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  • Public defence: 2017-08-25 10:00 Lecture Hall T1, Huddinge
    Raghothama, Jayanth
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Integrating Computational and Participatory Simulations for Design in Complex Systems2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The understanding and conceptualization of cities and its constituent systems such as transportation and healthcare as open and complex is shifting the debates around the technical and communicative rationales of planning. Viewing cities in a holistic manner presents methodological challenges, where our understanding of complexity is applied in a tangible fashion to planning processes. Bridging the two rationales in the tools and methodologies of planning is necessary for the emergence of a 'non-linear rationality' of planning, one that accounts for and is premised upon complexity. Simulations representing complex systems provide evidence and support for planning, and have the potential to serve as an interface between the more abstract and political decision making and the material city systems.

    Moving beyond current planning methods, this thesis explores the role of simulations in planning. Recognizing the need for holistic representations, the thesis integrates multiple disparate simulations into a holistic whole achieving complex representations of systems. These representations are then applied and studied in an interactive environment to address planning problems in different contexts. The thesis contributes an approach towards the development of complex representations of systems; improvements on participatory methods to integrate computational simulations; a nuanced understanding of the relative value of simulation constructs; technologies and frameworks that facilitate the easy development of integrated simulations that can support participatory planning processes.

    The thesis develops contributions through experiments which involved problems and stakeholders from real world systems. The approach towards development of integrated simulations is realized in an open source framework. The framework creates computationally efficient, scalable and interactive simulations of complex systems, which used in a participatory manner delivers tangible plans and designs.

  • Public defence: 2017-08-31 10:00 F3, Stockholm
    Carrander, Claes
    KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.
    Magnetizing Currents in Power Transformers: Measurements, Simulations, and Diagnostic Methods2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    This thesis demonstrates a method for transformer core diagnostics. The method uses the no-load current of the transformer as an indicator, and gives different characteristic signatures for different types of faults or defects. Using the no-load current for the diagnostic gives high sensitivity. The method is therefore able to detect defects that are too small to have an impact on the losses. In addition to different types of fault, the method can in some cases also distinguish between faults in different locations within the core.

    Both single-phase and three-phase transformers can be diagnosed using this method, and the measurements can be easily performed at any facility capable of measuring the no-load loss. There are, however, some phenomena that occur in large transformers, and in transformers with high rated voltages. Examples include capacitive resonance and magnetic remanence. This thesis proposes and demonstrates techniques for compensating for these phenomena. With these compensating techniques, the repeatability of the measurements is high. It is shown that units with the same core steel tend to have very similar no-load behavior.

    The diagnostics can then be performed either by comparing the transformer to another unit, or to simulations. The thesis presents one possible simulation method, and demonstrates the agreement with measurements.

    This topological simulation method includes both the electric circuit and an accurate model of the magnetic hysteresis. It is therefore also suitable for other, related, studies in addition to core diagnostics. Possible subjects include ferroresonance, inrush, DC magnetization of transformers, and transformer core optimization.

    The thesis also demonstrates that, for three-phase transformers, it is possible to compare the phases to each other. This technique makes it possible to diagnose a transformer even without a previous measurement to compare to, and without the data required to make a simulation.

  • Public defence: 2017-09-01 10:00 F3, Stockholm
    Everitt, Niklas
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Module identification in dynamic networks: parametric and empirical Bayes methods2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The purpose of system identification is to construct mathematical models of dynamical system from experimental data. With the current trend of dynamical systems encountered in engineering growing ever more complex, an important task is to efficiently build models of these systems. Modelling the complete dynamics of these systems is in general not possible or even desired. However, often, these systems can be modelled as simpler linear systems interconnected in a dynamic network. Then, the task of estimating the whole network or a subset of the network can be broken down into subproblems of estimating one simple system, called module, embedded within the dynamic network.

    The prediction error method (PEM) is a benchmark in parametric system identification. The main advantage with PEM is that for Gaussian noise, it corresponds to the so called maximum likelihood (ML) estimator and is asymptotically efficient. One drawback is that the cost function is in general nonconvex and a gradient based search over the parameters has to be carried out, rendering a good starting point crucial. Therefore, other methods such as subspace or instrumental variable methods are required to initialize the search. In this thesis, an alternative method, called model order reduction Steiglitz-McBride (MORSM) is proposed. As MORSM is also motivated by ML arguments, it may also be used on its own and will in some cases provide asymptotically efficient estimates. The method is computationally attractive since it is composed of a sequence of least squares steps. It also treats the part of the network of no direct interest nonparametrically, simplifying model order selection for the user.

    A different approach is taken in the second proposed method to identify a module embedded in a dynamic network. Here, the impulse response of the part of the network of no direct interest is modelled as a realization of a Gaussian process. The mean and covariance of the Gaussian process is parameterized by a set of parameters called hyperparameters that needs to be estimated together with the parameters of the module of interest. Using an empirical Bayes approach, all parameters are estimated by maximizing the marginal likelihood of the data. The maximization is carried out by using an iterative expectation/conditional-maximization scheme, which alternates so called expectation steps with a series of conditional-maximization steps. When only the module input and output sensors are used, the expectation step admits an analytical expression. The conditional-maximization steps reduces to solving smaller optimization problems, which either admit a closed form solution, or can be efficiently solved by using gradient descent strategies. Therefore, the overall optimization turns out computationally efficient. Using markov chain monte carlo techniques, the method is extended to incorporate additional sensors.

    Apart from the choice of identification method, the set of chosen signals to use in the identification will determine the covariance of the estimated modules. To chose these signals, well known expressions for the covariance matrix could, together with signal constraints, be formulated as an optimization problem and solved. However, this approach does neither tell us why a certain choice of signals is optimal nor what will happen if some properties change. The expressions developed in this part of the thesis have a different flavor in that they aim to reformulate the covariance expressions into a form amenable for interpretation. These expressions illustrate how different properties of the identification problem affects the achievable accuracy. In particular, how the power of the input and noise signals, as well as model structure, affect the covariance.

  • Public defence: 2017-09-08 14:00 Kollegiesalen
    Ebadat, Afrooz
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Experiment Design for Closed-loop System Identification with Applications in Model Predictive Control and Occupancy Estimation2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The objective of this thesis is to develop algorithms for application-oriented input design. This procedure takes the model application into account when designing experiments for system identification.

    This thesis is divided into two parts. The first part considers the theory of application-oriented input design, with special attention to Model Predictive Control (MPC). We start by studying how to find a convex approximation of the set of models that result in acceptable control performance using analytical methods when controllers with no closed-form control law, for e.g., MPC are employed. The application-oriented input design is formulated in time domain to enable handling of signals constraints. The framework is extended to closed-loop systems where two cases are considered i.e., when the plant is controlled by a general but known controller and for the case of MPC. To this end, an external stationary signal is designed via graph theory. Different sources of uncertainty in application-oriented input design are investigated and a robust application-oriented input design framework is proposed.

    The second part of this thesis is devoted to the problem of estimating the number of occupants based on the information available to HVAC systems in buildings. The occupancy estimation is first formulated as a two-tier problem. In the first tier, the room dynamic is identified using temporary measurements of occupancy. In the second tier, the identified model is employed to formulate the problem as a fused-lasso problem. The proposed method is further developed to be used as a multi-room estimator using a physics-based model. However, since it is not always possible to collect measurements of occupancy, we proceed by proposing a blind identification algorithm which estimates the room dynamic and occupancy, simultaneously. Finally, the application-oriented input design framework is employed to collect data that is informative enough for occupancy estimation purposes.