Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to ...

https://journals.sagepub.com/doi/pdf/10.1177/0020294019827330

Automated power management strategy for wind power generation system using pitch angle controller

The load–frequency control (LFC) problem has been one of the major subjects in a power system. In
practice, LFC systems use proportional–integral (PI) controllers. However since these controllers are designed
using a linear model, the non-linearities of the system are not accounted for and they are incapable of
gaining good dynamical performance for a wide range of operating conditions in a multi-area power system.
A strategy for solving this problem because of the distributed nature of a multi-area power system is
presented by using a multi-agent reinforcement learning (MARL) approach. It consists of two agents in each
power area; the estimator agent provides the area control error (ACE) signal based on the frequency bias
estimation and the controller agent uses reinforcement learning to control the power system in which genetic
algorithm optimisation is used to tune its parameters. This method does not depend on any knowledge of the
system and it admits considerable flexibility in defining the control objective. Also, by finding the ACE signal
based on the frequency bias estimation the LFC performance is improved and by using the MARL parallel, computation is
realised, leading to a high degree of scalability. Here, to illustrate the accuracy of the proposed approach, a
three-area power system example is given with two scenarios.

Load–frequency control : a GA-based multi-agent reinforcement learning

An Levenberg–Marquardt trained feed-forward back-propagation based intelligent pitch angle controller for wind generation system

Wind farms are enormous and complex control systems. It is challenging and valuable to control and optimize wind farms. Their applications are widely used in various industries. Artificial intelligent algorithms are effective methods for optimization problems due to their distinctive characteristics. They have been successfully applied to wind farms. In this paper, several issues in wind farms are presented. Applications of artificial intelligent algorithms in wind farm controllers, Mach number, wind speed prediction, wind power prediction and other problems of wind farms are reviewed. Two future research directions are pointed out to develop artificial intelligent algorithms for wind farm control systems and wind speed and power prediction.

A review of applications of artificial intelligent algorithms in wind farms

This article presents a study on set of PID parameters of blade pitch angle controller of wind turbine with fuzzy logic algorithm. Three individual control methods were used to control the wind turbine pitch angle. These control methods are conventional PI, fuzzy and fuzzy PID. With the use of these control methods, the system was protected from possible harms in high wind speed region and maintained changing of nominal output power. It was aimed to the control the wind turbine blade pitch angle in different wind speeds and to hold the output power stable in the set point by simulation of controllers with Matlab/Simulink Software. By evaluating the steady state time of output power received from the simulation results and steady state errors, the performances of the control systems have been measured and compared with one another. As a result of these simulation comparisons, it is clear that fuzzy PID controller performed better than PI and Fuzzy Controller.

Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller

Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm

A new fuzzy logic proportional controller approach applied to individual pitch angle for wind turbine load mitigation

Output powers of wind turbines (WTs) with variable blade pitch over nominal wind speeds are controlled by means of blade pitch adjustment. While tuning the blade pitch, conventional proportional–integral–derivative (PID) controllers and some intelligent genetic algorithms (IGAs) are widely used in hot systems. Since IGAs are community-based optimisation methods, they have an ability to look for multi-point solutions. However, the PID parameter setting optimisation of the IGA controllers is important and quite difficult a step in WTs. To solve this problem, while the optimisation is carried out by regulating mutation rates in some IGA controllers, the optimisation is conducted by altering crossover point numbers in others. In this study, a new IGA algorithm approach has been suggested for the PID parameter setting optimisation of the blade pitch controller. The algorithm rearranging both the mutation rate and the crossover point number together according to the algorithm progress has been firstly used. The new IGA approach has also been tested and validated by using MATLAB/Simulink software. Then, its superiority has been proved by comparing the other genetic algorithm (GAs). Consequently, the new IGA approach has more successfully adjusted the blade pitch of a WT running at higher wind speeds than other GA methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2016.0029

Proportional–integral–derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm

A control operation is the most important part of stabilization between load and frequency in a power system. The demand power change and the generated power change can disrupt the load-frequency balance. For this reason, the system frequency value should be controlled by some methods. In this study, a load frequency control was performed in a two-area power system. The renewable solar energy system and the interconnected power system were connected. In order to control the load-frequency balance, the classical PI, PID, Fuzzy Logic Controller (FLC) based PI and Bacterial Foraging Algorithm (BFA) based PI controller were separately designed and implemented to control the frequency value. All the trials were simulated and confirmed by the MATLAB/Simulink program. According to the results obtained from the control methods, the BFA-PI method provided better overshoots and BFA-PI had better settling times than the others.DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22281

/pdf/effects-on-load-frequency-control-of-a-solar-power-system-3pu21eafiw.pdf

Zafer Civelek

Papers

Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller

Optimization of fuzzy logic (Takagi-Sugeno) blade pitch angle controller in wind turbines by genetic algorithm

A new fuzzy logic proportional controller approach applied to individual pitch angle for wind turbine load mitigation

Proportional–integral–derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm

Effects on Load-Frequency Control of a Solar Power System with a Two-Area Interconnected Thermal Power Plant and its Control with a New BFA Algorithm