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Wave energy converters (WECs) face difficulties such as low operating range, low power output and, fluctuating power. A seamless synchronization among WEC components is required to get a better performance. For control system studies, the model should capture all the necessary dynamics involved in each conversion stages, however the interlinked complexity in each subsystem increases the computation time. This article presents a reduced order wave-to-wire (WTW) model of an oscillating water column (OWC) based WEC. The approach involves modeling of hydrodynamic and aerodynamic coupling of the capture chamber, aerodynamic and thermodynamic coupling inside the capture chamber, aerodynamic and rotor dynamic coupling in air turbine; and rotor dynamics and generator dynamics in the turbine generator coupling. The result shows that the model retains its fundamental dynamics and reduces the number of unknowns to describe the state space. The model indicates the correlation of each variable represented in the state space. The model predicted power output for different sea state. It also shows that the accuracy and the efficiency of the model are acceptable for OWC-WEC control system studies. The present model can be used as a time domain tool to design an effective control system for OWC device for different sea states, and the overall device performance can be improved significantly.

An oscillating water column (OWC) wave energy device can use a bidirectional flow impulse turbine for energy extraction. The airflow velocity profiles during inhalation and exhalation of the OWC are asymmetric, and the flowrate at exhalation is higher. Hence, this article presents an optimized turbine to work for asymmetrical airflow conditions. The rotor blade setting angle (γ) was made asymmetric with a non-zero setting angle, and the guide vane shape was optimized. The unsteady Reynolds-averaged Navier-Stokes equations (URANS) were solved to get the objective function values for the Latin hypercube sampling (LHS) generated design space. A pseudo-sinusoidal velocity profile replicating the flow inside the OWC device was used for the transient simulations. A weighted average surrogate model generated the initial population for a hybrid genetic algorithm to produce optimum designs. The effect of turbine induced damping and starting characteristics were analyzed under irregular flow conditions obtained from the Pierson–Moskowitz wave spectrum. The optimized turbine resulted in better starting and running characteristics. Also, the mean efficiency of the optimized turbine was increased relatively by 9.5%.

Internet of Things (IoT) encompasses numerous old and new technologies under its umbrella; which means there is a huge diversity in types of devices, protocols and mechanisms that are to be supported. Things by themselves need to inter-network and collaborate to provide the necessary level of service expected out of them, working in isolation does not help in many cases. Interoperability issues have been attempted to be solved in broadly five different ways — publishing standards, reference architectures and frameworks, defining protocols and media-type standards and by using abstract interface definition languages and semantic ontologies. An interoperable environment requires a well-established data-model to provide properties, behavior and message formats and a framework to support selecting the right protocol along with an ontology indicating what the data means based on the context. Application layer is the primary interface for interacting with the systems and services; hence it makes it important to provide an interoperable environment at this layer enabling dynamic composition and rapid development of new services. IoT requires a common construct that is adaptive, expressive and dynamic in nature abstracting out the communications, behavior and models that allows diverse set of device stacks to interoperate. Such constructs enables device vendors to publish changes systematically and incrementally without affecting the advances in the rapid development of applications.

This paper deals with the efficiency maximization of a wave energy converter (WEC). The WEC is an oscillating water column (OWC) device and drives a permanent magnet synchronous generator (PMSG) through a bidirectional flow impulse-turbine. The converter faces challenges such as large peak-to-average power ratio, low overall efficiency, and inefficient energy absorption for regular and irregular sea states. In this context, a higher order sliding mode controller (HOSMC) was proposed, and its gains were optimized to control through the best efficiency point tracking (BEPT) of the turbine. The flow through the turbine-passage was simulated by the computational fluid dynamics (CFD) technique, and the BEPT characteristics were obtained. An adaptive inertia-weight particle-swarm algorithm and a grouped grey-wolf algorithm were used for optimization. The Optimized HOSMC reduced chattering, minimized the reaching time and improved the mean efficiency by about 67% compared to the uncontrolled cases. In addition, the relative improvement of the mean efficiency was at least 4.8% compared to conventional controllers. The controller reduced the peak-to-average power ratio of at least 35.6% relative to the uncontrolled case of the turbine under different sea states.

This work deals with unsteady analysis and control of a bi-directional impulse turbine equipped in oscillating water column (OWC) wave energy converter (WEC). The Unsteady Reynolds-averaged Navier-Stokes equations (URANS) were solved to get the turbine characteristics and the maximum efficiency tracking for different axial velocities. The grouped grey wolf optimization (GGWO) algorithm finds the optimal gains of proportional-integral-derivative (PID) controllers of a permanent magnet synchronous generator (PMSG), such that a maximum efficiency tracking can be achieved. The URANS analysis show that the best efficiency point (BEP) occurs at a flow coefficient of 1.25. The GGWO based controller increases efficiency by 61.5% and 4% compared to the uncontrolled and conventionally controlled case, respectively and limits the peak-to-average power ratio by 38.6% compared to the uncontrolled case.