The power output and the efficiency of a thermophotovoltaic (TPV) system are determined by the radiation from a heat source, TPV cells, system parasitic losses, cell cooling subsystems, etc. The cells are core devices of a system, and the performance of the cells can be characterized by a number of parameters, including open circuit voltage (V_oc), short circuit current density (I_sc), and maximum output power density (P_m). The cell temperature has a great effect on these parameters. Although several papers have reported the dependence of these parameters on the cell temperature, few studies experimentally examined the temperature-dependent performance of the TPV cells or comparatively analyzed them. In this study, we investigated how the fundamental parameters of GaSb, Ge, and InGaAsSb cells varied with their operating temperatures using a home-built TPV prototype. The measured data indicate that the cell temperature significantly affects the performance of the TPV cells. Variations of these parameters with the cell temperature are different for various TPV cells. The V_oc and P_m of the GaSb, Ge, and InGaAsSb cells decreased linearly with increasing cell temperature, while the I_sc increased slightly. The normalized value of the temperature coefficient for the P_m of the GaSb was lower than those for the Ge and InGaAsSb cells, which indicated that the GaSb cells were relatively less sensitive to cell temperature. The results provided in this study are useful to improve cell performance and design TPV systems.

In recent years, the State Grid of China has placed significant emphasis on the monitoring of noise in substations, driven by growing environmental concerns. This paper presents a substation noise monitoring system designed based on an end-network-cloud architecture, aiming to acquire and analyze substation noise, and report anomalous noise levels that exceed national standards for substation operation and maintenance. To collect real-time noise data at substations, a self-developed noise acquisition device is developed, enabling precise analysis of acoustic characteristics. Moreover, to subtract the interfering environmental background noise (bird/insect chirping, human voice, etc.) and determine if noise exceedances are originating from substation equipment, an intelligent noise separation algorithm is proposed by leveraging the convolutional time-domain audio separation network (Conv-TasNet), dual-path recurrent neural network (DPRNN), and dual-path transformer network (DPTNet), respectively, and evaluated under various scenarios. Experimental results show that (1) deep-learning-based separation algorithms outperform the traditional spectral subtraction method, where the signal-to-distortion ratio improvement (SDRi) and the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of Conv-TasNet, DPRNN, DPTNet and the traditional spectral subtraction are 12.6 and 11.8, 13.6 and 12.4, 14.2 and 12.9, and 4.6 and 4.1, respectively; (2) DPTNet and DPRNN exhibit superior performance in environment noise separation and substation equipment noise separation, respectively; and (3) 91% of post-separation data maintains sound pressure level deviations within 1 dB, showcasing the effectiveness of the proposed algorithm in separating interfering noises while preserving the accuracy of substation noise sound pressure levels.

Gas–oil separation by membrane stands for a promising technique in dissolved gas analysis (DGA). Since the accuracy of DGA relies on the results of gas–oil separation to a great extent, it is necessary to study the influence factor of membrane for better performance. Although plentiful studies have been conducted aiming at membrane modification to obtain better separation performance, it cannot be ignored that the conditions of oil also affect the performance of membrane much. In this work, a photoacoustic spectroscopy-based sensor for DGA, which employed membrane for gas–oil separation, was established first. By detecting the photoacoustic signal, the performance of membrane could be evaluated. Furthermore, the influences of feed velocity and pressure have on the performance of membrane were analyzed. Both simulation and experiment were employed in this work to evaluate the influences by collecting the equilibrium time of membrane under different conditions. As a result, the simulation and experiment agreed with each other well. Moreover, it was reasonable to draw the conclusion that the equilibrium time was evidently reduced with the raise of feed velocity but remained with a minimum change when pressure changed. The conclusion may serve as a reference for the application of membrane in optical sensor and DGA.