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Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
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Turbine-Governor Control01:17

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Turbine-governor control is crucial for maintaining power system stability by balancing turbine mechanical power output with electrical load demand. This mechanism ensures that generator frequency and rotor speed are within acceptable limits during load variations. Turbine-generator units store kinetic energy due to their rotating masses; this energy is released to meet the load requirement when the load increases. The electrical torque of turbines rises to meet the demand, whereas the...
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Generator Voltage Control01:21

Generator Voltage Control

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Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
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Three-Winding Transformers01:19

Three-Winding Transformers

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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
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Controller Configurations01:22

Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Design Example: Calculating Safe Diameter for Wind-Exposed Disc01:17

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Assessing safety in wind-exposed installations is crucial to preventing potential failures. This example explores the calculation and design adjustments needed to mount a circular disc on a building facade, where wind forces are a primary concern. A 4-meter diameter disc was initially designed as an aesthetic feature facing winds at a velocity of 25 meters per second, with an air density of 1.25 kilograms per cubic meter. Given these conditions, the drag force on the disc was determined using...
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Updated: May 15, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Optimal hybrid type-3 fuzzy controller for horizontal axis wind turbines: Comparative study.

Adnan Qahtan Adnan1, Mohammed Khalil Hussain1, Ardashir Mohammadzadeh2

  • 1Department of Energy Engineering, University of Baghdad, Baghdad, Iraq.

ISA Transactions
|April 10, 2025
PubMed
Summary

This study introduces an optimal hybrid type-3 fuzzy-PID controller for wind turbine blade pitch angle control, outperforming type-1 and type-2 fuzzy logic controllers. The new controller ensures more stable power generation, especially under fluctuating wind conditions.

Keywords:
Fuzzy inference systemsGenetic algorithmHybrid fuzzy PID controllerInterval type-3 fuzzy logic controllerParticle swarm optimization

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Area of Science:

  • Renewable Energy Systems
  • Control Engineering
  • Artificial Intelligence in Engineering

Background:

  • Wind turbine power generation is significantly impacted by blade pitch angle (BPA) control, which is challenged by fluctuating wind speeds and inherent system uncertainties.
  • Existing type-1 and type-2 fuzzy logic controllers have limitations in managing high levels of uncertainty in wind turbine systems.

Purpose of the Study:

  • To propose and evaluate a novel optimal hybrid type-3 fuzzy logic controller for enhancing wind turbine blade pitch angle control (PAC).
  • To compare the performance of type-3 fuzzy logic controllers against type-1 and type-2 fuzzy logic controllers, as well as hybrid fuzzy-PID controllers.
  • To determine the optimal fuzzy inference system (FIS) and tuning method for improved power generation stability.

Main Methods:

  • Implementation and comparison of six controllers: Type-1 Fuzzy Logic Controller (T1-FLC), Interval Type-2 Fuzzy Logic Controller (IT2-FLC), Interval Type-3 Fuzzy Logic Controller (IT3-FLC), and their optimal hybrid fuzzy-PID controller (HT1-FPIDC, HT2-FPIDC, HT3-FPIDC) variants.
  • Evaluation of Mamdani and Sugeno fuzzy inference systems (FIS) for BPA control.
  • Optimization of PID parameters using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the hybrid controllers.

Main Results:

  • The Sugeno FIS demonstrated superior output power generation stability compared to the Mamdani FIS for the 500-kW horizontal axis wind turbine.
  • The optimal hybrid type-3 fuzzy-PID controller (HT3-FPIDC) based on Mamdani FIS with PSO achieved a 19.74% lower absolute summation error (ASE) than the optimal HT2-FLC with PSO (Sugeno FIS) and a 39.03% lower ASE than the optimal HT1-FLC with PSO (Sugeno FIS).
  • The proposed optimal HT3-FPIDC utilizing PSO and Mamdani FIS yielded the best results for consistent power output at the rated value.

Conclusions:

  • The optimal hybrid type-3 fuzzy-PID controller, particularly when tuned with PSO and employing Mamdani FIS, offers superior performance for wind turbine blade pitch angle control.
  • Type-3 fuzzy logic controllers provide a significant advantage in handling uncertainties, leading to more stable and consistent wind power generation.
  • The findings underscore the potential of advanced fuzzy logic control strategies for optimizing renewable energy systems.