Pitch Angle Optimization for Small Wind Turbines Based on a Hierarchical Fuzzy-PID Controller and Anticipated Wind Speed Measurement

Abstract

Wind energy is an alternative to meet the growing energy demand and protect the environment; however, in places with limited wind resources, only the installation of small horizontal-axis wind turbines (SHAWTs) is profitable. At the height of these turbines, the wind is usually unstable with gusts and turbulence due to obstacles in its path such as buildings and trees. The pitch angle must be adaptable to guarantee nominal rotation speed, and it is commonly regulated with a proportional-integral-derivative (PID) feedback controller. This controller works well when the wind is stable, but not with drastic changes in wind speed. To correct this problem, this article introduces a PID controller with automatic adjustment of the gain values using a fuzzy logic controller (FLC). The PID gain adjustment allows an optimal response speed of the system for different wind conditions. The membership functions of the FLC are determined from a methodology that includes: The measurement of the wind speed at a calculated distance, a statistical analysis of the wind variability, and a dynamic analysis of the wind path. In this way, it is possible to anticipate the response of the actuator to the arrival of a gust of wind to the rotor. The algorithm is implemented in 14 kW SHAWTs where the difference in performance with a conventional controller is quantified. Satisfactory results were obtained, the electrical output increased by 7%, and the risk of rotor damage due to vibrations or mechanical fatigue was reduced by 20%.

A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

“…According to the simulations presented, suggested self tuning PID method is clearly superior to the traditional PIDs and other similar methods. The main difficulties that have been overcome by the suggested scheme are that: (1) The controller dependency on the mathematical model of the plant is eliminated, (2) The performance is improved by the suggested GT2-FLS modeling, (3) Online optimization scheme improves the robustness against unpredicted disturbances, (4) A predictive scheme is added to the conventional PID.…”

“…This type of controller is very sensitive to measurement noise in process control and makes adjustment using trial and error more difficult. However, the use of a derivative mode slightly increases the response speed [1,2]. Then developing of tuning algorithms are essential to improve the performance.…”

Comparative Study between Flatness-Based and Field-Oriented Control Methods of a Grid-Connected Wind Energy Conversion System

“…The dynamic model of the electrical grid can be expressed by Equation (9), where L g and R g are the filter inductance and resistance, respectively, V gd and V gq present the d-q components of the grid voltage, V id and V iq stand for the d-q components of the inverter output voltage, I gd and I gq are the d-q axis components of the current measured on the grid, and w g is the fundamental frequency [2].…”

“…Wind energy is an alternative to meet the growing energy demand and to protect the environment [1]. The Global Wind Energy Council has reported a global capacity of 651 GW with 60.4 GW of new installations in 2019 and an expectation of growth of more than 100 GW in annual installations over the next decade [2,3]. Research on the wind power industry started to improve in the last century where the variable-speed wind turbine (WT) with a full-scale frequency converter was used in distributed power generation systems [4][5][6][7][8].…”

Reduction of Total Harmonic Distortion of Wind Turbine Active Power Using Blade Angle Adaptive PI Controller

“…Fuzzy logic control is more suitable to the nonlinear systems [13]. Fuzzy controller is used to extract more power at low wind-speed conditions, while protecting the system at high wind-speed condition [15]. Ref.…”