Contributions to uncertainty reduction in the estimation of PV plants performance

Esta tesis doctoral presenta un procedimiento integral de control de calidad en centrales fotovoltaicas, que comprende desde la fase inicial de estimación de las expectativas de producción hasta la vigilancia del funcionamiento de la instalación una vez en operación, y que permite reducir la incertidumbre asociada su comportamiento y aumentar su fiabilidad a largo plazo, optimizando su funcionamiento. La coyuntura de la tecnología fotovoltaica ha evolucionado enormemente en los últimos años, haciendo que las centrales fotovoltaicas sean capaces de producir energía a unos precios totalmente competitivos en relación con otras fuentes de energía. Esto hace que aumente la exigencia sobre el funcionamiento y la fiabilidad de estas instalaciones. Para cumplir con dicha exigencia, es necesaria la adecuación de los procedimientos de control de calidad aplicados, así como el desarrollo de nuevos métodos que deriven en un conocimiento más completo del estado de las centrales, y que permitan mantener la vigilancia sobre las mismas a lo largo del tiempo. Además, los ajustados márgenes de explotación actuales requieren que durante la fase de diseño se disponga de métodos de estimación de la producción que comporten la menor incertidumbre posible. La propuesta de control de calidad presentada en este trabajo parte de protocolos anteriores orientados a la fase de puesta en marcha de una instalación fotovoltaica, y las complementa con métodos aplicables a la fase de operación, prestando especial atención a los principales problemas que aparecen en las centrales a lo largo de su vida útil (puntos calientes, impacto de la suciedad, envejecimiento…). Además, incorpora un protocolo de vigilancia y análisis del funcionamiento de las instalaciones a partir de sus datos de monitorización, que incluye desde la comprobación de la validez de los propios datos registrados hasta la detección y el diagnóstico de fallos, y que permite un conocimiento automatizado y detallado de las plantas. Dicho procedimiento está orientado a facilitar las tareas de operación y mantenimiento, de manera que se garantice una alta disponibilidad de funcionamiento de la instalación. De vuelta a la fase inicial de cálculo de las expectativas de producción, se utilizan los datos registrados en las centrales para llevar a cabo una mejora de los métodos de estimación de la radiación, que es la componente que más incertidumbre añade al proceso de modelado. El desarrollo y la aplicación de este procedimiento de control de calidad se han llevado a cabo en 39 grandes centrales fotovoltaicas, que totalizan una potencia de 250 MW, distribuidas por varios países de Europa y América Latina. ABSTRACT This thesis presents a comprehensive quality control procedure to be applied in photovoltaic plants, which covers from the initial phase of energy production estimation to the monitoring of the installation performance, once it is in operation. This protocol allows reducing the uncertainty associated to the photovoltaic plants behaviour and increases their long term reliability, therefore optimizing their performance. The situation of photovoltaic technology has drastically evolved in recent years, making photovoltaic plants capable of producing energy at fully competitive prices, in relation to other energy sources. This fact increases the requirements on the performance and reliability of these facilities. To meet this demand, it is necessary to adapt the quality control procedures and to develop new methods able to provide a more complete knowledge of the state of health of the plants, and able to maintain surveillance on them over time. In addition, the current meagre margins in which these installations operate require procedures capable of estimating energy production with the lower possible uncertainty during the design phase. The quality control procedure presented in this work starts from previous protocols oriented to the commissioning phase of a photovoltaic system, and complete them with procedures for the operation phase, paying particular attention to the major problems that arise in photovoltaic plants during their lifetime (hot spots, dust impact, ageing...). It also incorporates a protocol to control and analyse the installation performance directly from its monitoring data, which comprises from checking the validity of the recorded data itself to the detection and diagnosis of failures, and which allows an automated and detailed knowledge of the PV plant performance that can be oriented to facilitate the operation and maintenance of the installation, so as to ensure a high operation availability of the system. Back to the initial stage of calculating production expectations, the data recorded in the photovoltaic plants is used to improved methods for estimating the incident irradiation, which is the component that adds more uncertainty to the modelling process. The development and implementation of the presented quality control procedure has been carried out in 39 large photovoltaic plants, with a total power of 250 MW, located in different European and Latin-American countries.

On the Velocity and Acceleration Estimation from Discrete Time-Position Sensors

In this work is addressed the topic of estimation of velocity and acceleration from digital position data. It is presented a review of several classic methods and implemented with real position data from a low cost digital sensor of a hydraulic linear actuator. The results are analyzed and compared. It is shown that static methods have a limited bandwidth application, and that the performance of some methods may be enhanced by adapting its parameters according to the current state.

Assessment of a Biomass Utilization Alternative for Colorado State Parks

Colorado State Parks is currently managing overstocked wildland fuels on critical Parklands through mechanical mastication projects, but wants to determine the feasibility of alternatively turning thinned materials into marketable biomass fuel. Accurate estimates of potential volume and associated production costs are critical to determining this feasibility. In this study, forest inventory data were used to estimate potential volume and combined with data from previous studies to estimate potential harvest and haul costs and revenues. This study reveals that a biomass utilization alternative may be an economically feasible option for State Parks to pursue and in many cases may be more economically favorable than current mechanical treatments.

Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown.

Algorithm for Correcting the Keratometric Error in the Estimation of the Corneal Power in Eyes With Previous Myopic Laser Refractive Surgery

Purpose: To calculate theoretically the errors in the estimation of corneal power when using the keratometric index (nk) in eyes that underwent laser refractive surgery for the correction of myopia and to define and validate clinically an algorithm for minimizing such errors. Methods: Differences between corneal power estimation by using the classical nk and by using the Gaussian equation in eyes that underwent laser myopic refractive surgery were simulated and evaluated theoretically. Additionally, an adjusted keratometric index (nkadj) model dependent on r1c was developed for minimizing these differences. The model was validated clinically by retrospectively using the data from 32 myopic eyes [range, −1.00 to −6.00 diopters (D)] that had undergone laser in situ keratomileusis using a solid-state laser platform. The agreement between Gaussian (PGaussc) and adjusted keratometric (Pkadj) corneal powers in such eyes was evaluated. Results: It was found that overestimations of corneal power up to 3.5 D were possible for nk = 1.3375 according to our simulations. The nk value to avoid the keratometric error ranged between 1.2984 and 1.3297. The following nkadj models were obtained: nkadj= −0.0064286r1c + 1.37688 (Gullstrand eye model) and nkadj = −0.0063804r1c + 1.37806 (Le Grand). The mean difference between Pkadj and PGaussc was 0.00 D, with limits of agreement of −0.45 and +0.46 D. This difference correlated significantly with the posterior corneal radius (r = −0.94, P < 0.01). Conclusions: The use of a single nk for estimating the corneal power in eyes that underwent a laser myopic refractive surgery can lead to significant errors. These errors can be minimized by using a variable nk dependent on r1c.

Estimation of Key Dates and Stages in Rice Crops Using Dual-Polarization SAR Time Series and a Particle Filtering Approach

Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods.

Handbook for estimation of labor supply by use of a community survey.

Title from cover.

Optimal design for model discrimination and parameter estimation for itraconazole population pharmacokinetics in cystic fibrosis patients

Optimal sampling times are found for a study in which one of the primary purposes is to develop a model of the pharmacokinetics of itraconazole in patients with cystic fibrosis for both capsule and solution doses. The optimal design is expected to produce reliable estimates of population parameters for two different structural PK models. Data collected at these sampling times are also expected to provide the researchers with sufficient information to reasonably discriminate between the two competing structural models.

Estimating state-contingent production frontiers

Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to conventional estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies, and other quantities of economic interest.

On parameter estimation in population models

We describe methods for estimating the parameters of Markovian population processes in continuous time, thus increasing their utility in modelling real biological systems. A general approach, applicable to any finite-state continuous-time Markovian model, is presented, and this is specialised to a computationally more efficient method applicable to a class of models called density-dependent Markov population processes. We illustrate the versatility of both approaches by estimating the parameters of the stochastic SIS logistic model from simulated data. This model is also fitted to data from a population of Bay checkerspot butterfly (Euphydryas editha bayensis), allowing us to assess the viability of this population. (c) 2006 Elsevier Inc. All rights reserved.

Estimating State-contingent Production Frontiers

Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to conventional estimation approaches, we find that estimating production frontiers in a statecontingent framework produces significantly different estimates of elasticities, firm technical efficiencies and other quantities of economic interest.

Putting people first? Chinese state-owned enterprises' adjustment to globalisation

This paper analyses the impact of FDI on the employment, productivity, profitability and survival performance of urban SOEs in China, with the aid of a rich panel data set over the period 1999–2005. Our estimation strategy controls for the endogeneity of a number of regressors and accounts for firm-level unobserved heterogeneity. Four key results emerge from the analysis: (i) Firmlevel foreign finance enhances the employment and productivity growth of SOEs, as well as their survival prospects; (ii) Competition from sectoral FDI has a deleterious impact on the growth and survival probability of SOEs without access to any foreign capital; (iii) Export-oriented FDI in downstream sectors has negative performance ramifications; and (iv) There are no discernible spillover effects that can be attributed to FDI in upstream sectors, suggesting limited linkages between multinational firms and SOEs.

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Hidden state models for time series

Amongst all the objectives in the study of time series, uncovering the dynamic law of its generation is probably the most important. When the underlying dynamics are not available, time series modelling consists of developing a model which best explains a sequence of observations. In this thesis, we consider hidden space models for analysing and describing time series. We first provide an introduction to the principal concepts of hidden state models and draw an analogy between hidden Markov models and state space models. Central ideas such as hidden state inference or parameter estimation are reviewed in detail. A key part of multivariate time series analysis is identifying the delay between different variables. We present a novel approach for time delay estimating in a non-stationary environment. The technique makes use of hidden Markov models and we demonstrate its application for estimating a crucial parameter in the oil industry. We then focus on hybrid models that we call dynamical local models. These models combine and generalise hidden Markov models and state space models. Probabilistic inference is unfortunately computationally intractable and we show how to make use of variational techniques for approximating the posterior distribution over the hidden state variables. Experimental simulations on synthetic and real-world data demonstrate the application of dynamical local models for segmenting a time series into regimes and providing predictive distributions.