A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research

Output power levelling for DFIG wind turbine system using intelligent pitch angle control

Blade pitch angle control, as an indispensable part of wind turbine, plays a part in getting the desired power. In this regard, several pitch angle control methods have been proposed in order to limit aerodynamic power gained from the wind turbine system (WTS) in the high-windspeed regions. In this paper, intelligent control methods are applied to control the blade pitch angle of doubly-fed induction generator (DFIG) WTS. Conventional fuzzy logic and neuro-fuzzyparticle swarm optimization controllers are used to get the appropriate wind power, where fuzzy inference system is based on fuzzy c-means clustering algorithm. It reduces the extra repetitive rules in fuzzy structure which in turn would reduce the complexity in neuro-fuzzy network with maximizing efficiently. In comparing the controllers at any given wind speed, adaptive neuro-fuzzy inference systems controller involving both mechanical power and rotor speed revealed better performance to maintain the aerodynamic power and rotor speed at the rated value. The effectiveness of the proposed method is verified by simulation results for a 9 MW DFIG WTS

Double Fed Induction Generator Control Design Based on a Fuzzy Logic Controller for an Oscillating Water Column System

Oscillating water column (OWC) systems are water power generation plants that transform wave kinetic energy into electrical energy by a surrounded air column in a chamber that changes its pressure through the waves motion. The chamber pressure output spins a Wells turbine that is linked to a doubly fed induction generator (DFIG), flexible devices that adjust the turbine speed to increase the efficiency. However, there are different nonlinearities associated with these systems such as weather conditions, uncertainties, and turbine stalling phenomenon. In this research, a fuzzy logic controller (FLC) combined with an airflow reference generator (ARG) was designed and validated in a simulation environment to display the efficiency enhancement of an OWC system by the regulation of the turbine speed. Results show that the proposed framework not only increased the system output power, but the stalling is also avoided under different pressure profiles.This research was funded by the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), Diputación Foral de Álava (DFA) through the project CONAVANTER, and to the UPV/EHU through the project GIU20/063

A New Sliding Mode Control Strategy for Variable-Speed Wind Turbine Power Maximization

This is the peer reviewed version of the following article: Khalfallah Tahir, Cheikh Belfedal, Tayeb Allaoui, Mouloud Denai, and M’hamed Doumi, ‘A new sliding mode control strategy for variable‐speed wind turbine power maximization’, International Transactions on Electrical Energy Systems, Vol. 28 (4): e2513, April 2018, which has been published in final form at https://doi.org/10.1002/etep.2513. Under embargo until 10 January 2019. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.The paper proposes a new sliding mode power control strategy for a wound-field synchronous generator-based variable speed wind energy conversion systems to maximize the power extracted from the wind turbine. The proposed controller can handle the inherent nonlinearities in wind energy conversion systems and the randomness of the wind speed as well as the uncertainties of the model and external disturbances. To reduce the chattering phenomenon that characterizes conventional sliding mode control, a sigmoid function with a variable boundary layer is proposed. The adaptive switching gains are adjusted on-line by using a fuzzy logic-based technique. Several simulation scenarios were performed to evaluate the performance of the proposed control scheme. The results demonstrate that this controller provides excellent response characteristics, is robust against parameter variations, and free from chattering phenomenon as compared with the conventional sliding mode control.Peer reviewedFinal Accepted Versio

Hybrid MPPT Control: P&O and Neural Network for Wind Energy Conversion System

In the field of wind turbine performance optimization, many techniques are employed to track the maximum power point (MPPT), one of the most commonly used MPPT algorithms is the perturb and observe technique (PO) because of its ease of implementation. However, the main disadvantage of this method is the lack of accuracy due to fluctuations around the maximum power point. In contrast, MPPT control employing neural networks proved to be an effective solution, in terms of accuracy. The contribution of this work is to propose a hybrid maximum power point tracking control using two types of MPPT control: neural network control (NNC) and the perturbation and observe method (PO), thus the PO method can offer better performance. Furthermore, this study aims to provide a comparison of the hybrid method with each algorithm and NNC. At the resulting duty cycle of the 2 methods, we applied the combination operation. A DC-DC boost converter is subjected to the hybrid MPPT control.  This converter is part of a wind energy conversion system employing a permanent magnet synchronous generator (PMSG). The chain is modeled using MATLAB/Simulink software. The effectiveness of the controller is tested at varying wind speeds. In terms of the Integral time absolute error (ITAE), using the PO technique, the ITAE is 9.72. But, if we apply the suggested technique, it is smaller at 4.55. The corresponding simulation results show that the proposed hybrid method performs best compared to the PO method. Simulation results ensure the performance of the proposed hybrid MPPT control.

Hybrid pitch angle controller approaches for stable wind turbine power under variable wind speed

The production of maximum wind energy requires controlling various parts of medium to large-scale wind turbines (WTs). This paper presents a robust pitch angle control system for the rated wind turbine power at a wide range of simulated wind speeds by means of a proportional–integral–derivative (PID) controller. In addition, ant colony optimization (ACO), particle swarm optimization (PSO), and classical Ziegler–Nichols (Z-N) algorithms have been used for tuning the PID controller parameters to obtain within rated stable output power of WTs from fluctuating wind speeds. The proposed system is simulated under fast wind speed variation, and its results are compared with those of the PID-ZN controller and PID-PSO to verify its effeteness. The proposed approach contains several benefits including simple implementation, as well as tolerance of turbine parameters and several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered a significant advantage of this strategy. Theoretical analyses, as well as simulation results, indicate that the proposed controller can perform better in a wide range of wind speed compared with the PID-ZN and PID-PSO controllers. The WT model and hybrid controllers (PID-ACO and PID-PSO) have been developed in MATLAB/Simulink with validated controller models. The hybrid PID-ACO controller was found to be the most suitable in comparison to the PID-PSO and conventional PID. The root mean square (RMS) error calculated between the desired power and the WT’s output power with PID-ACO is found to be 0.00036, which is the smallest result among the studied controllers

Multi-mode soft switching control for variable pitch of wind turbines based on T-S fuzzy weighted

Variable pitch control is an effective way to ensure the constant power operation of the wind turbines over rated wind speed. The pitch actuator acts frequently with larger amplitude and the increasing mechanical fatigue load of parts of wind turbines affects the output quality of generator and damages the service life of wind turbines. The existing switching control methods only switch at a certain threshold, which can result in switch oscillation. In order to deal with these problems, a multi-mode soft switching variable pitch control strategy was put forward based on Takagi-Sugeno (T-S) fuzzy weighted to accomplish soft switch, which combined intelligent control with classical control. The T-S fuzzy inference was carried out according to the error and its change rate, which was used to smooth the modal outputs of fuzzy control, radial basis function neuron network proportion integration differentiation (RBFNN PID) control and proportion integration (PI) control. This method takes the advantages of the three controllers into consideration. A multi-mode soft switch control model for variable pitch of permanent magnet direct drive wind turbines was built in the paper. The simulation results show that this method has the advantages of three control modes, switch oscillation is overcome. The integrated control performance is superior to the others, which can not only stabilize the output power of wind turbines but also reduce the fatigue load