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The conversion system is a major contributor to failure rates. These faults lead to time and cost consuming. Fault diagnosis capabilities pay as a solver to achieve a steady system. This paper presents a full analysis of permanent magnet synchronous generator wind system (PMSGWS) and proposes a special RMS voltage-based fault diagnosis method. The full analysis presents a comprehensive knowledge of faulty behaviors especially under arm current flowing or cutting off. Due to enough knowledge of faulty behaviors, the implementation of the detection method without threshold setting is contributed by the special RMS voltage. Its sample period is set longer than the time of the maximum pulse width ratio (MPR) and shorter than the fault show time of lower tube voltage. Due to this, the detection speed and robustness are achieved. By these simple settings for the fault diagnosis method, the faulty switch is detected in less than 1/4 of the period. Simulation and experimental results confirm the validity and feasibility of the new proposed fault detection method.

Once the short circuit damage accident of a large capacity 500 kV transformer occurs suddenly, it will seriously affect the safe operation of the power grid. The article expounds on the calculation principle and evaluation method of the transformer's ability to withstand short circuit, then introduces the design structure of 400 MVA/500 kV short circuit autotransformer and calculates the electromagnetic force and evaluates the ability to withstand short circuit under high voltage ‐ medium voltage (HV‐MV) and medium voltage ‐ low voltage (MV‐LV) conditions. By comparing with typical 110 kV and 220 kV transformers, the short‐circuit characteristics are analysed, the control strategy is proposed, and the largest capacity short circuit prototype with 400 MVA/500 kV autotransformer is developed. Last, the sudden short circuit test is carried out in the large current test centre. The results show that the large capacity autotransformer passed the big current impulse under HV‐MV and MV‐LV short circuit conditions for the first time in the world, and all indicators are good, which confirms the accuracy of the strength check and the effectiveness of the short circuit evaluation method and also proves that the 500 kV autotransformer has strong strength to withstand short circuits. [ABSTRACT FROM AUTHOR]

In this paper, an identification scheme for sandwich systems with a dead zone is proposed. In this scheme, a so-called combinational input is proposed to transform the sandwich model with a dead zone into a model with separated parameters. Then, the recursive general identification algorithm can be applied to the parameter-estimation, since the transformed model has a form that is linearized in coefficients. Finally, the proposed method is applied to the identification of an X–Y moving positioning stage. [ABSTRACT FROM PUBLISHER]

A novel method for fault diagnosis of analog circuits with tolerance based on wavelet packet (WP) decomposition and probabilistic neural networks using genetic algorithm (GPNN) is proposed in this paper. The fault feature vectors are extracted after feasible domains on the basis of WP decomposition of responses of a circuit being solved. Then by fusing various uncertain factors into probabilistic operations, GPNN methods to diagnose faults are proposed whose parameters and structure obtained form genetic optimisations resulting in best detection of faults. Finally, simulations indicated that GPNN classifiers are correct 7% more than BPNN of the test data associated with our sample circuits. [ABSTRACT FROM AUTHOR]