H. Bora Karayaka

Resonance control strategy for a slider crank WEC power take-off system

This paper presents a novel slider crank power take-off system and wave tracking control methodology for efficiently converting the energy of open ocean waves into electrical energy. The Wave Energy Converter (WEC) and Power Take-off System (PTOS) are modeled and a non-parametric control strategy to maximize energy extraction under regular sinusoidal wave condition is introduced. Energy extraction results from simulation are compared with theoretical optimums, and a number of factors that influence energy extraction are discussed. The study shows that a suboptimal wave energy conversion technique can be achieved.

The Development of a Rotational Wave Energy Conversion System: Design and Simulations

Electrical power generation by means of renewable energies is becoming a flourishing industry with many researchers focusing on investigating new and effective ways to harness this power. This paper presents a new proposed mechanism for harnessing the potential energy of open ocean waves and the procedures of efficiently converting this energy into electricity. Unlike many of the already proposed designs that utilize linear generators, this paper exposes some light on the idea of rotational conversion using ocean waves to make use of most-readily available generators. A simple buoy, piston, connecting rod and flywheel system fixed to a platform elevated from the sea level is designed and simulated to track the wave surface for converting the linear motion into rotational motion, which will eventually turn the generator. The results are validated with experimental data collected from a laboratory wave generator.

Energy extraction from a slider-crank wave energy converter under irregular wave conditions

A slider-crank wave energy converter (WEC) is a novel energy conversion device. It converts wave energy into electricity at a relatively high efficiency, and it features a simple structure. Past analysis on this particular WEC has been done under regular sinusoidal wave conditions, and suboptimal energy could be achieved. This paper presents the analysis of the system under irregular wave conditions; a time-domain hydrodynamics model is adopted and a rule-based control methodology is introduced to better serve the irregular wave conditions. Results from the simulations show that the performance of the system under irregular wave conditions is different from that under regular sinusoidal wave conditions, but a reasonable amount of energy can still be extracted.

An empirical method for estimating thermal system parameters based on operating data in smart grids

An experimental methodology was developed for online system identification of a thermal system or heated space. In this setting, the intelligent controller detects system parameters during normal operation and adapts its performance accordingly. The ultimate goal is to demonstrate that load leveling with demand side management can be used to reduce peak power consumption while maintaining residential room temperatures at a comfortable level. A prototype enclosure was built and equipped with a heater and thermal measuring equipment. Data was collected during a 17 hour temperature regulation experiment using a bang-bang controller similar to those commonly used for residential heating control. First and second order mathematical models were developed for thermal system identification. The mathematical models utilized the collected temperature data to estimate the net thermal resistance and capacitance using system identification techniques. Results showed the second order model to match the real system characteristics reasonably well. It was found that even for a small prototype enclosure, the estimated thermal parameters showed quite large values of thermal capacitance which can be a great asset for demand side management and control applications in a smart grid. The system identification method developed here is an important step toward the development of intelligent controllers.

An Investigation of Parametric Load Leveling Control Methodologies for Resistive Heaters in Smart Grids

The main goal in this study is to demonstrate that load levelling with demand side management in smart grids can be achieved to reduce peak power consumption while maintaining residential room temperatures at a comfortable level. A prototype enclosure was built and equipped with a heater and thermal measuring equipment. Data was collected during a 17 hour temperature regulation experiment using a traditional on-off (bang-bang) controller similar to those commonly used for residential heating control. A second order mathematical model was utilized to estimate the net thermal resistances and capacitances using system identification techniques at two different temperature set points. The enclosure system was used to determine if peak power could be reduced by slowly varying loads utilizing a different type of controller. Two different linear control techniques (using K-Factor and PI approaches) and the associated power electronics circuitry were implemented and tuned. Both controller systems successfully leveled the load and reduced the peak power demand.

Optimum parameter search for a slider-crank wave energy converter under regular and irregular wave conditions

In this work, a novel model of a wave energy converter that utilizes a slider crank power take-off system (PTOS) has been studied to determine maximum energy extraction. This system includes a buoy, slider crank linkage, and generator with a gear box. With a regular sinusoidal wave excitation force, effects of three parameters on the system are analyzed. These include the slider crank radius, connecting rod length, and phase lock offset. It is found that the slider crank radius has the largest effect on total energy extracted. Under irregular wave conditions only the effect of phase lock offset is analyzed. The results show that the control system can exhibit small percentages of performance degradation without large adverse effects on the overall energy production.

Solar farm hourly dispatching using super-capacitor and battery system

Utilizing solar energy is essential to having a clean and healthy Earth for generations to come. However, because solar power is intermittent, caused by weather changes such as clouds or temperature fluctuations, power distributers cannot rely on solar farms as a consistent power source. The solution is the integration of an energy storage system capable of absorbing and producing the necessary power to maintain a constant power for a specific amount of time, known as dispatching. This paper demonstrates a successful dispatching scheme of solar energy using a hybrid energy storage system (HESS) consisting of a battery energy storage system (BESS) and a supercapacitor energy storage system (SESS). The HESS utilizes the high energy density property (the ability to charge and discharge large amounts of energy, preferably at low frequency) of lead acid batteries and the high power density property (the ability to rapidly charge or discharge energy, at high frequency) of supercapacitors together to invoke a synergy of low-frequency and high-frequency energy storage components. The HESS is designed to increase the longevity of the traditional BESS while enabling the capability to dispatch the solar energy.

Irregular wave energy extraction analysis for a slider crank WEC power take-off system

Slider crank Wave Energy Converter (WEC) is a novel energy conversion device. It converts wave energy into electricity at a relatively high efficiency, and it features a simple structure. Past analysis on this WEC has been done under regular sinusoidal wave conditions, and a suboptimal energy could be achieved. This paper presents the analysis of the system under irregular wave conditions; a time-domain hydrodynamics model is adopted and the control methodology is modified to better serve the irregular wave conditions. Results from the simulations show that the performance of the system under irregular wave conditions is different from that under regular sinusoidal wave conditions, but still a reasonable amount of energy can be extracted.

A rotational wave energy conversion system development and validation with real ocean wave data

Recently, there has been a massive push in the research community towards green or renewable energies, specifically for electrical power generation. Fossil fuels are losing their popularity due to the associated environmental damage and hazards, and green energies are gaining momentum as significant energy resources. This paper presents a novel system and method for converting the power of open ocean waves into electrical energy that is validated utilizing real open ocean wave data in a simulation. The operation of the machine is simulated and validated under real oceanic conditions. The newly proposed system and method stand out from most wave conversion methods because this new machine utilizes rotational motion. As opposed to linear generators that most popular models currently use, this machine will convert the linear up-and-down motion of the waves into rotational motion to turn a generator.

Simulating micro-robots to find a point of interest under noise and with limited communication using Particle Swarm Optimization

This paper presents the simulation results of a swarm of micro-robots collaborating to find a point of interest in 2D space. Guided by a fitness function, the Particle Swarm Optimization (PSO) algorithm is highly efficient to explore the solution space and find such an optimum. However, in real-world scenarios in which the particles are micro-robots, there are practical constraints. The two most significant constraints are: (1) given communication and measurement noise, the fitness function evaluation will be noisy, (2) given the limited communication range of micro-robots, broadcasting the global best solution is too expensive. A neighborhood PSO (NPSO) algorithm is proposed to replace the global best by the neighborhood best. Different applications call for different fitness functions, and three benchmark functions, representing three typical scenarios, are examined: (1) a unimodal and symmetric scenario with only one global optimum, (2) a multi-modal scenario with one global optimum but many local optima, and (3) a uni-model but asymmetric scenario. For each fitness function, simulations on the effects of the two aforementioned constraints, individually or combined, are carried out. The results demonstrate that PSO is tolerant to noise up to certain level and NPSO is a practical adaptation to implement swarm intelligence in swarm robotics.