Nonlinear self-tuning control for power oscillation damping

Power systems exhibit nonlinear behavior especially during disturbances, necessitating the application of appropriate nonlinear control techniques. Lack of availability of accurate and updated models for the whole power system adds to the challenge. Conventional damping control design approaches consider a single operating condition of the system, which are obviously simple but tend to lack performance robustness. Objective of this research work is to design a measurement based self-tuning controller, which does not rely on accurate models and deals with nonlinearities in system response. Designed controller is required to ensure settling of inter-area oscillations within 10−12s, following disturbance such as a line outage. The neural network (NN) model is illustrated for the representation of nonlinear power systems. An optimization based algorithm, Levenberg-Marquardt (LM), for online estimation of power system dynamic behavior is proposed in batch mode to improve the model estimation. Careful study shows that the LM algorithm yields better closed loop performance, compared to conventional recursive least square (RLS) approach with the pole-shifting controller (PSC) in linear framework. Exploiting the capability of LM, a special form of neural network compatible with feedback linearization technique, is applied. Validation of the performance of proposed algorithm is done through the modeling and simulating heavy loading of transmission lines, when the nonlinearities are pronounced. Nonlinear NN model in the Feedback Linearization (FLNN) form gives better estimation than the autoregressive with an external input (ARX) form. The proposed identifier (FLNN with LM algorithm) is then tested on a 4−machine, 2−area power system in conjunction with the feedback linearization controller (FBLC) under varying operating conditions. This case study indicates that the developed closed loop strategy performs better than the linear NN with PSC. Extension of FLNN with FBLC structure in a multi-variable setup is also done. LM algorithm is successfully employed with the multi-input multi-output FLNN structure in a sliding window batch mode, and FBLC controller generates multiple control signals for FACTS. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed schemes: both MIMO and MISO against a conventional model based controller. A coefficient vector for FBLC is derived, and utilized online at each time instant, to enhance the damping performance of controller, transforming into a time varying controller

Measurements, Models, Systems and Design

531 s.

The fundamental effect of ingredients on the pasting and retrogradation properties of tapioca and corn starch in excess and limited water solvation medium

Native starches, low cost carbohydrate are employed by the food industry to control the textural and organoleptic properties of several starch based foods, such as baby foods, where starch co-exists and interacts with other food ingredients in the food matrix such as, sugars, acids, salts and lipids. This interaction unpredictably and unavoidably affects the organoleptic properties of the final product. This is why these food applications demand the knowledge of starch pasting and viscosity behaviour, because it is necessary to control the organoleptic properties of the finished products and also to obtain consistent viscosity from batch to batch. The objective of this study was to investigate the effect of ingredients on the viscoelastic properties of corn and tapioca starch used in the production of selected baby foods. In this study, the sugars and acids profile of the cooking medium where the starch is gelatinized was analyzed with a gas chromatograph coupled with flame ionization detector (GC-FID). The results were utilized to prepare sugars and acids starch model solutions for a process simulation to predict starch rheological behaviour. The viscoelastic properties of experiments were carried out with a Rapid Visco Analyser (RVA).Experimental results showed that all ingredients tested had a significant dilatant (shear thickening) phenomenon on the starch in excess water medium, while on the other hand, in limited water medium, the ingredients exhibited an inverse effect causing a significant pseudo plastic behaviour (shear thinning) on the starch samples. Lipids caused a significant thixotropic (shear thinning) behaviour and were evidently responsible for the irreproducibility of viscosity profile of starch types when added to the mixture. Acids caused shear thinning behaviour due to glucose hydrolysis and reduction in the degree of polymerization. Furthermore, starch lumps were formed in food matrix when starch dispersions were allowed to sediment and also gelatinized with delayed shearing, causing a clumping together of starch granules. It can be concluded that corn and tapioca starch gelatinization and pasting required for thickening, textural and organoleptic properties of puree-based baby food matrix gives a better viscoelastic properties in excess water solvation medium, in comparison to limited water medium, since cooking in excess water environment has enough solvent externally available for corn and tapioca amorphous growth ring, therefore resulting in total disruption of lamellar and crystalline order resulting in a non-thixotropic characteristics of the finished starch-based puree product. This study may be used as an applicable model for starch-based food product development. Keywords: RVA, GC-FID, sugars and acids, tapioca starch, corn starch, amylose and amylopectin, ascorbic acid, lipids, salts, viscosity, pasting and retrogradation. </p

Trellis Decoding And Applications For Quantum Error Correction

Compact, graphical representations of error-correcting codes called trellises are a crucial tool in classical coding theory, establishing both theoretical properties and performance metrics for practical use. The idea was extended to quantum error-correcting codes by Ollivier and Tillich in 2005. Here, we use their foundation to establish a practical decoder able to compute the maximum-likely error for any stabilizer code over a finite field of prime dimension. We define a canonical form for the stabilizer group and use it to classify the internal structure of the graph. Similarities and differences between the classical and quantum theories are discussed throughout. Numerical results are presented which match or outperform current state-of-the-art decoding techniques. New construction techniques for large trellises are developed and practical implementations discussed. We then define a dual trellis and use algebraic graph theory to solve the maximum-likely coset problem for any stabilizer code over a finite field of prime dimension at minimum added cost. Classical trellis theory makes occasional theoretical use of a graph product called the trellis product. We establish the relationship between the trellis product and the standard graph products and use it to provide a closed form expression for the resulting graph, allowing it to be used in practice. We explore its properties and classify all idempotents. The special structure of the trellis allows us to present a factorization procedure for the product, which is much simpler than that of the standard products. Finally, we turn to an algorithmic study of the trellis and explore what coding-theoretic information can be extracted assuming no other information about the code is available. In the process, we present a state-of-the-art algorithm for computing the minimum distance for any stabilizer code over a finite field of prime dimension. We also define a new weight enumerator for stabilizer codes over F_2 incorporating the phases of each stabilizer and provide a trellis-based algorithm to compute it.Ph.D

Planning and Control Strategies for Motion and Interaction of the Humanoid Robot COMAN+

Despite the majority of robotic platforms are still confined in controlled environments such as factories, thanks to the ever-increasing level of autonomy and the progress on human-robot interaction, robots are starting to be employed for different operations, expanding their focus from uniquely industrial to more diversified scenarios. Humanoid research seeks to obtain the versatility and dexterity of robots capable of mimicking human motion in any environment. With the aim of operating side-to-side with humans, they should be able to carry out complex tasks without posing a threat during operations. In this regard, locomotion, physical interaction with the environment and safety are three essential skills to develop for a biped. Concerning the higher behavioural level of a humanoid, this thesis addresses both ad-hoc movements generated for specific physical interaction tasks and cyclic movements for locomotion. While belonging to the same category and sharing some of the theoretical obstacles, these actions require different approaches: a general high-level task is composed of specific movements that depend on the environment and the nature of the task itself, while regular locomotion involves the generation of periodic trajectories of the limbs. Separate planning and control architectures targeting these aspects of biped motion are designed and developed both from a theoretical and a practical standpoint, demonstrating their efficacy on the new humanoid robot COMAN+, built at Istituto Italiano di Tecnologia. The problem of interaction has been tackled by mimicking the intrinsic elasticity of human muscles, integrating active compliant controllers. However, while state-of-the-art robots may be endowed with compliant architectures, not many can withstand potential system failures that could compromise the safety of a human interacting with the robot. This thesis proposes an implementation of such low-level controller that guarantees a fail-safe behaviour, removing the threat that a humanoid robot could pose if a system failure occurred

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Biodegradable Polymeric Biomaterials in Different Forms for Long-acting Contraception and Drug Delivery to the Eye and Brain

Efficacy of many of the new and existing therapeutics is often hampered by the lack of an effective and compliant method of delivery. Typically, drugs have poor water solubility, short half-lives, and low permeability across the biological membranes. The result is low bioavailability of the drugs at the target site and can cause toxicity and side effects at high doses. Often the conventional dosage forms fail to overcome these limitations. In the recent decades, biodegradable polymeric drug delivery systems have emerged as promising candidates to solve the challenges of poor solubility, low permeability and sustained release owing to the advantages of biocompatibility, versatility, and tunable drug release. Polyesters and polysaccharides are the most common polymers that were explored for drug delivery applications because of their unique advantages including non-toxic nature, wide availability, relatively low cost, and flexibility in chemistry. Although a major progress has been in the field of drug delivery, still there are unmet medical needs which require new materials for delivering drugs such as, injectable systems that can achieve long-term contraception (five months or longer) at low cost, and drug delivery systems that can enhance the permeability of drugs across ocular/blood-brain barriers and sustain release as well for treating chronic diseases such as diabetic retinopathy in the eye and Alzheimer’s disease in the brain. Therefore, this research has evaluated the potential of different biodegradable polymeric biomaterials based on polyesters or polysaccharides for long-acting contraception and drug delivery to the eye and brain to resolve the issues such as poor compliance and adherence to the existing contraceptive dosage forms or poor solubility and permeability of the drugs across ocular/blood-brain barriers. The first system includes polyester-based injectable in situ forming depot systems (ISD) for long-acting contraception. The aim of this project was to develop injectable ISD system containing levonorgestrel (LNG) for contraceptive effect for five months or longer after single shot that helps to reduce unintended pregnancies with high patient compliance and low cost. A series of LNG-containing ISD formulations were designed by employing unique strategies which include the use of poly(lactic acid-co-glycolic acid), poly(lactic acid) with different biodegradable properties, and blends of these polyesters, use solvent mixtures of N-methyl-2-pyrrolidone, triethyl citrate, benzyl benzoate, and vary the polymer/solvent ratios, and various drug loadings. The formulations were evaluated for viscosity, initial burst, in vitro and in vivo long-term release. In vivo investigation in rats showed the sustained-release pharmacokinetic profile of LNG from the ISD formulations for at least five months and continued for more than seven months depending on the composition, and the vaginal cytology studies have demonstrated that formulations have successfully suppressed the rat estrous cycle. After the end of the treatment, a rapid and predictable return of fertility was observed in rats. The optimized lead formulation has shown promising injectability (23 G) and low initial in vivo burst profiles. The results suggested that the developed LNG-ISD formulations have a great potential for developing into future robust affordable long-acting contraceptive products for improving patient compliance and adherence. Another type of polymeric biomaterial systems that were evaluated in this study includes polysaccharide-based biodegradable nanoparticles for drug delivery across ocular and blood-brain barriers. Depending on the need of the therapeutic application, two types of polysaccharide-based nanoparticles were investigated for their drug delivery feasibility which includes: (a) Poly(N-isopropylacrylamide-co-Dextran-lactateHEMA) nanogels for the potential delivery of hydrophilic peptide (insulin) across ocular barriers for the treatment of diabetic retinopathy. The in vitro, and ex vivo studies showed that the developed nontoxic nanogels have great potential to enhance the drug permeability across ocular barriers including the in vitro retinal pigment epithelium, sclera and cornea barriers for treating diabetic retinopathy; and (b) β-cyclodextrin-poly(β-amino ester) nanoparticles as potential drug carriers to enhance the solubility and blood-brain barrier (BBB) permeability of 17-N-allylamino-17-demethoxygeldanamycin (17-AAG) to treat Alzheimer’s disease. The nanoparticles sustained the release of 17-AAG for at least one week in vitro and showed increased permeability (2-fold) of the 17-AAG across BBB in vivo in mice, and resulted in enhanced expression of the Hsp70 protein in the brain. In conclusion, the developed biodegradable polymeric biomaterials have shown potential to be used in long-acting contraception and drug delivery to the eye and brain