Wyniki wyszukiwania - BazTech

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Diagnosis of inter-turn short circuit fault in IPMSMs based on the combined use of greedy tracking and random forest

Wang Jianping, Ma Jian, Meng Dean, Zhao Xuan, Zhang Kai, Liu Qiquan, Xu Kejie

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

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Vol. 72, nr 2

art. no. e148943

EN

Inter-turn short circuit (ITSC) is a frequent fault of interior permanent magnet synchronous motors (IPMSM). If ITSC faults are not promptly monitored, it may result in secondary faults or even cause extensive damage to the entire motor. To enhance the reliability of IPMSMs, this paper introduces a fault diagnosis method specifically designed for identifying ITSC faults in IPMSMs. The sparse coefficients of phase current and torque are solved by clustering shrinkage stage orthogonal matching tracking (CcStOMP) in the greedy tracking algorithm.The CcStOMP algorithm can extract multiple target atoms at one time, which greatly improves the iterative efficiency. The multiple features are utilized as input parameters for constructing the random forest classifier. The constructed random forest model is used to diagnose ITSC faults with the results showing that the random forest model has a diagnostic accuracy of 98.61% using all features, and the diagnostic accuracy of selecting three of the most important features is still as high as 97.91%. The random forest classification model has excellent robustness that maintains high classification accuracy despite the reduction of feature vectors, which is a great advantage compared to other classification algorithms. The combination of greedy tracing and the random forest is not only a fast diagnostic model but also a model with good generalisation and anti-interference capability. This non-invasive method is applicable to monitoring and detecting failures in industrial PMSMs.

2

Quantifying urban vegetation coverage change with a linear spectral mixing model: a case study in Xi’an, China

Zhao Xuan, Liu Jianjun

Ecological Chemistry and Engineering. S

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2021

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Vol. 28, nr 1

87--100

EN

With the rapid development of urban area of Xi’an in recent years, the contradiction between ecological environmental protection and urban development has become prominent. The traditional remote sensing classification method has been unable to meet the accuracy requirements of urban vegetation monitoring. Therefore, how to quickly and accurately conduct dynamic monitoring of urban vegetation based on the spectral component characteristics of vegetation is urgent. This study used the data of Landsat 5 TM and Landsat 8 OLI in 2011, 2014 and 2017 as main information source and LSMM, region of variation grid analysis and other methods to analyse the law of spatial-temporal change of vegetation components in Xi’an urban area and its influencing factors. The result shows that: (1) The average vegetation coverage of the study area from 2011 to 2017 reached more than 50 %, meeting the standard of National Garden City (great than 40 %). The overall vegetation coverage grade was high, but it had a decreasing trend during this period. (2) The vegetation in urban area of Xi’an experienced a significant change. From 2011 to 2017, only 30 % of the low-covered vegetation, 24.39 % of the medium-covered vegetation and 20.15 % of the high-covered vegetation remained unchanged, while the vegetation in the northwest, northeast, southwest and southeast of the edge of the city’s third ring changed significantly. (3) The vegetation quality in urban area of Xi’an has decreased from 2011 to 2014 with 6.9 % of vegetation coverage reduced; while from 2014 to 2017, the overall vegetation quality of this area has improved with 2.1 % of the vegetation coverage increased, which was mainly attributed to urban construction and Urban Green Projects. This study not only can obtain the dynamic change information of urban vegetation quickly, but also can provide suggestions and data support for urban planning of ecological environmental protection.

3

Data-driven temporal-spatial model for the prediction of AQI in Nanjin

Zhao Xuan, Song Meichen, Liu Anqi, Wang Yiming, Wang Tong, Cao Jinde

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 4

255--270

EN

Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.

4

Thermodynamic model of steam injection pipeline considering the effect of time and phase change

Zhao Xuan, Niu Chengcheng, Wang Jun, Zhu Xiuxing

Journal of Power Technologies

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2019

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Vol. 99, nr 2

123--130

EN

Thermodynamic parameters in heavy oil thermal recovery wells form the basis for evaluating the thermal efficiency of steam injection. However, various factors in wellbores affect the variation law of thermodynamic parameters, hindering attempts to make an accurate description of them. A thermodynamic model of wellbores is proposed in this study which factors in the effects of time and phase change with a view to: (i) improving the accuracy of thermodynamic parameter analysis, and (ii) identifying the main factors and rules that govern thermal efficiency. With the time factor considered, the transient conduction function of a coupled wellbore-formation was established, and the heat loss during steam injection was analyzed. Meanwhile, a wellbore pressure gradient equation was established using the Beggs-Brill model with consideration of the influence of phase transformation in wellbore. Steam pressure, which varies with flow pattern, was also analyzed. The accuracy of the proposed model was verified by comparing the results of the analysis with the test data. Taking this approach, the influence of steam injection parameters on thermal efficiency was studied. The results demonstrate that the relative error of the pressure analysis result of proposed model is 1.06% and the relative error of temperature is 0.24%. The main factor affecting thermal efficiency is water in the annulus of the wellbore, followed by the steam injection rate. The thermal efficiency of the wellbore is about 80% when the water depth in the annulus is 300 m. An increase in the injection rate or extension of the injection time can improve thermal efficiency, whereas an increase in steam injection pressure reduces thermal efficiency. The proposed method provides good prospects for optimizing high efficiency steam injection parameters of heavy oil thermal recovery wells.