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The relationship between the short circuit ratio (SCR) and static voltage stability is analyzed in this paper. According to eigenvalue decomposition method, a novel concept named generalized short circuit ratio (gSCR) has been proposed for multi-infeed LCC-HVDC (MIDC) systems to mathematically measure the connected AC strength from the point view of voltage stability, which can overcome the rule-of-thumb basis of existing multi-infeed short circuit ratio (MISCR) concept. In gSCR, two indices, the critical gSCR (CgSCR) and the boundary gSCR (BgSCR) are developed to quantitatively evaluate if the connected AC system is strong or weak, in which CgSCR=2 and BgSCR=3 are two critical values for strength evaluation. Finally, numerical simulations are conducted to validate the effectiveness of the proposed gSCR concept.Index Terms--Static voltage stability, multi-infeed HVDC system, multi-infeed short circuit ratio, generalized short circuit ratio.

Fault detection can increase the reliability and efficiency of power electronic converters employed in power systems. Among the converters in the power system, a Neutral Point Clamped (NPC) three-level inverter is most commonly used to drive electric motors. In this paper, a new approach for open-circuit fault detection and location of the NPC three-level inverter for a shifting process using a constant voltage-to-frequency ratio is proposed. In order to diagnose open-circuit fault in as short a time as possible, an adaptive electrical period partition (AEPP) algorithm is proposed to pick single electrical periods from real-time three-phase current signals. The Maximal Overlap Discrete Wavelet Transformation (MODWT) and Park’s Vector Modulus (PVM) are used for feature analysis and normalization of electrical period signals. The statistical characteristics of the electrical period signals are extracted, and a random forest model is constructed to realize the state classification. Compared with the traditional fault diagnosis method, the proposed algorithm finds fault locations quickly and accurately. The effectiveness and accuracy of the proposed algorithm are verified by experiments.

Virtual prototyping of power electronic modules aims to allow rapid evaluation of potential designs without building and testing physical prototypes. Among the interests in thermal models of the virtual modules, process of compact thermal models needs effective methodology to fast generate small models describing the thermal performance of a potential design. This study chooses the Generalized Minimized Residual (GMRES) Algorithm to process thermal models due to its efficiency. Based on that, a machine learning aided surrogate model is proposed for the prediction of thermal performance since existing approaches take much time to determine the thermal response to a particular input power. This surrogate model is created by training a dedicated artificial neural network (ANN) on simulation data, after that this model can quickly map the module temperature and the power input in time domain. In the training process, crossvalidation method is introduced to determine which neuron structure should be selected for the practical data generated by thermal equations. The test group is noted in cross-validation to give the prediction performance of structure candidates. To verify the proposed method, the resulting data of trained surrogate models are compared with the accurate simulation data after the ANN based cross-validation.

The major source of loss in modern compressors is the secondary loss. Non-axisymmetric endwall profile contouring is now a well established design methodology in axial flow turbines. However, flow development in axial compressors is differ from turbines, the effects of non-axisymmetric endwall to axial compressors requires flow analysis in detail. This paper presents both experimental and numerical data to deal with the application of a non-axisymmetric hub endwall in a high-subsonic axial-flow compressor. The aims of the experiment here were to make sure the numerically obtained flow fields is the physical mechanism responsible for the improvement in efficiency, due to the non-axisymmetric hub endwall. The computational results were first compared with available measured data of axisymmetric hub endwall. The results agreed well with the experimental data for estimation of the global performance. The coupled flow of the compressor rotor with non-axisymmetric hub endwall was simulated by a state-of-the-art multi-block flow solver. The non-axisymmetric hub endwall was designed for a subsonic compressor rotor with the help of sine and cosine functions. This type of non-axisymmetric hub endwall was found to have a significant improvement in efficiency of 0.45% approximately and a slightly increase for the total pressure ratio. The fundamental mechanisms of non-axisymmetric hub endwall and their effects on the subsonic axial-flow compressor endwall flow field were analyzed in detail. It is concluded that the non-axisymmetric endwall profiling, though not optimum, can mitigate the secondary flow in the vicinity of the hub endwall, resulting in the improvement of aerodynamic performance of the compressor rotor.

Abstract. A benchmark dataset of radiation, heat, and CO2 fluxes is crucial to land–atmosphere interaction research. Due to rapid urbanization and the development of agriculture, the land–atmosphere interaction processes over the Yangtze River Delta (YRD) of China, which is a typical East Asian monsoon region, are becoming various and complex. To understand the effects of various land cover changes on land–atmosphere interactions in this region, a comprehensive long-term (2011–2019) in situ observation campaign, including 30 min resolution meteorological variables (air temperature, humidity, pressure, wind speed, and wind direction), surface radiative flux, turbulent heat flux, and CO2 flux, was conducted at four sites with two typical surface types (i.e., croplands and suburbs) in the YRD. Analysis of the dataset showed that all four radiation components, latent heat flux, sensible heat flux, soil heat flux, and CO2 flux varied seasonally and diurnally at the four sites. Surface energy fluxes exhibited great differences among the four sites. On an annual basis, for the two cropland sites, the dominant consumer of net radiation was latent heat flux. For the two suburban sites, in contrast, latent heating dominated from April to November, whereas sensible heating dominated during the other months. Our present work provides convincing evidence that the dataset has potential for multiple research fields, including studying land–atmosphere interactions, improving boundary layer parameterization schemes, evaluating remote sensing algorithms, validating carbon flux modeling and inversion, and developing climate models for typical East Asian monsoon regions. The dataset is publicly available at https://doi.org/10.5281/zenodo.6552301 (Duan et al., 2022).

-The introduction of renewable energy sources into the AC grid can change and weaken the strength of the grid, which will in turn affect the stability and robustness of the doubly-fed induction generator (DFIG) wind farm. When integrated with weak grids, the DFIG wind turbine with vector power control often suffers from poor performance and robustness, while the DFIG wind turbine with synchronized control provides better stability. This paper investigates the critical short circuit ratios of DFIG wind turbine with vector power control and synchronized control, to analyze the stability boundary of the DFIG wind turbine. Frequency domain methods based on sensitivity and complementary sensitivity of transfer matrix are used to investigate the stability boundary conditions. The critical capacity of DFIG wind farm with conventional vector power control at a certain point of common coupling (PCC) is obtained and is further increased by employing synchronized control properly. The stability boundary is validated by electromagnetic transient simulation of an offshore wind farm connected to a real regional grid.