The study of rotor blade aerodynamic performances of wind turbine has been presented in this thesis. This study was focused on aerodynamic effects changed by blade surface distribution as well as grid solution along the airfoil. The details of numerical calculation from Fluent were described to help predict accurate blade performance for comparison and discussion with available data. The direct surface curvature distribution blade design method for two-dimensional airfoil sections for wind turbine rotors have been discussed with the attentions to Euler equation, velocity diagram and the factors which affect wind turbine performance and applied to design a blade geometry close to an existing wind turbine blade, Eppler387, in order to argue that the blade surface drawn by direct surface curvature distribution blade design method contributes aerodynamic efficiency. The FLUENT calculation of NACA63-215V showed that the aerodynamic characteristics agreed well with the available experimental data at lower angles of attack although it was discontinuities in the surface curvature distributions between 0.7 and 0.8 in x/c. The discontinuities were so small that the blade performance could not be affected. The design of Eppler 387 blade performed to reduce drag force. The discontinuities of surface distribution matched the curve of the pressure coefficients. It was found in the curvature distribution that the leading edge pressure side had difficulties to connect to Bezier curve and also the trailing edge circle was never be tangent to the lines of trailing edge pressure and suction sides due to programming difficulties.

Aerodynamics of wind turbines

A Survey of Artificial Neural Network in Wind Energy Systems

Connecting legs in parallel in a voltage source inverter is a way to increase the output current and, thus, its rated power. The connection can be made using either coupled or uncoupled inductors, and achieving an even contribution to the output current from all the legs is a crucial issue. Circulating currents produce additional losses and stress to the power devices of the converter. Therefore, they should be controlled and minimized. An efficient technique to attain such balance when coupled inductors are used is presented in this paper. The proposed technique can also be used when the inductors are uncoupled, since it is a particular case where the coupling coefficients are zero. This technique does not include proportional–integral controllers and does not require any parameter tuning either. The exact control action needed to reach current balance is straightforwardly calculated and applied. Experimental results are shown in this paper to verify the efficiency of the proposed balancing method.

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Current-Balancing Technique for Interleaved Voltage Source Inverters With Magnetically Coupled Legs Connected in Parallel

A new hybrid GA–GSA algorithm for tuning damping controller parameters for a unified power flow controller

The position sensor is one of the most used devices in Adjustable Speed Drives (ASDs). Its use is mandatory in electric machines vector control. In this paper, an interest was addressed to this field. Indeed, a bibliographical review, about Fault Detection and Isolation (FDI) and Fault Tolerant Control (FTC) in ASDs, is presented. Thus, the paper deals with position sensor FDI and sensorless control-based FTC in ASDs. Moreover, this issue is mainly addressed to position sensor faults in ASDs. This paper is based on a wide literature review referring to scientific papers and manufacturer׳s technical documents. In total, 186 references in the open literature, dating back to 1981, have been investigated in order to perform this study.

Speed/position sensor fault tolerant control in adjustable speed drives - A review.

Low Frequency Oscillations (LFO) occur in power systems because of lack of the damping torque in order to dominance to power system disturbances as an example of change in mechanical input power. In the recent past Power System Stabilizer (PSS) was used to damp LFO. FACTs devices, such as Unified Power Flow Controller (UPFC), can control power flow, reduce sub-synchronous resonance and increase transient stability. So UPFC may be used to damp LFO instead of PSS. UPFC damps LFO through direct control of voltage and power. In this research the linearized model of synchronous machine (Heffron-Philips) connected to infinite bus (Single Machine-Infinite Bus: SMIB) with UPFC is used and also in order to damp LFO, adaptive neuro-fuzzy controller for UPFC is designed and simulated. Simulation is performed for various types of loads and for different disturbances. Simulation results show good performance of neuro-fuzzy controller in damping LFO.

Damping of Low Frequency Oscillations in power systems with neuro-fuzzy UPFC controller

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

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Robust fault detection of wind energy conversion systems based on dynamic neural networks.

Low frequency oscillations (LFO) in power system occur usually because of lack of damping torque to overcome disturbances in power system such as changes in mechanical power. Traditionally, power system stabilizers (PSS) are being used to damp these oscillations. Unified power flow controller (UPFC) is a well-known FACTS device that can control power flow in transmission lines. It can also replace PSS to damp low frequency oscillations effectively through direct control of voltage and power. In this paper a linearized Heffron-Philips model of a (single machine-infinite bus) power system with a unified power flow controller is developed. The designed fuzzy-based UPFC controller adjusts four UPFC inputs by appropriately processing of the input error signal, and provides an efficient damping. The results of the simulation show that the UPFC with fuzzy-based controllers is more effective in damping LFO compared to UPFC with PID controllers.

Fuzzy logic based UPFC controller for damping low frequency oscillations of power systems

Structure of wind energy conversion systems (WECSs) must be robust against faults. In order to accurately study WECSs during occurrence of faults and to explore the impact of faults on each component of the WECSs, a detailed model is required in which both mechanical and electrical parts of the WECSs are properly involved. In addition, a fault detection system (FDS) is required to diagnose the occurred faults at the appropriate time in order to ensure a safe system operation, avoid heavy economic losses, prevent damage to adjacent relevant systems and facilitate timely repair of failed components. This can be performed by subsequent actions through fast and accurate detection of faults. In this paper, by utilising a comprehensive dynamic model of the WECS, an FDS is presented using dynamic recurrent neural networks. In industrial processes, dynamic neural networks are known as a good mathematical tool for fault detection. The proposed FDS detects faults of the generator's angular velocity sensor, pitch an...

Fault detection of wind energy conversion systems using recurrent neural networks

Power inverters are used to acquire an ac output voltage from a dc power supply. The parallel connection of inverters results in several advantages, like improvement of the system reliability and increasing of the power capacity. The control problem for parallel-connected inverters is proper voltage regulation and equal current distribution. In this paper, a characteristic loci based compensator (Approximate commutative compensator) is used to obtain design objectives. In order to compare results, H ae and LQG/LTR controllers, are also implemented on parallel inverters. Simulation results of the single-module, two-module and three-module inverter systems with different kinds of loads and parametric uncertainties, have demonstrated the feasibility of the proposed control scheme in proper voltage regulation and equal current distribution.

Nasser Talebi

Papers

Damping of Low Frequency Oscillations in power systems with neuro-fuzzy UPFC controller

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

Fuzzy logic based UPFC controller for damping low frequency oscillations of power systems

Fault detection of wind energy conversion systems using recurrent neural networks

Current and voltage control of paralleled multi-module inverter systems