μ-Synthesis Control of Photovoltaic System

2014 ◽  
Vol 494-495 ◽  
pp. 1706-1709
Author(s):  
Zi Heng Yin ◽  
Peng Li

Traditional control methods are usually difficult to achieve a good compromise between nominal performance and robust performance, in order to improve the flexibility and robustness of the PV power grid-connection system without affecting its nominal performance, aiming at the effect on flexible PV power grid-connection by component parameter perturbation, a μ-synthesis robust control method of flexible grid-connection of photovoltaic power based on model matching is proposed. Model matching problem including the actual controlled objects like inverters, is transformed into a generalized system with uncertainty block being abstracted, thus further is attributed to a structured uncertainty problem, which can be solved by DK iteration. The μ-analysis and simulation results show that the designed μ-synthesis controller, makes the robust performance of the closed-loop system satisfied, and advantages are especially more obvious in the case of system component parameter perturbation.

2021 ◽  
Author(s):  
Minglei Jiang ◽  
Yuting Bao ◽  
Qunying Li ◽  
Jingying Yang ◽  
Peng Liu ◽  
...  

SIMULATION ◽  
1968 ◽  
Vol 11 (2) ◽  
pp. 57-60
Author(s):  
Robert Gonzalez ◽  
Milo Muterspaugh

This paper describes a simple method of steepest-descent optimization of a criterion functional F(α1, α2,...) depend ing on the state variables y i(t;α1,α2,...) of a dynamic system with design parameters α 1, α 2.... The system is simulated on a fast analog computer and the parameters are given mutually orthogonal sequences of binary pertur bations during successive computer runs. Simple correla tion of each parameter perturbation with the criterion functional value at the end of each computer run yields ap proximations for the gradient components ∂ F/∂α k needed for steepest-descent optimization with a minimum- of dig ital logic. As an example, two- and three-parameter model- matching problems are solved by iterative computation at 1000 iterations per second. A course term-paper for first-year graduate students


2020 ◽  
Vol 12 (1) ◽  
pp. 1-19
Author(s):  
Mostefai Abdelkader ◽  
Ignacio García Rodríguez de Guzmán

This paper formulates the process model matching problem as an optimization problem and presents a heuristic approach based on genetic algorithms for computing a good enough alignment. An alignment is a set of not overlapping correspondences (i.e., pairs) between two process models(i.e., BP) and each correspondence is a pair of two sets of activities that represent the same behavior. The first set belongs to a source BP and the second set to a target BP. The proposed approach computes the solution by searching, over all possible alignments, the one that maximizes the intra-pairs cohesion while minimizing inter-pairs coupling. Cohesion of pairs and coupling between them is assessed using a proposed heuristic that combines syntactic and semantic similarity metrics. The proposed approach was evaluated on three well-known datasets. The results of the experiment showed that the approach has the potential to match business process models effectively.


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