Operation Status Research Articles

Optimized Wavelet Transform for the Development of an Algorithm Designed for the Analysis of Digital Substation Electrical Equipment Parameters

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored.

An 11-bit low power column-parallel single slope ADC with comparator toggle prediction technique for CMOS image sensor

This paper proposes an 11-bit low power column-parallel SS ADC with DCDS for CMOS image sensors. The proposed SS ADC reduces the power consumption in three ways. Firstly, using a combination of coarse and fine quantization can reduce power consumption of counter. Secondly, changing the operation state can remove the bit width inverter. Thirdly, the comparator power is reduced by making each column of comparators switch off after the end of the comparison function. The proposed ADC is fabricated in a 110 nm 1P4M CMOS technology and has a DNL of +0.64/−0.54 LSB and an INL of +0.04/−6.7 LSB at a sampling frequency of 29.9 kS/s. The single column SS ADC has the power consumption ranging from 39.6 μW to 74.87 μW. Compared with the conventional SS ADC, the minimum and the maximum reduction of the power consumption are 20.1 % and 43.8 %, respectively. The average power saving is 34.5 %.

Graph-logical models for (n, f, k) – and consecutive - k-out-of-n – systems

The article is devoted to methods of constructing graph-logical models of fault-tolerant multiprocessor systems. In particular, systems of the type (n, f, k), linear consecutive-k-out-of-n and circular consecutive-k-out-of-n are considered, which are characterized by the failure of the system when a certain number of consecutive processors fail. Graph-logical models can be used to estimate the reliability parameters of fault-tolerant multiprocessor systems by conducting statistical experiments with models of their behavior in the failure flow. The graph-logical models under construction are based on the basic models with a minimum of lost edges. It is determined that to build a graph-logical model of systems of this type, it is sufficient to calculate the maximum possible number of failed processors at which the system remains in operation. A graph-logical model of a basic system that can handle this number of failures is built, without taking into account the sequence of these failures. The next step is to identify all possible consecutive failures that cause the system to fail. Then, the base model is modified in such a way as to reflect the failure of the system when consecutive failures occur. This means weakening the base model on the previously determined vectors. The proposed methods of model construction can be used both for linear and circular consecutive-k-out-of-n systems and for (n, f, k) systems. A minor difference will be in the calculation of some parameters. The paper describes the calculation of such parameters as the maximum allowable number of failures at which the system remains in an operational state, as well as the calculation of the number of all combinations of consecutive failures at which the system fails. Experiments have been conducted to confirm the model's compliance with the system's behavior in the failure flow. Examples are given to demonstrate the process of building graph-logical models for linear consecutive-k-out-of-n, circular consecutive-k-out-of-n and (n, f, k) systems using the proposed methods.

Bi-level expansion planning of power system considering wind farms based on economic and technical objectives of transmission system operator

Planning for the expansion of generation and transmission infrastructure, with a focus on integrating wind farms is presented in this study according to the goals of economic efficiency, operational performance, security, and reliability of the transmission system operator. The approach incorporates a bi-level optimization framework, with the upper level outlining the formulation of power system expansion planning to reduce construction and operational costs while adhering to the investment budget limits. In the lower-level model, which economic reliable-secure functioning is formulated for the transmission network. The objective function of the problem aims to minimize operational costs, energy losses, and anticipated energy not supplied, and the voltage security index. The constraints of this problem include AC optimal power flow model, security, and reliability limits. Scheme extracts a convex-linear model for the formulation using the conventional linearization technique. Pareto optimization driven by the aggregation of weighted functions results in a single-objective framework for the lower-level problem, and the Karush-Kuhn-Tucker technique presents a single-level model for the scheme. The approach utilizes stochastic optimization has been adopted to model load, wind farms, and network equipment availability uncertainties. Finally, the study cases yielded numerical findings that showcase the efficacy of the proposed scheme in achieving the desired economic, operational, security, and reliability status within the transmission network. This is particularly true in situations where there is appropriate generation and transmission expansion.

Influence analysis of position error to the operating characteristics of opposed-piston free-piston engine generator with position observation optimization

Stable operation is a crucial challenge for opposed-piston free-piston engine generator (FPEG). Due to the absence of mechanical constraints, variations position-based operation parameters would have a significantly impact on the operation performance of the system. This paper conducted a comprehensive investigation into the effects of position fluctuations on the system's dynamic characteristics and thermodynamic performance by leveraging a detail system model with stable operation. Additionally, a novel real-time position observation approach for the mover was proposed, leveraging observation coils. The findings indicate that the opposed-piston FPEG system can quickly recover to stable operational state within a few cycles for a one-time position error, if there is no misfire in the power cylinder. The acceptable range for stable operation of the opposed-piston FPEG is between +35 % and −48 % for the one-time position error. With the continuous position error varying from +20 % to −30 %, there is a noticeable decrease in the compression ratio, operating frequency, and overall system performance. A significant linear relationship has been found between the flux linkage value of the observation coil and the position of the mover. The maximum difference between the observed position calculated by the proposed approach and the actual position was below 4 %.

Experimental study and seismic assessment on base isolated precast concrete frame structure with box connectors

The precast construction method offers fast and environmentally friendly solutions for sustainable development in construction. Various connection methods, including wet and dry, have been proposed to enhance assembly convenience. However, challenges remain in achieving complete assembly and considering disassembly processes, hindering post-earthquake restoration. This study introduces a precast RC structure system with easily assembled joints that facilitate disassembly. To enhance the structural system's adaptability to regions with high seismic intensity, rubber-bearing base isolation technology is employed to optimize its seismic performance. Shaking table tests were conducted to evaluate the structure system's dynamic response and seismic performance. Test results indicate that the isolation layer significantly decreases the acceleration response, inter-story drift ratios, floor shear forces, and strain of reinforcements. The isolation structure has a high probability of entering operational and immediate occupancy states during rare earthquakes. The deformation of the isolation bearings has a decisive impact on the overall seismic fragility of the isolation structure system.

Digital twin technology for sewage sludge smoldering process and CO/NOx emissions based on back propagation neural network: A laboratory experimental study

Smoldering has broad prospects for application in the treatment of sewage sludge with high moisture content, but it faces the problem of high CO/NOx emission concentrations. To improve smoldering velocity and reduce emission concentrations of gas pollutants, intelligent control and refined treatment of sewage sludge smoldering need to be achieved. In this paper, the digital twin-driven sewage sludge smoldering treatment system is proposed, and the overall framework, operational process and key technologies of the system are described in detail. A digital twin system based on the back propagation neural network model is constructed, which achieves the accurate prediction of the variation trends and average values of CO/NOx emission concentrations as well as smoldering temperature and velocity. Nondominated Sorting Genetic Algorithm II is used for multiobjective optimization, providing effective control strategies for sewage sludge with distinctive characteristics. Smoldering features, emission concentrations of gas pollutions and equipment operating status are visualized using WebGL technology. Results show the maximum increase in smoldering velocity is 49%, whilst CO can be reduced by 8% to 60% and the maximum reduction in NOx is 51%. This system can assist in applications such as monitoring state of sewage sludge smoldering, timely warnings and intelligent control.

Multi-modal signal adaptive time-reassigned multisynchrosqueezing transform of mechanism

High-end mechanical equipment often operates under non-stationary conditions, such as varying loads, changing speeds, and transient impacts, which can lead to failures. Time-frequency analysis (TFA) integrates time and frequency parameters, allowing for detailed signal analysis and is widely used in this context. To improve the accuracy of assessing the operational status of mechanical equipment, this paper proposed a multi-modal signal adaptive time reassignment multiple synchrosqueezing transform (MSST) TFA method. This method enhances the MSST method by using a local maximum technique to address energy ambiguity in TFA. Additionally, the optimal window width for each function is determined through iterative processes to better concentrate energy in the TFA. Multi-modal signals are jointly analyzed using an impulse feature extraction method for signal reconstruction, enabling multi-dimensional fault analysis. The proposed method is validated with both simulation and experimental data from a planar parallel mechanism (PPM) and is compared against classical and advanced techniques. The results show that the method effectively captures shock features in multi-modal signals, offering a more consolidated time-frequency representation (TFR) than existing TFA algorithms.

Dynamic Clock Switching Scheme for Enhanced Power Efficiency in System-on-Chip Architectures

High power consumption and thermal management are critical challenges in traditional System- on-Chip (SoC) architectures due to multiple active clock sources feeding individual IP blocks, even during idle states. This paper introduces a dynamic clock switching power-saving scheme that consolidates clock sources by dynamically switching to a single lower-frequency clock during low-power modes. A Clock Control Agent (CCA) monitors real-time operational status, power consumption, and performance needs, leveraging AI for predictive adjustments. Experimental results demonstrate a significant reduction in idle power consumption and improved thermal management, without compromising performance or data integrity. This scheme addresses inefficiencies in existing methods such as Dynamic Voltage and Frequency Scaling (DVFS) and clock gating, offering a robust and efficient solution for next-generation SoC designs.

Optimization of the power–transportation coupled power distribution network based on stochastic user equilibrium

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load have generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power–transportation-coupled distribution network optimization strategy based on stochastic user traffic equilibrium theory. First, a stochastic user equilibrium-mixed traffic flow allocation model that expresses user non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained using an improved method of successive algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power–transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines.

Experimental and theoretical exploration of bactericidal and photo-catalytical activities of hierarchically porous AuNRs@CuO nanocomposite

In this study, a novel binary nanocomposite consisting of CuO nanosheets functionalized with gold nanorods (AuNRs@CuO), was successfully prepared using a two-step synthetic procedure and further investigated via Density Functional Theory. The physicochemical properties of prepared nanomaterials were analysed using Fourier transform infrared spectroscopy, X-ray diffraction, Field emission scanning electron microscopy, High-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy confirming successful fabrication with improved surface properties. Experimental analyses, including antibacterial assays and morphological studies of bacterial cells demonstrates superior bactericidal properties of AuNRs@CuO nanocomposite compared to pristine CuO nanosheets. Furthermore, molecular docking score provide theoretical insights into the underlying mechanism governing the enhanced antibacterial activity, highlighting the favorable interaction with the bacterial cells. The synergistic effect of CuO nanosheets and AuNRs further improves the photocatalytic efficacy, culminating in a high-yield photocatalyst that achieves 98.7% degradation for Indigo carmine under Xenon lamp irradiation compared to 40.8% and 62.4% for AuNRs and CuO nanosheets alone. Improved charge separation and generation of H+ as major reactive oxygen species from the nanocomposite to Indigo carmine was revealed by radical scavenger activity and photoluminescence studies. After cycles of operation, the morphology and surface state of the photocatalyst remained unchanged, as investigated by FESEM and HRTEM analysis. The novelty of this study lies in the integration of AuNRs and CuO nanosheets as an efficient photocatalytic material for the remediation of toxic organic dyes in wastewater, presenting a promising advancement in sustainable water treatment technologies.

A mathematical treatment of the impact of p53 signalling on the expression of the transcription factor E2F in the CycE/Cdk2 subsystem in cancerous cells

Complex networks of proteins and genes control cell growth until mitosis. These components play crucial roles in determining the characteristics and timing of each reaction in the cell. A family of cyclins and cyclin-dependent kinases regulates the progression of the cell from one stage to the next, while certain inhibitors regulate cell proliferation, significantly impacting this process. We examine the impact of p53 protein production in the cyclin E/cyclin-dependent kinase 2 (CycE/CDK2) subsystem. Although the precise functioning of the p53 protein is not fully understood, it is known to exhibit pulsatile behaviour, triggered in response to DNA damage. Consequently, the expression of the p53 protein within the cell system increases with the severity of DNA damage. Similarly, levels of CycE/CDK2 are substantially elevated in instances of DNA damage.To investigate this subsystem, we have developed a mathematical model of the CycE/CDK2 subsystem, presented as non-linear ordinary differential equations. Our primary aim is to determine the conditions that could theoretically result in tumorigenesis. We conduct a dynamical systems analysis to investigate the presence of stable limit cycles, which indicate the standard operational states of cells. Our investigation reveals the levels of p53 protein at which the cell can correct defects, undergo apoptosis, or enter quiescence, and where the system exhibits a Hopf-bifurcation (limit cycles). This is the novelty of our study, as previous research has primarily focused on numerical simulations.Using MATLAB simulations, we produced various results in response to different expressions and concentrations of the p53 protein, allowing us to assess its influence on malignant cells. By altering the concentration of protein p53, we ascertain the specific cellular circumstances that facilitate its natural growth, self-correction in the presence of defective activity, and entry into a state of quiescence.This investigation is motivated by the significant role of oscillations in driving, controlling, and managing the cellular oversight machinery. These aspects are incorporated into developing a mathematical model that emulates cellular operations. From this research, we establish that E2F is a critical target to mitigate oncogenic activity, achievable by managing the pulsatile behaviour of the inhibitor p53 or inhibiting its production process within the cell machinery.

Operational status effect on the seismic risk assessment of oil refineries

The operational status of an oil refinery (type and scale of operations that take place at any time instance) largely determines the amount of fuel produced, circulated within the facility, and stored in tanks. This status is affected by seasonality, periods of peak or low demand, as well as periods of routine maintenance. However, it is an aspect that is typically neglected even though it stands out among the factors that determine the seismic performance of several critical industrial assets, such as the storage tanks, as well as the consequences of any potential failure. An open-source refinery testbed is employed herein to demonstrate the effect of the refinery's operational status on the seismic risk estimates. Alternative realistic operational scenarios are developed following typical industry practices and are arranged over a time period between two refinery major maintenance shutdown events. The most probable damage state is selected for each asset to identify the most vulnerable ones. Based on the type and importance of the impacted assets, the potential consequences are determined at the facility level. Resulting estimates are very different if an earthquake strikes during a regular/high/low-demand period, or a maintenance period. The framework can be utilized to identify the locations within the refinery that may trigger cascading failures and secondary damages, should their assets be damaged by a seismic event. The outcomes can be exploited by stakeholders, risk engineers, and emergency action planners for developing customized and businesslike procedures to enhance the seismic resilience of the facility.

Optimal weighted multi-scale entropy-energy ratio feature for machine fault diagnosis

Identifying the sensitive characteristics of mechanical equipment components is crucial for effective fault diagnosis. However, focusing solely on a specific feature at a single time scale fails to comprehensively capture the device’s operational state. Inspired by the concept of multi-scale analysis and recognizing the complementary strengths of permutation entropy (PE) and root mean square (RMS) in fault characterization, we propose a novel feature called the Optimal Weighted Multi-Scale Entropy-Energy Ratio (OWMEER). This feature aims to enhance fault characterization by optimally combining the strengths of PE and RMS, thereby providing a more comprehensive assessment of the device’s condition. The effectiveness and superiority of OWMEER in fault characterization have been validated through experimental data, including both public and self-test datasets, when combined with the commonly used pattern recognition methods such as random forest (RF) and support vector machine (SVM). The results demonstrate that using OWMEER as a fault feature not only yields better results than using the original features RMS and PE, but also maintains strong diagnostic performance across different classifiers and datasets.

Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning

Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.

Analysis on temperature vacuum swing adsorption from wet flue gas carbon capture by using solid amine adsorbent with co-adsorption equilibrium models

Adsorption carbon capture has attracted extensive attention due to its low regeneration heat consumption, eco-friendly impacts and simplicity. However, there is a lack of comprehensive analysis of its energy consumption, economy and environment impact of adsorption carbon capture. This study aims to investigate the working performance using temperature vacuum swing adsorption (TVSA) from wet flue gas carbon capture utilizing co-adsorption equilibrium models for metal–organic framework (MOF) based amine materials. TVSA model is then carried out in terms of the continuous operational state, the dynamic changes in physical properties inside the bed, and the differences in properties at different positions within the bed layer. It is found that they often cannot simultaneously meet high CO2 recovery rates, productivity, and equivalent work. The analysis reveals that the optimal conditions, namely an adsorption temperature of 318 K and a CO2 component of 0.02 kmol during desorption, collectively result in reduced equivalent work and heightened productivity. The maximum productivity could reach 46.35 kg·m−3·h−1 for 5.6 v% water content and 308 K adsorption temperature. It is demonstrated that temperature vacuum swing adsorption for wet flue gas carbon capture using MOF based amine adsorbent is quite promising in terms of energy consumption and CO2 productivity in the future.

Experimental demonstration of frequency- downconverted arm-length stabilization for a future upgraded gravitational wave detector.

Ground-based laser interferometric gravitational wave detectors (GWDs) consist of multiple optical cavity systems whose lengths need to be interferometrically controlled. An arm-length stabilization (ALS) system has played an important role in bringing these interferometers into an operational state and enhancing their duty cycle. The sensitivity of these detectors can be improved if the thermal noise of their test mass mirror coatings is reduced. Crystalline AlGaAs coatings are a promising candidate for this. However, the current ALS system with a frequency-doubled 532 nm light is no longer an option with AlGaAs coatings because the 532 nm light is absorbed by AlGaAs coatings due to the narrow bandgap of GaAs. Therefore, alternative locking schemes must be developed. In this Letter, we describe an experimental demonstration of a novel ALS scheme, to the best of our knowledge, which is compatible with AlGaAs coatings. This ALS scheme will enable the use of AlGaAs coatings in current and future terrestrial gravitational wave detectors.

越南汉语学习者低龄化趋势：成因与现状分析——以胡志明市商业华语培训中心为例

<p>In recent years, there has been a noticeable trend of younger learners taking up Chinese globally. Since the Vietnamese Ministry of Education included Chinese as an elective foreign language in primary schools in 2021, the number of primary and secondary school students learning Chinese has significantly increased. To gain a more comprehensive understanding of the current state of Chinese learning in Vietnamese primary and secondary schools, identify potential challenges in the process of younger learners taking up Chinese, and provide valuable data support for the formulation of long-term Chinese education development plans, this study uses the Hoavan SHZ Chinese Training Center as a case study. It explores the enrollment situation and operational status of young learners over the past ten years. Through data analysis and interviews, the study examines changes in the age groups of young learners, the causes of the trend toward younger learners, the prospects for development, and the challenges faced. The results show that the number of young Chinese learners in Vietnam has been steadily increasing, with the age of learners continuously decreasing, including preschool children starting to learn Chinese. National education policies and the deepening of China-Vietnam cooperation are the main driving forces. To respond to this trend and meet the needs of parents and students, this paper advocates cultivating students' interest in learning, improving the quality of teachers, and refining teaching materials and methods.</p>

Modeling and simulation study of end around taxiway operation of Shanghai Hongqiao Airport

Abstract With the rapid expansion of the aviation industry, an increasing number of Close Spaced Parallel Runway (CSPR) airports are either planning or constructing End Around Taxiways (EAT) to alleviate field operation pressures and enhance safety. Taking Shanghai Hongqiao Airport’s typical CSPR EAT configuration as a case study, this research integrates the airport’s current operational status with the anticipated requirements for future structural renovations and increased flight volumes. Various operational scenarios are established, and simulation research on optimising EAT operations is conducted in advance. The simulation study proceeds as follows: first, an AirTOP simulation model is constructed based on Hongqiao Airport’s actual operational construction. Subsequently, leveraging existing operational scenarios, five simulation scenarios are devised by activating EATs at the departure and approach ends of the eastern zone. The merits and drawbacks of these scenarios are thoroughly analysed. The findings indicate that, with escalating flight volumes, the utilisation of EAT for larger aircraft can curtail their holding duration by nearly 8 min, consequently reducing overall arrival holding duration by 6 min. Departures from gates proximate to T1 experience a 3-min reduction in holding duration through the adoption of EAT at the approach end. Despite an increase in taxi distance due to a higher proportion of aircraft taxiing around, the overall taxi time is diminished. Activating EATs at the departure and approach ends of the eastern zone effectively mitigates the adverse effects of heightened flight volumes on field operational efficiency.

A 6 month-long real-time time scale steered to NIM-Sr1 optical lattice clock

Abstract NIM-Sr1 optical lattice clock has been operated intermittently for seven consecutive months with an operation uptime around 13.8%, which serves as the reference to steer a hydrogen maser for generating a real-time time scale TS(Sr1). The peak-to-peak time difference of TS(Sr1) compared to UTC is 1.8 ns within 180 days and is less than 0.5 ns within the last month, after correcting the time error induced by a time transfer link abnormality. A dedicated automation system with hardware and software is developed to monitor the operation status of NIM-Sr1, process the measurement data, and periodically predict the hydrogen maser frequency. According to the simulation results, a composite algorithm based on both Kalman filtering (KF) and weighted least squares fitting (WLSF) is utilized for generating TS(Sr1). The KF is utilized when the unavailability of NIM-Sr1 is shorter than one day, and the WLSF with fitting interval T fit=30 d is utilized in other conditions leveraging the long-term predictability of the hydrogen maser. In addition, it is demonstrated by simulation and the post-processing of earlier experimental data that, a time scale with smaller time error can be achieved by shortening the WLSF interval from 30 days to about 5 days under the condition of sufficient measurement data points for reducing the statistical error.