# Investigation of ANN performance for tracking the optimum points of PV module under partially shaded conditions
> Syafaruddin

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_79951662401
Jurnal / Konferensi: 2010 9th International Power and Energy Conference IPEC 2010
Tahun terbit: 2010
DOI: https://doi.org/10.1109/IPECON.2010.5697002
Citations: 20

## Authors
- Syafaruddin

## Abstract
Solving partially shaded condition still remains an important task in the PV system practice. Under such condition, the global maximum point shifts continuously in wide voltage range from the local maxima, which make it difficult for conventional controllers. Intelligent techniques based on artificial neural network are well-known as the promising methods to identify the global maxima. However, there are many variants of neural networks and they have strong and weak points during the implementation. This paper investigates the performance of radial basis function (RBF) neural network and three layered feed-forward neural network (TFFN) under partial shadow operation of PV module. These two ANN structures are well-recognized for optimization, forecasting and control in PV system application due to the simplicity of structure and high performance accuracy. The investigation is focused on the network structure, training and validation process of these methods. To determine to which method is preferable to handle this task, the adaptive neuro-fuzzy inference system (ANFIS) is used as the comparator. The proposed method is verified and tested using developed real-time simulator.

## Keywords
- Adaptive neuro fuzzy inference system
- Computer science
- Artificial neural network
- Process (computing)
- Artificial intelligence
- Maxima and minima
- Radial basis function
- Control theory (sociology)
- Fuzzy logic
- Fuzzy control system
- Control (management)
- Mathematics
- Operating system
- Mathematical analysis

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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