Guolian Hou

Dynamic modeling and multivariable control of organic Rankine cycles in waste heat utilizing processes

In this paper, the dynamics of organic Rankine cycles (ORCs) in waste heat utilizing processes is investigated, and the physical model of a 100 kW waste heat utilizing process is established. In order to achieve both transient performance and steady-state energy saving, a multivariable control strategy for the waste heat recovery system is proposed by incorporating a linear quadratic regulator (LQR) with a PI controller. Simulations demonstrate that the proposed strategy can obtain satisfactory performance.

Cascade control of superheated steam temperature with neuro-PID controller.

In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neuro-PID controller besides the sequences of tracking error in outer loop control system, hence, feedback control is combined with feedforward control in the proposed neuro-PID controller. The convergent condition of the neural networks is analyzed. The implementation procedures of the proposed cascade control approach are summarized. Compared with the neuro-PID controller using minimizing squared error criterion, the proposed neuro-PID controller using minimizing error entropy criterion may decrease fluctuations of the superheated steam temperature. A simulation example shows the advantages of the proposed method.

Constrained predictive control based on state space model of Organic Rankine Cycle system for waste heat recovery

Due to the advantages of recovering energy from low-grade heat sources, Organic Rankine Cycle (ORC) system is adopted as the primary technology for waste heat recovery. As the ORC system is a multivariable system with high complexity and nonlinear coupling, the conventional control strategy may not achieve satisfactory results. In this paper, the application of constrained model predictive control (MPC) for an ORC system is demonstrated. The simulation results are given to illustrate the efficiency and feasibility of the proposed control strategy.

Application of GA for three-area power system after deregulation with valve position limit

In recent years, electric power industry has been moving towards a deregulated framework all over the world. Many problems have appeared in the new situation. The valve position limit exhibits nonlinear character in the deregulated environment. In this work, distribute companies participation matrix (DPM) is brought in to analyze the effect of the DPM to the dynamic performance of the system. T-S model is proposed to construct valve position nonlinear constraint. Real-coded GA is used and the initial population is obtained by uniform sampling. It takes less time than the common algorithm. The simulation results of the three-area power system are then introduced to demonstrate the validity of the results.

Statistical Information Based Single Neuron Adaptive Control for Non-Gaussian Stochastic Systems

Based on information theory, the single neuron adaptive control problem for stochastic systems with non-Gaussian noises is investigated in this paper. Here, the statistic information of the output within a receding window rather than the output value is used for the tracking problem. Firstly, the single neuron controller structure, which has the ability of self-learning and self-adaptation, is established. Then, an improved performance criterion is given to train the weights of the single neuron. Furthermore, the mean-square convergent condition of the proposed control algorithm is formulated. Finally, comparative simulation results are presented to show that the proposed algorithm is superior to the PID controller. The contributions of this work are twofold: (1) the optimal control algorithm is formulated in the data-driven framework, which needn’t the precise system model that is usually difficult to obtain; (2) the control problem of non-Gaussian systems can be effectively dealt with by the simple single neuron controller under improved minimum entropy criterion.

Multivariable robust control for Organic Rankine Cycle based waste heat recovery systems

Organic Rankine Cycle (ORC) is particularly suitable for the recovery of energy from low-quality heat sources. In this paper, based on robust theory, a multivariable robust controller was designed for an ORC system. This simple-structured and easy-to-realized controller does not require a precise math model. The integral action included in it compensates unknown factors of the plant. The simulation result shows that the proposed control strategy can obtain satisfactory performance in set point tracking ability. Also, the excellent robust performance withstanding the disturbances and dynamical uncertainties are obtained by using the multivariable robust control.

Controller design for a heat exchanger in waste heat utilizing systems

This paper presents a method of designing self-tuning PID controller based on genetic algorithm for an evaporator in an Organic Rankine Cycle system for waste heat recovery. Compared with Ziegler-Nichols PID controller, the self-tuning PID controller can achieve better control performance.

Improved minimum entropy control for two-input and two-output networked control systems

In this paper, the problem of control algorithm design for a class of nonlinear two-input and two-output (TITO) networked control systems (NCSs) with non-Gaussian random time delays is investigated, where a general non-linear auto-regressive moving average with exogenous model is used to describe the plant. Due to the non-Gaussian random time delays involved in the systems, it is insufficient to obtain a satisfactory optimal control algorithm by only controlling the expected value of the tracking errors. The Renyi entropies of the tracking errors and control inputs are adopted to characterize the randomness of the closed-loop system. The formulations of the probability density functions (PDFs) of the tracking errors and control inputs are deduced. By minimizing the new performance index, a recursive optimal control algorithm is obtained. Furthermore, the local stability condition of the closed-loop systems is established. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

Multiple model predictive control for organic rankine cycle (ORC) based waste heat energy conversion systems

In this paper, a multiple model predictive control (MMPC) strategy is applied into an organic Rankine cycle (ORC) based waste heat energy conversion system (WHECS). The rotating speed of the pump and the shaft torque of the expander are manipulated simultaneously to provide the optimal (suboptimal) evaporating pressure and superheating temperature for nonlinear WHECSs under different operating conditions. Simulation results confirm the efficacy of the proposed control scheme.

Virtual negative impedance droop method for parallel inverters in microgrids

Most microgrid converters are controlled by droop method, which could ensure microgrid converter to realize load distribution under low-speed communication. Because the microgrid line impedance is resistive, which makes the output active power and reactive power coupling each other and reduces the stability of microgrid. Introducing virtual impedance can adapt droop control for resistive lines. However, virtual impedance may lead to great voltage drop and harmonic amplification. The traditional droop control strategy were analyzed, and a droop control strategy based on voltage and current double-loop for multi-inverters parallel operation in microgrid was proposed by introduction of virtual negative resistance . By offsetting part of line resistance by the virtual negative resistance, equivalent performance can be achieved with smaller virtual impedance, which improves voltage quality. Matlab simulation confirms the effectiveness of the proposed method.