Analysis of the effects of a passing cloud on a grid-interactive photovoltaic system with battery storage using neural networks
Journal Article
·
· IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers)
In this paper, a combined radial-basis-functions (RBF) and backprop network is used to predict the effects of passing clouds on a Utility-Interactive Photovoltaic (PV) system with battery storage. Using the irradiance as input signal, the network models the effects of random cloud movement on the electrical variables of the Maximum Power Point Tracker (MPPT) and the variables of the utility-linked inverter over a short period of time. During short time intervals, the irradiance is considered as the only varying input parameter affecting the electrical variables of the system. The advantages of Artificial Neural Network (ANN) simulation over standard linear model is that it does not require the knowledge of internal system parameters, involves less computational effort, and offers a compact solution for multiple-variable problems. The model can be easily integrated into a typical utility system and resulting system behavior can be determined. The viability of the battery-supported PV system as dispatchable unit is also investigated. The simulated results are compared with the experimental results captured during cloudy days. This model can be a useful tool in solar Energy Engineering design and in PV-integrated utility operation.
- Research Organization:
- Univ. of Massachusetts, Lowell, MA (US)
- OSTI ID:
- 20080379
- Journal Information:
- IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers), Journal Name: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers) Journal Issue: 4 Vol. 14; ISSN 0018-9383; ISSN ITCNE4
- Country of Publication:
- United States
- Language:
- English
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