Maximum Power Point Tracking Control for Non-Gaussian Wind Energy Conversion System by Using Survival Information Potential

Abstract

In this paper, a wind energy conversion system is studied to improve the conversion efficiency and maximize power output. Firstly, a nonlinear state space model is established with respect to shaft current, turbine rotational speed and power output in the wind energy conversion system. As the wind velocity can be descried as a non-Gaussian variable on the system model, the survival information potential is adopted to measure the uncertainty of the stochastic tracking error between the actual wind turbine rotation speed and the reference one. Secondly, to minimize the stochastic tracking error, the control input is obtained by recursively optimizing the performance index function which is constructed with consideration of both survival information potential and control input constraints. To avoid those complex probability formulation, a data driven method is adopted in the process of calculating the survival information potential. Finally, a simulation example is given to illustrate the efficiency of the proposed maximum power point tracking control method. The results demonstrate that by following this method, the actual wind turbine rotation speed can track the reference speed with less time, less overshoot and higher precision, and thus the power output can still be guaranteed under the influence of non-Gaussian wind noises.

Integrating Physical and Data-Driven System Frequency Response Modelling for Wind-PV-Thermal Power Systems

“…The frequency time series of power grids in North America, Japan, and Europe were investigated in [33], in which the power grid frequency fluctuations are non-Gaussian. With the advancement of information theoretic learning techniques, some statistical indices have been used to investigate system identification, filtering, and control strategies of non-Gaussian systems, such as entropy, correntropy, high order moments, SIP, and so on [34][35][36][37][38]. The SIP of a random variable is defined in terms of function instead of the probability density function (PDF) [36].…”

“…The SIP of a random variable is defined in terms of function instead of the probability density function (PDF) [36]. The SIP criterion outperforms the widely used minimum error entropy criterion when dealing with non-Gaussian disturbances [37][38]. For example, it is easy to estimate and has no translation invariance.…”

Recent Advances in Non-Gaussian Stochastic Systems Control Theory and Its Applications

“…Based on the system model, minimum entropy control can be achieved using disturbance observer [18]. In addition, the entropy index can be particularly replaced by various types of entropies, such as (h, phi)-entropy [19], correntropy [20]. Motivated by the information theory, the performance criterion can also be described by information potential, which can be estimated by data and equivalent to entropy regarding the randomness attenuation, such as the survival information potential [21].…”