Performance comparison analysis of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for intelligent management of distributed generation
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Full Text |
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Author |
Alias Khamis, Gan Chin Kim, Mohd Shahrieel Mohd Aras, Nor Aira Zambri and Zairi Ismael Rizman
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e-ISSN |
1819-6608 |
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On Pages
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574-586
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Volume No. |
20
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Issue No. |
10
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Issue Date |
August 5, 2025
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DOI |
https://doi.org/10.59018/052573
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Keywords |
artificial neural network, adaptive neuro‐fuzzy inference system, distribution generation, microgrid.
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Abstract
To control the voltage source inverter (VSI) of the PV/Fuel Cell/Battery cell system, conventional methods of controlling voltage and current modes were developed with improved controllers of the proportional Integral (PI) Neural Network (NN) of both the output voltage and the internal current control loop. The suggested neural network PI controller performs better than the adaptive neurofuzzy inference system (ANFIS) controller while maintaining the PI controller's reliability and ease of use. VSI controllers have employed space vector type pulse width modulation to produce sine-shaped waves. The proposed inverter-based Distributed Generation (DG) model is applied to the micro-grid-system to review its effectiveness as a complete model as well as to evaluate the performance of its use in large network systems. Since the VSI model is built on a P-Q control scheme that allows separate control of active and reactive power output, DG can operate based on active and reactive power references on the inverter. A new technique has been developed to manage active and reactive power reference for DG by using an ANN to ensure that the DG unit operates at optimal power values while reducing the amount of power loss, as well as maintaining the voltage profile within acceptable limits. The results showed that the proposed artificial NN technique could accurately predict the active and reactive power references of DG with minimal error. A comparison was made between the ANFIS DG-logic controller and the NN PI controller for the VSI in terms of the generation of evaluation metrics.
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