ANN-Based Airflow Control for an Oscillating Water Column Using Surface Elevation Measurements

Abstract

Oscillating water column (OWC) plants face power generation limitations due to the stalling phenomenon. This behavior can be avoided by an airflow control strategy that can anticipate the incoming peak waves and reduce its airflow velocity within the turbine duct. In this sense, this work aims to use the power of artificial neural networks (ANN) to recognize the different incoming waves in order to distinguish the strong waves that provoke the stalling behavior and generate a suitable airflow speed reference for the airflow control scheme. The ANN is, therefore, trained using real surface elevation measurements of the waves. The ANN-based airflow control will control an air valve in the capture chamber to adjust the airflow speed as required. A comparative study has been carried out to compare the ANN-based airflow control to the uncontrolled OWC system in different sea conditions. Also, another study has been carried out using real measured wave input data and generated power of the NEREIDA wave power plant. Results show the effectiveness of the proposed ANN airflow control against the uncontrolled case ensuring power generation improvement.

Self-Adaptive Global-Best Harmony Search Algorithm-Based Airflow Control of a Wells-Turbine-Based Oscillating-Water Column

“…Hence, the resulting turbine torque is affected by the stalling behavior (see Figure 4d). The Wells turbine's stalling behavior can be evaded if the flow coefficient is constantly regulated [24,25]. From the expression (9), the flow coefficient relies on the airflow velocity in the turbine duct.…”

Symmetry-Breaking for Airflow Control Optimization of an Oscillating-Water-Column System

“…As a result, the attained turbine torque illustrated in Figure 6 shows no stalling during the first case and a significant decline at the crest of the second case because of the stall effect which reduces the obtained torque in terms of average value. The Wells turbine's stalling behavior can be evaded if the flow coefficient is constantly regulated [39,45]. From Expression (9), the flow coefficient relies on the airflow velocity in the turbine duct.…”

“…Airflow velocity evolution with different wave amplitudes and periods[45].The stall effect is demonstrated by investigating the uncontrolled OWC system with different sea states. The first sea condition is waves with a 10 s period and 0.9 m wave amplitude beginning at 0 s until 22.5 s. The second sea condition is waves with a 10 s period and a 1.2 m wave amplitude beginning at 22.5 s until 50 s. It may be observed inFigure 5that during the first case the OWC offers a flow coefficient below the threshold value 0.3, but, during the second case, it surpasses 0.3.…”

Centralized Airflow Control to Reduce Output Power Variation in a Complex OWC Ocean Energy Network

“…In [37], a fuzzy gain scheduled and PI-type airflow controller for an OWC, which was an advanced version of the airflow controller, were presented earlier in [23,24]. Recently, some artificial intelligence-based airflow controller techniques have been proposed [38][39][40]. A self-adaptive global best harmony search algorithm-based airflow control of OWC was proposed by [38] wherein four variants of the harmony search algorithm were implemented and tested to optimize the control design of the PID controller in the airflow control scheme.…”

“…In [39], another approach for airflow control was proposed which utilized symmetry-breaking concept to design the controller. Furthermore, an artificial neural network-based airflow controller was designed using surface elevation measurements [40]. is study considered real measured wave input data and generated power of the NEREIDA wave power plant.…”