An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

Abstract

Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system. In view of the difficulty in modeling the "S" characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning machine model (AE-PLS-ELM) was proposed to describe the pump turbine characteristics. First, a mathematical model was formulated to describe the flow and moment characteristic curves. The improved Suter transformation was employed to transfer the original curves into WH and WM curves. Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network. The ELM-Autoencoder technique was employed to obtain the initial weights of the Autoencoder based extreme learning machine (AE-ELM) model. The PLS method was exploited to avoid the multicollinearity problem of the Moore-Penrose generalized inverse. Lastly, the effectiveness of the proposed AE-PLS-ELM model has been verified using real data from a pumped storage unit in China. The results demonstrated that the AE-PLS-ELM model can obtain better modeling accuracy and generalization performance than the traditional models and, thus, can be exploited as an effective and sufficient approach for the modeling of pump turbine characteristics.

Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor

“…Calculate the objective function values for each agents according to (25), and update the position of bellwether X B (t) Calculate the bellwether vector X i bw (t) by (19) Calculate the self-awareness vector X i self (t) by (20) Enter ASA and set gen = 1, initialize locations of sheep flock X i (t) according to input constraints (26)- (27) and stair-like control strategy 28 Complexity relevant part from the optimal solution to lead the state into the terminal region at the next sample time, as shown in…”

“…If applied in PSPs, where control laws are often determined within 40 milliseconds, and they may suffer from slow error convergence and can only be trained offline. Hence, the prediction models in these previous studies are not suitable for practical use in PSPs [25]. To overcome the shortcomings of BP, extreme learning machine (ELM), which is a fast machine learning algorithm based on single hidden layer feedforward networks (SLFNs), was proposed by Huang in 2006 [26].…”

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

“…To date, various powerful prediction models have been applied in complex nonlinearity and optimization problems in pump turbine characteristics identified [7], flood interval prediction [8], stock price prediction [9], and wind speed forecasting [10]. In the field of dam deformation prediction based on prototypical observations, many prediction models have also been established, such as multiple linear regression [11], neural network [5], support vector machines [12], extreme learning machine [13], boosted regression trees [14], and Gaussian process regression [15].…”

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

“…The well-known least-squares (LS) has been widely used as a standard method of model parameters estimation in geodetic applications and many others branches of modern science [29][30][31][32][33][34][35][36][37][38][39][40][41][42]. This is due to the flexibility of the LS, since no concepts from probability theory are used in formulating the least-squares minimisation problem.…”