Robustification of the H∞ controller combined with fuzzy logic and PI&PID-Fd for hybrid control of Wind Energy Conversion System Connected to the Power Grid Based on DFIG

This research aims to propose the optimal control method combined with the neuron network for an induction motor. In the proposed system, the induction motor is a nonlinear object which is controlled at each working point. At these working-points, the state equation of the induction motor is linear, so it is possible to apply the linear quadratic regular algorithm for the induction motor. Therefore, the parameters of the state feedback controller are the functions. The output-input relationships of these functions are set through the neural network. The numerical simulation results show that the quality of the control system of the induction motor is very high: The response speed always follows the desired speed with the short transition time and the small overshoot. Furthermore, the system is robust in the case of changing the load torque, and the parameters of the induction motor are incorrectly defined

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The Neural Network-Combined Optimal Control System of Induction Motor

The integration of wind turbines (WTs) in variable speed drive systems belongs to the main factors causing low stability in electrical networks. Therefore, in order to avoid this issue, WTs hybridization with a storage system is a mandatory. This paper investigates WT system operating at variable speed. The system contains of a permanent magnet synchronous generator (PMSG) supported by a battery storage system (BSS). To enhance the quality of active and reactive power injected into the network, direct power control (DPC) scheme utilizing space-vector modulation (SVM) technique based on proportional-integral (PI) control is proposed. Meanwhile, to improve the rendition of this method (DPC-SVM-PI), the rooted tree optimization technique (RTO) algorithm-based controller parameter identification is used to achieve PI optimal gains. To compare the performance of RTO-based controllers, they were implemented and tested along with some other popular controllers under different working conditions. The obtained results have shown the supremacy of the suggested PIRTO algorithm compared to competing controllers regarding total harmonic distortion (THD), overshoot percentage, settling time, rise time, average active power value, overall efficiency, and active power steady-state error. 

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Rooted Tree Optimization for Wind Turbine Optimum Control Based on Energy Storage System

An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization parameter adjustment is needed. The choice of controller parameters is explained, along with the methodology behind the criterion for objective function value calculation. The optimized controller is implemented as an outer-loop speed controller for Predictive Current Control (PCC) of an induction machine. The performance of the proposed control method is compared with that of several other model predictive control methods. The results show a 55% decrease in speed tracking error and 74% decrease in torque overshoot.

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Optimization of Fuzzy Controller for Predictive Current Control of Induction Machine

As an important data analysis method, LR has been widely used in many fields. In applications to practical classification problems, LR tends to give good results. However, traditional LR has significant weaknesses in overcoming the complexity and redundancy of the solution. To this end, many solutions have been proposed, which tend to be a common approach with few results. The main purpose of this paper is to research and design the debugging method of digital grid connection (GC) based on the logistic regression model (LRM). On this basis, this paper proposes an accurate autoregressive model and provides detailed information to ensure the reliability and excellence of the model, which is verified by experiments. The experimental results show that using different classification methods, the test error and selection of data sets have different results. It can be seen that the ten-fold cross-validation error for adaptive elastic net LR is 1/38 and the test error is 1/34. Compared with other methods, the classification performance of adaptive elastic net LR is also superior, even better than other classification methods.

Grid-connected Debugging Method of Digital Power Grid Based on LR Model

This article proposes a control system for a ship power station using a doubly-fed-induction generator (DFIG). Firstly, the author analyzes the characteristics of the power generation system using DFIG based on the rotor sync signal technique and then proposes a control system for adjusting the active and reactive power of the generator supplied to the grid. The advantage of the rotor sync technique is that two control channels of active and reactive power are independent. It is favorable for the author proposes the Fuzzy-PID controller for each control channel. The result is that active and reactive power always follow the desired values in a fast response time. Thus, the author applies this proposed system for automatic load division in a parallel power grid on the ship. The entire generator system has well ensured the load distribution between the shaft-generator and the grid in the case of changes in load consumption and rotor speed of the generator.

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The Power-Sharing System of DFIG-Based Shaft Generator Connected to a Grid of the Ship

This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.

http://ijai.iaescore.com/index.php/IJAI/article/download/20928/pdf

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

Miniature Aerial Vehicles with four rotors is called Quad-rotor MAV, popularly used in aspects of life such military, civilian products, processes and remote sensor, etc. In this paper, the authors present the suitable structure of control system for the Quad-rotor MAV. The first, the Six Degrees of Freedom (6 DOF) of the Quad-rotor MAV dynamic model is built. After, the control structure with the single loop is built. But in the single-loop system, only four control signals of Quad-rotor MAV can be controlled, so the Quad-rotor MAV can be reached the height only and keep the stability. However, it is important to note that we have to well-known the orbit of the Quad-rotor MAV flight; the Quad-rotor MAV must fly point-to-point exactly, so the six coordinate variables must be controlled. So, the double loop control structure system is proposed to do that. Finally, the simulation results analysis and the experimental results of the real model are explored to prove the effectiveness of the proposed structure.

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Duc-Minh Nguyen

Papers

The Power-Sharing System of DFIG-Based Shaft Generator Connected to a Grid of the Ship

A neural network combined with sliding mode controller for the two-wheel self-balancing robot

A Suitable Structure to Control the System of Quad-rotor Miniature Aerial Vehicles