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Data collected from the supervisory control and data acquisition (SCADA) system, used widely in wind farms to obtain operational and condition information about wind turbines (WTs), is of important significance for anomaly detection in wind turbines. The paper presents a novel model for wind turbine anomaly detection mainly based on SCADA data and a back-propagation neural network (BPNN) for automatic selection of the condition parameters. The SCADA data sets are determined through analysis of the cumulative probability distribution of wind speed and the relationship between output power and wind speed. The automatic BPNN-based parameter selection is for reduction of redundant parameters for anomaly detection in wind turbines. Through investigation of cases of WT faults, the validity of the automatic parameter selection-based model for WT anomaly detection is verified. [ABSTRACT FROM AUTHOR]

Pulsewidth-modulated (PWM) inverters are used more and more to operate electrical machines and to interface renewable energy systems With the utility grid. However, there are abundant high-frequency harmonics in the output voltage of a PWM inverter, which increase the iron losses and result in derating of the machine or transformer connected to them. Predicting the iron losses caused by the PWM supply is critical for the design of electrical machines and transformers operated by PWM inverters. These losses are primarily attributed to eddy-current loss caused by the PWM Supply. In this paper, after analyzing the harmonic components of PWM voltage, we derive the effects of different parameters of PWM switching on the eddy-current loss. We compare the iron losses modeled with the proposed analytical methods on a three-phase transformer, a dc motor, and an induction motor with the results of time-stepping finite-element analysis and experiments. We provide detailed equations for the prediction of iron losses. These equations can be directly applied in the design and control of PWM converters and electric motors to improve energy efficiency in electrical machines and transformers operated from PWM converters. [ABSTRACT FROM AUTHOR]

This paper discusses the need for modeling and simulation of electric and hybrid vehicles. Different modeling methods such as physics-based Resistive Companion Form technique and Bond Graph method are presented with powertrain component and system modeling examples. The modeling and simulation capabilities of existing tools such as Powertrain System Analysis Toolkit (PSAT), ADvanced Vehicle SimulatOR (ADVISOR), PSIM, and Virtual Test Bed are demonstrated through application examples. Since power electronics is indispensable in hybrid vehicles, the issue of numerical oscillations in dynamic simulations involving power electronics is briefly addressed. [ABSTRACT FROM AUTHOR]