Fuzzy Modelling Algorithms and Parallel Distributed Compensation for Coupled Electromechanical Systems

Modelling and controlling an electrical Power Generation System (PGS), which consists of an Internal Combustion Engine (ICE) linked to an electric generator, poses a significant challenge due to various factors. These include the non-linear characteristics of the system’s components, thermal effects...

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Vydáno v:Algorithms Ročník 17; číslo 9; s. 391
Hlavní autoři: Reyes, Christian, Ramos-Fernández, Julio C., Espinoza, Eduardo S., Lozano, Rogelio
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.09.2024
Témata:
Algorithms
Artificial intelligence
Closed loops
Compensation
Control algorithms
Control systems design
Controllers
Design optimization
Electric generators
Electric power
Electric power systems
Electric utilities
Electrical loads
Electrical noise
Energy efficiency
fuzzy c-means
Fuzzy control
Fuzzy logic
Fuzzy systems
Generators
Internal combustion engines
Machine learning
Mathematical analysis
Mathematical functions
Mathematical models
Modelling
Nonlinear control
Numerical analysis
Optimization
Parameter identification
Particle swarm optimization
PDC controller
power generation
Temperature effects
Unmanned aerial vehicles
Vehicles
Mexico
ISSN:1999-4893, 1999-4893
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Shrnutí:Modelling and controlling an electrical Power Generation System (PGS), which consists of an Internal Combustion Engine (ICE) linked to an electric generator, poses a significant challenge due to various factors. These include the non-linear characteristics of the system’s components, thermal effects, mechanical vibrations, electrical noise, and the dynamic and transient impacts of electrical loads. In this study, we introduce a fuzzy modelling identification approach utilizing the Takagi–Sugeno (T–S) structure, wherein model and control parameters are optimized. This methodology circumvents the need for deriving a mathematical model through energy balance considerations involving thermodynamics and the non-linear representation of the electric generator. Initially, a non-linear mathematical model for the electrical power system is obtained through the fuzzy c-means algorithm, which handles both premises and consequents in state space, utilizing input–output experimental data. Subsequently, the Particle Swarm Algorithm (PSO) is employed for optimizing the fuzzy parameter m of the c-means algorithm during the modelling phase. Additionally, in the design of the Parallel Distributed Compensation Controller (PDC), the optimization of parameters pertaining to the poles of the closed-loop response is conducted also by using the PSO method. Ultimately, numerical simulations are conducted, adjusting the power consumption of an inductive load.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a17090391