By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao
The quantity LNCS 9377 constitutes the refereed court cases of the twelfth overseas Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The fifty five revised complete papers awarded have been conscientiously reviewed and chosen from ninety seven submissions. those papers conceal many subject matters of neural network-related examine together with clever regulate, neurodynamic research, memristive neurodynamics, computing device imaginative and prescient, sign processing, computing device studying, and optimization.
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Extra info for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings
Liu and D. Qian Conclusions A terminal reaching law based sliding mode control method for the LFC problem is proposed in this article. The scheme is implemented in an interconnected power system with GRC and wind turbines. Moreover, RBF NNs are adopted to compensate and approximate the system uncertainties. The simulation results have validated the desirable frequency regulation performance against the system uncertainties, the GRC nonlinearity and wind power fluctuation. Compared with the SMC only, the superiority of the improved NNs-based sliding mode controllers has been illustrated.
Vqr(t) and iqr(t) are the q-axis components of the rotor voltage and the rotor current. w(t) is the rotational speed, Tm(t) is the mechanical power, Ht is the equivalent inertia constant, Pe(t) is the active power. X2=1⁄Rr, X3=Lm ⁄Lss, T1=L0 ⁄(wsRs), L0=Lrr+Lm2 ⁄Lss, and Lss=Ls+Lm, Lrr=Lr+Lm, here Lm is the magnetizing inductance, Rr and Rs are the rotor and stator resistances, Lr and Ls are the rotor and stator leakage inductances, Lrr and Lss are the rotor and stator self-inductances, ws is the synchronous speed.
In order to verify the effectiveness of the proposed control method, ∆PL1 =∆PL2 =1% pu and ∆Tm1 =∆Tm2 =1% pu are applied to the system at t = 5s simultaneously. It can be seen from Fig. 3 that the ∆f and ACE are damped to zero with small oscillations. These results embody the performances against load disturbance and wind power fluctuation of the designed terminal reaching law based SMC scheme. From the comparisons in Fig. 3, the performances of the frequency regulation with RBF NNs (blue curves) is superior to those without RBF NNs (red curves) in term of settling time.
Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings by Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao