## 6 Conclusions  
The system's overall efficiency is heavily influenced by the efficiency of each sub-unit. This is due to the OWC-WEC attaching to the total system in the order of chamber-turbine-generator. The chamber and turbine, which are the first sub-units, are directly affected by the physical damping term that is produced from the generator and have a mutual influence on each other. Therefore, it is difficult to strategize a real-time method to maximize the entire system efficiency. Otherwise, analysis of the time domain is subject to the Newtonian mechanics, and it does not require the constant angular speed of the turbine that was assumed in the frequency domain analysis, since the angular acceleration level of the turbine can be addressed in the Newtonian mechanics. Figure 16: Time averaged results of the CAV controller under the PM irregular wave model with various \(T_{p}\) and \(H_{s}\) for \(T_{m}=5000\) s when \(\Omega_{d}=550\) rad\(/\)s: (**a**) relative power extraction from turbine; (**b**) relative average turbine efficiency. Figure 15: Time averaged results of the CAV controller under the PM irregular wave model with various \(T_{p}\) and \(H_{s}\) for \(T_{m}=5000\) s when \(\Omega_{d}=400\) rad\(/\)s: (**a**) relative power extraction from turbine; (**b**) relative average turbine efficiency. Thus, we have discussed the real-time control strategy under the assumption that it is related to an ideal electric DC generator, of which we can only control its terminal load. We designed the controller with two objectives in mind: (1) maximizing the turbine's instant efficiency (i-MET); and (2) regulating the turbine's spin speed (CAV). We then demonstrated the effectiveness of the controller by applying it to an OWC-turbine combined WEC system under an irregular wave environment, running a simulation in the time domain and comparing it to the maximum when the terminal load is constant with \(b_{g}\) between \(10^{-3}\) and one. The i-MET controller, which tracks the turbine's maximum efficiency, requires the function of the input and torque coefficients, as well as real-time measuring of the turbine's angular velocity and air flow speed. This simulation assumes that the turbine's characteristics are quasi-static. As seen in the results of Section 4, the turbine's instant efficiency was at its maximum within its functional intervals. Since tracking the turbine's maximum efficiency does not guarantee the optimal operation of the WEC-system as a whole, we compared the power extraction obtained by the controllers with the maximum power extraction that has no control under a constant damping coefficient with irregular waves that have varying wave heights and periods. Figure 13, which depicts the relative power extractions and relative efficiencies, reveals that the system maintains a relative power extraction over 0.94 for most wave states. This system also manages to achieve relative power extractions greater than one in certain states and also provides a relative turbine efficiency over 0.94 in most areas. In addition, it can be inferred that there is no correlation between relative power extractions and relative efficiencies with the i-MET controller. As seen in Figures 4 and 5, the angular velocity of the rotor fluctuates with the i-MET controller that yields a very poor quality of electricity. However, the value of this controller rests on the fact that we can measure the maximum power extraction without knowledge of the ideal damping load in a chamber-turbine-generator combined system. The i-MET controller can be regarded as a type of MPPT (maximum power point tracking) controller in the aspect of a controller for maximum output. While the general MPPT algorithm decides the load condition by comparison between the averaged current and past performances [52], the i-MET controller decides the load condition by comparison between the current and reference states.