Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine

2010 ◽  
Vol 35 (9) ◽  
pp. 2008-2014 ◽  
Author(s):  
M. Jafarian ◽  
A.M. Ranjbar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Turbine ◽  
Fuzzy Modeling ◽  
Energy Output ◽  
Modeling Techniques ◽  
Artificial Neural

scholarly journals Artificial Neural Networks to Predict the Power Output of a PV Panel

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Valerio Lo Brano ◽  
Giuseppina Ciulla ◽  
Mariavittoria Di Falco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Power Output ◽  
Department Of Energy ◽  
Electricity Production ◽  
Energy Output ◽  
Weather Data ◽  
Output Data ◽  
Artificial Neural

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

scholarly journals Fault Detection of Wind Turbine Sensors Using Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ayse Gokcen Kavaz ◽  
Burak Barutcu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Wind Turbine ◽  
High Performance ◽  
Detection Algorithm ◽  
Sensor Calibration ◽  
Isolation Technique ◽  
Measurement Validation ◽  
Artificial Neural

This paper proposes a method for sensor validation and fault detection in wind turbines. Ensuring validity of sensor measurements is a significant part in overall condition monitoring as sensor faults lead to incorrect results in monitoring a system’s state of health. Although identifying abrupt failures in sensors is relatively straightforward, calibration drifts are more difficult to detect. Therefore, a detection and isolation technique for sensor calibration drifts on the purpose of measurement validation was developed. Temperature sensor measurements from the Supervisory Control and Data Acquisition system of a wind turbine were used for this aim. Low output rate of the measurements and nonlinear characteristics of the system drive the necessity to design an advanced fault detection algorithm. Artificial neural networks were chosen for this purpose considering their high performance in nonlinear environments. The results demonstrate that the proposed method can effectively detect existence of calibration drift and isolate the exact sensor with faulty behaviour.

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