OMP)-based data processing technique to detect rail damage by reconstructing defect-related reflective wave from complex raw signals. First, a finite element model (FEM) is constructed to simulate ultrasonic guided wave (UGW) signals and subsequent signal reconstruction using OMP demonstrates capabilities. In the experimental part, a rail segment with a vertical crack is installed with a fiber Bragg grating (FBG) sensor to receive UGW. The reconstructed signals confirm the effectiveness of our method in identifying the rail web crack location. This method offers a novel approach for rail crack identification, promoting more efficient inspection methods for extensive rail transportation networks.

Influence of Winding Configurations and Stator/Rotor Pole Combinations on Field Back-EMF Ripple in Switched Flux Hybrid Excited Machines

Similar to armature back electromotive force (armature back-EMF), the back-EMF also exists in the field winding of hybrid excited machines. However, the existence of field back electromotive force (field back-EMF) is harmful to the safe and stable operation of machine systems, e.g., higher losses, lower efficiency, higher torque ripple, and reduced control performance. This paper systematically investigates the influence of armature/field winding configurations together with stator/rotor pole combinations on the field back-EMF ripple in hybrid excited machines with switched-flux stators. The two-dimensional (2D) time-stepping finite element modeling and prototyping experiments are used for the research. The investigated field and armature coil pitches equal to 1, i.e., non-overlapped windings. The influential factors that are investigated in this paper mainly include the number of layers of field/armature windings, the number of field/armature coils, and the stator/rotor pole combinations. The results show that the field back-EMF’s harmonic order and peak-to-peak value are closely associated with field/armature winding configurations and stator/rotor pole combinations under various conditions. Finally, for validation of the results predicted by the finite element method, a prototype machine is built and tested. Overall, non-overlapped double-layer armature and field windings are recommended for the hybrid excited switched flux machines with various stator/rotor pole combinations to realize relatively lower field back-EMF under different conditions.

Informer learning framework based on secondary decomposition for multi-step forecast of ultra-short term wind speed

Accurate and dependable wind speed prediction holds paramount importance in facilitating the dispatch and safe operation of power systems. Nonetheless, the inherent instability of wind speed makes wind speed prediction challenging. Consequently, a short-term wind speed prediction framework, amalgamating secondary decomposition (SD)-Informer, has been proposed in this paper. Initially, the variational mode decomposition (VMD) is applied to decompose the primary wind speed sequence. Through the VMD feature decomposition module, it effectively filters and eliminates superfluous noise from wind speed data. Subsequently, the complete ensemble empirical mode decomposition with adaptive noise technique is introduced for a secondary decomposition targeting the high-frequency components derived from the initial decomposition. To address the limitation of neural network models in capturing essential information from lengthy sequential data concurrently, a predictive model based on Informer is proposed as wind speed prediction module, thereby enhancing prediction accuracy. The validation of this hybrid model encompasses four distinct time ranges. Multiple models are scrutinized through comparative analysis to ascertain the superior performance of the proposed hybrid model. The root mean square error of the proposed method is reduced by 33.02%、25.46%、24.26%, and 23.12% compared to gate recurrent unit (GRU), vision Transformer (ViT), attention (AT)-ViT, and CNN-atteneion (CA)-Bi-directional long short-term memory (BiLSTM) respectively. The mean absolute error of the proposed method in the first quarter is 0.432, with model comparison values reduction of 36.19%、22.99%、20.44%, and17.71% respectively. The experimental results indicate that the proposed model exhibits a strong capability in capturing the long-term dependencies between the input and output sequences of wind speed. It can perform multi-step predictions while ensuring high prediction accuracy.

Experimental Investigation to Study Impact of Low-Concentration Hybrid ZnO and TiO<sub>2</sub> Nanoparticles on Thermo-Physical Properties of Transformer Oil

Transformer oil (Naphthenic Oil) is vital for the safe and efficient operation of transformer systems. The current experimental investigation mainly concentrates on the impact of low-concentration TiO2 and ZnO hybrid nanoparticles of 0.001 and 0.005 vol. % concentrations on the thermophysical properties of Naphthenic oil (Transformer oil). This investigation separately determines the flash and fire point, viscosity, thermal conductivity, pH, breakdown voltage and strength, and zeta potential analysis for both fresh and used Naphthenic oil and adding hybrid nanoparticles. The addition of hybrid nanoparticles to fresh and used Naphthenic oil increases the flash and fire points, viscosity, thermal conductivity, and pH values but the breakdown strength of the fresh oil decreases and the used oil increases.

Constraint Optimal Model-Based Disturbance Predictive and Rejection Control Method of a Parabolic Trough Solar Field

The control of the field outlet temperature of a parabolic trough solar field (PTSF) is crucial for the safe and efficient operation of the solar power system but with the difficulties arising from the multiple disturbances and constraints imposed on the variables. To this end, this paper proposes a constraint optimal model-based disturbance predictive and rejection control method with a disturbance prediction part. In this method, the steady-state target sequence is dynamically corrected in the presence of constraints, the lumped disturbance, and its future dynamics predicted by the least-squares support vector machine. In addition, a maximum controlled allowable set is constructed in real time to transform an infinite number of constraint inequalities into finite ones with the integration of the corrected steady-state target sequence. On this basis, an equivalent quadratic programming constrained optimization problem is constructed and solved by the dual-mode control law. The simulation results demonstrate the setpoint tracking and disturbance rejection performance of our design under the premise of constraint satisfaction.

Robust Dynamic State Estimation for Power System Based on Generalized Correntropy Loss Function and Unscented Kalman Filter

Dynamic state estimation (DSE) of the power system is one of the most important means to ensure a safe and stable operation of the power system. However, the electromagnetic field environment, communication noise and equipment failure often lead to the appearance of non‐Gaussian noise and bad data, which ultimately cause performance degradation of traditional DSE algorithms based on the mean‐square error criterion. In this paper, a DSE method based on the generalized correntropy loss (GCL) function is proposed, which is well adapted to estimate the dynamic characteristics of the power system. The new robust DSE method can effectively deal with problems such as non‐Gaussian noise and bad data, thus improving the state estimation accuracy. Simulation results carried out on the IEEE 39‐bus system with synchronous generators demonstrate the robustness of the proposed DSE method. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Design and modeling of three-mode nonlinear hybrid piezoelectric-electromagnetic vibration energy harvester for harvesting multi-band vibration energy on the freight wagon axle box

Abstract With the rapid development of rail transit system, it is becoming more demanding for structural health monitoring (SHM) of the train. It is crucial to supply power for sensing devices on the freight wagon to ensure the safe operation of rail transit systems. Three-mode nonlinear hybrid piezoelectric-electromagnetic vibration energy harvester (TNHVEH) with a three-layer pickup system has been designed and applied to efficiently harvest vibration energy on the axle box of freight wagons for providing the power of the sensors for online SHM. Dynamic coupling model of the vehicle-harvester system is built to devote the relationship between the operation bandwidth and the nonlinear degree of TNHVEH for broadening the operation frequency band. Simulation and experimental results demonstrate that the resonant frequencies of the three pickup systems of TNHVEH are concentrated around 27, 70, and 120 Hz, matching the representative frequencies of axle box vibrations. A maximum output voltage of 2.97 V and output power of 29.4 μW under an optimal load of 300 kΩ at 0.5 g acceleration is achieved. It successfully lights up 53 LEDs with ‘ECJTU’ patterns, providing a solution to the power supply problem of microelectronic devices on freight wagons.

System-level Prognostics and Health Management for Complex Industrial Systems

Prognostics and health management (PHM) has become essential to guarantee aware and safe system operation and to inform economic decision-making. However, due to the nature of detection, diagnostics, and prognostic methods, applications have mainly been limited to the component level. In practice, most industrial systems consist of multiple interacting components whose partial degradation could lead to the system's failure (or subsystems).This research addresses the limitations of traditional component-level PHM techniques by proposing a novel system-level framework. By implementing a hierarchical structure of components and subsystems, we will select an optimal method for each subsystem to aggregate its component health assessments. The overall system health can then be estimated by further combining the obtained estimates. The research considers simplified and holistic modeling techniques, margin-based methods, and hybrid graphical models. This approach aims to provide reliable system health predictions and online components' sensitivity measures to enhance maintenance decision-making. We consider an application in the context of the nuclear industry, characterized by strict safety and economic requirements. Using a SIMULINK model to approximate a Pressurized Water Reactor (PWR) with real industrial inputs, we plan to add component degradation modules and use simulated sensor data and reliability information to test the proposed framework. Initial results on artificial case studies show the feasibility of integrating component-level health predictions.

An Ultra‐Short‐Term Multi‐Step Prediction Model for Wind Power Based on Sparrow Search Algorithm, Variational Mode Decomposition, Gated Recurrent Unit, and Support Vector Regression

ABSTRACTAccurate ultra‐short‐term wind power prediction techniques are crucial for ensuring the efficient and safe operation of wind farms and power systems. Combined models based on data decomposition‐prediction techniques have shown excellent performance in ultra‐short‐term wind power forecasting. This study introduces a novel ultra‐short‐term multi‐step prediction model for wind power, which integrates the sparrow search algorithm (SSA), variational mode decomposition (VMD), gated recurrent unit (GRU), and support vector regression (SVR). An optimization variational mode decomposition technique is developed by adaptively determining VMD hyperparameters using SSA. The optimization VMD decomposes the original wind power sequence into sub‐modes, and the resulting sequence of decomposed sub‐modes calculates permutation entropy (PE) values. Sub‐modes with similar PE values are combined, reorganized, and categorized into high‐frequency and low‐frequency. High‐frequency sub‐modes data with high complexity and non‐stationarity are predicted by the GRU neural network. Low‐frequency sub‐modes data with low complexity and strong nonlinearity are predicted with SVR. The proposed model was evaluated against seven others using three error metrics: MAE, RMSE, and R2, along with their corresponding enhancement percentages. Experimental results indicate that the proposed model extracts detailed and trend information from the wind power series more effectively and stably than the comparison models. It also demonstrates superior multi‐step prediction performance, offering significant value for practical engineering applications.

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety.

Design and optimization of line CT energy harvesting device

Abstract The aim of this study is to design and optimize an energy harvesting device for line current transformers (CT). The main function of this device is to extract energy from CT on the line to supply power to related electronic devices such as sensors and monitoring equipment. Firstly, an in-depth analysis of the working principles and characteristics of CT was conducted to determine the design requirements and performance indicators of the energy harvesting device. Subsequently, an energy-harvesting device based on the principle of magnetic coupling was designed. Different circuit principles were developed to accommodate low-load and high-load scenarios on the line, and the structure and parameters were optimized to improve energy conversion efficiency and output stability. Through experimental validation, the performance and stability of the designed energy harvesting device under different working conditions were verified. Finally, the design scheme of the energy harvesting device was optimized, and further improvement and application suggestions were proposed. This study provides a theoretical and practical basis for improving the performance and reliability of line CT energy harvesting devices, which is of great significance for the safe operation of power systems and equipment monitoring.

A novel economic dispatch of energy-renewable multi-area power systems with group-based differential evolution

Spinning reserve capacity can handle the effects of load and renewable energy output forecasting errors. To ensure the safe and stable operation of the power systems and enhance economic benefits, it is meaningful to develop reasonable reserve capacity model for multi-area power systems. In this paper, a calculation method for reserve capacity, based on multi-area joint reserve capacity, is proposed to handle the uncertainty of renewable energy output. Multi-area interconnected economic dispatch with renewable energy, based on multi-area joint reserve capacity, can comprehensively consider the characteristics of power generation resources in each areas, and then reasonably allocate reserve capacity in each areas. Furthermore, to solve the multi-area interconnected economic dispatch with renewable energy, a sorted grouping-based adaptive multi-strategy differential evolution algorithm is designed, which divides the population into four groups according to the current convergence and balance, then adopts several new mutation strategies, and uses adaptive schemes for each group of populations separately. Notably, the sorted grouping-based adaptive multi-strategy differential evolution algorithm has advantages over several differential evolution variants and several well-known algorithms. Finally, the proposed method is compared with individual reserve capacity, which proves that the superiority of the proposed method in reserve capacity programming.

Failure modes and non-destructive testing techniques for fiber-reinforced polymer composites

Prognostics and health management (PHM) is an enabling technology essential for the safe operation and conditional maintenance of any engineering system. Over the past two decades, the use of fiber-reinforced polymer (FRP) composites has increased to develop high-strength, lightweight, and durable composite structures. PHM technology has been widely adopted in the composites industry to address challenges such as damage detection, localization, remaining useful life (RUL) prediction, and maintenance scheduling. However, implementing PHM technologies for FRPs necessitates a thorough understanding of their failure modes and the non-destructive testing (NDT) techniques commonly used to assess their health condition. Therefore, this review provides a comprehensive discussion on the failure modes of FRP composites based on practical loading conditions, examines several NDT techniques employed on FRP composites, detailing their advantages and limitations, and highlights the current challenges while providing future directions for the use of NDT techniques in PHM of composite structures. Thus, the review aids practitioners, the scientific community, and new researchers in understanding the failure modes of FRP composites and exploring recent advancements in NDT techniques to address the current challenges and limitations in PHM of composite structures.

Ceramic matrix composites for corrosion-resistant next-generation nuclear reactor systems: A conceptual review of enhancements in durability against molten salt attack

Ceramic matrix composites (CMCs) are emerging as a key material class for enhancing corrosion resistance in next-generation nuclear reactor systems, particularly in high-temperature molten salt environments. These environments, critical for advanced nuclear reactors such as molten salt reactors (MSRs), present severe challenges, including aggressive chemical attacks that degrade traditional structural materials over time. This conceptual review explores the development and application of CMCs to improve the durability and corrosion resistance of nuclear reactors exposed to molten salt attacks. CMCs, which consist of ceramic fibers embedded in a ceramic matrix, offer significant advantages, such as high thermal stability, mechanical strength, and improved corrosion resistance compared to conventional materials. This review examines how CMCs can be tailored to withstand harsh operational conditions, with a focus on the selection of ceramic phases, fiber-matrix interactions, and innovative fabrication techniques that enhance their protective capabilities. Key challenges addressed include the optimization of composite design to resist molten salt corrosion, the effects of temperature on the material properties, and the long-term stability of CMCs under extreme conditions. Advances in surface treatments, coatings, and the development of hybrid CMC systems are also discussed, highlighting their potential to further enhance durability. The review outlines the use of advanced characterization techniques, such as high-temperature corrosion testing and in situ microscopy, to evaluate CMC performance in molten salt environments. Additionally, it identifies knowledge gaps in current research, emphasizing the need for long-term studies on CMC behavior under realistic reactor conditions. This review concludes by proposing future research directions and technological advancements required to integrate CMCs into next-generation nuclear reactor designs, aiming to improve system reliability and operational safety.

State of health estimation for lithium-ion batteries with Bayesian optimized Gaussian process regression

The estimation of the state of health (SOH) of lithium-ion batteries is of great importance to ensure the safe and stable operation of a lithium-ion battery management system (BMS). Accurate estimation of SOH can effectively prevent unnecessary losses of lithium-ion batteries due to failures. Therefore, a Bayesian-optimized Gaussian process regression (BO-GPR) method is proposed for SOH estimation. Firstly, the ageing characteristics reflecting the SOH of the battery are extracted from the charge/discharge time and incremental capacity (IC) curve, and the correlation between the health characteristics and the SOH is analysed by means of grey correlation (GRA). On the other hand, a Bayesian optimization algorithm is used to automatically find the optimal hyperparameters of the GPR model and trained using the battery dataset to obtain the SOH estimating model. To validate the effectiveness of the method, the datasets provided by NASA and Oxford are used as experimental objects and compared with different data-driven models to verify the accuracy and reliability of the proposed model. The experimental results show that the method proposed in this paper has a high accuracy in the estimation of the SOH, with the maximum average estimation error being within 1%.