Outline

  • Highlights
  • Abstract
  • Keywords
  • Nomenclature
  • 1. Introduction
  • 2. Pv System Modeling
  • 3. Maximum Power Point Searching Method
  • 4. Ann Based Maximum Power Point Prediction
  • 5. Case Study
  • 6. Conclusions and Future Work
  • References

رئوس مطالب

  • چکیده
  • کلید واژه ها
  • 1. مقدمه
  • 2. ماژول سازی سیستم PV
  • 3. شیوه جستجوی نقطه حداکثر توان
  • 4. پیش بینی نقطه حداکثر توان بر پایه ANN
  • 5. مطالعه موردی
  • 6. نتیجه گیری و کار آتی

Abstract

This paper proposes a novel Maximum Power Point Tracking (MPPT) method suitable for any application in which very fast changing and not uniform shading conditions continuously occur, as in case of photovoltaic systems (PVs) installed in the roof of electric vehicles. Basically, an Artificial Neural Network (ANN) based approach is utilized to automatically detect the global maximum power point of the PV array by using a preselected number of power measurements of the PV system. The method requires only the measure of PV voltages and currents, thus avoiding the use of additional sensors providing information about the environmental operating conditions and temperature of PV modules. The time interval required to achieve the maximum power generation from the PV modules is about constant and established a priori. The greater the number of power–voltage characteristic scansions, the greater the ANN’s ability to meet the maximum and its prediction accuracy. The algorithm is cost-effective, with no additional hardware requirements and limited dependence on system parameter variations. Numerical simulations have validated the effectiveness of the proposed method, and have highlighted the tradeoff between the preselected number of power–voltage characteristic scansions, the size of the ANN and its prediction accuracy.

Keywords: - - - - -

Conclusions and future work

The paper has presented a novel MPPT method that provides an accurate and fast estimation of the GMPP in a PV system subjected to continuous and rapidly changing shadowing patterns. Some quality indices have been proposed in order to compare the performance of different ANN structures and they have been computed in the case study considering numerous random generated scenarios. The results have also highlighted a good robustness of the method to parameter variations of PV system. In particular, this work have investigated, by means of a detailed analysis based on numerical simulations, the benefits of a novel implementation of a global MPPT algorithm, highlighting its effectiveness and suitability when applied to small PV systems installed, for instance, on the roof of electrical vehicles. In future works, experimental tests of the proposed MPPT could highlight which hardware solutions are more suitable also considering the economical investment point of view. In particular, the trade-off among implementation costs and energy losses could be investigated.

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