Operation Of Systems Research Articles

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.

Research on carbon emission accounting and low carbon operation methods for urban water supply systems

Under the environment of global warming, how to promote the innovative and efficient development of water supply system is the key to sustainable urban development. There is still room for improvement in the study of carbon emissions in urban water supply systems and there is a lack of appropriate rationale for low-carbon operation and management of waterworks. A water-energy-carbon (WEC) framework is established in Fuzhou to account for the carbon emissions from 2013 to 2022 and the level of coupling and coordination development is evaluated in this study. A multi-objective optimization model and a system dynamics (SD) model were developed to optimize the low-carbon operation of the water supply system and predict future climate impacts. We also provide a quantitative and qualitative evaluation method for low-carbon operation. The results show that carbon emissions generated by electricity consumption and chemical use in the water supply system contribute greatly, and the coupling coordination level is high. Under different scenarios, the optimal solutions of carbon emissions, carcinogenic risk factors and unit energy economy range from 1.63 to 3.82 kt CO2-eq, 4.4 × 10−5 to 2.7 × 10−4, and 0.26 to 0.73 RMB/MJ. In the SD model, the water supply system can achieve 17 % and 20 % carbon reduction in 2025 and 2030. Its carbon reduction direction is mainly reflected in public service water use. This study provides a reference for optimizing the operation of water supply systems.

Field Experiment on Transient Thermal Comfort in a Chinese Railway Station – High Temperature Exposures

Thermal alliesthesia describes the phenomenon where the perception of a given environmental thermal stimulus can be either pleasant or unpleasant, depending on the individual's internal thermal state relative to homeostasis. Quantifying the thermal alliesthesia effect in human thermal perception of periodic transient metabolic rates and thermal environmental exposures can potentially enhance the thermal experience of temporarily occupied built environments, such as a railway station. A field experiment was conducted in a railway station in southern China during hot and humid summer months. Sixteen college-age participants were recruited to undergo simulations of entering the railway station after having been exposed to various intensities of heat, and then sitting quietly in the station waiting hall. Thermal environmental parameters, thermal physiological parameters, and subjective perceptions of participants were registered throughout each field experiment. The results indicate that the effect of prior heat exposures on skin temperatures and subjective perceptions were extinguished within 10 minutes after participants entered the station. Step-changes from low-to-high intensity activity induced a more immediate and pronounced change in metabolic heat production, compared to the reverse scenario. A transient thermal perception model is proposed with the input parameters including the environmental temperature and metabolic rate transients, inter alia. The algorithm in the model indicates that thermal alliesthesia induced by temperature step-change within transition spaces could be negligible, whereas metabolic transients exert a more decisive influence. The study provides insight into the alliesthesia experienced in transitional spaces and guidance for designs and operations of HVAC systems.

Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors

Transfer learning (TL) has the inspiring potential for artificial intelligence in heating, ventilation and air conditioning (HVAC) system with insufficient data labels. However, traditional TL-based methods are limited when applied across different conditions, systems, and operations.Unfortunately, public building HVAC systems encounter challenges related to data acquisition and richness, making it difficult to obtain data from similar HVAC systems conditions, scenarios and operations. It proposes a novel TL-based method that combines energy and mass balance constraint equation (EBCe) to diagnose the sensor faults in HVAC systems across different systems, conditions and operations.Firstly, it utilizes laboratory data as the source domain data and constructes EBCe based on the common physical laws of HVAC system to reduce the data differences between laboratory and public buildings. Then, an laplacian kernel domain-adaptive neural network (LkDaNN) is proposed to generalize more efficiently feature differences between the source domain data and target domain data. Finally, experiment analyzes the non-fault and four control-sensors fault under both cross-operation and non-cross operation conditions. The experimental results demonstrate that the EBCe-LkDaNN method achieves satisfactory fault detection and diagnosis (FDD) performance.The overall FDD accuracy of porposed method can reach 90.72% and 88.64% under different cross-operation, respectively. Practical application of the EBCe-LkDaNN strategy for HVAC sensor FDD are discussed at last.

Guest Editorial Special Section on Advancing Power Electronics Reliability: Components, Systems, and Intelligent Operation

Guest Editorial Special Section on Advancing Power Electronics Reliability: Components, Systems, and Intelligent Operation

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.

Optimizing Effectiveness and Defense of Drone Surveillance Missions via Honey Drones

This work aims to develop a surveillance mission system using unmanned aerial vehicles (UAVs) or drones when Denial-of-Service (DoS) attacks are present to disrupt normal operations for mission systems. In particular, we introduce the concept of cyber deception using honey drones (HDs) to protect the mission system from DoS attacks. HDs exhibit fake vulnerabilities and employ stronger signal strengths to lure DoS attacks, unlike the legitimate drones called mission drones (MDs) deployed for mission execution. This research formulates an optimization problem to identify an optimal set of signal strengths of HDs and MDs to best prevent the system from DoS attacks while maximizing mission performance under the resource constraints of UAVs. To solve this optimization problem, we leverage deep reinforcement learning (DRL) to achieve these multiple objectives of the mission system concerning system security and performance. Particularly, for efficient and effective parallel processing in DRL, we utilize a DRL algorithm called the Asynchronous Advantage Actor-Critic (A3C) algorithm to model attack-defense interactions. We employ a physical engine-based simulation testbed to consider realistic scenarios and demonstrate valid findings from the realistic testbed. The extensive experiments proved that our HD-based approach could achieve up to a 32% increase in mission completion, a 20% reduction in energy consumption, and a 62% decrease in attack success rates compared to existing defense strategies.

TreeHouse: An MLIR-based Compilation Flow for Real-Time Tree-based Inference

Tree-based ensembles stand as the prominent resource-efficient approaches for real-time inference. To optimize their performance, researchers have developed several solutions to accommodate their unique program structure, i.e., consecutive branches and eliminate the floating-point arithmetic operations, which are usually costly at the embedded computing systems. Most of them are realized at the level of source code and a standard compilation is applied subsequently. Therefore, an end-to-end compilation flow may consolidate these methods and provide a holistic optimization. In this work, we introduce TreeHouse, a compilation flow based on MLIR designed for real-time inference of tree ensembles. First, we optimize the layout of basic blocks to reduce the expected branches during inference. Moreover, we provide a solution to facilitate LLVM register allocation for further efficiency. In addressing the suboptimal performance of floating-point operations in edge systems, we incorporate methods to convert data into integer format. We implemented and evaluated TreeHouse using two tree-based ensembles: boosting trees and random forests. Overall, boosting trees exhibited performance gains ranging from 1.38 × to 8.24 × on x86, 1.70 × to 6.61 × on ARMv8, and 1.75 × to 4.52 × on RISC-V compared to state-of-the-art decision tree research. Random forests achieved performance gains of up to 1.6 × on x86, 2.03 × on ARMv8, and 2.9 × on RISC-V.

Refinement of Recloser Operation and Safety Enhancement in Distribution Systems: A Study Based on Real Data

Refinement of Recloser Operation and Safety Enhancement in Distribution Systems: A Study Based on Real Data

EDH-STNet: An Evaporation Duct Height Spatiotemporal Prediction Model Based on Swin-Unet Integrating Multiple Environmental Information Sources

Given the significant spatial non-uniformity of marine evaporation ducts, accurately predicting the regional distribution of evaporation duct height (EDH) is crucial for ensuring the stable operation of radio systems. While machine-learning-based EDH prediction models have been extensively developed, they fail to provide the EDH distribution over large-scale regions in practical applications. To address this limitation, we have developed a novel spatiotemporal prediction model for EDH that integrates multiple environmental information sources, termed the EDH Spatiotemporal Network (EDH-STNet). This model is based on the Swin-Unet architecture, employing an Encoder–Decoder framework that utilizes consecutive Swin-Transformers. This design effectively captures complex spatial correlations and temporal characteristics. The EDH-STNet model also incorporates nonlinear relationships between various hydrometeorological parameters (HMPs) and EDH. In contrast to existing models, it introduces multiple HMPs to enhance these relationships. By adopting a data-driven approach that integrates these HMPs as prior information, the accuracy and reliability of spatiotemporal predictions are significantly improved. Comprehensive testing and evaluation demonstrate that the EDH-STNet model, which merges an advanced deep learning algorithm with multiple HMPs, yields accurate predictions of EDH for both immediate and future timeframes. This development offers a novel solution to ensure the stable operation of radio systems.

THE ROLE OF INFORMATION FLOWS IN THE OPERATIONAL MANAGEMENT OF TRANSPORT

The management of transport flows is based on transmitting, receiving and processing the information related to them by applying the principles of the system approach. In a theoretical-applied aspect, it manifests itself as an economic activity for the development and implementation of methods for collecting, storing, processing and distributing information for specific functions and operations in transport infrastructure systems.

Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, overwhelming engineers with a deluge of often correlated alerts. Therefore, effective alert aggregation is crucial in isolating root causes and accelerating failure resolution. Existing approaches typically rely on either semantic similarity or statistical methods, both of which have significant limitations, such as ignoring causal relationships or struggling to handle infrequent alerts. To overcome these drawbacks, we propose a novel two-phase alert aggregation approach. We employ temporal–spatial clustering to group alerts based on their temporal proximity and spatial attributes. In the second phase, we utilize large language models to trace the cascading effects of service failures and aggregate alerts that share the same root cause. Experimental evaluations on datasets from real-world cloud platforms demonstrate the effectiveness of our method, achieving superior performance compared to traditional aggregation techniques.

Adaptive Generalized Extended State Observer for a Single Phase PV Grid-connected System Operating Under the Sudanese-Sahelian Climate of Cameroon

Knowing the health of a system allows to guarante its efficiency and sustainability. The state observer is one of several techniques used by authors to estimate system state. This paper focuses on the problem of simultaneous states estimation of DC (Direct Current) and AC (Alternating Current) sides of a single-phase Photovoltaic (PV) grid-connected operating under the Sudanese-Sahelian climate of Cameroon. A generalized extended state observer (GESO) has been designed to simultaneously estimate the three states and the three disturbances of the system. A good estimation of the state and disturbances is achieved by the appropriate choice of the observer gain and the disturbance compensation gain resulting from the correct pole placement. The GESO robustness has been tested by varying the PV voltage and grid voltage. When there are no input fluctuations, the estimation errors of nominal states and disturbances converge to zero. The fluctuation in PV voltage resulting from partial shading has a significant impact on the boost converter current. The boost converter current varies proportionally with the drop in voltage due to partial shading from 55% to 59%. Under the grid voltage fluctuation, the boost converter current remains stable while the DC bus voltage and inverter current are significantly affected. The proposed GESO prove its robustness to perturbations from the PV array and grid side into the Single-Phase PV Grid-connected System. This paper contributes to the study of observers applied to the PV system and points the way to future work on diagnosing faults in PV systems operating in Cameroon's Sudanese-Sahelian climate.

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively.

The Development of a 3D Magnetic Field Scanner Using Additive Technologies

Visualizing magnetic fields is essential for studying the operation of electromagnetic systems and devices that use permanent magnets or magnetic particles. However, commercial devices for this purpose are often expensive due to their complex designs, which may not always be necessary for specific research needs. This work presents a method for designing an automated laboratory setup for magnetic cartography, utilizing a 3D printer to produce structural plastic components for the scanner. The assembly process is thoroughly described, covering both the hardware and software aspects. Spatial resolution and mapping parameters, such as the number of data points and the collection time, were configured through software. Multiple tests were conducted on samples featuring flat inductive coils on a printed circuit board, providing a reliable model for comparing calculated and measured results. The scanner offers several advantages, including a straightforward design, readily available materials and components, a large scanning area (100 mm × 100 mm × 100 mm), a user-friendly interface, and adaptability for specific tasks. Additionally, the integration of a pre-built macro enables connection to any PC running Windows, while the open-source microcontroller code allows users to customize the scanner’s functionality to meet their specific requirements.

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.

The Effects of an Adaptive Ventilation Control System on Indoor Air Quality and Energy Consumption

Indoor air quality (IAQ) and energy consumption (Q) are well-known building estimators, but they are used separately. Energy consumption is used during the design stage, while IAQ is used during operation. The novelty of our approach is that we propose using both estimators simultaneously during building operations. The purpose of this study was to find an adaptive ventilation strategy that maintained good indoor air quality with minimal energy consumption. The second novelty of our approach consists of IAQ estimation. While the operation of ventilation systems depends only on the indoor carbon dioxide (CO2) concentration at present, our novel approach uses a more global IAQ index that includes four different air pollutants. Physical models are used for the hourly prediction of the two indices: global IAQ and Q. This study presents a comparative analysis of several ventilation operations strategies: fixed versus adaptive. The main findings show that a decrease in the ventilation rate, na, from 3.5 h−1 to 2.0 h−1 leads to a diminishment in energy consumption of 42.9%, maintaining the global IAQ index under the limited health risk value (VRL). Moreover, an adaptive ventilation strategy of na, maintaining the global IAQ index value under VRL, achieves a further reduction in energy consumption of 72.9%, highlighting its efficiency.

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.

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.

Advanced Design of Naval Ship Propulsion Systems Utilizing Battery-Diesel Generator Hybrid Electric Propulsion Systems

As advanced sensors and weapons require high power, naval vessels have increasingly adopted electric propulsion systems. This study aims to enhance the efficiency and operability of electric propulsion systems over traditional mechanical propulsion systems by analyzing the operational profiles of modern naval vessels. Consequently, a battery-integrated generator-based electric propulsion system was selected. Considering the purpose of the vessel, a specification selection procedure was developed, leading to the design of a hybrid electric propulsion system (comprising one battery and four generators). The power management control technique of the proposed propulsion system sets the operating modes (depending on the specific fuel oil consumption of the generators) to minimize fuel consumption based on the operating load. Additionally, load distribution control rules for the generators were designed to reduce energy consumption based on the load and battery state of charge. MATLAB/Simulink was used to evaluate the proposed system, with simulation results demonstrating that it maintained the same propulsion performance as existing systems while achieving a 12-ton (22%) reduction in fuel consumption. This improvement results in cost savings and reduced carbon dioxide emissions. These findings suggest that an efficient load-sharing controller can be implemented for various vessels equipped with electric propulsion systems, tailored to their operational profiles.