On-line neural network training for maximum power point tracking of PV power plant

Zhang, L.; Bai, Yun

doi:10.1177/0142331207076374

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…Many authors have used neural networks to estimate and forecast the global solar radiation and the clearness index [15][16][17][18][19]. There are also some examples where this approach has been used as a tool to track the maximum power point of a PV generator; see, for instance, [20,21].…”

Section: Neural Network Applied To Photovoltaic Simulationmentioning

confidence: 99%

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Piliougine

Elizondo

Mora‐López

et al. 2014

Progress in Photovoltaics

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A neural network for modelling photovoltaic modules using angle of incidence and clearness index is proposed. Engineers require methods to estimate the output of a photovoltaic plant depending on meteorological conditions. Therefore, models for the grid inverter and the generator must be provided, and their outputs must be combined. The connection between both models is related to the maximum power point of the generator and how it is tracked by the inverter. That maximum power point under specific conditions of irradiance and module temperature is determined by the I-V curve of the module, which must be simulated under those conditions. Algebraic procedures were used to simulate the I-V curve. Recently, neural networks have been used for the same purpose. Previous methods only take into account the irradiance and the module temperature. The model proposed is based on neural networks, and it uses not only the irradiance and the module temperature but also the angle of incidence and the instantaneous clearness index as additional inputs. The normalised clearness replaces the standard clearness index because it allows the removal of the hourly trend found in this last index. This new model improves the results obtained with previous ones as it can distinguish amongst samples with the same solar irradiance and temperature values but with different angle of incidence and instantaneous clearness index. Results show that this model could be used to improve the accuracy of the tools used to forecast the output of photovoltaic plants.

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Section: Neural Network Applied To Photovoltaic Simulationmentioning

confidence: 99%

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Piliougine

Elizondo

Mora‐López

et al. 2014

Progress in Photovoltaics

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“…. In addition, there are also many examples where this approach has been used as a tool to track the maximum power point of a PV generator, see for instance .…”

Section: Neural Network Applied To Pv Simulationmentioning

confidence: 99%

“…Neural networks have been used to estimate and forecast the global solar radiation or the clearness index such as the works by Elizondo et al [21], Yona et al [22], and Mora-López et al [23]. In addition, there are also many examples where this approach has been used as a tool to track the maximum power point of a PV generator, see for instance [24,25].…”

Section: Neural Network Applied To Pv Simulationmentioning

confidence: 99%

Photovoltaic module simulation by neural networks using solar spectral distribution

Piliougine

Elizondo

Mora‐López

et al. 2012

Progress in Photovoltaics

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A novel methodology based on artificial neural networks is proposed as an alternative to algebraic and numerical procedures to determine the I‐V curve of a module under different conditions. Although there are methods that use neural networks for approximating the I‐V curve, this is the first time that the measurement of the spectrum is incorporated as an input. In addition, a suitable selection of the training samples used to build the model is fundamental in order to get an accurate approximation. This is why a training sample selection based on a Kohonen self‐organizing map is performed in this paper instead of a random selection. With the use of this preliminary step, the performance of the network trained with spectral information improves over the one without spectral information. Copyright © 2012 John Wiley & Sons, Ltd.

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“…NNs are well established in pattern recognition and other applications (Wang et al, 2012;Wong et al, 2009;Zhang and Bai, 2008). NNs are preferred over other existing clustering methods (fuzzy or SVMs), because they can be easily trained using the Kohonen learning algorithm (Kohonen, 1990), and are computationally efficient to implement online.…”

Section: Second Stage: Classification Of Energy Consumption Patternsmentioning

confidence: 99%

Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems

Cao

Pang

Gan

et al. 2012

Transactions of the Institute of Measurement and Control

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To reduce energy consumption for sustainable and energy-efficient manufacturing, continuous energy monitoring and process tracking of industrial machines are essential. In this paper, we introduce a novel approach to reduce the number of required sensors in process tracking by identifying the operational states based on real-time energy data. Finite-state machines are used to model the engineering processes, and a two-stage framework for online classification of real-time energy measurement data in terms of machine operational states is proposed for energy audit and machine scheduling. The first stage uses advanced signal processing techniques to reduce noise while preserving important features, and the second stage uses intelligent pattern recognition algorithms to cluster energy consumption patterns. Our proposed two-stage framework is evaluated on an industrial injection moulding system using a Savizky-Golay filter and a neural network, and our experimental results show a 95.85% accuracy in identification of machine operational states.

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On-line neural network training for maximum power point tracking of PV power plant

Cited by 19 publications

References 10 publications

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Modelling photovoltaic modules with neural networks using angle of incidence and clearness index

Photovoltaic module simulation by neural networks using solar spectral distribution

Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems

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