Abstract
Self-Constructing Fuzzy-Neural-Network-Imitating Sliding-Mode Control for Parallel-Inverter System in Grid-Connected Microgrid
Highlights
Grid-connected inverters, which usually operate in parallel to the point of common coupling (PCC), are required for distributed generation (DG) systems to construct a microgrid (MG), and provide electric energy to the utility grid (UG) [1]
The elements of the sliding-surface vector are taken as the inputs of the designed self-constructing fuzzy neural network (SFNN) to imitate the sliding-mode control (SMC) law, and the rules of the SFNN can be generated online from null according to the instantaneous inputs based on the rulegenerating mechanism with the dynamic threshold
The pruning mechanism is carried by a dynamic Petri net to fire the significant rules, which is utilized to recall the rules corresponding to the reconnected slave inverters
Summary
Grid-connected inverters, which usually operate in parallel to the point of common coupling (PCC), are required for distributed generation (DG) systems to construct a microgrid (MG), and provide electric energy to the utility grid (UG) [1]. The aforementioned self-organizing FNN frameworks used in [20]-[26] possess structure and parameter selflearning abilities according to the input signals and the working conditions of the systems, which fully exert the generalization ability of the FNN and reduce the complexity and computational burden of the control system on the premise of effectively improving the control performance. A model-free self-constructing FNN scheme with dynamic rule-generating and rule-pruning mechanisms is designed to imitate an SMC for grid-connected current tracking and current sharing control of the parallel-inverter system in a grid-connected MG. The proposed SFNNISMC could be more appropriate for systems with unknown uncertainties and frequently changing operational conditions due to structure learning and parameter adaptation It can realize the best balance between the control performance and the computational time in comparison to an FNN with a fixed network structure. Where 1 denotes the 1-norm operator, and ρg =[ g1 gk gn ]T , in which gk k 1, ,n are given positive constants
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