Abstract:The harmonic impact caused by wind turbines should be carefully investigated before wind turbines are interconnected. However, the harmonic currents of wind turbines are not easily predicted due to the variations of wind speed. If the harmonic current outputs can be predicted accurately, the harmonic impact of wind turbines and wind farms for power grids can be analyzed efficiently. Therefore, this paper analyzes the harmonic current characteristics of wind turbines and investigates the feasibility of developing harmonic current predictors. Field measurement, data sorting, and analysis are conducted for wind turbines. Two harmonic current predictors are proposed based on the measured harmonic data. One is the Auto-Regressive and Moving Average (ARMA)-based harmonic current predictor, which can be used for real-time prediction. The other is the stochastic harmonic current predictor considering the probability density distributions of harmonic currents. It uses the measured harmonic data to establish the probability density distributions of harmonic currents at different wind speeds, and then uses them to implement a long-term harmonic current prediction. Test results use the measured data to validate the forecast ability of these two harmonic current predictors. The ARMA-based predictor obtains poor performance on some harmonic orders due to the stochastic characteristics of harmonic current caused by the variations of wind speed. Relatively, the prediction results of stochastic harmonic current predictor show that the harmonic currents of a wind turbine in long-term operation can be effectively analyzed by the established probability density distributions. Therefore, the proposed stochastic harmonic current predictor is helpful in OPEN ACCESSEnergies 2013, 6 1315 predicting and analyzing the possible harmonic problems during the operation of wind turbines and wind farms.

This research aims to develop an artificial intelligence (AI) estimator for the coating touch panel (TP) film. The AI estimator could estimate the transmittance of touch panel (TP) with different layers coating. It also could be used for helping the technician precisely set the relevant control parameters of evaporator in advance so that the optical quality of TP film could fit the customers request. In order to catch the unknown relationship between the films transmittance and its all possible influencing factors, the neural network (NN) is taken as the AI tool. In other words, a fast and precise intelligent manufacturing mechanism for TP evaporation process is expected to be developed and this intelligent mechanism could be practically used in the real industrial applications.

This research aims to estimate the optical property of touch panel (TP) with different layers coating. The neural network (NN) model is used to catch the complex relationship among the chromatic aberration, i.e. L. a. b. values, and their relevant influencing factors. An artificial intelligent (AI) estimator is expected to be developed so that the optical property of TP decoration film with different layers coating could be precisely estimated before the evaporation process is taken. Such an AI estimator can help the technician to set the control parameters of evaporator in advance and make the films optical property could fit the customers request. From the simulation results shown, the estimations of chromatic aberration of TP film are very accurate. In other words, such an AI estimator is possibly to be developed and it is quite promising and potential in the real industrial applications.

This paper presents an automatic quality inspection system for the riveting process by using neural network (NN) techniques. Two types of neural models were used in studies. One is the conventional neural network and the other one is the quantum neural network which is expected to deal with the signals with fuzziness and uncertainty. The well-trained neural network could make an immediate diagnosis of the riveting quality based on the impact signals sensed. Thus, such NN inspection system can not only monitor the real time riveting process, but also give the assistance on the riveting quality verification. In order to demonstrate the superiority of neural network inspection system developed, the experimental data provided Chinese Air Force Institute of Technology was studied and simulated. The method of riveting quality index (RQI) was also performed as a comparison. From the simulation results shown, both of the proposed neural network inspection systems have the better verification accuracy than RQI method.