Abstract
The noise pollution caused by urban substations is an increasingly serious problem, as is the issue of local residents being disturbed by substation noise. To accurately assess the degree of noise annoyance caused by substations to surrounding residents, we established a noise annoyance prediction model based on transfer learning and a convolution neural network. Using the model, we took the noise spectrum as the input, the subjective evaluation result as the target output, and the AlexNet network model with a modified output layer and corresponding parameters as the pre-training model. In a fixed learning rate and epoch setting, the influence of different mini-batch size values on the prediction accuracy of the model was compared and analyzed. The results showed that when the mini-batch size was set to 4, 8, 16, and 32, all the data sets had convergence after 90 iterations. The root mean square error (RMSE) of all validation sets was lower than 0.355, and the loss of all validation sets was lower than 0.067. As the mini-batch size increased, the RMSE, loss, and mean absolute error (MAE) of the verification set gradually increased, while the number of iterations and the training duration decreased gradually. In this test, a mini-batch size value of four was appropriate. The resultant convolutional neural network model showed high accuracy and robustness, and the error between the prediction result and the subjective evaluation result was between 2% and 7%. The model comprehensively reflects the objective metrics affecting subjective perception, and accurately describes the subjective perception of urban substation noise on human ears.
Highlights
With the continued popularization of electric vehicles, more charging piles are being installed, creating a corresponding increase in the demand for power supply, which has led to the construction of more urban substations and other basic power grid facilities.the location of the new urban substations is increasingly problematic due to the associated noise pollution
The local residents frequently complain of noise pollution, and the social problems caused by residents who are adversely affected by the noise from the substations have become more pronounced
Theperformance performanceofofthe themethod method used this study was verified by the urban substanoise recorded and the corresponding subjective evaluation results of a previous study tion noise recorded and the corresponding subjective evaluation results of a previous These urban substation noise samples were recorded using a data acquisition study [12]
Summary
The location of the new urban substations is increasingly problematic due to the associated noise pollution. The local residents frequently complain of noise pollution, and the social problems caused by residents who are adversely affected by the noise from the substations have become more pronounced. The accurate evaluation and prediction of the degree of substation noise disturbance to residents is important in the construction of new substations and with respect to implementing noise control measures for existing substations, which is a common problem in the power grid industry which needs to be solved urgently. Low-frequency noise is characterized by strong penetration, slow attenuation, and long transmission distance, and it is perceived by local residents. The most typical low-frequency substation noise occurs at
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.