Papers by elizabeth rita samuel
Laguerre Expansion Series Based Reduced Order Interval Systems
IEEE Transactions on Circuits and Systems II: Express Briefs
Model order reduction techniques have attracted researchers for the analysis of higher-order syst... more Model order reduction techniques have attracted researchers for the analysis of higher-order systems. The brief presents the order reduction of the interval system in state-space form using the Laguerre polynomial method with singular value decomposition. Laguerre - SVD model reduction technique is approved for the algorithms reduced computational complexity and stability perseverance. The proposed algorithm applies Laguerre approximation to interval system to compute an interval Krylov matrix using the Interval laboratory MATLAB toolbox. Then singular value decomposition is performed on the concatenated matrix with the upper limit and lower limit of the obtained interval Krylov matrix. The resultant column orthogonal matrix yields the reduced interval system through congruence transformation. The technique preserves the original system properties and is proved to be stable. Pertinent numerical results validate the proposed technique with examples from literature.
IETE Journal of Research
The load-frequency control is a matter of interest in power system operation as it addresses the ... more The load-frequency control is a matter of interest in power system operation as it addresses the problem of controlling the system in response to the disturbances. A well-known solution to this problem is feedback stabilization through Linear Quadratic Regulator. The observability condition has to be satisfied for the implementation of LQR, but for certain cases of multi-source power systems this criteria fails. Also, to obtain the desired response, proper selection of the weighting matrices Q and R for LQR controller is inevitable. On a trial and error based choice of these matrices, the classical optimal controller may fail to achieve an optimum set point tracking, due to the absence of an integral control action. In this paper, a balanced reduction technique is proposed to obtain a minimal realization of the unobservable system, which makes the optimal control design possible for the control of change in frequency. Thus the paper also applies PID output feedback controller designed through LQR. The impact of reduction for minimal realization and the LQR based PID controller in the frequency stabilization of the considered systems are highlighted in this paper through pertinent cases.
Parameter Study of Electric Vehicle (EV), Hybrid EV and Fuel Cell EV Using Advanced Vehicle Simulator (ADVISOR) for Different Driving Cycles
Springer Transactions in Civil and Environmental Engineering, 2020
The ever-increasing developments in the transportation industry has smoothened the life of mankin... more The ever-increasing developments in the transportation industry has smoothened the life of mankind a great deal. However, these improvements have pumped out chocking exhausts into the environment making earth a difficult place to live in. The present fearsome condition of our nature has led man to think of an alternative to the diminishing fossil fuel reserves and hazardous exhausts being given out to environment. Electrification of transportation industry is one of the emerging solutions to the above-mentioned issues resulting in higher fuel economy, better performance and lower emissions. The various configurations of EV including parallel hybrid electric vehicles (HEVs) and fuel cell electric vehicles (FCEVs) are taken into account for various driving cycles for performing the parameter comparative study in ADVISOR software which works in the MATLAB/Simulink platform. It primarily quantifies the fuel economy, the performance, State of Charge (SoC) and the emissions of the vehicle that uses fuel cells, batteries, electric motors and internal combustion engines in hybrid configuration. For various drive cycles, the performance curves for each configuration of EV were obtained. A comparative study has been done to obtain the suitable vehicle configuration for a particular drive cycle. Simulation results are presented and discussed to compare SoC and emissions.
Variance Based Analysis for an Isolated Power System Using Kalman Filter and LQR
2018 15th IEEE India Council International Conference (INDICON), 2018
Automatic Generation Control (AGC) is an important approach to acquire the stability to ensure re... more Automatic Generation Control (AGC) is an important approach to acquire the stability to ensure reliable operation of power systems. For stable operation of power systems, the frequency of the system should be reserved within the nominal value. Towards this, the estimation of states is of paramount importance. In this paper, a comparison is made of (i) the continuous estimation of the states from the measurement of one of the state variable of a power system using Kalman filter, followed by a pole placement to ensure stability and (ii) the optimal control approach viz Linear Quadratic Regulator (LQR) to the Automatic Generation Control (AGC), implementing control from direct measurement of all state variables. The comparison is made on the basis of the mean of variances of frequency estimate of both the approaches under different noise levels on the measurements made, from independent Monte Carlo simulations. The comparison is done on an isolated power system, modelled using Simulink...
Analysis of Optimised LQR Controller Using Genetic Algorithm for Isolated Power System
Automatic Generation Control (AGC) is an important tool to ensure the stability and reliability o... more Automatic Generation Control (AGC) is an important tool to ensure the stability and reliability of power systems. For stable operation of power systems, the frequency of the system should be reserved within the nominal value. Towards this, the estimation of states is of supreme implication. In this paper, a comparison is made on the estimation of the states using Kalman filter method and optimal control approach to the Automatic Generation Control (AGC) of an isolated power system. The performance of optimised Linear Quadratic Regulator (LQR) in pole placement is compared with Kalman filter estimating the states for pole placement. Genetic Algorithm is used to optimise the weighting matrices Q and R of an LQR controller. Kalman filter based controller estimates the states of the system by measuring only one output signal i.e. the frequency output of the system considered. The comparison is made on the basis of the mean of the variances of frequency estimate of both the approaches un...
A Review of various Internal Combustion Engine and Electric Propulsion in Hybrid Electric Vehicles
2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 2019
Nowadays the development of Hybrid Electric Vehicle (HEV) is one of the major research area under... more Nowadays the development of Hybrid Electric Vehicle (HEV) is one of the major research area under automobile sector. HEV is a combination of an internal combustion engine and electric motor, which still has a drawback of harmful gas emissions. The torque produced is also a concern for the design and control of HEV. This paper reviews the drawback of various internal combustion engines and electric propulsion combinations used in Hybrid Electric Vehicle based on their performance characteristics.
PSO based LQR‐PID output feedback for load frequency control of reduced power system model using balanced truncation
International Transactions on Electrical Energy Systems, 2021
Protection and Control of Modern Power Systems, 2020
Combined estimation of state and feed-back gain for optimal load frequency control is proposed. L... more Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and th...
International Journal of Hybrid Intelligent Systems, 2019
Automatic Generation Control (AGC) is an important tool to ensure the stability and reliability o... more Automatic Generation Control (AGC) is an important tool to ensure the stability and reliability of power systems. For stable operation of power systems, the frequency of the system should be reserved within the nominal value. Towards this, the estimation of states is of supreme implication. In this paper, a comparison is made on the estimation of the states using Kalman estimator method and optimal control approach to the Automatic Generation Control (AGC) of an isolated power system. The performance of optimized Linear Quadratic Regulator (LQR) in pole placement is compared with Kalman estimator. Optimization algorithms such as Genetic Algorithm and Particle Swarm Optimization are used to optimize positive definite matrices Q and R, weighting matrices of a LQR controller. Kalman estimator estimates the states of the system by measuring only one output signal which in this paper is mentioned as the change in frequency for the system considered. The comparison is made on the basis of the mean of the variances of the output, using the mentioned approaches. Study is conducted under different noise levels for independent Monte Carlo simulations. Modeling of an isolated power system is done using Simulink/MATLAB.
A hybrid adaptive sampling algorithm for obtaining reduced order models for systems with frequency dependent state-space matrices
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2015
This paper proposes a hybrid adaptive sampling algorithm to automate the generation of reduced or... more This paper proposes a hybrid adaptive sampling algorithm to automate the generation of reduced order models for systems described by large-scale frequency dependent state-space models. The evaluation of the frequency dependent state-space model for each frequency sample can be computationally expensive. The distribution of frequency samples must be optimized to avoid oversampling and undersampling. In order to have an optimum number of frequency samples, the proposed algorithm uses the reflective exploration technique for the adaptive selection of samples, and the sampling is further refined using a binary search to validate the frequency dependent reduced order models. Projection-based model order reduction techniques are used for obtaining the reduced order model. The projection matrix for each frequency sample is merged to obtain a common projection matrix for all samples. However, in certain cases when the number of sample points increases, the merged projection matrix also increases in dimension and might fail to provide a satisfactory reduction in model size. Thus, the merged projection matrix is truncated based on its singular values to obtain a compact common projection matrix. Then, the reduced order state-space matrices per frequency are interpolated over the frequency range of interest to obtain the system response. Pertinent examples validate the proposed hybrid adaptive sampling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.
2015 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2015
An adaptive frequency sampling algorithm is proposed in this paper to automate the generation of ... more An adaptive frequency sampling algorithm is proposed in this paper to automate the generation of reduced order models for systems with delays which can be represented as frequency dependent state-space matrices. Reflective exploration technique is used to obtain an optimum number of frequency samples for which the reduced state-space matrices per frequency is computed using a common projection matrix and is then interpolated to obtain the frequency response. The algorithm is illustrated using a numerical example.
Mathematics in Industry, 2016
, tom.dhaene}@ugent.be. Summary. Reduced order models obtained by model order reduction methods m... more , tom.dhaene}@ugent.be. Summary. Reduced order models obtained by model order reduction methods must be accurate over the whole frequency range of interest. Multipoint reduction algorithms allow to generate accurate reduced models. In this paper, we propose the use of reflective exploration technique for obtaining the expansion points adaptively for the reduction algorithm. At each expansion point the corresponding projection matrix is computed. Then, the projection matrices are merged and truncated based on their singular values to obtain a compact reduced order model.
IEEE Transactions on Antennas and Propagation, 2016
This paper describes a data-driven method to model the radiation patterns (over a large angular r... more This paper describes a data-driven method to model the radiation patterns (over a large angular region) and scattering parameters of antennas as a function of the geometry of the antenna. The radiation pattern model consists of a linear combination of characteristic basis function patterns (CBFPs), where the expansion coefficients of the CBFPs are functions of geometrical features of the antenna. Scattering parameters are modeled by means of parameterized state-space matrices. The obtained models are quick to evaluate and are thus suitable for design activities where multiple simulations are required. The proposed method is validated through illustrative examples.
Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics, 2014
Reduced state-space models obtained by model order reduction methods must be accurate over the wh... more Reduced state-space models obtained by model order reduction methods must be accurate over the whole frequency range of interest and must also preserve passivity. In this paper, we propose multipoint reduction technique using reflective exploration for adaptively choosing the expansion points. The projection matrices obtained from the expansion points are merged to form the overall projection matrix. In order to obtain a more compact model the projection matrix is truncated based on its singular values. Finally, the reduced order model is obtained, while ensuring that the passivity of the reduced system is preserved during the reduction process.
Lecture Notes in Electrical Engineering, 2014
A judicious choice of the state-space realization is required in order to account for the assumed... more A judicious choice of the state-space realization is required in order to account for the assumed smoothness of the state-space matrices with respect to the design parameters. The direct parameterization of poles and residues may be not appropriate, due to their possible nonsmooth behavior with respect to design parameters. This is avoided in the proposed technique, by converting the a pole-residue description to a Sylvester description which is computed for each root macromodel.This technique is used in combination with suitable parameterizing schemes for interpolating a set of state-space matrices, and hence the poles and residues indirectly, in order to build accurate parametric macromodels. The key features of the present approach are first the choice of a proper pivot matrix and second, finding a well-conditioned solution of a Sylvester equation. Stability and passivity are guaranteed by construction over the design space of interest. Pertinent numerical examples validate the proposed Sylvester technique for parametric macromodeling.
Reduced order delayed systems by means of Laguerre functions and Krylov subspaces
2014 IEEE 18th Workshop on Signal and Power Integrity (SPI), 2014
ABSTRACT In this paper, we propose an algorithm for model order reduction of large scale systems ... more ABSTRACT In this paper, we propose an algorithm for model order reduction of large scale systems that can be described by delayed differential equations. The algorithm is based on the combination of a Laguerre expansion technique for the delays and higher order Krylov subspaces. Pertinent numerical results validate the accuracy and efficiency of the proposed model order reduction approach.
Large scale systems are present in many fields of engineering and for many applications, such as ... more Large scale systems are present in many fields of engineering and for many applications, such as circuit simulation and time dependent control problems. In large systems, the system dimension or number of internal state space variables is quite large, with respect to the number of input and output ports. When the system dimension is large, the underlying mathematical model becomes computationally intractable, due to memory insufficency, time limitations and ill-conditioning. The common approach to overcome this is by means of model order reduction. The resulting reduced model ultimately replaces the original model in real-world simulations or can be used to develop a low dimensional controller suitable for practical applications.
Parameterized reduced order models are important for the design and analysis of microwave structu... more Parameterized reduced order models are important for the design and analysis of microwave structures and systems. Quite often, a large set of models (nodes) with respect to a design parameter variation are uniformly chosen in the parameter design space, which are subjected to model order reduction algorithms and interpolated into a multidimensional model. In order to preserve passivity in the parameterization step, positive interpolation operators are frequently used. This paper demonstrates the importance of sequential sampling for selecting the nodes and building the parameterized models. It is shown that sequential sampling algorithms can significantly reduce the model evaluation cost. The present approach is validated by means of a microstrip example.
IEEE Transactions on Circuits and Systems II: Express Briefs, 2015
A novel parametric model order reduction (PMOR) technique based on matrix interpolation for multi... more A novel parametric model order reduction (PMOR) technique based on matrix interpolation for multicondutor transmission lines with delays having design parameter variations is proposed in this paper. Matrix interpolation overcomes the oversize problem caused by input-output system level interpolation based parametric macromodels. The reduced state-space matrices are obtained using a higher-order Krylov subspace based model order reduction technique which is more efficient in comparison to the Gramian based parametric modeling where the projection matrix is computed using a Cholesky factorization. The design space is divided into cells and then the Krylov subspaces computed for each cell is merged and then truncated using an adaptive truncation algorithm with respect to their singular values to obtain a compact common projection matrix. The resulting reduced order state-space matrices and the delays are interpolated using positive interpolation schemes making it computationally cheap and accurate for repeated system evaluations under different design parameter settings. The proposed technique is successfully applied to RLC and multiconductor transmission line circuits with delays.
2012 IEEE 16th Workshop on Signal and Power Integrity (SPI), 2012
We present a novel parameterized model order reduction method based on matrix interpolation. The ... more We present a novel parameterized model order reduction method based on matrix interpolation. The design space is sampled over an estimation grid and for each estimation point a Krylov subspace is computed. A common projection matrix is generated by the truncation of the singular values of the merged Krylov subspaces of all estimation points from the design space. The reduced matrices are then interpolated using positive interpolation schemes to build guaranteed passive parameterized reduced order models. The technique is validated by means of a pertinent numerical simulation.
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Papers by elizabeth rita samuel