| Patent application number | Description | Published |
|---|
| 20110125685 | Method for identifying Hammerstein models - The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part. | 05-26-2011 |
| 20110125686 | Method for identifying Hammerstein models - The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used. | 05-26-2011 |
| 20110125687 | Method for hammerstein modeling of steam generator plant - The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID). | 05-26-2011 |
| 20110242313 | Contamination monitoring of high voltage insulators - Contamination monitoring of high voltage insulators provides a system and method producing an early predictor for high voltage insulator failure, allowing repairmen to either already be on site when a high voltage insulator fails in order to expedite repair time, or allowing repair and/or replacement of a faulty insulator before the failure actually occurs. The system and method provide transmission of an alarm signal when contaminant levels (such as equivalent salt deposit density (ESDD) levels) formed on a high voltage insulator exceed pre-selected threshold values, indicating the likelihood of high voltage insulator failure. | 10-06-2011 |
| 20110257799 | Sliding mode AGC controller and method - The sliding mode AGC controller and method includes Genetic Algorithms (GA) to find the optimal feedback gains and switching vector values of the controller. In order to provide enhancement of the system dynamical performance and a reduction in the SMC chattering, two objective functions are provided in the optimization process. The tested two-area interconnected AGC model incorporates nonlinearities in terms of Generation Rate Constraint (GRC) and a limiter on the integral control value. Comparison with previous AGC methods reported in the literature validates the significance of the sliding mode AGC controller. | 10-20-2011 |
