Adaptive intelligent hydro turbine speed identification with water and random load disturbances

Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm

“…The nonlinear system of the PSU is complex in both structure and parameters [8,9]. To achieve an accurate description of the intrinsic characteristics of the PTGS, research on model identification of the PTGS is mainly based on data-driven models such as fuzzy system [3,[10][11][12], neural networks [13,14], grey system theory [15], and the Gaussian mixture model [16].…”

“…Due to its powerful nonlinear fitting and forecasting ability, the neural networks technique has been widely used in the modeling and identification of various nonlinear systems [13,17]. The main process of the model identification of a hydroelectric generating unit or PSU based on neural networks is optimizing the weights and biases of each neuron in the network with the help of training algorithms such as back-propagation, gradient descent, and intelligent optimization algorithms, and then, the nonlinear characteristics of HTGS or PTGS can be effectively described using the identification model.…”

“…The main process of the model identification of a hydroelectric generating unit or PSU based on neural networks is optimizing the weights and biases of each neuron in the network with the help of training algorithms such as back-propagation, gradient descent, and intelligent optimization algorithms, and then, the nonlinear characteristics of HTGS or PTGS can be effectively described using the identification model. In Kishor et al [13], the neural networks nonlinear autoregressive network with exogenous signal (NNARX) model and the adaptive network-based fuzzy inference system (ANFIS) were applied to simulate the hydropower plant model at random load disturbances. The ANFIS model turned out to perform better than the NNARX model in identifying the turbine/generator speed.…”

Reply to the comment on “Prediction of ground vibrations resulting from the blasting operations in an open pit mine by adaptive neuro-fuzzy inference system” by Tarkan Erdik

“…;Chatterjee et al (2006);Chen and Linkens (2001);Ciaramellaa et al (2005);Farag et al (1998);Gaweda and Zurada (2003);Hassine et al (2003);Huang and Ren (1999);Hunt et al (1996);Juang and Lin (1999);Kim and Kasabov (1999);Kishora et al (2007);Lin and Chen (2005); Lin and Ho(2005); Mitrakis et al (2008); Paiva and Dourado…”

Gait Recognition Based on Invariant Leg Classification Using a Neuro-Fuzzy Algorithm as the Fusion Method

“…Neuro-fuzzy system has been proved to have significant results in modeling nonlinear functions. Neuro-fuzzy system has been used frequently in the literature as fishing predictions [23], vehicular navigation [24], identifying the turbine speed dynamics [25], radio frequency power amplifier linearization [26], microwave application [27], image denoising [28,29], prediction in cleaning with high pressure water [30], sensor calibration [31], fetal electrocardiogram extraction from ECG signal captured from mother [32], and identification of normal and glaucomatous eyes [33].…”