Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18

Rotor Current Control Design for DFIG-based Wind Turbine Using PI, FLC and Fuzzy PI Controllers

Due to the rising demand for electricity with increasing world population, maximizing renewable energy capture through efficient control systems is gaining attention in literature. Wind energy, in particular, is considered the world’s fastest-growing energy source it is one of the most efficient, reliable and affordable renewable energy sources. Subsequently, well-designed control systems are required to maximize the benefits, represented by power capture, of wind turbines. In this thesis, a 2.0-MW Doubly-Fed Induction Generator (DFIG) wind turbine is presented along with new controllers designed to maximize the wind power capturer. The proposed designs mainly focus on controlling the DFIG rotor current in order to allow the system to operate at a certain current value that maximizes the energy capture at different wind speeds. The simulated model consists of a single two-mass wind turbine connected directly to the power grid. A general model consisting of aerodynamic, mechanical, electrical, and control systems are simulated using Matlab/Simulink. An indirect speed controller is designed to force the aerodynamic torque to follow the maximum power curve in response to wind variations, while a vector controller for current loops is designed to control the rotor side converter. The control system design techniques considered in this work are Proportional-Integral (PI), fuzzy logic, and fuzzy-PI controllers. The obtained results show that the fuzzy-PI controller meets the required specifications by exhibiting the best steady-state response, in terms of steady-state error and settling time, for some DFIG parameters such as rotor speed, rotor currents and electromagnetic torque. Although the fuzzy logic controller exhibits smaller peak overshoot and undershoot values when compared to the fuzzy-PI, the peak value difference is very small, which can be compensated using protection equipment such as circuit breakers and resistor banks. On the other hand, the PI controller shows the highest overshoot, undershoot and settling time values, while the fuzzy logic controller does not meet the requirements as it exhibits large, steady-state error values

Fuzzy logic tuning of a PI controller to improve the performance of a wind turbine on a semi-submersible platform under different wind scenarios

The integration of renewable energy sources in power systems, specially wind energy, is growing as environmental concerns arise in society. Nevertheless, the low amount of viable sites onshore or in shallow waters restricts the use of wind energy. In this sense, offshore semi-submersible platforms appear as an option, which in addition enables the integration of complementary elements, for instance wave energy converters. However, the complexity of the system increases due to the interactions between the platform movements and the wind turbine, and traditional control techniques do not enable to cope with these interactions in an easy way, hence limiting the efficiency of energy harvesting. Intelligent control techniques are an option with a great potential to take full account of the said interactions and to improve energy production efficiency. Still, it is required to have simulation models including those effects beforehand, so that the effects of a designed controller on the system can be evaluated. This paper presents an original fuzzy logic controller that tunes a reference controller, improving its performance according to a developed methodology that allows evaluation of controllers for wind turbines in semi-submersible platforms. The resulting fuzzy logic controller allows higher efficiency concerning mechanical loads in the system, electric energy production and tracking error of the speed reference.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Development and Evaluation of Fuzzy Logic Controllers for Improving Performance of Wind Turbines on Semi-Submersible Platforms under Different Wind Scenarios

Among renewable energy technologies, wind energy features one of the best possibilities for large-scale integration into power systems. However, there are specific restrictions regarding the installation areas for this technology, thus resulting in a growing, yet restricted, rate of penetration of the technology because of the limited viable sites onshore or in shallow waters. In this context, the use of offshore semi-submersible platforms appears as a promising option, which additionally enables the incorporation of other elements, such as wave energy converters or aquaculture. Nevertheless, this kind of offshore facility involves interactions between platform movements and the wind turbine, increasing the complexity of the system, causing traditional control techniques to not be able to fully cope with the dynamics of the system, and thus limiting the efficiency of energy extraction. On the contrary, the use of intelligent control techniques is an interesting option to take full account of the said interactions and to improve energy capture efficiency through the control of the pitch of the blades, especially under turbulent, above-rated wind profiles. This work presents an original fuzzy logic controller that has been validated by comparing it with previously validated controllers, following a developed methodology that allows comparison of controllers for wind turbines in semi-submersible platforms using performance indexes.This work was partially supported by the Ministry of Economy and Competitiveness (Government of Spain) and European Union (RTC-2016-5712-3); by the European Union, CDTI (Spain) and BEISS (UK) through the call H2020 ERA-NET DEMOWIND (WIP10+ project); by the Regional Government of Andalusia and European Union (UMA-CEIATECH-18); and finally, by partial funding for open access charge from the Universidad de Málaga. Partial funding for open access charge: Universidad de Málag

DESIGN OF ECO GREEN WIND TURBIN USING FUZZY LOGIC ON SAHARA DESERT

In development of wind turbine, the government of Libya has assignment Libya electric co. To develop wind turbine in Sahara Desert beginning of 2001. The officer the took a surveying of wind speed and the environment of the Sahara Desert. The surveying has shown that wind speed of Sahara Desert is class  4 and 5. Therefore is suitable to develop wind turbine to produce electrical power for RL (Resistor Load). Based on the argument above, so this research entitled: “Analysis Of Eco Green Wind Turbine Design For Sahara Desert Using  Fuzzy Logic”. The objectives of this research are: to design and development of eco green vertical axis wind turbines (VAWT) in Sahara Desert, to analysis of suitable eco green vertical axis wind turbines (VAWT) in Sahara Desert using MATLAB and to analysis of how fuzzy logic use in wind turbine design. Research model used input process in Matlab simulation and output is the design and analysis of efficiency wind turbines

Hardware-In-The-Loop Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator

To enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real- time solutions have to implement the proposed fault tolerant control strategy. Finally, a high- fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations

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

Hybrid Optimized Fuzzy Pitch Controller of a Floating Wind Turbine with Fatigue Analysis

Floating offshore wind turbines (FOWTs) are systems with complex and highly nonlinear dynamics; they are subjected to heavy loads, making control with classical strategies a challenge. In addition, they experience vibrations due to wind and waves. Furthermore, the control of the blade angle itself may generate vibrations. To address this issue, in this work we propose the design of an intelligent control system based on fuzzy logic to maintain the rated power of an FOWT while reducing the vibrations. A gain scheduling incremental proportional–derivative fuzzy controller is tuned by genetic algorithms (GAs) and combined with a fuzzy-lookup table to generate the pitch reference. The control gains optimized by the GA are stored in a database to ensure a proper operation for different wind and wave conditions. The software Matlab/Simulink and the simulation tool FAST are used. The latter simulates the nonlinear dynamics of a real 5 MW barge-type FOWT with irregular waves. The hybrid control strategy has been evaluated against the reference baseline controller embedded in FAST in different environmental scenarios. The comparison is assessed in terms of output power and structure stability, with up to 23% and 33% vibration suppression rate for tower top displacement and platform pitch, respectively, with the new control scheme. Fatigue damage equivalent load (DEL) of the blades has been also estimated with satisfactory results.This work has been partially supported by the Spanish Ministry of Science and Innovation under the project MCI/AEI/FEDER number RTI2018-094902-B-C21 and PDI2021-123543OB-C21