In view of the shortcomings of current image detection methods for icing thickness on power transmission lines, an image measuring method for icing thickness based on remote online monitoring was proposed. In this method, a Canny operator is used to get the image edge, in addition, a Hough transform and least squares are combined to solve the problems of traditional Hough transform in the parameter space whereby it is easily disturbed by the image background and noises, and eventually the edges of iced power transmission lines and un-iced power transmission lines are accurately detected in images which have low contrast, complex grayscale, and many noises. Furthermore, based on the imaging principle of the camera, a new geometric calculation model for icing thickness is established by using the radius of power transmission line as a reference, and automatic calculation of icing thickness is achieved. The results show that proposed image recognition method is rarely disturbed by noises and background, the image recognition results show good agreement with the real edges of iced power transmission lines and un-iced power transmission lines, and is simple and easy to program, which suggests that the method can be used for image recognition and calculation of icing thickness.

The failure rate is an important indicator to assess the reliability of substation equipment, and the failure rate prediction is an effective way to master the operation status of substation equipment. According to the characteristics of failure rate data, such as failure period segmentation and stochastic variation, this paper proposes a combined prediction method by the data decomposition of failure rate time series and the segmentation theory of failure periods, and then establishes a new failure rate prediction model of substation equipment based on Weibull distribution and time series analysis. Compared with the traditional Weibull distribution function model which can’t describe the stochastic variation of failure rate data and can’t identify failure period automatically, the proposed model in this paper uses the minimum sum algorithm of residual squares obtained by the Weibull distribution to accurately identify the demarcation point between different failure periods, and then establishes Autoregressive Moving Average (ARMA) model to achieve combined prediction. Finally, the effectiveness of the proposed model is verified by the engineering example and comparison with traditional failure rate prediction methods.

The efficient utilization of solar energy significantly contributes to energy efficiency in buildings. Solar photovoltaic thermal (PVT) heat pumps, a hybrid of photovoltaic and solar-assisted heat pumps, have demonstrated a significant development trend due to their multi-generational capacity for heating, power, and cooling with reliable operational performance. This research work presents and investigates a single-stage compression PVT heat pump system, along with the operation principle of the system’s heating and power co-generation throughout the winter and transitional season. The construction of the testing facility, data reduction, error analysis, and performance evaluation indices of the system are all explained theoretically. A continuous experiment research project focusing on system heating and power performance was carried out in Dalian during the transition season (November in this study) and winter season (December in this study) as part of our investigation into the potential uses for space heating, residential hot water, and power supply in northern China. The findings of the experimental research demonstrate that the proposed system can generate electricity and heat at high efficiency during the winter and transitional seasons, with long-term stable performance. The system’s average heating COP_t is 5 during the transitional season and 4.4 during the winter season. Meanwhile, the average photovoltaic power efficiency under both weather conditions is 11.9% and 10.2%, with a peak value of 15.7% and 12.0%, respectively. Additionally, the system compression ratio’s variation range is 2 to 3.88, which is lower than the standard heat pump system. As a result, the entire system heating operating process remains constant.

Statistical modeling and analysis of satellite failure is an effective way to master the satellite operating status, and the results can provide important theoretical basis for the performance improvement of satellite reliability. In view of the dual-period characteristics of satellite failure distribution, a 2-Weibull segmented model for statistical modeling of satellite failure has been proposed in this paper. In order to improve the robustness of the parameter estimation for 2-Weibull segmented model, a parameter estimation method for 2-Weibull segmented model based on least squares Hough transform algorithm is continued to be proposed in this paper. And the feasibility and effectiveness of the parameter estimation method are verified by simulation experiments. The 2-Weibull segmented model is used for statistical modeling of 1584 satellite failure samples. The results show that, compared with the traditional single Weibull distribution model, the 2-Weibull segmented model proposed in this paper improves the fitting accuracy of satellite failure distribution by 46.15%.

Due to system anomalies, BeiDou navigation satellite system (BDS) sometimes updates the broadcast ephemeris at non-integral hours. The gross errors contained in the non-integer-hour data blocks of broadcast ephemeris will lead to the signal-in-space (SIS) anomalies of BDS satellites, and then affect the SIS continuity and availability performance of BDS satellites. In order to study the performance of non-integer-hour data blocks in BDS broadcast ephemeris, a gross error identification method for BDS non-integer-hour broadcast ephemeris based on precise ephemeris has been proposed in this paper to identify the 2017–2021 BDS non-integer-hour broadcast ephemeris data into gross error data blocks, normal data blocks and unknown data blocks. For the normal data blocks in BDS non-integer-hour broadcast ephemeris, the orbit performance and clock performance have been assessed in detail. And then, the judgment conditions for the normal data blocks and gross error data blocks have been further studied. The research results show that the normal data blocks of BDS non-integer-hour broadcast ephemeris satisfy the relationship of TOC(time of clock)-TOE(time of ephemeris)=720Ns (N=1, 2, 3 and 4), while the gross error data blocks satisfy the relationship of TOC-TOE $\ne 720$ Ns (N=1, 2, 3 and 4).