Quasi-Polynomial Control of a Synchronous Generator

A simple dynamic model of permanent magnet synchronous generator, that is used for electrical energy generation is investigated in this work using a nonlinear technique based on the quasi-polynomial representation of the dynamical model. It is well known that dynamical systems with smooth nonlinearities can be embedded in a quasi-polynomial model. Quasipolynomial systems are good candidates for a general nonlinear system representation since their global stability analysis is equivalent to the feasibility of a LMI. Moreover, the stabilizing quasi-polynomial state feedback controller design problem is equivalent to the feasibility of a bilinear matrix inequality. The classical stabilizing state feedback problem for quasipolynomial systems has been extended in this work with the ability of tracking time-dependent reference signals. It is shown, that the stabilizing quasi-polynomial servo controller design is equivalent to a bilinear matrix inequality. The results are applied to the model of a synchronous generator

Generalized persistent fault detection in distribution systems using network flow theory

Persistent faults are steady state anomalies with a magnitude which does not necessary trigger general protective gear. It is present in various types of distribution networks, as leak in pipe networks or as high-impedance fault in electric systems. As smart meters come into general use, distribution systems are upgraded to have advanced metering infrastructure. The amount of consumer data is thereby increased, which can be used for diagnostic purposes. Different kind of detection methods, improvements are presented in different physical domains. However, persistent fault detection lies on basic physical principles like the conservation of energy. In order to be able to develop and evaluate methods, which target basically the same problems, the notions of the well established general network theory are being used. New formal definitions are presented to handle measurement data. In this framework a general extension of Flow networks is presented which enables the detection of faults which are not present from the beginning in our model, between metered points. The solution to this problem is presented in the form of a two-stage evolutionary algorithm. Finally the working of the methods is illustrated and verified through a simple simulation based case study

Aggregation of Heterogeneous Flexibility Resources Providing Services for System Operators and the Market Participants

Power systems characterized by large, centralized generation sources and the typical flow of energy from the transmission grid to the distribution grid towards consumers are evolving. The increasing penetration of intermittent and distributed renewable energy generation is forcing system operators to increase the volume of balancing capabilities and procure flexibility services at the distribution grid level that must be supported by the aggregation of small-scale resources connected at the distribution grid. This paper suggests an aggregator framework that provides services for both operators of transmission and distribution systems while optimizes its portfolio to perform on wholesale energy trading markets too. Overlaying phases of multi-period optimization runs are proposed that incorporate stochastic renewable energy generation as well as load forecasts and, moreover, the continuously changing business context while enabling cooperation between optimization phases throughout the business process

Deep Learning Based SoH Estimation of Lithium-ion Batteries

An artificial intelligence-based approach of estimating remaining useful life for Li-ion batteries has been used in this work, where two different recursive neural networks were set up, trained and investigated for two different scenarios. The investigated battery type is the widely used 18650 battery class. The training and prediction of both networks are performed on a publicly available high-quality dataset that serves as a base for several related research works. The batteries are charged/discharged until they reach their end of life by means of capacity degradation. The charge and discharge were performed under different charging current/load profiles. Out of the available data-driven methods, LSTM (long-short term memory) and GRU (Gated Recurrent Unit) neural networks are the most promising candidate since they are capable of the handling of long-term processes, such as battery aging. The two networks are parameterized, trained and tested for two different scenarios

MODELLING A THREE-PHASE CURRENT SOURCE INVERTER

A current source inverter model has been developed in the given paper that is constructed from six LTI models for the different switching modes. The overall model is in a piecewise affine form that supports the use of model predictive control. The model has been verified against engineering expectations and its open-loop performance shows that it is a promising basis of model predictive control structures

REFRIGERATOR OPTIMAL SCHEDULING TO MINIMISE THE COST OF OPERATION

The cost optimal scheduling of a household refrigerator is presented in this work. The fundamental approach is the model predictive control methodology applied to the piecewise affine model of the refrigerator. The optimisation could not be solved using off-the-shelf tools, e.g. Multi-Parametric Toolbox, so a binary tree-based optimal scheduling algorithm has been developed for this problem

Catalytic Co-Processing of delayed coker light naphtha with other refinery gasoline streams

Upgrading of delayed coker light naphtha is very difficult due to its high diolefin and silicon content. Mixtures of light straight run naphtha and delayed coker light naphtha fractions were hydrotreated in two stages over NiMo/Al2O and CoMo/Al2O catalysts (diolefin saturation followed by hydrodesulphurization ). The results showed that naphtha fractions free of diolefins, olefins, sulphur and silicon can be produced with the two stage hydrogenation. These are excellent feeds for naphtha isomerization. One-stage selective hydrodesulphurization tests were also conducted with blends of coker naphtha (up to 5 vol%) and fluid catalytic crackers gasoline over CoMo/Al2O. Diolefin-free products of < 10 mg/kg sulphur could be produced with a research octane number loss of max. 3

Coordination of cell division and differentiation in plants in comparison to animals

During animal and plant development all cells are originated from a single fertilized oocyte, the zygote. To generate an adult organism from the single-celled zygote many rounds of cell division are required to be completed. Cell division is manifested through a well-defined series of molecular and cellular events that is often referred as the cell cycle. Studies in various model organisms demonstrated that the eukaryotic cell cycle is regulated in a conserved manner with cyclin-dependent kinases (CDKs) in the centre. It is widely believed that cells must exit the cell cycle for cell differentiation. Accordingly, cell division and differentiation do not happen at the same time. The main questions in developmental biology are how these processes are coordinated during development, how do cells stop division before differentiation, and why and how cells maintain or re-initiate cell division activity? Recent studies indicate direct links between molecular cell cycle and cell differentiation machineries. The basic mechanisms regulating the balance between cell proliferation and differentiation are remarkably similar in plants and animals despite their fundamentally different developmental strategies. There is considerable dissimilarity, however, in the upstream signalling pathways affecting this balance in developmental and environmental contexts. In this chapter we focus our attention on the molecular regulatory mechanism controlling and coordinating cell division and differentiation both in animals and plants with emphasis on the entry and exit points of the cell cycle

Online Assessment and Game-Based Development of Inductive Reasoning

The aims of the study were (1) to develop a domain-general computer-based assessment tool for inductive reasoning and to empirically test the theoretical models of Klauer and Christou and Papageorgiou; and (2) to develop an online game to foster inductive reasoning through mathematical content and to investigate its effectiveness. The sample was drawn from fifth-grade students for the assessment (N = 267) along with the intervention study (N = 122). The online figurative test consisted of 54 items: nine items were developed for each of the six inductive reasoning processes. The digital game-based training program included 120 learning tasks embedded in mathematical content with differential feedback and instructional support. The test had good psychometric properties regarding reliabilities, means, and standard deviations. Confirmatory factor analyses revealed that the six processes of inductive reasoning and the three latent factors of Similarity, Dissimilarity, and Integration could be empirically confirmed. The training program was effective in general (corrected effect size = .38); however, the process of cross-classification was not developed significantly. Findings could contribute to a more detailed understanding of the structure and the modifiability of inductive reasoning processes and could reveal further insights into the nature of fluid intelligence