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Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives
Moulay Rachid DOUIRI, Mohamed CHERKAOUI
Frontiers in Energy. 2013, 7 (4): 456-467.
https://doi.org/10.1007/s11708-013-0264-8
In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.
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Optimal generation scheduling in power system using frequency prediction through ANN under ABT environment
Simarjit KAUR, Yajvender Pal VERMA, Sunil AGRAWAL
Frontiers in Energy. 2013, 7 (4): 468-478.
https://doi.org/10.1007/s11708-013-0282-6
In a competitive and deregulated power scenario, the utilities try to maintain their real electric power generation in balance with the load demand, which creates a need for the precise real time generation scheduling (GS). In this paper, the GS problem is solved to perform the unit commitment (UC) based on frequency prediction by using artificial neural network (ANN) with the objective to minimize the overall system cost of the state utility. The introduction of availability-based tariff (ABT) signifies the importance of frequency in GS. Under-prediction or over-prediction will result in an unnecessary commitment of generating units or buying power from central generating units at a higher cost. Therefore, an accurate frequency prediction is the first step toward optimal GS. The dependency of frequency on various parameters such as actual generation, load demand, wind power and power deficit has been considered in this paper. The proposed technique provides a reliable solution for the input parameter different from the one presented in the training data. The performance of the frequency predictor model has been evaluated based on the absolute percentage error (APE) and the mean absolute percentage error (MAPE). The proposed predicted frequency sensitive GS model is applied to the system of Indian state of Tamilnadu, which reduces the overall system cost of the state utility by keeping off the dearer units selected based on the predicted frequency.
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Characteristics and application of road absorbing solar energy
Zhihua ZHOU, Shan HU, Xiaoyan ZHANG, Jian ZUO
Frontiers in Energy. 2013, 7 (4): 525-534.
https://doi.org/10.1007/s11708-013-0278-2
If the heat of road surface can be stored in summer, the road surface temperature will be decreased to prevent permanent deformation of pavement. Besides, if the heat stored is released, it can supply heat for buildings or raise the road surface temperature for snow melting in winter. A road-solar energy system was built in this study, and the heat transfer mechanism and effect of the system were analyzed according to the monitored solar radiant heat, the solar energy absorbed by road and the heat stored by soil. The results showed that the road surface temperature was mainly affected by solar radiation, but the effect is hysteretic in nature. The temperature of the solar road surface was 3°C–6°C lower than that of the ordinary road surface. The temperature of the solar road along the vertical direction was 2°C–5°C lower than that of the ordinary road. The temperature difference increased as the distance to the heat transfer tubes decreased. The average solar collector efficiency of the system was 14.4%, and the average solar absorptivity of road surface was 36%.
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