Jihong Wang

Mathematical Modeling Study of Scroll Air Motors and Energy Efficiency Analysis—Part II

A scroll air motor, also known as a scroll expander, is a relatively new concept to pneumatic actuators. In recent years, scroll air motors have been adopted by combined heat and power boilers, uninterrupted power supply, and some other energy storage systems as a new mechatronic device for energy conversion. The paper aims to develop a complete mathematical model of the scroll air motor for dynamic characteristic and energy efficiency analysis. In Part I of the paper, the geometry description of the scroll air motor is presented and the scroll driving torque is derived. In this, Part II, the mathematical model for the scroll dynamic process is described and verified by comparing the simulated and measured results. Then, the initial analysis of scroll energy efficiency is given, which explains the scrolls high ability in energy conversion.

Study of a New Strategy for Pneumatic Actuator System Energy Efficiency Improvement via the Scroll Expander Technology

Although pneumatic actuators have been widely used in industry and other application areas, its weakness in low-energy efficiency is well known. Aiming for energy efficiency improvement, this paper presents a new hybrid pneumatic system that will recover energy from the exhaust compressed air through a scroll expander. The scroll expander drives a generator to convert the exhaust compressed air energy to electrical energy; thus, the proposed system is entitled “pneumatic-electrical” system. A closed-loop coordinate control strategy is engaged and proven to be essential in maintaining proper actuator operation status, while the scroll expander is connected in. The overall system mathematical model is derived and simulation results are presented in this paper. A test rig is set up to verify the feasibility of the proposed system structure. Both simulation and test results indicate that the proposed scheme is realistic and work well.

A new stability and time-delay tolerance analysis approach for Networked Control Systems

The key feature of Networked Control Systems (NCSs) is that the information is exchanged through a network among control system components. So the network induced time delay is inevitable in NCSs. The time delay, either constant (up to jitter) or random, may degrade the performance of control systems and even destabilize the systems if the systems are designed without considering the effects of the time delays properly. Once the structure of a NCS is confirmed, it is essential to identify what the maximum time delay is allowed for maintaining the system stability which, in turn, is also associated with the process of controller design. Some studies have been reported in estimation of the maximum time-delay allowed for maintaining system stability. However, most of the reported methods are normally over complicated for practical applications. A new finite difference approach is proposed in this paper for estimating the maximum time-delay tolerance, which has a simple structure and is easy to apply.

Mathematic modeling and condition monitoring of power station tube-ball mill systems

The paper presents a newly developed nonlinear Tube-Ball mill model for model based on-line condition monitoring. This mathematical model is derived through analyzing energy transferring, heat exchange and mass flow balances. Evolutionary techniques are adopted to identify the unknown system parameters using the on-site measurement data. The identified system parameters are then validated using multiple on-line measurement data. Validation has been conducted by comparing the measured and simulated values. The results indicate that the model can represent the coal mill dynamics and can be used to predict the mill dynamic performance. Then the model is implemented on-line and it can run on-line along with the real milling process. It is then adopted for on-line condition and safety monitoring, fault detection, and control to improve the efficiency of combustion.

Generalized Predictive Control for superheated steam temperature regulation in a supercritical coal-fired power plant

The design and implementation of a Generalized Predictive Control (GPC) strategy for the superheated steam temperature regulation in a supercritical (SC) coal-fired power plant is presented. A Controlled Auto-Regressive MovingAverage (CARMA) model of the plant is derived from using the experimental data to approximately predict the plants future behavior. This model is required by the GPC algorithm to calculate the future control inputs. A new GPC controller is designed and its performance is tested through extensive simulation studies. Compared with the performance of the plant using a conventional PID controller, the steam temperature controlled by the GPC controller is found to be more stable. The stable steam temperature leads to more efficient plant operation and energy saving, as demonstrated by the simulation results. Plant performance improvement is also tested while the plant experiences the load demand changes and disturbances resulting from the malfunctioning of coal mills.

Comparison of two magnetic saturation models of induction machines

The paper considers two models of induction machines accounting for magnetic saturation. The first is a systematically-designed model based on fundamental principles, while the second is a simplified model that neglects certain terms in the first model. The paper shows that both models predict the same responses in the linear region, as well as in the nonlinear region but only for steady-state operation. To investigate the possible superiority of one model over the other, the paper considers the measurement of transient responses where the models predict different behaviors. Experimental conditions are planned by computing the eigenvalues of the linearized systems and selecting conditions with maximal differences in settling time. Surprisingly, however, the experimental data shows relatively little differences between the predictions of the models and fails to favor one model over the other.

Modeling and Experimental Study of a Wind Turbine System in Hybrid Connection With Compressed Air Energy Storage

This paper presents a new hybrid wind turbine system that is formed by a continuously variable transmission connection of the turbine drive shaft with an air expander/compressor. A mechanical power split device is designed to synthesize the power delivered by the wind turbine and the air expander/compressor. A small-scale hybrid wind turbine system is mathematically modeled, analyzed, and validated using a laboratory-scale experimental test rig. By utilizing compressed air energy storage, it is shown that the hybrid wind turbine system can provide smooth power output under fluctuating wind speed conditions. Such a direct connection structure reduces the overall system cost by using one generator instead of two compared with the conventional CAES system structure. The study demonstrates the benefit of improved efficiency and flexibility brought to the turbine operation by the hybridization of wind energy and stored energy.

Robust stabilization of Networked Control Systems using the Markovian jump system approach

The key feature of Networked Control Systems (NCSs) is that the information is exchanged through a network among control system components. Transmitting control signals through shared networks induces time delays and data losses which may destabilize the system. This time delay may be constant periodic or random. The random time delay can be modeled using Markov Chains and the NCS can be modeled as Markovian jump system. The stochastic stability of the system has the form of Bilinear Matrix Inequality (BMI). The V-K iteration algorithm is used to solve the BMI and hence to design the stabilizing controller. A modified V-K iteration algorithm is presented in this paper where the decay rate is maximized in both the V- and K-loops. The V-K algorithm method is applied to the cart and inverted pendulum problem which shows that the decay rate is improved with the modified algorithm.

Predictive control of coal mills for improving supercritical power generation process dynamic responses

The paper is to study new control strategies for improvement of dynamic responses of a supercritical power generation process through an improved control to the associated fuel preparation performed by the coal milling process. Any control actions taking for the milling process will take a long time to show their influences onto the boiler, turbine and generator responses as the whole process experiences coal transmission, grinding, drying and blowing to the furnace. The control philosophy behind the work presented in the paper is to develop a control strategy to achieve prediction of the future demand for fuel input and implement control actions at the earliest possible time. The paper starts from description of the nonlinear mathematical model developed for the supercritical coal fired power plant and then moves onto control strategy development. Finally, the simulation study has been carried out to demonstrate the effect of the new predictive control.

The dynamic neural network model of a ultra super-critical steam boiler unit

Thermal power unit is an energy conversion system consisting of the boiler, the turbine and their auxiliary machines respectively. It is a complicated multivariable system with strong nonlinearity, uncertainty and multivariable coupling. These characters will be more evident with the unit tending to large-capacity and high-parameter. It is expensive to build the model of the unit using conventional method. The paper presents modeling of a 1000MW ultra supercritical once-through boiler unit. Based on these field data, two different neural networks are used to model the thermal power unit. The simulation results validate the efficiency of the neural networks in modelling the ultra supercritical unit.