Moving Horizon Estimation for JModelica.org

In this thesis a Moving Horizon Estimator (MHE) has been implemented for the JModelica.org platform. JModelica.org is an open-source software platform for simulation and optimization of systems described in the modeling language Modelica. MHE is an optimization-based strategy for state estimation where, at each time step, a finite horizon optimization problem is solved to generate an estimate of the current state values. The goal has been to implement an MHE that works with many already existing Modelica models and that has an intuitive user interface. The performance of the implemented MHE is evaluated using both linear and nonlinear systems in a series of simulation examples. The results indicate that the MHE performs well

Moving-Horizon Dynamic Power System State Estimation Using Semidefinite Relaxation

Accurate power system state estimation (PSSE) is an essential prerequisite for reliable operation of power systems. Different from static PSSE, dynamic PSSE can exploit past measurements based on a dynamical state evolution model, offering improved accuracy and state predictability. A key challenge is the nonlinear measurement model, which is often tackled using linearization, despite divergence and local optimality issues. In this work, a moving-horizon estimation (MHE) strategy is advocated, where model nonlinearity can be accurately captured with strong performance guarantees. To mitigate local optimality, a semidefinite relaxation approach is adopted, which often provides solutions close to the global optimum. Numerical tests show that the proposed method can markedly improve upon an extended Kalman filter (EKF)-based alternative.Comment: Proc. of IEEE PES General Mtg., Washnigton, DC, July 27-31, 2014. (Submitted

Moving Horizon Estimation with Dynamic Programming

Moving Horizon Estimation(MHE) is a optimization based strategy to state estimation. It involves computation of arrival cost, a penalty term, based on the MHE cost function. Minimization of this arrival cost is done through various methods. All these methods use nonlinear programming optimization technique which gives the estimate. The main idea of MHE revolves around minimizing the estimation cost function. The cost function is dependent on prediction error computation from data and arrival cost summarization. The major issue that hampers the MHE is choosing the arrival cost for ensuring stability of the overall estimation and computational time. In order to attain this stability, this thesis incorporates dynamic programming algorithm to estimate MHE cost function. Dynamic programming is an algorithm for solving complex problems. The MHE cost function algorithm has been modied based on dynamic programming algorithm in order to ensure stability of the overall estimation. In order to apply this algorithm, a specic non-linear lter, particle lter is used for the initialization of MHE. The reason of using particle lter for initialization of MHE is due to fact that dynamic programming algorithm works on principle of samples and particle lter provides the samples. A comparison of mean squared error(MSE) using the nonlinear programming optimization and dynamic programming optimization is veried for the proposed theory of using dynamic programming algorithm in estimation of cost functio

Moving Horizon Estimation with Dynamic Programming

Moving Horizon Estimation(MHE) is a optimization based strategy to state estimation. It involves computation of arrival cost, a penalty term, based on the MHE cost function. Minimization of this arrival cost is done through various methods. All these methods use nonlinear programming optimization technique which gives the estimate. The main idea of MHE revolves around minimizing the estimation cost function. The cost function is dependent on prediction error computation from data and arrival cost summarization. The major issue that hampers the MHE is choosing the arrival cost for ensuring stability of the overall estimation and computational time. In order to attain this stability, this thesis incorporates dynamic programming algorithm to estimate MHE cost function. Dynamic programming is an algorithm for solving complex problems. The MHE cost function algorithm has been modied based on dynamic programming algorithm in order to ensure stability of the overall estimation. In order to apply this algorithm, a specic non-linear lter, particle lter is used for the initialization of MHE. The reason of using particle lter for initialization of MHE is due to fact that dynamic programming algorithm works on principle of samples and particle lter provides the samples. A comparison of mean squared error(MSE) using the nonlinear programming optimization and dynamic programming optimization is veried for the proposed theory of using dynamic programming algorithm in estimation of cost functio