Robust speed control for a variable speed wind turbine

Modern wind turbines are designed in order to work in variable speed opera-tions. To perform this task, these turbines are provided with adjustable speed generators, like the double feed induction generator (DFIG). One of the main advantages of adjustable speed generators is improving the system efficiency compared with _xed speed generators, because turbine speed can be adjusted as a function of wind speed in order to maximize the output power. However, this system requires a suitable speed controller in order to track the optimal reference speed of the wind turbine. In this work, a sliding mode control for variable speed wind turbines is proposed. The proposed design also uses the vector oriented control theory in order to simplify the DFIG dynamical equations. The stability analysis of the proposed controller has been carried out under wind variations and pa-rameter uncertainties using the Lyapunov stability theory. Finally, the simulated results show on the one hand that the proposed controller provides a high-performance dynamic behavior, and on the other hand that this scheme is robust with respect to parameter uncertainties and wind speed variations, which usually appear in real systems.

Adaptive variable structure control law for a variable speed wind turbine

Presentado en el 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11

Sliding mode control law for a variable speedwind turbine

Modern wind turbines are designed in order to work in variable speed operations. To perform this task, wind turbines are provided with adjustable speed generators, like the double feed induction generator. One of the main advantage of adjustable speed generators is improving the system efficiency compared to fixed speed generators, because turbine speed can be adjusted as a function of wind speed in order to maximize the output power. However this system requires a suitable speed controller in order to track the optimal reference speed of the wind turbine. In this work, a sliding mode control for variable speed wind turbines is proposed. An integral sliding surface is used, because the integral term avoids the use of the acceleration signal, which reduces the high frequency components in the sliding variable. The proposed design also uses the vector oriented control theory in order to simplify the generator dynamical equations. The stability analysis of the proposed controller has been carried out under wind variations and parameter uncertainties by using the Lyapunov stability theory. Finally simulated results show, on the one hand that the proposed controller provides a high-performance dynamic behavior, and on the other hand that this scheme is robust with respect to parameter uncertainties and wind speed variations, that usually appear in real systems.

Sliding mode control strategy for variable speed wind turbines

EFTA 2009

Somatisation in primary care: experiences of primary care physicians involved in a training program and in a randomised controlled trial

Background: A new intervention aimed at managing patients with medically unexplained symptoms (MUS) based on a specific set of communication techniques was developed, and tested in a cluster randomised clinical trial. Due to the modest results obtained and in order to improve our intervention we need to know the GPs' attitudes towards patients with MUS, their experience, expectations and the utility of the communication techniques we proposed and the feasibility of implementing them. Physicians who took part in 2 different training programs and in a randomised controlled trial (RCT) for patients with MUS were questioned to ascertain the reasons for the doctors' participation in the trial and the attitudes, experiences and expectations of GPs about the intervention. Methods: A qualitative study based on four focus groups with GPs who took part in a RCT. A content analysis was carried out. Results: Following the RCT patients are perceived as true suffering persons, and the relationship with them has improved in GPs of both groups. GPs mostly valued the fact that it is highly structured, that it made possible a more comfortable relationship and that it could be applied to a broad spectrum of patients with psychosocial problems. Nevertheless, all participants consider that change in patients is necessary; GPs in the intervention group remarked that that is extremely difficult to achieve. Conclusion: GPs positively evaluate the communication techniques and the interventions that help in understanding patient suffering, and express the enormous difficulties in handling change in patients. These findings provide information on the direction in which efforts for improving intervention should be directed.

Variable structure control for maximum wind power extraction

EuroPES 2009

Sea-level rise in Hawaii: Implications for future shoreline locations and Hawaii coastal management

Management of coastal development in Hawaii is based on the location of the certified shoreline, which is representative of the upper limit of marine inundation within the last several years. Though the certified shoreline location is significantly more variable than long-term erosion indicators, its migration will still follow the coastline's general trend. The long-term migration of Hawaii’s coasts will be significantly controlled by rising sea level. However, land use decisions adjacent to the shoreline and the shape and nature of the nearshore environment are also important controls to coastal migration. Though each of the islands has experienced local sea-level rise over the course of the last century, there are still locations across the islands of Kauai, Oahu, and Maui, which show long- term accretion or anomalously high erosion rates relative to their regions. As a result, engineering rules of thumb such as the Brunn rule do not always predict coastal migration and beach profile equilibrium in Hawaii. With coastlines facing all points of the compass rose, anthropogenic alteration of the coasts, complex coastal environments such as coral reefs, and the limited capacity to predict coastal change, Hawaii will require a more robust suite of proactive coastal management policies to weather future changes to its coastline. Continuing to use the current certified shoreline, adopting more stringent coastal setback rules similar to Kauai County, adding realistic sea-level rise components for all types of coastal planning, and developing regional beach management plans are some of the recommended adaptation strategies for Hawaii. (PDF contains 4 pages)

Regenerative amplifier with continuously variable pulse duration used in an optical parametric chirped-pulse amplification laser system for synchronous pumping

A Nd:glass regenerative amplifier has been set up to generate the pumping pulse with variable pulse width for an optical parametric chirped-pulse amplification (OPCPA) laser system. Each pulse of the pulse train from a cw self-mode-locking femtosecond Ti:sapphire oscillator is stretched to approximate to300 ps at 1062 nm to be split equally and injected into a nonlinear crystal and the Nd:glass regenerative amplifier, as the chirped signal pulse train and the seed pulse train of the pumping laser system, respectively. By adjusting the cavity length of the regenerative amplifier directly, the width of amplified pulse could be varied continuously from approximate to300 ps to approximate to3 ns. The chirped signal pulse for the OPCPA laser system and the seed pulse for the pumping laser system come from the same oscillator, so that the time jitter between the signal pulse and the pumping pulse in optical parametric amplification stages could be <10 ps. (C) 2003 Society of Photo-Optical Instrumentation Engineers.

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.