Trong-Thang Nguyen

Bio: Trong-Thang Nguyen is an academic researcher from Water Resources University. The author has contributed to research in topics: Control theory & Control system. The author has an hindex of 4, co-authored 12 publications receiving 43 citations.

The fuzzy-PID based-pitch angle controller for small-scale wind turbine

Abstract: This paper aims to design the pitch angle control based on proportional–integral–derivative (PID) controller combined with fuzzy logic for small-scale wind turbine systems. In this control system, the pitch angle is controlled by the PID controller with their parameter is tuned by the fuzzy logic controller. This control system can compensate for the nonlinear characteristic of the pitch angle and wind speed. A comparison between the fuzzy-PID-controller with the conventional PID controller is carried out. The effectiveness of the method is determined by the simulation results of a small wind turbine using a permanent magnet generator (PMSG).

The neural network-based control system of direct current motor driver

Abstract: This article aims to propose an adaptive control system for the direct current motor driver based on the neural network. The control system consists of two neural networks: the first neural network is used to estimate the speed of the direct current motor and the second neural network is used as a controller. The plant in this research includes motor and the driver circuit so it is a complex model. It is difficult to determine the exact parameters of the plant so it is difficult to build the controller. To solve the above difficulties, the author proposes an adaptive control system based on the neural network to control the plant reach the high quality in the case of unknowing the parameters of the plant. The results are that the control quality of the system is very good, the response speed always follows the desired speed and the transition time is small. The simulation results of the neural network control system are shown and compared with that of a PID controller to demonstrate the advantages of the proposed method.

The maximum power point tracking based-control system for small-scale wind turbine using fuzzy logic

Abstract: This paper presents the research on small-scale wind turbine systems based on the Maximum Power Point Tracking (MPPT) algorithm. Then propose a new structure of a small-scale wind turbine system to simplify the structure of the system, making the system highly practical. This paper also presented an MPPT-Fuzzy controller design and proposed a control system using the wind speed sensor for small-scale wind turbines. Systems are simulated using Matlab/Simulink software to evaluate the feasibility of the proposed controller. As a result, the system with the MPPT-Fuzzy controller has much better quality than the traditional control system.

Sliding mode control-based system for the two-link robot arm

Abstract: In this research, the author presents the model of the two-link robot arm and its dynamic equations. Based on these dynamic equations, the author builds the sliding mode controller for each joint of the robot. The tasks of the controllers are controlling the Torque in each Joint of the robot in order that the angle coordinates of each link coincide with the desired values. The proposed algorithm and robot model are built on Matlab-Simulink to investigate the system quality. The results show that the quality of the control system is very high: the response angles of each link quickly reach the desired values, and the static error equal to zero.

The multilevel inverter with clamped-diode

Abstract: In this study, the author analyzes the advantages and disadvantages of multi-level inverter compared to the traditional two-level inverter and then chose the suitable inverter. Specifically, the author analyzes and designs the three-level inverter, including the power circuit design and control circuit design. All designs are verified through the numerical simulation on Matlab. The results show that even though the three-level inverter has a low number of switches (only 12 switches), but the quality is very good: the total harmonic distortion is small; the output voltage always follows the reference voltage.

Power Enhancement With Grid Stabilization of Renewable Energy-Based Generation System Using UPQC-FLC-EVA Technique

Abstract: The proposed work focuses on the power enhancement of grid-connected solar photovoltaic and wind energy (PV-WE) system integrated with an energy storage system (ESS) and electric vehicles (EVs). The research works available in the literature emphasize only on PV, PV-ESS, WE, and WE-ESS. The enhancement techniques such as Unified Power Flow Controller (UPFC), Generalized UPFC (GUPFC), and Static Var Compensator (SVC) and Artificial Intelligence (AI)-based techniques including Fuzzy Logic Controller (FLC)-UPFC, and Unified Power Quality Conditioner (UPQC)-FLC have been perceived in the existing literature for power enhancement. Further, the EVs are emerging as an integral domain of the power grid but because of the uncertainties and limitations involved in renewable energy sources (RESs) and ESS, the EVs preference towards the RES is shifted away. Therefore, it is required to focus on improving the power quality of the PV-WE-ESS-EV system connected with the grid, which is yet to be explored and validated with the available technique for enhancing power quality. Furthermore, in the case of the bidirectional power flow from vehicle-to-grid (V2G) and grid-to-vehicle (G2V), optimal controlling is crucial for which an electric vehicle aggregator (EVA) is designed. The designed EVA is proposed for the PV-WE-ESS-EV system so as to obtain the benefits such as uninterruptible power supply, effective the load demand satisfaction, and efficient utilization of the electrical power. The power flow from source to load and from one source to another source is controlled with the support of FLC. The FLC decides the economic utilization of power during peak load and off-peak load. The reduced power quality at the load side is observed as a result of varying loads in the random fashion and this issue is sorted out by using UPQC in this proposed study. From the results, it can be observed that the maximum power is achieved in the case of PV and WE systems with the help of the FLC-based maximum power point tracking (MPPT) technique. Furthermore, the artificial neural network (ANN)-based technique is utilized for the development of the MPPT algorithm which in turn is employed for the validation of the proposed technique. The outputs of both the techniques are compared to select the best-performing technique. A key observation from the results and analysis indicates that the power output from FLC-based MPPT is better than that of ANN-based MPPT. Thus, the proper and economical utilization of power is achieved with the help of FLC and UPQC. It can be inferred that the EVs can play a vital role in imparting the flexibility in terms of power consumption and grid stabilization during peak load and off-peak load durations provided that the proper control techniques and grid integration are well-established.

Redes neuronales y aprendizaje por refuerzo en el control de turbinas eólicas

Abstract: El control del angulo de las palas de las turbinas eolicas es complejo debido al comportamiento no lineal de los aerogeneradores, y a las perturbaciones externas a las que estan sometidas debido a las condiciones cambiantes del viento y otros fenomenos meteorologicos. Esta dificultad se agrava en el caso de las turbinas flotantes marinas, donde tambien les afectan las corrientes marinas y las olas. Las redes neuronales, y otras tecnicas del control inteligente, han demostrado ser muy utiles para el modelado y control de estos sistemas. En este trabajo se presentan diferentes configuraciones de control inteligente, basadas principalmente en redes neuronales y aprendizaje por refuerzo, aplicadas al control de las turbinas eolicas. Se describe el control directo del angulo de las palas del aerogenerador y algunas configuraciones hibridas de control. Se expone la utilidad de los neuro-estimadores para la mejora de los controladores. Finalmente, se muestra un ejemplo de aplicacion de algunas de estas tecnicas en un modelo de turbina terrestre.

Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control

Abstract: This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.