# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 5 Conclusions  
The present manuscript presents the modeling and application of an oscillating-water-column plant containing a doubly fed induction generator driven by a Wells turbine. The paper introduces a new ANN-based rotational speed control strategy for the stalling phenomenon within the Wells turbine that proved to limit the produced power. Figure 28: Flow coefficients for uncontrolled case and ANN-based rotational speed control. Figure 30: Produced power for uncontrolled OWC and ANN-based rotational speed controlled OWC. Figure 29: Turbine torque for uncontrolled case and ANN-based rotational speed control. The suggested ANN rotational speed control contains a trained ANN to identify strong incoming waves susceptible of provoking the stalling phenomenon in the oscillating water column. The wave surface elevation measurements considered to train the artificial neural network were recorded by an ADCP. Once trained, the network can generate the suitable rotational speed reference. The rotational speed reference was used within the ANN-based rotational speed control scheme. For the development of a proper ANN structure for the problem various simulations were carried out with the forward approach-based trial-and-error rule. The best network structure was chosen by taking into consideration the best performance index, the number of epochs and the least complex structure. The ANN training process revealed that the best MSE was achieved using MLP networks. The selected network structure converged in a tolerable number of epochs. With the trained ANN within the rotational speed control scheme, it was able to identify strong waves to evade the stalling phenomenon. The results demonstrate the efficiency of the proposed control for various sea conditions. Moreover, a demonstrative study case considering measured surface elevation data of waves in Mutriku displayed decent avoidance of the stalling behavior when compared to the measured power of an OWC of NEEIDA facility. Since the idea is to generate a rotational speed reference that solely depends on the wave amplitude and period, with a proper database of wave data, the ANN training is neither hard nor time-consuming and it is easily implemented in a conventional RSC control scheme which might already exist in existing plants. This makes the proposed control transferable to any similar sites. All authors contributed to the modeling and implementation of the OWC plant. All authors conceived, developed and implemented the control techniques. All authors analyzed and validated the results. All authors contributed to writing-review and editing the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Basque Government, through project IT1207-19 and by the MCIU/MINECO through RTI2018-094902-B-C21/RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE). The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
ADCP Acoustic Doppler Current Profiler  
AWAC Acoustic Wave and Current  
ANN Artificial Neural Network  
BTB Back-to-back converter  
DC Direct Current  
DFIG Doubly Fed Induction Generator  
EVE Ente Vasco de la Energia (Basque Energy Agency)  
GHG Greenhouse Gases  
GSC Grid-Side Converter  
IPCC Intergovernmental Panel on Climate Change  
LMA Levenberg-Marquardt Algorithm  
MLP Multi-Layer Perceptron  
MPPT Maximum Power Point Tracking  
MSE Mean Squared Error  
OWC Oscillating Water Column  
PI Proportion Integral  
PLL Phase Locked Loop  
PWM Pulse Width Modulation  
RSC Rotor-Side Converter  
SWL Still Water Level  
WEC Wave Energy Converter