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In practical power systems, it is still very challenging to figure out a cost-effective undervoltage load shedding (UVLS) scheme that can reliably and adaptively react to the short-term voltage stability (SVS) problem. Faced with this challenge, this paper develops an intelligent data-driven predictive UVLS scheme for online SVS enhancement. Inspired by valuable ideas in model predictive control and supplementary excitation control in power systems, a novel deep feedback learning machine (DFLM) is designed to precisely predict future voltage violations after UVLS execution. With the help of the DFLM, the UVLS scheme is aware of potential effects of various candidate UVLS actions. Owing to this desirable nature, it can adaptively respond to diverse SVS conditions and optimize UVLS decisions in a non-iterative way. Further, two well-designed strategies, i.e., stepwise constraint relaxation and incremental DFLM adaptation, are introduced to enhance the scheme's reliability and adaptability during online application. Numerical test results on the Nordic test system and the realistic North GZ Power Grid in China showcase the excellent performances of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a special control solution called virtual voltage source (VVS) is developed for no-load tests of induction machines. This concept is applied to a 2 × 27 MVA high-power machine test bench where the test bench is requested to precisely characterize, e.g., 3 MW machines at no-load currents. The developed VVS thoroughly solves the stability issue of induction machines at low-speed ranges. A set of optimizations is presented here that systematically improves the voltage precision of the test bench. Experimental results prove that the proposed concept achieves 0.08–0.12% voltage unbalance, less than 3% current unbalance, with less than 0.3% harmonic-voltage-factor, and verified by more than 100 induction machines. [ABSTRACT FROM AUTHOR]