Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control |
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| Institution: | 1. Czech Technical University in Prague, University Centre for Energy Efficient Buildings, Trinecka 1024, Bustehrad 273 43, Czech Republic;2. Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Control Engineering, Karlovo namesti 13, 121 35, Prague 2, Czech Republic;1. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, Hunan, 412007, China;2. Zoomlion Heavy Industry Science And Technology Co.,Ltd., Changsha, Hunan, 410000, China;1. College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou, Hunan, 412007, China;2. Guangxi Power Grid Company Guilin Power Supply Bureau, Guilin,Guangxi, 541000, China;1. Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education), School of Electrical Engineering and Automation, Hefei 230601, China;2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong;3. Discipline of Engineering and Energy; Centre for Water, Energy & Waste, Harry Butler Institute, Murdoch University, Perth, WA6150, Australia;4. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China;1. School of Macaronic Engineering and Automation, Shanghai University, Shanghai 200444, China;2. School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China |
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| Abstract: | This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models. |
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