Electric Power Research Articles (Page 1)

ABSTRACT Particle size distribution analysis of a coal ball-mill product stream remains a major challenge for engineers who are manufacturing coal products in several coal firms, due of improper identification of optimum-retained mass yield regions. In our study, experimental data generated from ball-milling of five (5) different coal samples (the run-of-mine coal, cobble coals, nuts coals, peas coals and fine coal type) were reviewed. A new approach was employed that uses a combination of the population balance model (PBM) approach and the volumetric retained mass yield against particle size, obtained from twenty (20) laboratory trials of dry batch grinding. Moreover, the gross power required to generate adequate mass yield at desired size classes was successfully measured using the Stephen Morrell Power Law Model. The resultant estimations were then used to scale up the cost(s) of milling from laboratory to industrial scale. Based on the conclusions, the monetary inputs required for milling different coal types changed proportionally with the coal grindability and hardness of the coal material observed under each coal grade. Coal macerals are formed from fossil fuel deposits and its usage in Africa and the rest of the world is increasingly gaining traction and more than 25% of the world’s electrical power is generated from coal. Additionally, coal size reduction through milling is extremely energy intensive when targeting to process large concentrates of the product stream, hence a proper and more reliable technique that can precisely calibrate a desired particle size and particle size distribution (PSD) is a vital aspect in coal beneficiation.

Abstract Wide-area monitoring, protection, and control (WAMPAC) systems are essential for ensuring stability in modern electric power grids with large shares of converter-based renewable generation. Laboratory environments allow risk-free validation before field deployment. Building on our Control Center Laboratory and Real-Time Simulation (CLARO) framework, we implement complete WAMPAC workflows that couple real-time grid simulation (Opal-RT), standardized communication protocols (IEEE C37.118, IEC 61850, IEC 60870-5-104, Modbus), and modern information-technology/operational-technology (IT/OT) architectures (Apache Kafka, Kubernetes, containerization). A real-time automation controller executes corrective actions using Generic Object Oriented Substation Event (GOOSE) messages based on a pre-computed catalog of measures. On a synthetic 118-bus benchmark, phasor-measurement-unit (PMU) driven end-to-end delays from disturbance to control action remain below 250 ms, while PMU-enhanced state estimation reduces bus-voltage magnitude error by over 40 % compared with Supervisory Control and Data Acquisition (SCADA) only. These results demonstrate the feasibility of realistic, lab-based WAMPAC validation and highlight how integrating modern IT/OT stacks strengthens grid resilience.

This study presents the perceived implementability of the digital Hospital-Based Cancer Registry (HBCR) and the Adapted-Resource Implementation Application (ARIA) to enhance data systems and treatment standards at Pediatric Oncology unit (POU). A 2-year (2023-2025) implementation study on the integrated application of ARIA and HBCRs is being conducted at Jimma University Medical Center (JUMC) and St. Paul Hospital Millennium Medical College (SPHMMC). This article reports the formative assessment results, guided by the Consolidated Framework for Implementation Research (CFIR), involved eight focus group discussions, four in-depth interviews, and two co-design workshops with diverse healthcare providers and hospital management/leadership personnel. The integrated implementation of HBCR-ARIA was viewed as innovative and adaptable. Digital HBCR was perceived as more effective than manual methods for managing pediatric oncology data. Similarly, ARIA was perceived as effective and feasible for providing patient-specific standardized care. Workflows and responsibilities were co-defined separately for the respective POUs. The co-designed implementation strategy includes residents filling demographic and diagnostic information of patients' on HBCR interim document and then cross-checked by the pediatric Hematology and Oncology (PHO) fellows. The Medical Monitor (PHO senior) approves the validity of the document before entry into REDCap by the data clerk. ARIA is filled by PHO fellows and approved by second PHO fellow or PHO seniors based on the availability. Facilitators in both the inner (hospital) and outer (external) settings outweighed the barriers. Facilities and motivated human resources are in place to implement the digital HBCR and ARIA strategies at the respective POU. However, challenges such as inconsistent electric power, unreliable internet services, and logistic-supply issues. The implementation strategies for digitized HBCR and ARIA, co-designed to fit the specific contexts of two POU, appear promising but require further evaluation.

To address the issue of existing automatic irrigation systems’ excessive reliance on electrical power and communication networks, a one-inlet, four-outlet Hydraulically Actuated Irrigation Control Valve (HAICV) was designed that operates based on water pressure variations. Its hydraulic characteristics and flow field features were investigated through experimental and numerical simulation methods. The results indicated that power–function relationships exist between pressure and flow rate, as well as between flow rate and head loss. The flow coefficient and resistance coefficient were found to range within [77.46, 81.06] and [15.94, 17.46], respectively. Dynamic simulations based on User-Defined Functions (UDF) revealed that during the opening process, the internal pressure of the valve spool remains high, with the primary pressure drop concentrated in the outlet region, and the low-pressure zone shrinks as the opening degree increases. A high-velocity band forms at the outlet, with jet flow and turbulence observed at small to medium openings, while the flow field stabilizes after full opening. The unique spool shape and non-straight flow passage structure of the HAICV result in relatively high energy loss, making it suitable for self-pressure irrigation systems. This study provides a theoretical foundation for evaluating its performance and broader applications.

Plug-in Hybrid Electric Vehicles (PHEVs) are increasingly favored for their low emissions and freedom from range anxiety, combining electric efficiency with the extended range of a gasoline engine. While previous research on PHEV energy consumption has predominantly focused on powertrain design and energy management strategies, there is growing recognition of the critical role played by driver behavior in determining real-world energy efficiency. Based on multi-mode vehicle data collected from real-world driving scenarios, we propose a novel dual-layer LSTM-Transformer model, named DLLT, for real-time prediction of energy consumption and driving dynamics in multi-mode PHEVs. The first layer employs an LSTM network to perform mode clustering, while the second layer conducts energy consumption regression using a Transformer model with integrated mode information. This hierarchical architecture enables adaptation to diverse driving and braking modes, significantly enhancing the model’s ability to accurately identify vehicle operation modes and precisely predict energy consumption. To more accurately validate the effectiveness of DLLT in modeling eco-driving behavior for PHEVs, we collected a large amount of multidimensional time-series data from real-world PHEVs. Experimental results demonstrate that the model achieves a 93% accuracy rate in vehicle mode prediction. Under unseen driving conditions, it attains R2 values of 0.99 for fuel consumption, 0.86 for acceleration, and 0.81 for electric power, outperforming existing models across all evaluation metrics. With its high prediction accuracy and robust generalization capability, DLLT shows great potential for applications in PHEV eco-driving behavior analysis, intelligent energy management systems, and future autonomous driving control strategies.

The purpose of the study is to evaluate the environmental performance of two systems for space heating and hot water provision in a residential building. In both cases, a ground-source heat pump is used. In the baseline system, the heat pump is driven by electrical power from the grid. In the alternative system, photovoltaic thermal collectors are integrated into the building for domestic hot water preparation and the production of electricity. Excess heat produced in the summer is introduced to the borehole and extracted later, in the cooler part of the year. Environmental benchmarking of the two systems was conducted using the Life Cycle Assessment method. A cradle-to-grave approach was applied, taking into account all life cycle stages of the system and its operation over 20 years. Results show that the alternative system yields significantly lower impacts in terms of Global Warming Potential (36% decrease) and Resources (36% decrease). In terms of Human Health, the decrease is minor (6%). However, in terms of Ecosystem, the alternative system shows a 47% higher impact than the baseline system. This increase is primarily attributed to the additional components required in the alternative configuration.

Abstract. Fault detection in electric power facilities is a crucial component of power grid maintenance, with hidden faults posing greater challenges compared to overt faults. Notably, hidden faults often coincide with localized heating, making infrared imaging an effective detection modality. However, automatic identification of power equipment in infrared images remains challenging; traditional methods are often inefficient and lack accuracy, while deep learning approaches are hindered by limited sample availability and accuracy issues. Furthermore, the temperature-based criteria for diagnosing hidden faults lack robustness. To address these challenges, this study proposes a comprehensive approach: first, employing the Segment Anything Model (SAM) for rapid annotation of power facilities in infrared images; second, leveraging these annotations to iteratively optimize a U-Net model for automated power equipment identification; and third, integrating temperature information to identify abnormal regions using dynamic threshold segmentation, thereby locating potential fault components. Experimental validation was conducted on a transmission line in Jiaxing City, Zhejiang Province, demonstrating a detection success rate exceeding 90%. The results indicate high detection accuracy and efficiency, presenting a promising solution for intelligent inspection of electric power infrastructure.

Smart sensor networks play important roles in structural monitoring, health diagnosis, and data transmission. Given their extensive distributed energy requirements, piezoelectric energy harvesting, which aims to convert mechanical vibrational energy into electrical power, can serve as a viable alternative or supplement to power supplies owing to its compact size, high power density, and excellent stability. Piezoelectric energy harvesting involves three key components: piezoelectric materials responsible for mechanical-to-electrical energy conversion, mechanical structures enabling mechanical-to-mechanical energy transmission, and power-management systems used to efficiently extract electrical energy. For electromechanical conversion, state-of-the-art piezoelectric materials, including crystals, ceramics, polymers, and composites, are analyzed. Regarding mechanical energy transmission, the focus is on methodologies to achieve high power output, wide bandwidth, and multi-directional vibration capability. Several widely adopted electrical circuits are comprehensively reviewed in terms of power management. From an application perspective, practical energy harvesters are categorized into magneto-mechano-electric, fluid-based, biomechanical, and ultrasound-induced types. Additionally, future theoretical and practical challenges in piezoelectric energy harvesting are discussed.

Detecting patched anomaly regions in spatial electric field power spectrum images

Abstract Arc fault detection is critical for ensuring the safety and reliability of electrical power systems, as it helps prevent fires and equipment damage. Data models demonstrate significant advantages across various fields and have become a focal point of arc fault detection. However, the generalization ability under small sample conditions and interpretability of these models remain key challenges in this field. This article presents an end-to-end arc fault detection method that integrates experience.First, based on current waveforms, three types of artificial experience are summarized. Then, a one-dimensional adaptive neural decision tree based arc fault detection method is explored by combining neural networks and decision tree to perform feature extracting, path selecting, and label classifying. Finally, by embedding experience into the one-dimensional adaptive neural decision tree to guide model training, the trained model is capable of effectively detecting arc faults across various load types. The results show that the proposed method achieves a high
 fault detection performance on datasets of different sizes, and is compared with other reported methods to verify its superiority.
 Moreover, it can visualize the decision-making process.

The electric power industry, as a High-Reliability Organization (HRO), demands a mature safety culture. However, existing diagnostic tools often lack industry specificity. This study addresses this gap by developing and validating a specialized safety culture diagnostic tool for the Korea Electric Power Corporation (KEPCO). A mixed-methodology was employed, resulting in a 50-item questionnaire tailored to the power industry’s unique risks. The tool assesses four domains: ‘Hierarchical Safety Leadership’, ‘Safety and Health Management System’, ‘Worker Participation’, and ‘Accident and Risk Management’. The survey was administered to 680 personnel (426 KEPCO internal; 254 contractors). The tool’s reliability was validated using Cronbach’s alpha (internal $\alpha$ = 0.991; contractor $\alpha$ = 0.989), and its structural validity was confirmed through confirmatory factor analysis (CFA). The overall safety culture score was favorable, with a mean of 4.25 (out of 5.0). However, the analysis revealed significant perception gaps between organizational groups. Contractors reported the highest score (4.46), whereas the headquarters (HQ) scored the lowest (4.00). Notably, the HQ showed weaknesses in ‘Worker Participation’ (3.93) and ‘Accident and Risk Management’ (3.98). These results suggest that KEPCO’s culture is currently in the ‘Calculative’ stage. It also demonstrates a significant gap between the HQ’s administrative management and the contractors’ on-site execution. This study expanded the scope of safety research by including contractors, who are often overlooked. This validated tool enables KEPCO to quantitatively diagnose these perception gaps. This supports data-driven interventions and the transition toward a ‘Proactive’ safety culture.

In micro gas turbines, electrical power from the high-speed generator is delivered to the grid through a converter that influences overall efficiency and energy quality. This subsystem is often overlooked in efforts to improve turbine performance, which have traditionally focused on combustors and turbomachinery. This study investigates how replacing conventional silicon switching devices in the converter with silicon carbide technology can directly reduce greenhouse gas emissions from micro gas turbines. Although silicon carbide is widely used in electric vehicles and distributed energy systems, its emission reduction impact has not been assessed in micro gas turbines. A MATLAB-based model of a 100 kW Ansaldo Energia micro gas turbine was used to compare the performance of silicon and silicon carbide converters across the 20–100 kW operating range. Silicon carbide reduced total converter losses from 4.316 kW to 3.426 kW at full load, a decrease of 0.889 kW. This improvement lowered carbon dioxide emissions by 5.7 g/kWh and increased net electrical efficiency from 30.03% to 30.29%. Each turbine can therefore avoid about 1.53 tonnes of carbon dioxide annually, or 11.61 tonnes over a 50,000 h service life, without altering turbine design, combustor geometry, or fuel composition. This work establishes the first quantitative link between wide-bandgap semiconductor performance and direct greenhouse gas mitigation in micro gas turbines, demonstrating that upgrading converter technology from silicon to silicon carbide offers a deployable pathway to reduce emissions from micro gas turbines and, by extension, lower the carbon intensity of distributed generation systems.

Renewable-powered water electrolysis provides a carbon-neutral route to hydrogen, yet large-scale deployment is constrained by reliance on stable but carbon-intensive grid electricity. Direct integration with fluctuating renewable power requires catalysts and devices that can endure dynamic operating conditions. Here we present a transient-promoter strategy for NiFe oxyhydroxide oxygen evolution reaction (OER) catalysts, realized from Ni3Fe1.2Cr0.8Ox precursor, for kilowatt-scale anion exchange membrane water electrolyzers (AEMWEs). Ex situ and operando spectroscopy establish that Cr (i) modulates Ni/Fe oxidation states to enrich positive charge and facilitate oxyhydroxide formation, (ii) induces porosity that enhances electrolyte penetration and OH- adsorption, and (iii) leaches sacrificially to protect Ni/Fe active sites. Lab-scale AEMWE device achieves an industrially relevant current density of 1 A cm- 2 at a cell voltage of 1.68V and sustains continuous operation for over 30 days under both constant and fluctuating loads. Scaling from 1 cm2 AEMWE to an 8-cell, 512 cm2 stack, the system can handle an electrical power of 2.5kW at peak, and delivers 1 A cm- 2 at 1.78V per cell at 60 °C. The stack remains resilient over 13 simulated solar cycles (>50h), underscoring the feasibility of integrating renewable electricity with durable, NiFe oxyhydroxide OER catalyst based AEMWEs.

A GA-SLP-Based Dynamic Allocation Method for Electric Power Emergency Materials Considering Disaster Impact Differences

Electric aircraft powered by lithium batteries (LIBs) have seen rapid development in recent years, making research into their thermal runaway (TR) characteristics crucial for ensuring flight safety. This study focused on the individual battery cells of a specific electric aircraft power battery system, conducting TR experiments under both the aircraft’s service ceiling temperature (−8.5 ± 2 °C) and ground ambient temperature (30 ± 2 °C). The experiments analyzed changes in battery temperature, voltage, and mass during TR. Experimental results indicate that the peak TR temperatures reached 589.6 °C and 654 °C under the two environments, respectively, with maximum heating rates of 8.6 °C/s and 16.9 °C/s. At ambient ground temperatures, battery voltage drops more rapidly, with the voltage of a 100% SOC battery decreasing over just 10 s. Peak mass loss during TR reached 265.48 g and 247.52 g, respectively. Combining TR temperature data with the Semenov thermal runaway model, the minimum ambient temperature causing TR in this electric aircraft power battery under sustained external heating was determined to be approximately 39 °C. Finally, a multi-level protection strategy covering the “airframe–battery compartment–cabin” was established. The findings from this research can serve as a reference for subsequent safety design of this aircraft type and the formulation of relevant airworthiness standards.

One of the most plentiful, sustainable, and clean renewable energy sources that humanity has access to is solar energy. It is useful in a variety of fields, including as manufacturing, agriculture, environmental management, heating, and energy generation. By efficiently converting solar energy into electrical and thermal power, photovoltaic and solar thermal technologies can lessen reliance on fossil fuels and reduce greenhouse gas emissions. Crop drying procedures, greenhouses, and solar-powered irrigation systems all increase agricultural productivity while advancing sustainability. Similar to this, both domestic and commercial uses—like process heat, space conditioning, and solar water heating—help to lower operating costs and increase energy efficiency. In addition to its technological benefits, solar energy promotes economic growth by stabilizing long-term energy prices, guaranteeing energy security, and generating job opportunities. By lowering carbon emissions and air pollution, it also contributes significantly to environmental conservation. The future utility of solar energy keeps growing because to developments in smart grid integration, hybrid renewable systems, and solar-powered vehicles. All things considered, solar energy is a key component of international initiatives to address climate change and achieve sustainable development.

Abstract Renewable‐powered water electrolysis provides a carbon‐neutral route to hydrogen, yet large‐scale deployment is constrained by reliance on stable but carbon‐intensive grid electricity. Direct integration with fluctuating renewable power requires catalysts and devices that can endure dynamic operating conditions. Here we present a transient‐promoter strategy for NiFe oxyhydroxide oxygen evolution reaction (OER) catalysts, realized from Ni 3 Fe 1.2 Cr 0.8 O x precursor, for kilowatt‐scale anion exchange membrane water electrolyzers (AEMWEs). Ex situ and operando spectroscopy establish that Cr (i) modulates Ni/Fe oxidation states to enrich positive charge and facilitate oxyhydroxide formation, (ii) induces porosity that enhances electrolyte penetration and OH − adsorption, and (iii) leaches sacrificially to protect Ni/Fe active sites. Lab‐scale AEMWE device achieves an industrially relevant current density of 1 A cm − 2 at a cell voltage of 1.68 V and sustains continuous operation for over 30 days under both constant and fluctuating loads. Scaling from 1 cm 2 AEMWE to an 8‐cell, 512 cm 2 stack, the system can handle an electrical power of 2.5 kW at peak, and delivers 1 A cm − 2 at 1.78 V per cell at 60 °C. The stack remains resilient over 13 simulated solar cycles (>50 h), underscoring the feasibility of integrating renewable electricity with durable, NiFe oxyhydroxide OER catalyst based AEMWEs.

Brain cancer treatment using low intensity electrotherapy techniques is gaining interest. Localized electric field delivery via an implanted array of electrodes, termed Intratumoral Modulation Therapy (IMT), was found efficacious against brain cancers preclinically. With prior IMT studies supporting the transition towards patient application, we consider optimizing the design of electrodes, such that power consumption is minimized while retaining tumor field coverage and field shaping capability. Cylindrical multi-contact electrodes were modelled with variable radius, spacing between contacts and contact length, and applied to spherical tumors ranging from 20-40 mm in diameter. Stimulation programming was optimized and the overall power analyzed for each design such that target coverage was maintained. To investigate the field shaping potential, designs were further optimized on 11 glioma patient MR images with irregular shaped tumors. The IMT electrode parameters found to minimize power consumption were maximal electrode radius (0.8 mm) and minimal contact spacing (1 mm). Analysis of treatment plans on patient images found 4 mm contact length to minimize complexity (total number of contacts) while maintaining field shaping capability. In this study, electrodes were designed specifically for IMT that minimized power consumption while maintaining field coverage and shaping. This design was robust in its applicability to patient samples. Due to the complexity of dynamic IMT electric field delivery, the established planning system and the custom IMT hardware designed in this study are necessary precursors to human applications. With this work we are one step closer to treating patients with brain cancer.

New behavioral indices for analyzing locational aspects of market power in Transmission-Constrained electricity markets

A Novel Multi-Step Short-Term Power Forecasting Model for Electric Power Systems Based on Deep-Learning