| 1. |
- Ulker, Muhammed Akif, et al.
(författare)
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Maximum likelihood ensemble filter state estimation for power systems fault diagnosis
- 2017
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Ingår i: 2017 2nd International Conference on System Reliability and Safety (ICSRS). - : IEEE. - 9781538633229 ; , s. 140-145
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Konferensbidrag (refereegranskat)abstract
- Maximum Likelihood Ensemble Filter (MLEF) is a deterministic filtering approach that employs the ensembles. The method applies low dimensional ensemble space for the computation of a nonlinear cost function Hessian preconditioning and implements the optimization of the cost function. The MLEF is utilized as state estimation instrument that estimates states of dynamic systems and contributes to reliable and safe operation and monitoring of dynamic systems. In this article, MLEF is employed as a state estimation tool to track the states of a nonlinear power system to assist the fault diagnosis and bad data analysis of the system. A three-node benchmark power system model is considered in this study and a disconnection event is implemented as a fault scenario on the system with measurement data which contains some bad data. The scenario refers to a discontinuous problem which has non-derivable points and this is contrary to gradient based techniques. The MLEF practice on the introduced problem is examined and the results are illustrated. The obtained results shows that the estimation convergence of the MLEF technique on the considered benchmark model is satisfactory.
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| 2. |
- Ulker, Muhammed Akif, et al.
(författare)
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Simplex optimization for particle filter joint state and parameter estimation of dynamic power systems
- 2017
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Ingår i: IEEE EUROCON 2017 CONFERENCE PROCEEDINGS. - : IEEE. - 9781509038435 - 9781509038442 - 9781509038428
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Konferensbidrag (refereegranskat)abstract
- The incidence of sudden unanticipated variations in power system states and parameters will tend to increase due to higher intermittent renewable energy penetration in distributed generation. It is needed to have proper state and parameter estimation tools that can follow-up these variations and can reflect the real-time system dynamics. In this paper, a particle filter with Nelder-Mead simplex optimization algorithm is implemented to estimate the states and a parameter of a three-node benchmark test model. The performance of Bayesian particle filter for joint estimate of the states and parameter for the benchmark non-linear power system model has been analysed and favorable results were obtained by minimizing approximated negative log-likelihood function via Nelder-Mead simplex algorithm.
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| 3. |
- Uzunoglu, Bahri, et al.
(författare)
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Maximum Likelihood Ensemble Filter State Estimation for Power Systems
- 2018
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9456 .- 1557-9662. ; 67:9, s. 2097-2106
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Tidskriftsartikel (refereegranskat)abstract
- Maximum likelihood ensemble filter (MLEF) is an ensemble-based deterministic filtering method. It optimizes a nonlinear cost function through maximum likelihood and utilizes low-dimensional ensemble space on the calculation of Hessian preconditioning of the cost function. This paper implements the MLEF as a state estimation tool for the estimation of the states of a power system, and presents the first MLEF application study on a power system state estimation. The MLEF methodology is introduced into power systems and the simulations are implemented for a three-node benchmark power system and 68-bus test system which have been employed in several previous studies to address a discontinuous problem where derivative is not defined. This is in contrast to gradient-based methods in the literature that needs gradient and Hessian information which is not defined in jumps. The performance of the filter on the presented problem is analyzed and the results are presented. Results indicate that the estimation convergence is achieved with the MLEF method.
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| 4. |
- Uzunoglu, Bahri, et al.
(författare)
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Particle filter joint state and parameter estimation of dynamic power systems
- 2016
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Ingår i: 2016 57th International Scientific Conference On Power And Electrical Engineering Of Riga Technical University (RTUCON). - 9781509037315
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Konferensbidrag (refereegranskat)abstract
- Intermittent renewable energy sources in distributed generation will increase the chance of sudden unpredictable changes in the system states and parameters of dynamic power systems. To track the changes of the power systems, system state and parameter estimation methods that can track the near real-time dynamics of the power systems are needed. Power system operators still employ simulation studies using off-line models that are built based on prior knowledge gained through information via simulated typical scenarios which does not make use of posterior knowledge of neither parameter space nor state space of the dynamics of the power systems. Dynamic models of a power system has increasingly more important role in power system operations since they impact the operational conditions of dynamical power system. In this study, we propose a particle filter based state and parameter estimation method to improve modelling accuracy, which determines the best set of model parameters using realtime measurement data. This can be achieved via measurements by Phasor Measurement Units (PMU) or Remote Terminal Units (RTU) that can capture the system dynamic responses in real time. In addition, parameters of the system can also be estimated. Herein the load will he the parameter of the system that needs to be estimated jointly with the states. Joint state and parameter estimation for power systems via employing Bayesian particle filter is being introduced in this study.
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