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The condition monitoring system of the hydropower generator unit (HGU) contains a vast amount of monitoring data related to equipment performance. It is challenging to reveal the HGU performance change law from these data. This paper constructed a correlation transmissibility damage indicator (CTDI) to analyze HGU degradation performance based on the transmissibility function (TF). Unlike traditional TF damage indicators, CTDI can use different types of monitoring data to obtain system TF to increase TF combination and improve detection sensitivity. The analysis is performed using actual monitoring data from a hydropower plant in Guangxi, China. Firstly, a new frequency concept is defined based on the characteristics of HGU obtaining data every 10 minutes. Then, the HGU is considered a system and divided into three subsystems according to the sensor installation locations of the monitoring system. The damage indicator is used to analyze the system and its subsystems at the time scales of day, week, and month to locate and quantify the damage of HGU. The final results of this analysis show that the method can accurately detect and locate faults, applies to different operating conditions, and reveals the HGU performance deterioration pattern, which is of great significance for HGU maintenance.

Precipitation data from ground-based observatories in the Dongting Lake basin are often missing, resulting in large errors in surface precipitation data obtained by interpolation, which affects the accuracy of hydro-meteorological studies. Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) is the main high-resolution precipitation product, which is available to supplement measured missing data. To evaluate the applicability of this product in the Dongting Lake basin at multiple spatial and temporal scales, this paper analyzes daily, monthly, seasonal, annual, and extreme precipitation events of the three latest IMERG precipitation products (IPPs) (IMERG-F, IMERG-E, and IMERG-L) using eight statistical evaluation metrics. We find that the spatial and temporal performance of IMERG precipitation products varies over different time scales and topographic conditions. However, all three metrics (CC, RMSE, and RB) of the IMERG-F precipitation products outperform the IMERG-E and IMERG-L precipitation products for the same period. In the comparison of IMERG and TRMM (Tropical Rainfall Measuring Mission) precipitation products on monthly and seasonal scales, IMERG-F performed the best. IPPs can capture precipitation more accurately on seasonal scales and perform better in winter, indicating good detection of trace precipitation. Both high and low altitudes are not favorable for the satellite detection of extreme precipitation in both general and extreme precipitation events. Overall, the accuracy of IMERG-F with correction delay is slightly better than that of IMERG-E and IMERG-L without correction under near-real-time conditions, which is applicable in the Dongting Lake basin. However, the correction process also exacerbates overestimation of the precipitation extent.