A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup>
In this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump system). The main goal of the paper is to propose...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/6/1355 |
| _version_ | 1798038808808128512 |
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| author | Piotr Kozierski Jacek Michalski Joanna Zietkiewicz Marek Retinger and Wojciech Retinger Wojciech Giernacki |
| author_facet | Piotr Kozierski Jacek Michalski Joanna Zietkiewicz Marek Retinger and Wojciech Retinger Wojciech Giernacki |
| author_sort | Piotr Kozierski |
| collection | DOAJ |
| description | In this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump system). The main goal of the paper is to propose an object that potentially can have similar to the power system grid properties, but with the number of state variables reduced twice (only one state variable for each node, while there are two in the case of power systems). Transition and measurement functions are proposed in the paper, and two types of transition functions are considered: dependent on one or many state variables. In addition, 10 types of measurements are proposed both for branch and nodal cases. The experiments are performed for 14 different, four-dimensional systems. Plants are both linear and highly nonlinear. The results include information about the state estimation quality (based on the mean squared error indicator) and the values of the effective sample size. It is observed how the higher effective sample size resulted in the better estimation quality in subsequent cases. It is also concluded that the very low number of significant particles is the main problem in particle filtering of multivariable systems, and this should be countered. A few potential solutions for the latter are also presented. |
| first_indexed | 2024-04-11T21:45:25Z |
| format | Article |
| id | doaj.art-f09df3fc9f664e049e182166a8b67e9a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T21:45:25Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f09df3fc9f664e049e182166a8b67e9a2022-12-22T04:01:26ZengMDPI AGEnergies1996-10732020-03-01136135510.3390/en13061355en13061355A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup>Piotr Kozierski0Jacek Michalski1Joanna Zietkiewicz2Marek Retinger and Wojciech Retinger3Wojciech Giernacki4Faculty of Computing and Telecommunications, Institute of Computing Science, Poznan University of Technology, 60-965 Poznań, PolandFaculty of Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznań, PolandFaculty of Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznań, PolandFaculty of Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznań, PolandFaculty of Control, Robotics and Electrical Engineering, Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznań, PolandIn this paper, a new object in the form of a theoretical network is presented, which is useful as a benchmark for particle filtering algorithms designed for multivariable nonlinear systems (potentially linear, nonlinear, and even semi-Markovian jump system). The main goal of the paper is to propose an object that potentially can have similar to the power system grid properties, but with the number of state variables reduced twice (only one state variable for each node, while there are two in the case of power systems). Transition and measurement functions are proposed in the paper, and two types of transition functions are considered: dependent on one or many state variables. In addition, 10 types of measurements are proposed both for branch and nodal cases. The experiments are performed for 14 different, four-dimensional systems. Plants are both linear and highly nonlinear. The results include information about the state estimation quality (based on the mean squared error indicator) and the values of the effective sample size. It is observed how the higher effective sample size resulted in the better estimation quality in subsequent cases. It is also concluded that the very low number of significant particles is the main problem in particle filtering of multivariable systems, and this should be countered. A few potential solutions for the latter are also presented.https://www.mdpi.com/1996-1073/13/6/1355sequential importance resamplingmultidimensional systemsparticle filternonlinear plantsstate estimationpower system grid |
| spellingShingle | Piotr Kozierski Jacek Michalski Joanna Zietkiewicz Marek Retinger and Wojciech Retinger Wojciech Giernacki A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup> Energies sequential importance resampling multidimensional systems particle filter nonlinear plants state estimation power system grid |
| title | A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup> |
| title_full | A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup> |
| title_fullStr | A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup> |
| title_full_unstemmed | A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup> |
| title_short | A New Network for Particle Filtering of Multivariable Nonlinear Objects <sup>†</sup> |
| title_sort | new network for particle filtering of multivariable nonlinear objects sup † sup |
| topic | sequential importance resampling multidimensional systems particle filter nonlinear plants state estimation power system grid |
| url | https://www.mdpi.com/1996-1073/13/6/1355 |
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