Data-driven approach is promising for predicting impedance profile of grid-connected voltage source converters (VSCs) under a wide range of operating points (OPs). However, the conventional approaches rely on a one-to-one mapping between operating points and impedance profiles, which, as pointed out in this article, can be invalid for multiconverter systems. To tackle this challenge, this article proposes a stacked-autoencoder-based machine learning framework for the impedance profile predication of grid-connected VSCs, together with its detailed design guidelines. The proposed method uses features, instead of OPs, to characterize impedance profiles, and hence, it is scalable for multiconverter systems. Another benefit of the proposed method is the capability of predicting VSC impedance profiles at unstable OPs of the grid-VSC system. Such prediction can be realized solely based on data collected during stable operation, showcasing its potential for rapid online state estimation. Experiments on both single-VSC and multi-VSC systems validate the effectiveness of the proposed method.

The fundamental frequency of ac power-electronic-based power systems may deviate from its nominal value, and it is highly affected by converter control dynamics. To capture the dynamics of fundamental frequency, two impedance modeling methods for voltage-source converters (VSCs) are reported, with respect to the selection of system reference frame. The first method is to model VSCs in a reference frame with the nominal frequency, while the second method models VSCs in a reference frame with varying fundamental frequency, and hence, the fundamental frequency is represented as an additional terminal variable in the impedance model. This article mathematically proves that the two impedance models are essentially equivalent, provided that the frequency dynamics is accounted in the modeling of control delay and power stage of VSCs in the second method. This equivalence is demonstrated for both grid-following (GFL) and grid-forming (GFM) VSCs. Stability predictions based on two methods are further compared based on an interconnected GFM and GFL VSC system. The results are also found to be identical. Finally, experiments validate the correctness of the theoretical analysis.

Aromatic compounds serve as key feedstocks in the chemical industry, typically undergoing functionalization or full reduction. However, partial reduction via dearomative sequences remains underexplored despite its potential to rapidly generate complex three-dimensional scaffolds and the existing dearomative strategies often require metal-mediated multistep processes or suffer from limited applicability. Herein, a photocatalytic radical cascade approach enabling dearomative difunctionalization through selective spirocyclization/imination of nonactivated arenes is reported. The method employs bifunctional oxime esters and carbonates to introduce multiple functional groups in a single step, forming spirocyclic motifs and iminyl functionalities via N–O bond cleavage, hydrogen-atom transfer, radical addition, spirocyclization, and radical-radical cross-coupling. The reaction constructs up to four bonds (C−O, C−C, C−N) from simple starting materials. Its broad applicability is demonstrated on various substrates, including pharmaceuticals, and it is compatible with scale-up under flow conditions, offering a streamlined approach to synthesizing highly decorated three-dimensional frameworks.

This paper derives closed-form solutions for grid-forming converters with power synchronization control (PSC) by subtly simplifying and factorizing the complex closed-loop models. The solutions can offer clear analytical insights into control-loop interactions, enabling guidelines for robust controller design. It is proved that 1) the proportional gains of PSC and alternating voltage control (AVC) can introduce negative resistance, which aggravates synchronous resonance (SR) of power control, 2) the integral gain of AVC is the cause of sub-synchronous resonance (SSR) in stiff-grid interconnections, albeit the proportional gain of AVC can help dampen the SSR, and 3) surprisingly, the current controller that dampens SR actually exacerbates SSR. Controller design guidelines are given based on analytical insights. The findings are verified by simulations and experimental results.

Impedance-based stability analysis has been widely adopted for voltage source converters (VSCs). Considering unknown controller parameters, impedance measurement based on frequency scan is always required for stability evaluation, which endures complicated implementation and can only be conducted under small amount of stable operating conditions. To solve this problem, a novel impedance profile prediction method for grid-connected VSCs has been proposed. A combined structure of stacked autoencoder (AE) and principal component analysis (PCA) is firstly proposed to extract VSC admittance feature under stable operating points, and a comprehensive VSC admittance set can be further predicted through searching on an enlarged feature space with unstable scenarios included. The stability can then be evaluated on the predicted VSC admittances with a stability boundary derived. Simulations and experiments prove the effectiveness of the proposed method.

The past years have seen a rapid increase in the deployment of large-scale wind power plants (WPPs) in transmission grids. The dynamic interactions between wind turbines (WTs), power transmission cables, and other electrical infrastructure of WPPs pose challenges to the stability and quality of electricity supply, particularly under diverse grid conditions. The interactions tend to be worsened with longer transmission cables [1]. A harmonic instability issue that features a 451 Hz resonance is manifested in an offshore WPP located in the North Sea [2]. During a submarine cable outage, an offshore WPP situated in England encountered instability due to sub-synchronous resonance at around 8.5 Hz [3].

In power-electronic-based power systems like wind farms, conducting stability analysis necessitates a comprehensive understanding of the system impedance across a wide frequency range, from sub-harmonic frequencies up to the Nyquist frequency of control systems of power converters. The cable aging effect can significantly impact the cable impedance, while accurately estimating the degree of aging proves challenging. To avoid the requirement for precise aging prognostic, this paper proposes an approach based on Artificial Neural Networks (ANN) that enables the estimation of AC cable impedance in a wind farm solely through fundamental frequency measurements. The data used for training the ANN is obtained from the cable model in PSCAD, incorporating physical and geometrical parameters, which accurately approximates real cables within power systems. The training results of the ANN validate the accuracy of the proposed identification approach. As a result, the proposed approach effectively eliminates the potential misjudgment of system stability caused by the aging effect of power cables.