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Qinghai-Tibet region has abundant solar energy resources. The photovoltaic power generation can be utilized to solve the electricity-supply problem in most parts of the region having no access to electricity. General household photovoltaic power can solve the basic problem of electrical lighting, but in some cases where large-power electricity supply is needed, such power cannot be utilized, which means its scope of application is limited. We employ the STM32 intelligent microprocessor controlling method to collect parameters and features of storage batteries and photovoltaic modules. The system can automatically expand capacity and output power, which is very practical in making general household photovoltaic power more widely applied.

Peak detection is a crucial preprocessing step in the analysis of various spectral signals. The method based on the continuous wavelet transform is more practical and popular, and has better detection accuracy and reliability by identifying peaks across scales in the wavelet space and implicitly removing noise as well as the baseline. However, there are inevitably overlapping peaks in the measured spectra, and the formed composite ridges affect peak detection accuracy. Most peak detection methods have limited applicability to overlapping peaks. A weighted continuous wavelet transform (WCWT) peak detection algorithm is proposed to improve the adaptive ability of the peak detection method. This method yields more obvious spectral peak characteristics in low-scale regions. Composite ridges can be successfully truncated by setting a noise threshold based on the standard deviation of the spectral signal. In addition, the maximum value in the ridges was compressed and shifted to a smaller scale, which could determine the peaks more accurately. The method was applied to the peak detection of simulated spectra, Romanian database of Raman spectra, and real liquid electrode glow discharge spectra. The results show that the proposed method exhibits good peak detection performance.INDEX TERMS Peak detection, weighted continuous wavelet transform, overlapping peaks, peak ridges.

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