parameter estimation

So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is essential to study motor performance and predict its behavior. Therefore, an efficient, accurate and reliable parameter estimation method is needed. In this article, the problem of estimating the parameters of the transfer function of the inverter-fed BLDC motor set has been solved using particle swarm algorithms (PSO). The results of using this algorithm have been compared with the results of other optimization algorithms. The comparison of these results has shown that the PSO algorithm is an efficient, accurate and reliable method for solving the transfer function parameter estimation problem.

Smart grids are complex, nonlinear and uncertain systems. Smart grids may consist of one or more cost functions, in which their optimal values have to be obtained. Moreover. In these systems the optimal condition has to be find with small computation burden, high accuracy, and relatively fast in real time. In this paper, a novel approach is proposed to find the minimum value of the total cost for the energy consumption in smart grids, in which the total cost is considered as uncertain objective function. The advantages of this technique are its stability, fast convergence, insensitivity with respect to initial condition and real time implementation. Some simulation results are provided to show the effectiveness of the proposed approach.

In this paper, to estimate the parameters of a three-phase induction motor, the current measured in one phase and Harris Hawk’s optimization algorithm (HHO) as well as the recently introduced improved technique of this algorithm (IHHO) are used. This method includes estimating the electrical equivalent circuit parameters of a three-phase induction motor, including rotor and stator resistance, leakage inductance, magnetizing inductance, as well as mechanical parameters including the moment of inertia and friction coefficient. As three-phase induction motors are widely used in various industries, it is necessary to identify their parameters to implement adjustable speed control systems for such motors. Therefore, it is necessary to develop appropriate algorithms to accurately estimate the induction motor parameters. The main inspiration of the proposed method is the behavior of the Harris hawk group in nature. The objective function is defined as the calculation of the least mean squares of the error resulting from the numerical simulation of the dynamic equations of the motor and the measured value of the current of one motor`s phase. The proposed method is applied to the dynamic model of a specific motor and the results of the proposed method are compared with other previously published methods. The obtained results demonstrate the remarkable capability of the proposed method and its rapid convergence.

In this paper, the parameters of the dynamic model of a three-degree-of-freedom simulator based on air bearing, including moment of inertia, center of mass, using experimental data in a maneuver in two ways: 1- hybrid optimization method of genetic algorithm and second-order programming and 2- The method of least squares error is estimated. To do this, a position maneuver is performed using reaction wheels, and torque values as well as angular velocities around three axes are recorded. The table parameters are then estimated using the stored data and the implementation of the two methods. The results show that with the least squares method, unlike the hybrid optimization algorithm, with a attitude control test from the non-zero initial point to the origin, it is not possible to derive a good estimate and different tests are needed to stimulate all system modes. While in the hybrid optimization method, by performing the same experiment, the desired results of the system estimation can be presented. To validate the implemented algorithm, the performance of the closed loop of the table in the laboratory environment and the simulated model were evaluated and compared, which indicates the appropriate accuracy (less than 5% error) of the estimation methods.

Parameter estimation of photovoltaic cell model based on measured characteristics is difficult due to the nonlinear behavior of the diode in the model and its exponential relation. Existence of two diodes in double-exponential model makes this estimation more difficult. A hybrid optimization algorithm is implemented based on explorative characteristic of invasive weed algorithm, probabilistic models of distribution estimation and utilizing dispersion capacities of a mixed Gaussian–Cauchy distribution to estimate parameters of the single and double diode models of photovoltaic cells as an optimization problem. The fitness function is defined as the root mean square error between current-voltage curves of the model output and the measured points. The proposed method is implemented for a practical photovoltaic cell based on measured characteristic and the obtained results are compared with other 8 optimization algorithms. The statistical comparison of the difference between current-voltage curve of the optimized circuit model and the measurements verifies more suitable performance of the implemented algorithm compared with others.

In this paper, we identify "variable time systems with slow changes" using Legendre basic functions. Physical phenomena behave with time-varying behavior for a variety of reasons. To model these systems, we need models with time-dependent parameters. In this paper, in order to identify, we consider a system that has time-varying parameters with slow changes. Select the input of this time-varying system of PRBS type, which is a frequency-rich signal. The results for noise cases are also largely acceptable. In this article, the system in question is called STV system for short.