IEEE Open Journal of Industry Applications最新文献

{"title":"Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks","authors":"Mohamed Massaoudi;Tassneem Zamzam;Maymouna Ez Eddin;Ali Ghrayeb;Haitham Abu-Rub;Shady S. Refaat","doi":"10.1109/OJIA.2024.3426334","DOIUrl":"10.1109/OJIA.2024.3426334","url":null,"abstract":"The transient power grid stability is greatly affected by the unpredictability of inverter-based resources of today's interconnected power grids. This article introduces an efficient transient stability status prediction method based on deep temporal convolutional networks (TCNs). A grey wolf optimizer (GWO) is utilized to fine-tune the TCN hyperparameters to improve the proposed model's accuracy. The proposed model provides critical information on the transient grid status in the early stages of fault occurrence, which may lead to taking the proper action. The proposed TCN-GWO uses both synchronously sampled values and synthetic values from various bus systems. In a postfault scenario, a copula of processing blocks is implemented to ensure the reliability of the proposed method where high-importance features are incorporated into the TCN-GWO model. The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. The proposed algorithm is evaluated on the 68-bus system and the Northeastern United States 25k-bus synthetic test system with credible contingencies using the PowerWorld simulator. The obtained results prove the enhanced performance of the proposed technique over competitive state-of-the-art transient stability assessment methods under various contingencies with an overall accuracy of 99% within 0.64 s after the fault clearance.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"267-282"},"PeriodicalIF":7.9,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10592636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141585847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-Energy Dynamic Avalanche to Failure by Incremental Source-Voltage Increase in Symmetric Double-Trench & Asymmetric Trench SiC MOSFETs","authors":"Mana Hosseinzadehlish;Saeed Jahdi;Xibo Yuan;Jose Ortiz-Gonzalez;Olayiwola Alatise","doi":"10.1109/OJIA.2024.3409153","DOIUrl":"https://doi.org/10.1109/OJIA.2024.3409153","url":null,"abstract":"The unclamped inductive switching (UIS) measurements can be categorized as “low energy” and “high energy” avalanche. The conventional approach to these tests is to increase the stress by either increasing the pulse length, or decreasing the inductor's size. However, for evaluation of the novel trench SiC \u0000<sc>mosfet</small>\u0000s, increase of electric field by voltage can be more influential to detect the degradation patterns and exact point of failure. This article, for the first time, investigates the avalanche rating to failure of the similarly rated SiC power \u0000<sc>mosfet</small>\u0000s in planar, symmetric double-trench and asymmetrical trench structures through incremental increase of applied voltage as the “high energy” technique to investigate the mechanisms of dynamic avalanche under elevated electric fields. Using this approach, the electrothermal stress is induced by incremental increases of voltage source on UIS at a range of temperatures between 25 °C and 175 °C. Silvaco technology computer-aided design (TCAD) simulations have been developed, validated, and analyzed to evaluate the stress mechanisms to failure. The measurements, validated by TCAD, show that some failure mechanisms when stress is elevated by increase of source voltage are different than the case of “high current” avalanche initiation by increase of pulse durations as reported in the past. In planar device, the peak electric field plays a key role in failure, as is the failure in symmetric device at low case temperatures. In asymmetric device, the critical avalanche energy of failure in both cases of 25 °C and 175 °C are very close, suggesting independence from the thermal headroom.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"235-252"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noninvasive Measurement of Three-Phase Currents","authors":"Prasad Shrawane;Tarlochan S. Sidhu","doi":"10.1109/OJIA.2024.3381856","DOIUrl":"10.1109/OJIA.2024.3381856","url":null,"abstract":"This article presents a noninvasive method of measuring three-phase currents using magnetic sensors that can be used for continuous monitoring, automation, and protection of power grids. The nonintrusive nature of these sensors gives operational and economic benefits in installing them at the existing distributed generation sites and power substations. These sensors are linear in operation, free of saturation, and need minimum duration or no outage for installation as compared to the conventional current transformers. This article describes magnetic field simulation, calibration, and experimental validation of magnetic sensors for accurate measurement of three-phase currents. Laboratory experiment results of three-phase low current measurements for two types of overhead structures: triangular and horizontal are rendered as a validation of the proposition. The performance verification of these sensors is further achieved by conducting field experiments for measuring currents up to 1500 A. The sensors yield promising results with a maximum error of 1.15% in the estimation of three-phase currents. The magnetic sensors showed satisfactory performance in accurately reproducing current waveforms consisting of fundamental frequency and harmonics that are typically present in modern power grids.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"143-154"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function","authors":"Rony Ibrahim;Ryad Zemouri;Antoine Tahan;Bachir Kedjar;Arezki Merkhouf;Kamal Al-Haddad","doi":"10.1109/OJIA.2024.3380249","DOIUrl":"10.1109/OJIA.2024.3380249","url":null,"abstract":"In the field of electrical machines maintenance, accurate and timely diagnosis plays a crucial role in ensuring reliability and efficiency. Variational autoencoder (VAE) techniques have emerged as a promising tool for fault classification due to their robustness in handling complex data. However, the inherent nondeterministic aspect of the VAE creates a significant challenge as it leads to varying cluster locations for identical health states across different machines. This variability complicates the creation of a standardized applicable diagnostic tool and challenges for the implementation of effective real-time health monitoring and prognostics. Addressing this issue, a novel approach is proposed wherein a desirability function-based term is integrated into the cost function of the VAE. The enhancement achieved by this approach arises from the standardization of classification, guaranteeing that analogous faults are assigned to identical geolocations within a 2-D user-friendly space. This method's efficacy is validated through two separate case studies: one analyzing vibration data from two diverse designs of large existing hydrogenerators, and the other utilizing vibration data sourced from an open-access dataset focused on bearing fault. The findings of both studies show that the model can cluster 97% of similar faults into preset zones, compared with 40% when the desirability term is excluded.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"106-116"},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10478716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140312142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DC Arc Flash Measurements From a 1000 V Valve Regulated Lead Acid Battery System","authors":"Nicolaus Jennings;David Wetz;Rick Langley;Nancy LaFlair;John Heinzel","doi":"10.1109/OJIA.2024.3400208","DOIUrl":"10.1109/OJIA.2024.3400208","url":null,"abstract":"As the Navy moves forward with implementing electrochemical energy sources in their power systems, a need to understand the hazards present to electric workers comes to light. Understanding the effects these sources have on arc flash hazards drives the need to study relevant energy sources that are deployed on naval platforms. A 1000-V valve regulated lead acid (VRLA) battery system presents the greatest need to study as incident energy models can suggest dramatic personal protective equipment (PPE) especially with the inclusion of scale factors. As a part of a collaborative effort between the Electric Power Research Institute (EPRI) and the University of Texas at Arlington (UTA) arc flash studies of VRLA battery systems at voltages as high as 936 V have been performed. Findings show that below 800 V, the battery sourced incident energy as high as 0.26 cal/cm\u0000²\u0000. Above 800 V, incident energy surpassed 1.2 cal/cm\u0000²\u0000 and approached category 1 PPE (4 cal/cm\u0000²\u0000). A model derived from this work is used to emphasize the impact enclosure based scale factors have on incident energy estimates. This work has also compared relevant dc incident energy models to the measured incident energy sourced from this battery system.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"168-176"},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10529540","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bidirectional Hybrid DC Circuit Breaker With Zero Voltage and Current Switching for Radar Power System","authors":"Nandakumar Saminathan;Aditya P;Satish Naik Banavath;Alessandro Lidozzi;Marco Di Benedetto;Vengadarajan A","doi":"10.1109/OJIA.2024.3399603","DOIUrl":"10.1109/OJIA.2024.3399603","url":null,"abstract":"This article proposes a novel zero-voltage switching (ZVS) and zero-current switching (ZCS)-based hybrid dc circuit breaker for a radar power system. Long-range radars demand huge power, in the order of hundreds of kW. Radar's phased array antenna houses a large number of electronic devices and works primarily on a dc power supply. Typically, military systems are required to have the highest operational reliability, as a result, electrical system protection plays a crucial role. A high power 310 V dc electrical power grid in radar carries hundreds of amperes of current under nominal operating conditions, results in significant fault current due to very low impedance, and demands a very fast fault interruption device. This article proposes and demonstrates the complete operation of a hybrid dc circuit breaker topology for radar applications. The proposed dc circuit breaker employs a mechanical switch that carries the entire current during the nominal operating conditions, and a power electronic module (PEM) connected in parallel helps in diverting the fault current from the main path. Fault current transfers to the PEM branch in a fraction of a second (5 \u0000<inline-formula><tex-math>$mu s$</tex-math></inline-formula>\u0000), which ensures faster load-side isolation. During the fault interruption process, mechanical switch contact opening experiences both ZVS and ZCS features, resulting in arcless operation, and also helps in faster contact separation. The ZVS and ZCS features greatly improve the reliability of the mechanical switch. The proposed concept does not involve any capacitors or corresponding precharging circuits for the ZVS/ZCS features. The proposed dc circuit breaker is analyzed theoretically, and also by simulations in LTspice. Additionally, an experimental prototype with a dc system rating of 310 V–10 A is developed to experimentally validate the performance of the proposed breaker topology. The article also presents a detailed design and comparative analysis, along with a discussion on the limitations of the proposed dc circuit breaker, and the scope for improvements.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"224-234"},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10528799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Control of Synchronous Reluctance Motor Based on Automatic Disturbance Rejection","authors":"Angelo Accetta;Maurizio Cirrincione;Filippo D'Ippolito;Marcello Pucci;Antonino Sferlazza","doi":"10.1109/OJIA.2024.3399009","DOIUrl":"10.1109/OJIA.2024.3399009","url":null,"abstract":"This article proposes the theoretical development and experimental application of the active disturbance rejection control (ADRC) to synchronous reluctance motor (SynRM) drives. The ADRC is a robust adaptive extension of the input-output feedback linearization control (FLC). It performs the exact linearization of the SynRM model by a suitable nonlinear transformation of the state based on the online estimation of the corrective term by the so-called extended state observers (ESO). Consequently, any unmodeled dynamics or uncertainty of the parameters are properly addressed. The control strategy has been verified successfully both in numerical simulations and experimentally on a suitably developed test set-up that provides the ADRC robustness versus parameters variations which cannot be obtained with other model-based nonlinear control techniques (e.g., FLC). Simulation results show the capability of the ADRC to maintain its dynamic performance, even in the presence of quick variations of the SynRM dynamic inductances. Experimental results confirm the robustness of the ADRC versus any model parameter uncertainty. The proposed ADRC has been experimentally compared with a previously developed FLC, in both a tuned and detuned working configuration, with the classic rotor oriented control, and with a finite state model predictive control (MPC), where speed control is integrated into the MPC. Experimental results show far better robustness versus any parameter variation.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"209-223"},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10526420","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI for Industry 5.0: Vision, Architecture, and Potential Directions","authors":"Chandan Trivedi;Pronaya Bhattacharya;Vivek Kumar Prasad;Viraj Patel;Arunendra Singh;Sudeep Tanwar;Ravi Sharma;Srinivas Aluvala;Giovanni Pau;Gulshan Sharma","doi":"10.1109/OJIA.2024.3399057","DOIUrl":"10.1109/OJIA.2024.3399057","url":null,"abstract":"The Industrial Revolution has shifted toward Industry 5.0, reinventing the Industry 4.0 operational process by introducing human elements into critical decision processes. Industry 5.0 would present massive customization via transformative technologies, such as cyber-physical systems (CPSs), artificial intelligence (AI), and big data analytics. In Industry 5.0, the AI models must be transparent, valid, and interpretable. AI models employ machine learning and deep learning mechanisms to make the industrial process autonomous, reduce downtime, and improve operational and maintenance costs. However, the models require explainability in the learning process. Thus, explainable AI (EXAI) adds interpretability and improves the diagnosis of critical industrial processes, which augments the machine-to-human explanations and vice versa. Recent surveys of EXAI in industrial applications are mostly oriented toward EXAI models, the underlying assumptions. Still, fewer studies are conducted toward a holistic integration of EXAI with human-centric processes that drives the Industry 5.0 applicative verticals. Thus, to address the gap, we propose a first-of-its-kind survey that systematically untangles EXAI integration and its potential in Industry 5.0 applications. First, we present the background of EXAI in Industry 5.0 and CPSs and a reference EXAI-based Industry 5.0 architecture with insights into large language models. Then, based on the research questions, a solution taxonomy of EXAI in Industry 5.0 is presented, which is ably supported by applicative use cases (cloud, digital twins, smart grids, augmented reality, and unmanned aerial vehicles). Finally, a case study of EXAI in manufacturing cost assessment is discussed, followed by open issues and future directions. The survey is designed to extend novel prototypes and designs to realize EXAI-based real-time Industry 5.0 applications.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"177-208"},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10526434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140939890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Techniques to Predict Voltage Unbalance in a Power Transmission System","authors":"Jonathan D. Boyd;Donald R. Reising;Anthony M. Murphy;Justin D. Kuhlers;C. Michael McAmis;James B. Rossman","doi":"10.1109/OJIA.2024.3369993","DOIUrl":"10.1109/OJIA.2024.3369993","url":null,"abstract":"Voltage unbalance is a growing issue that, among other things, can impact three-phase motor and drive loads, result in nuisance tripping of generation units and capacitor banks, and prevent optimization of conservative voltage regulation strategies. This difference between the three phases of voltage delivered to customers can damage the equipment of these customers as well as negatively impact the power system itself. This work presents an approach for predicting voltage unbalance using machine learning. Historical megawatt and megavar data–obtained through a Supervisory Control And Data Acquisition (SCADA) system–are used to train an artificial neural network model as a binary classifier with a portion of the data serving to validate the trained model. Voltage unbalance is predicted at an accuracy above 95% for eight substations within the power utility's extra-high voltage transmission network and over 91% for all 42 substations. The trained model is tested in a manner that would be employed using simulated data generated by state estimation software. This simulated data validates the model's capacity to predict the substation buses that would experience voltage unbalance.","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"86-93"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10448538","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140002408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/OJIA.2024.3354443","DOIUrl":"https://doi.org/10.1109/OJIA.2024.3354443","url":null,"abstract":"","PeriodicalId":100629,"journal":{"name":"IEEE Open Journal of Industry Applications","volume":"5 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10444962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139976195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}