jun Yan

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.

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.

Nacelle orientation based health indicator for wind turbines

A nacelle is the center hub of a wind turbine, with blades attached to it, and it is a key component to be controlled to ensure that the wind turbine face the incoming wind to capture the most energy. Ideally, the nacelle orientation and the wind direction should be opposite to each other. However, a noneffective control system may not be able to adjust the nacelle to its proper position, which can be a soft failure; or, in the worst case scenario like a spindle failure, the nacelle orientation is completely fixated at one direction since the turbine is shut down, which is a hard failure. Besides the well-known diagnostics tools using power and wind data to estimate the health state of a wind turbine, nacelle orientation is yet another informative variable to be added to the set of diagnostics tools, especially to detect the soft failures. In the SCADA (supervisory control and data acquisition) data set of wind turbines, there are dozens of measurement variables, on ambient weather, power, other electrical quantities, and some mechanical quantities. We have observed that the nacelle orientation exhibits a much more irregular or volatile pattern than the other variables, but the relationship between the wind direction and nacelle orientation is still effective to indicate an abnormal health state. We develop a metric to use nacelle orientation as the health indicator of wind turbines, and evaluate it using the data of nearly a hundred of wind turbines at a wind farm. Our method to construct the health indicator by nacelle orientation is driven by the SCADA data, without any pre-determined modeling, and hence it is automatic, adaptive and widely applicable on arbitrary number of wind turbines.

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.

1.18 Ocean (Marine) Energy

Marine energy is one of the most widely available types of renewable energy – 71% of the Earth that is covered by oceans could potentially satisfy the electricity demands of the whole world. Marine energy encompasses wave energy, tidal energy, ocean thermal energy, osmotic energy, and ocean current energy; most marine energy harnessing technologies are still in their infancy. The promising prospect of marine energy attracts worldwide attention from researchers and industry developers. This article introduces the state of the art of marine energy and discusses its economic and environmental impacts.

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.

Molecule binding parameter estimation from Surface Plasmon Resonance measurements

Surface Plasmon Resonance (SPR) is a highly sensitive technique that utilizes the properties of surface plasmons to detect the subtle changes in mass caused by adsorption or desorption of molecules. The SPR technique is appealing because the amount of adsorption or desorption is detected in real time, without the need to label the adsorbate or prepare samples using a complex procedure. Although much progress has been made in the analysis of SPR images several challenges remain. In this paper, we addressed two of these challenges, 1) automatic detection of regions of interest (ROIs) and 2) accurate estimation of the molecular association and disassociation parameters. With hundreds of ROIs on a single SPR video frame image, our procedure to automatically detect the ROIs greatly reduces the labor and time. The gray level values of the ROIs were extracted over time and used to estimate the molecule binding parameters, which are vital in biosensing applications. The parameters were estimated using Particle Swarm Optimization (PSO) and the standard Levenberg-Marquardt (LM) algorithm. A comparison of the results revealed that the PSO algorithm achieved a much lower mean squared error (MSE), and hence more accurate, than the LM algorithm for all of the active ROIs.

Size optimization of battery-supercapacitor hybrid energy storage system for 1MW grid connected PV array

This paper studies the optimum (most economical) scaling of a battery and supercapacitor hybrid storage for 1 MW photovoltaic (PV) arrays for a one hour dispatching period for an entire day. The optimization is based on the time constant of a low pass filter (LPF) that is used to allocate the power between a battery and a supercapacitor (SC). This paper also presents the price comparison between lead-acid and li-ion battery frameworks. Extensive simulations were conducted for thorough analysis of various hybrid energy storage system (HESS) combinations. According to the results, HESS outperforms battery or SC-only operation.

Prognostics by interacting multiple model estimator

In prognostics, the modeling of the failure models is complicated even for a single component, such as fatigue crack growth. For a complex system, there are a large number of components, and hence the failing models can be even more complicated due to diverse sub-systems and their components. The remaining useful life (RUL) of the system, as a whole, depends on many factors and there are often sudden changes in its progression pattern. The interacting multiple model (IMM) estimator is a filtering technique that tracks multiple models and reports the probability of each model. The information fusion ability of IMM with a built-in probabilistic metric is highly desirable in failure model tracking and higher level fusion. A general framework is proposed to describe the system health by a health index, then the RUL can be evaluated as the current health value divided by the degradation rate of the health index at that moment. Within the general framework of a health index, an IMM estimator is proposed to identify the failure models and evaluate both the values and the confidence interval of the RUL. Simulations on various health degradation models are carried out to illustrate the effectiveness of the IMM based RUL estimation. Specifically, the RUL sub-models can be with nearly constant degradation rate, with accelerated growing degradation rate, or some drastic break-down due to environmental changes such as a hard failure. In simulations, the truth is known, and hence the performance of the RUL estimator can be precisely assessed. This paper has not only proposed a fusion scheme to handle various failure models, but also presented the data generation procedure of health index in various situations. Such data sets can be used as benchmarks to compare various prognostics techniques.