SIMULATED MATHEMATICAL MODEL FOR HIGH PRESSURE CYLINDER WALL TEMPERATURE OF HOMEMADE TURBINE

In order to balance the contradiction between the demand of high precision and that of short time interval of model computing for the power plant simulator, a set of simulated mathematical models are constructed. The model describes the cylinder wall temperature located at four key positions of the high pressure cylinder. The simulated model is confirmed to be not only simple but also precise via comparison between the simulated results and the autoptic data of a power plant.

Experimental and Numerical Investigation of the Solar Chimney Power Plant’s Turbine

The solar chimney power plant is a relatively new electricity generation concept, based on renewable energy, combining the greenhouse effect with the chimney suction. The solar chimney powerplant consists of three parts, the solar collector, the chimney and the turbine generator unit, of which the study was focused on the later part. To evaluate the turbine performance inside the solar chimney powerplant, experimental system was constructed in Aswan, Egypt that has a metrological site (23°58'N and 32°47'E) occurs. The system was constructed to evaluate the performance of the solar chimney turbine and power generation characteristic in the hottest site where Aswan is located at the nearest of the Tropic of Cancer at the summer season. Velocity, electric power generation and the turbine efficiency are studying in this work. The numerical analyses were performed by using a commercial code CFX, ANSYS 16.1 to simulate the flow through the turbine and overall system. The study shows that the range of power generated (1.2 W - 4.4 W). It can be estimated, according to the results, the variation trend in pressure drops with the turbine rotation speed increase with small differences when the turbine rotation speed surpasses 1800 rpm with average efficiency of 57%. It is concluded that the theoretical model is basically valid for the system under study, and the CFD simulation can be used conveniently to predict the performance of the system, the comparison between them and experimental result shows a good agreement.

Model of DFIG Wind Farm and Study on Its LVRT Capability

As a typical clean and renewable energy, wind power is becoming more and more widely used in electrical industry. However, its characteristics of random and intermittent have brought serious problems to the power system, such as voltage fluctuation and insufficient reactive power. Based on the K-means clustering algorithm, this paper classifies the doubly-fed induction generators （DFIG） according to the operation of propeller pitch angle control. At the same time, to obtain the optimal parameter, advanced particle swarm optimization （PSO） is used. Then the dynamic model of DFIG under the network fault condition is built. What is more, the role that crowbar circuit plays in low voltage ride through （LVRT） is discussed. Finally, simulations in DigSILENT verify the model.

Model-based and Fuzzy Logic Approaches to Condition Monitoring of Operational Wind Turbines

It is common for wind turbines to be installed in remote locations on land or offshore, leading to difficulties in routine inspection and maintenance. Further, wind turbines in these locations are often subject to harsh operating conditions. These challenges mean there is a requirement for a high degree of maintenance. The data generated by monitoring systems can be used to obtain models of wind turbines operating under different conditions, and hence predict output signals based on known inputs. A model-based condition monitoring system can be implemented by comparing output data obtained from operational turbines with those predicted by the models, so as to detect changes that could be due to the presence of faults. This paper discusses several techniques for model-based condition monitoring systems: linear models, artificial neural networks, and state dependent parameter "pseudo" transfer functions.The models are identified using supervisory control and data acquisition(SCADA) data acquired from an operational wind firm. It is found that the multiple-input single-output state dependent parameter method outperforms both multivariate linear and artificial neural network-based approaches. Subsequently, state dependent parameter models are used to develop adaptive thresholds for critical output signals. In order to provide an early warning of a developing fault, it is necessary to interpret the amount by which the threshold is exceeded, together with the period of time over which this occurs. In this regard, a fuzzy logic-based inference system is proposed and demonstrated to be practically feasible.

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

Gain-scheduling control of a floating offshore wind turbine above rated wind speed

This paper presents an application of gain-scheduling(GS) control techniques to a floating offshore wind turbine on a barge platform for above rated wind speed cases. Special emphasis is placed on the dynamics variation of the wind turbine system caused by plant nonlinearity with respect to wind speed. The turbine system with the dynamics variation is represented by a linear parameter-varying(LPV) model, which is derived by interpolating linearized models at various operating wind speeds. To achieve control objectives of regulating power capture and minimizing platform motions, both linear quadratic regulator(LQR) GS and LPV GS controller design techniques are explored. The designed controllers are evaluated in simulations with the NREL 5 MW wind turbine model, and compared with the baseline proportional-integral(PI) GS controller and non-GS controllers. The simulation results demonstrate the performance superiority of LQR GS and LPV GS controllers, as well as the performance trade-off between power regulation and platform movement reduction.

An intelligent control method for a large multi-parameter environmental simulation cabin

The structure and characteristics of a large multi-parameter environmental simulation cabin are introduced.Due to the diffculties of control methods and the easily damaged characteristics,control systems for the large multi-parameter environmental simulation cabin are diffcult to be controlled quickly and accurately with a classical PID algorithm.Considering the dynamic state characteristics of the environmental simulation test chamber,a lumped parameter model of the control system is established to accurately control the multiple parameters of the environmental chamber and a fuzzy control algorithm combined with expert-PID decision is introduced into the temperature,pressure,and rotation speed control systems.Both simulations and experimental results have shown that compared with classical PID control,this fuzzy-expert control method can decrease overshoot as well as enhance the capacity of anti-dynamic disturbance with robustness.It can also resolve the contradiction between rapidity and small overshoot,and is suitable for application in a large multi-parameter environmental simulation cabin control system.