Superheating Control of ORC Systems via Minimum (h,φ)-Entropy Control

Abstract

The Organic Rankine Cycle (ORC) is one kind of appropriate energy recovery techniques for low grade heat sources. Since the mass flow rate and the inlet temperature of heat sources usually experience non-Gaussian fluctuations, a conventional linear quadratic performance criterion cannot characterize the system uncertainties adequately. This paper proposes a new model free control strategy which applies the (h,φ)-entropy criterion to decrease the randomness of controlled ORC systems. In order to calculate the (h,φ)-entropy, the kernel density estimation (KDE) algorithm is used to estimate the probability density function (PDF) of the tracking error. By minimizing the performance criterion mainly consisting of (h,φ)-entropy, a new control algorithm for ORC systems is obtained. The stability of the proposed control system is analyzed. The simulation results show that the ORC system under the proposed control method has smaller standard deviation (STD) and mean squared error (MSE), and reveals less randomness than those of the traditional PID control algorithm.

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

“…Similarly, a data-driven minimum entropy control algorithm was brought up by Yunlong Liu [17] using Taylor's expansion of the performance criterion. 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].…”