A novel adaptive power system stabilizer design using the self-recurrent wavelet neural networks via adaptive learning rates

作者：Ganjefar Soheil^*; Alizadeh Mojtaba

来源：International Transactions on Electrical Energy Systems, 2013, 23(5): 601-619.

DOI：10.1002/etep.1616

• power system stabilizer
• self-recurrent wavelet neural network
• adaptive learning rates
• intelligent control

In this article, the self-recurrent wavelet neural network (SRWNN) is used as a controller in both direct and indirect adaptive control structures to damp the low-frequency power system oscillations when only the inputs and outputs of synchronous generator are accessible for measurement. The gradient descent method using adaptive learning rates (ALRs) is applied to train all weights of SRWNN. The ALRs are derived from the discrete Lyapunov stability theorem, which was applied to guarantee the convergence of the proposed control schemes. Finally, the proposed control schemes are evaluated on a single machine infinite bus power system under different operating conditions and disturbances to demonstrate their effectiveness and robustness.

• 出版日期2013-7