Comparative Analysis of Advanced Controllers for Standalone WECs for DC Microgrid Applications

The small-scale wind energy generation system is one of the solutions to empower the isolated loads and provides a promising solution to decrease the greenhouse effect. This chapter describes the simulation analysis for wind energy conversion system incorporated with maximum power point tracking feature. The MPPT algorithms like variable current perturb and observe algorithm and variable step perturb and observe algorithm are incorporated with WECS. The comparative analysis is done in the closed-loop model in continuous time-varying wind speed. The closed-loop simulation is performed using a conventional fixed gain controller. To address the limitations of the fixed gain controller, the analysis is done using the gain scheduling proportional integral controller and the good gain method to tune the proportional integral controller. The comparative analysis between the fixed gain controller, the gain scheduling proportional integral controller, and the good gain method to tune proportional integral controller for above-stated MPPT methods is shown.

A compensation scheme applied on wind turbine blade pitch control for the reduction of non-torque main shaft loads

It has been well established that non-torque main shaft loads influence the internal drive train loads. This paper proposes a scheme that compensates for non-torque loads in the blade pitch controller. The compensation scheme is implemented on a dynamic model developed in FAST/Simulink. Three wind conditions of 8, 11.4 and 20 m/s are examined. The dynamic analysis of the bending moment in the low-speed shaft showed a reduction in bending moment by 3 % for the rated wind speed (11.4 m/s) and 1.8 % for the above-rated wind speed (20 m/s), highlighting the effectiveness of the proposed scheme. However, a reduction in bending moment also slightly decreased the shaft’s speed by 2.3 % and 0.5 %, respectively. Similarly, the turbine power was decreased by 9 % and 1 %, respectively. In comparison, further gain scheduling within the compensation scheme reduces the power loss to as low as 0.3 %. The 2 to 3 % reduction in the low-speed shaft bending moment can significantly influence the drive train loads and easily outweigh any loss resulting in the shaft rotational speed and turbine power. Thus, this paper shows that using bending moment error as feedback within the compensation scheme positively affects the low-speed shaft’s bending moment with the eventual potential of reducing drivetrain loads.

A Joint-space Position Control-based Approach to Haptic Rendering of Stiff Objects using Gain Scheduling

Haptic rendering often deals with interactions between stiff objects. A traditional way of force computing models the interaction using a spring-damper system, which suffers from stability issues when the desired stiffness is high. Instead of computing a force, this paper continues to explore shifting the focus to rendering an interaction with no penetration, which can be accomplished by using a position controller in the joint space using the encoders as feedback directly. In order to make this approach easily adaptable to any device, an alternative way to model the dynamics of the device is also presented, which is to linearize a detailed simulation model. As a family of linearized models is used to approximate the full dynamic model of the system, it is important to have a smooth transition between multiple sets of controller gains generated based on these models. Gain scheduling is introduced to improve the performance in certain areas and a comparison among three controllers is conducted in a simulation setup.

Active and Reactive Power Control for Wind Turbines Based DFIG Using LQR Controller with Optimal Gain-Scheduling

This paper proposes an optimal gain-scheduling for linear quadratic regulator (LQR) control framework to improve the performance of wind turbines based Doubly Fed Induction Generator (DFIG). Active and reactive power decoupling is performed using the field-oriented vector control which is used to simplify DFIG’s nonlinearity and derive a compact linearized state-space model. The performance of the optimal controller represented by a linear quadratic regulator is further enhanced using the whale optimization algorithm in a multiobjective optimization environment. Adaptiveness against wind speed variation is achieved in an offline training process at a discretized wind speed domain. Lookup tables are used to store the optimal controller parameter and called upon during the online implementation. The control framework further integrates the effects of pitch angle control mechanism for active power ancillary services and possible improvements on reactive power support. The results of the proposed control framework improve the overall performance of the system compared to the conventional PI controller. Comparison is performed using the MATLAB Simulink platform.