scholarly journals Assessment of Power Plant Based on Unsafe Behavior of Workers through Backpropagation Neural Network Model

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Juan Shi ◽  
Dingyi Chang

Safety is an essential topic for electric power plants. In recent years, accidents caused by unsafe behaviors of electric power plant employees are frequent. To promote the sustainable development and safety of electric power plants, studies on the assessment of unsafe behavior are becoming increasingly important and urgent. In this study, accident statistical analysis, literature review, and expert survey are adopted to select more comprehensive and accurate assessment indicators of unsafe behavior of the workers in electric power plants. Data about indicator and unsafe behavior were obtained through a questionnaire survey, and 27 indicators were used as inputs, and the unsafe behavior was taken as the output of a backpropagation (BP) neural network based unsafe behavior assessment model. An assessment indicator system about power plant workers’ unsafe behavior composed of 4 first-level indicators and 27 second-level indicators was established and the weights of the assessment indicators were determined. A three-layer feedforward BP neural network assessment model of “27-13-1” layers was found to be a suitable model. The proposed model can demonstrate the nonlinear complex relationship between the assessment indicator and the unsafe behavior of power plant workers. The model can be helpful to evaluate, predict, and monitor the safety performance of electric power plants.

2018 ◽  
Vol 25 (2) ◽  
pp. 132-139 ◽  
Author(s):  
Andrzej Tomporowski ◽  
Józef Flizikowski ◽  
Weronika Kruszelnicka ◽  
Izabela Piasecka ◽  
Robert Kasner ◽  
...  

Abstract This paper describes identification and components of destructiveness of energy, economic and ecologic profits and outlays during life cycle of offshore wind electric power plants as well as the most useful models for their design, assembly and use. There are characterized technical conditions (concepts, structures, processes) indispensable for increasing profits and/or decreasing energy, economic and ecological outlays on their operation as well as development prospects for global, European and domestic markets of offshore wind electric power industry. A preliminary analysis was performed for an impact of operators, processed objects, living and artificial environmental objects of a 2MW wind electric power plant on possible increase of profits and decrease of outlays as a result of compensation of destructiveness of the system, environment and man.


Author(s):  
Andrei Khitrov ◽  
Alexander Khitrov ◽  
Evgeny Veselkov ◽  
Vyacheslav Tikhonov

Autonomous low power electric power plants working with variable speed energy sources or electric subsystems of cogeneration plants of some type need to increase the low speed or the low voltage of the system. In this paper the investigations and the results of the experiments conducted using different structures are given.


2013 ◽  
Author(s):  
James M. Wolfe ◽  
Morgan M. Fanberg

The traditional electric power load analysis (EPLA) uses a very basic routine of assigning demand factors to each connected electric load, then summing these to arrive at an estimated power plant load. This method is overly simplistic, gives a false sense of certainty, and does not accurately reflect vessel operations. This paper will describe an alternative to traditional methods of determining ratings and configurations for electric power plants during vessel concept and preliminary design. This method uses statistical methods to calculate a range of possible power plant demand. Resulting data can be used to evaluate power plant configurations with respect to design risk, vessel operating profiles, and potential limitations. The ability to better evaluate the complete range of required electric power across all operating profiles increases in importance as vessel power plants become more sophisticated with the introduction of variable speed generation, battery/hybrid power systems, DC power distribution, and distributed load centers.


Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.


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