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IEEE IAS Conference on Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Liu, Yating Yang, Ming Yu, Yixiao Ding, Tingting Si, Zhiyuan Li, Menglin Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Jinan Peoples R China

ISBN: (纸本)9781665434980

High-precision short-term wind generation prediction results are conducive to making a scientific generation plan and improving the wind power absorption capacity of the power grids. Based on the analysis of the relationship between the numerical weather prediction and wind power, this paper proposes a short-term wind generation combined forecast model considering meteorological similarity to improve the prediction accuracy of short-term wind power. In this method, the meteorological similarity day model, the extreme gradient boosting algorithm and the back propagation neural network algorithm are selected for achieving the short-term wind power prediction. Then, the particle swarm optimization algorithm is applied to determine the weight of each single forecasting model. Finally, the prediction results are obtained through the combination of the single model prediction results. With the realistic wind power data collected from a wind farm in Xinjiang province, the short-term wind forecasting task is achieved by the proposed method. The simulation results illustrate that the combined model proposed in this paper can effectively improve the forecasting performance of the benchmark models.

关键词： bp neural network algorithm Gaussian transform particle swarm optimization algorithm XGBoost short-term wind power combined forecast

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Research of Rice Appearance Quality Recognition 7

Research of Rice Appearance Quality Recognition

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Seventh International Conference on Advanced Communication and networking (ACN)

作者： Zhong, Weifeng Ma, Lixia Yin, Yuheng Harbin Univ Sci & Technol Sch Automat Harbin Peoples R China

ISBN: (纸本)9781467379557

Rice is the common food in people's daily life, different grades of rice have a great difference to the influence of taste, therefore, it is important to identify the rice quality. In order to improve the accuracy and real-time performance of the rice quality identification, PCA algorithm (Principal Component Analysis) and bp neural network algorithm are proposed to identify the rice quality. The feature variables of rice are extracted by PCA algorithm, then training the bp neural network parameters with the variables as its input, finally the bp neural network trained is used to identify the rice quality. The algorithm is implemented into MATLAB software. The simulation shows that the PCA-bp algorithm is greatly well than bp algorithm in accuracy and real-time performance.

关键词： Quality identification Rice PCA bp neural network algorithm

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Research on Fine Water Injection Flow Field Regulation Technology of Reservoir Based on Computer neural network Simulation

Research on Fine Water Injection Flow Field Regulation Techn...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Geng, Ruijie Li, Minglin Wei, Yaqiong Li, Mingzhu Li, Guoyong Yu, Chenglin Jidong Oilfield Company PetroChina Hebei Province Tangshan City063000 China

ISBN: (纸本)9798350383348

The role of technical evaluation in the fine water injection flow field control technology of reservoirs is very important, but there is a problem of inaccurate evaluation of results. The traditional control technology cannot solve the technical evaluation problem in the fine water injection flow field control technology of reservoirs, and the evaluation is unreasonable. Therefore, this paper proposes a bp neural network algorithm for innovative technical evaluation and analysis. Firstly, the control theory is used to evaluate the technical personnel, and the indicators are divided according to the technical evaluation requirements to reduce the interference factors in the technical evaluation. Then, the control theory evaluates the technology of fine water injection flow field regulation of the reservoir, forms a technical evaluation scheme, and comprehensively analyzes the technical evaluation results. MATLAB simulation shows that under certain evaluation criteria, the technical evaluation accuracy and oil recovery of the fine water injection flow field control method of the reservoir by the bp neural network algorithm are better than those of the traditional control technology. © 2024 IEEE.

关键词： bp neural network algorithm control theory regulation technology water injection flow field

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Capacity Optimization Configuration of Aircraft Energy Storage Units Based on Particle Swarm Optimization and neural network algorithm 2

Capacity Optimization Configuration of Aircraft Energy Stora...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Wang, Zhiliang Fan, Xiaodong Wang, Meng Liu, Hongxu Cui, Wanqiang Liu, Guihua Beijing Electro-mechanical Engineering Institute Beijing China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350313765

In order to reduce the problem of excessive capacity allocation of energy storage units and achieve comprehensive optimization of aircraft power supply system volume, weight, and price, this paper proposes a capacity optimization configuration method that combines particle swarm optimization (PSO) with bp neural network algorithm. This method first establishes a capacity allocation strategy, and on this basis, uses PSO algorithm to provide the capacity optimization configuration results of the energy storage unit. Part of the optimization results are used to train the bp neural network model, while the other part is used to verify the optimization results of the bp neural network. Then, the bp neural network model is trained using the data from the PSO algorithm to obtain the mapping relationship between the energy storage unit parameters and the optimization configuration results. When optimizing new energy storage units, this mapping can be directly utilized without the need for intermediate capacity allocation strategies and particle swarm optimization algorithm steps. Compared with PSO algorithm, this method can avoid the problem of non-convergence of calculation results, improve calculation accuracy, reduce optimization iteration process, and significantly shorten calculation time. The simulation results verify the correctness of the method. © 2023 IEEE.

关键词： aircraft bp neural network algorithm capacity allocation strategy mapping relationship particle swarm optimization

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Research on the Integration Path of Computer-Aided Technology and Higher Vocational English Teaching 4

Research on the Integration Path of Computer-Aided Technolog...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Zhang, Xiushan Chongqing City Vocational College Chongqing402160 China

ISBN: (纸本)9798350305258

Computers have entered thousands of households as a result of the ongoing development of high technology and the progressive rise in people's living standards. As a result, computers have become an essential component of people's lives and a must for students. The education sector has once again started off a wave of teaching reform, supporting the flipped classroom teaching style and the merging of online and offline teaching in response to the rapid expansion of the information age and the full support of computers. Many different teaching platforms have grown in response to the requirements of the times and have developed into the fundamental tools for education and instruction in higher education institutions such as colleges and universities. The current way of instruction has experienced a radical transformation in comparison to the more conventional approaches of education. English instruction in higher vocational schools is not, of course, an exception. Teaching English with the assistance of computers in higher vocational colleges has the potential to both boost students' interest in learning and the effectiveness of English instruction in higher vocational institutions. Teaching English with the assistance of computers has emerged as the dominant methodology in the expansion of higher-level vocational English education. However, despite the fact that computers make studying more convenient, they also provide a number of temptations and difficulties. In this paper, bp neural network method is proposed, after an in-depth analysis has been conducted on the basis of computer-aided technology in higher vocational English education. The goals of this initiative are to advance the logical application of computers in higher vocational English instruction. © 2023 IEEE.

关键词： bp neural network algorithm classroom teaching computer-aided technology vocational English

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Multi-Source DC Transmission Lines Icing Prediction Model Based on Adaptive Weight Feature 6

Multi-Source DC Transmission Lines Icing Prediction Model Ba...

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6th International Conference on HVDC, HVDC 2024

作者： Ma, ShuYang Yang, Yang Dong, Xinsheng Zhao, Mingguan Li, Meng Wang, Hongxia Electric Power Research Institute State Grid Xin Jiang Company Limited Urumqi China State Grid Xin Jiang Company Limited Dept. Electric Power Research Institute Urumqi China

ISBN: (纸本)9798350387759

This paper analyzes and studies the relationship between line ice cover and its influencing factors by collecting and organizing transmission line ice cover-micro-meteorological monitoring data, meteorological automatic station monitoring data and ECMWF numerical prediction data of Xinjiang Power Grid, establishes an ice cover prediction model by bp neural network algorithm for the three types of data respectively, and finally performs dynamic fusion prediction on the results of the multi-source ice cover prediction. The main innovation of this paper is that a dynamic fusion method of multi-source ice cover prediction with adaptive weight features is adopted to fuse the multi-source ice cover prediction results, which improves the accuracy and reliability of the prediction by combining the measured data and numerical prediction data. © 2024 IEEE.

关键词： bp neural network algorithm ice cover prediction line icing

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Research on Several Calculation Methods for Lightning Protection and Grounding of Overhead Transmission Lines 3

Research on Several Calculation Methods for Lightning Protec...

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3rd International Conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Zhang, Kun China Power Construction Group Guizhou Electric Power Design and Research Institute Co. Ltd Guizhou Province GuiyangCity550008 China

ISBN: (纸本)9798350319125

With the increase of demand for power energy in social life, more and more overhead transmission lines have been built. In order to ensure the safety and stability of the transmission network, lightning protection and grounding design has always been the focus of power system research. According to statistical analysis, many of the faults on the transmission line are caused by lightning strikes, so the lightning protection design of the transmission line is a very important content. Therefore, this paper proposes a bp neural network algorithm to evaluate and analyze several calculation methods for lightning protection design and grounding. Lightning protection and grounding of overhead lines is a very important link in power system, which can effectively protect power equipment and personal safety. In this paper, several calculation methods for lightning protection grounding of overhead lines are compared, and their steps, functions and significance are discussed. line. The safety is superior to the classical method. © 2023 IEEE.

关键词： bp neural network algorithm lightning protection technology theory Power system transmission lines

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Temperature field evolution of mass concrete: From hydration dynamics, finite element models to real concrete structure

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JOURNAL OF BUILDING ENGINEERING 2023年 65卷

作者： Xie, Yudong Du, Wenxiang Xu, Yugui Peng, Beibei Qian, Chunxiang Southeast Univ Sch Materials Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Res Ctr Green Bldg Mat & Carbon Utilizat Nanjing 211189 Peoples R China China Construct Mat Ind Key Lab Microbial Biomineralizat Technol Nanjing 211189 Peoples R China Southeast Univ Jiangsu Key Lab Construct Mat Nanjing 211189 Peoples R China Nanjing Jiangbei New Dist Cent Business Dist Inve Nanjing 211189 Peoples R China

Due to the hydration of cement is especially obvious in mass concrete, it is challenging to accurately predict the temperature field and prevent the formation of temperature cracks. In this study, the parameters of the kinetic model of cement hydration applicable to this composite cementitious system were first obtained by the back propagation (bp) neural network algorithm, and then the three-dimensional temperature field time evolution under different heat source functions was obtained from the kinetic model of cement hydration, isothermal and adiabatic conditions of cumulative hydration exotherm by developing a finite element calculation model for the mass slab concrete, respectively. The calculation results show that the kinetic model of cement hydration as a new heat source function is suitable for the calculation of the temperature field of mass concrete, and the hydration heat of isothermal calorimetry yields low results. In addition, in the temperature field calculation of mass concrete, it is more accurate to consider the effect of temperature on cement hydration than not to consider temperature.

关键词： Hydration heat Mass concrete bp neural network algorithm Thermodynamic calculations Finite element model

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Short-Term Power Generation Forecasting of a Photovoltaic Plant Based on PSO-bp and GA-bp neural networks

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FRONTIERS IN ENERGY RESEARCH 2022年 9卷

作者： Li, Yuanqi Zhou, Lei Gao, Peiqi Yang, Bo Han, Yiming Lian, Chang SGCC Jiangsu Elect Power Co Ltd Suzhou Peoples R China Kunming Univ Sci & Technol Fac Elect Power Engn Kunming Yunnan Peoples R China SGCC Henan Elect Power Co Ltd Pingdingshan Peoples R China

With the improvement in the integration of solar power generation, photovoltaic (PV) power forecasting plays a significant role in ensuring the operation security and stability of power grids. At present, the widely used backpropagation (bp) and improved bp neural network algorithm in short-term output prediction of PV power stations own the drawbacks of neglection of meteorological factors and weather conditions in inputs. Meanwhile, the existing traditional bp prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. Therefore, based on the PV power plant in Lijiang, considering the related factors that influence PV output such as solar irradiance, environmental temperature, atmospheric pressure, wind velocity, wind direction, and historical generation data of the PV power station, three neural network algorithms (i.e., bp, GA-bp, and PSO-bp) are utilized respectively in this work to construct a short-term forecasting model of PV output. Simulation results show that GA-bp and PSO-bp network forecasting models both obtain high prediction accuracy, which indicates GA and PSO methods can effectively reduce the prediction errors in contrast to the original bp model. In particular, PSO owns better applicability than GA, which can further reduce the errors of the PV power prediction model.

关键词： photovoltaic power short-term forecasting bp neural network algorithm GA-bp algorithm PSO-bp algorithm solar energy

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THE USE OF NONLINEAR DYNAMIC SYSTEM AND DEEP LEARNING IN PRODUCTION CONDITION MONITORING AND PRODUCT QUALITY PREDICTION

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2022年第2期30卷 2240068-2240068页

作者： Li, Jun Wang, Ruoqi Alhodaly, Mohammed Sh Huang, Jingfeng Qi, Lin Zhejiang Wanli Univ Sch Informat & Intelligent Engn Ningbo 315100 Peoples R China Zhejiang Wanli Univ Dept Sci Res Ningbo 315100 Peoples R China King Abdulaziz Univ Fac Sci Dept Math Nonlinear Anal & Appl Math NAAM Res Grp POB 80203 Jeddah 21589 Saudi Arabia

To improve the product quality and production efficiency in industrial production, the industrial production process is explored. First, for the production state in industrial production, the radio frequency identification technology (RFID) is used to monitor the state of the nodes in the production process and collect data. Based on the problem of blind area in industrial production, an adaptive controller design based on uncertain nonlinear dynamic systems is introduced;second, based on this, for the problem that product quality is difficult to guarantee in the process flow, the improved multi-layer feedforward network (back propagation, bp) algorithm is used to predict it. Finally, the control diagnosis algorithm based on multi-order least squares support vector machine (LS-SVM) is used to analyze the abnormal cause of the product. The results show that the use of the RFID can monitor the production process in real time and obtain real-time product quality information data. The proposed adaptive controller based on uncertain nonlinear dynamic system has good applicability in solving the problem of blind area. The improved bp neural network algorithm improves the prediction accuracy in the process of product quality prediction and increases the convergence speed of the algorithm. The control diagnosis algorithm based on multi-order least squares that support vector machine (SVM) can classify and learn the historical data of products, as well as perform multi-order search and analysis on the causes of abnormal quality. It helps relevant personnel to carry out process improvement and control, thereby achieving the purpose of improving product quality. Through this investigation, it is found that the method proposed can monitor the state of industrial production in real time and help relevant personnel to control and predict product quality. It can be applied in actual processes.

关键词： Radio Frequency Identification Technology Uncertain Nonlinear Dynamic System Least Squares Support Vector Machine bp neural network algorithm Quality Control

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