The detection of harmonics is an important foundation for analysis and control of harmonics of power systems. This paper puts forward a new harmonic detection method based on a rotating coordinate transformation of multi-frequency, and expatiates of the basic theory of rotating coordinate orthogonal transformation of multi-frequency and its actualization processes to detect harmonics. Finally, computer simulation of harmonic detection based the proposed method and simulative results are given. The proposed detection method can detect the instantaneous values of random harmonic components and random fundamental components in real time with none principle error, and has the characteristics of high detection precision, good real time and simple actualization.

Stationary models are usually applied for wind characteristics analysis. However, nonstationarity has been found in the field measurements of typhoons in recent studies; therefore, using traditional models with stationary assumptions to conduct wind characteristics is inadequate. In this research, data acquisition of typhoon wind speeds and monsoon are conducted based on the wind field measurements. Wind speeds of typhoon “Maria” passing through Pintan, Fujian Province, China and the monsoon from 2017.10–2018.10 were obtained to investigate wind characteristics. The run test method is utilized to show that non-stationarity exists in both typhoon and monsoon wind speed, and the percent of non-stationary increases with the increase in time interval. Additionally, results show that stronger non-stationarity exists in typhoon wind speed compared with monsoons. Based on a self-adaptive procedure to extract time varying mean wind speed, a non-stationary model is established to compare with the non-stationary model, which has been applied in the traditional wind characteristic analysis. The fluctuating wind characteristics such as turbulence intensity, gust factor, turbulence integral scale, and wind speed spectrum are analyzed to compare the two models. Results show that the difference of such characteristics between the two models increases with the time interval, indicating the necessity of consideration of non-stationary models, especially for design specifications with larger time intervals. Influences of time intervals are investigated, and relevant recommendations are provided for wind resistance specifications. Our conclusions may provide reference for wind resistance design in engineering applications.

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Machine learning algorithms becomes popular for intelligent classification of rock images. In this paper, it selects Resnet 50 neural network model to divide the data sets based on the rock pictures taken under the white light lamp. By continuously adjusting the parameters of each layer, the intelligent classification of rocks is carries out. The training final validates accuracy reached 94.12%.

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