Madeleine Martinsen, E. Dahlquist, Anders Lönnermark, Örjan Säker
Sensors play a key role today and have been developed to be used in many applications that can be life critical as with e.g. fire alarms. When mines now start investing in information systems and information technology infrastructure, they have taken one step closer to digitization. This in turn creates opportunities for the mines to become completely autonomous in the future. Controlling, monitoring and planning such production requires new digitized solutions. Part of such a solution could for example be to mount different types of sensors in the mining process. Data gathering from sensors with diagnostics supported by predefined set-points enables early alarms allowing production personnel to react before a fire is a fact. This paper describes the conducted experimental study aiming at identifying risk for fire caused by mining vehicles in underground mines. The test result shows that some types of sensors have potential to early detect fire hazards.
{"title":"Gas Sensors for Early Detection of Fire Hazards Caused by Vehicles in Underground Mines","authors":"Madeleine Martinsen, E. Dahlquist, Anders Lönnermark, Örjan Säker","doi":"10.3384/ecp2017085","DOIUrl":"https://doi.org/10.3384/ecp2017085","url":null,"abstract":"Sensors play a key role today and have been developed to be used in many applications that can be life critical as with e.g. fire alarms. When mines now start investing in information systems and information technology infrastructure, they have taken one step closer to digitization. This in turn creates opportunities for the mines to become completely autonomous in the future. Controlling, monitoring and planning such production requires new digitized solutions. Part of such a solution could for example be to mount different types of sensors in the mining process. Data gathering from sensors with diagnostics supported by predefined set-points enables early alarms allowing production personnel to react before a fire is a fact. This paper describes the conducted experimental study aiming at identifying risk for fire caused by mining vehicles in underground mines. The test result shows that some types of sensors have potential to early detect fire hazards.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116392160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emerging technologies of Industry 4.0 have introduced novel ways of perceiving maintenance management, which has developed from being perceived as a “necessary evil” to become proactive with a holistic focusing on entire systems rather than single machines from Maintenance 3.0. In this context, the industry has begun to really appreciate the unique opportunities followed by system dynamics and simulation tools capabilities of representing the real world. However, maintenance management and performance are complex aspects of asset’s operation that is difficult to justify because of its multiple inherent trade-offs. Although the majority areunanimous when it comes to the expected impact maintenance plays on company profitability, this is in most cases challenging to determine and quantify. Moreover, relevant literature is considered as limited, especially with regards to impact simulation of Maintenance 4.0. Therefore, this paper focuses on the supportive function system dynamics, and modeling and simulation tools can be of help to assess behavior and predicting the future outcome of Maintenance 4.0 in the era of Industry 4.0. This includes developing a conceptualized model that enables simulating the future expected behavior i.e. (un)availability and cost by implementing such a maintenance system. In this context, a centrifugal compressor with the function of exporting gas to Europe is applied as a case study.
{"title":"Operations Dynamics of Gas Centrifugal Compressor: Process, Health and Performance Indicators","authors":"H. Nordal, I. El-Thalji","doi":"10.3384/ecp20170229","DOIUrl":"https://doi.org/10.3384/ecp20170229","url":null,"abstract":"Emerging technologies of Industry 4.0 have introduced novel ways of perceiving maintenance management, which has developed from being perceived as a “necessary evil” to become proactive with a holistic focusing on entire systems rather than single machines from Maintenance 3.0. In this context, the industry has begun to really appreciate the unique opportunities followed by system dynamics and simulation tools capabilities of representing the real world. However, maintenance management and performance are complex aspects of asset’s operation that is difficult to justify because of its multiple inherent trade-offs. Although the majority areunanimous when it comes to the expected impact maintenance plays on company profitability, this is in most cases challenging to determine and quantify. Moreover, relevant literature is considered as limited, especially with regards to impact simulation of Maintenance 4.0. Therefore, this paper focuses on the supportive function system dynamics, and modeling and simulation tools can be of help to assess behavior and predicting the future outcome of Maintenance 4.0 in the era of Industry 4.0. This includes developing a conceptualized model that enables simulating the future expected behavior i.e. (un)availability and cost by implementing such a maintenance system. In this context, a centrifugal compressor with the function of exporting gas to Europe is applied as a case study.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130385682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Thapa, S. Thapa, Rajan Jaiswal, N. C. Furuvik, Britt M. E. Moldestad
The effect of ash deposition on fluid dynamic behavior in a fluidized bed gasification reactor has been studied using experimental and computational methods. The experiments were carried out using sand particles as bed material and air as a fluidizing agent. A 3D computational model has been developed for a bubbling fluidized bed gasification reactor. First, the model was simulated using only sand particles and air. The results are compared with the experimental results. The comparison shows good agreement between the two sets of the results. The model was further used to study the effect of ash accumulation on the fluid dynamic properties of a biomass gasification reactor. The bed material was mixed with 2 and 4vol% of ash and simulated in cold conditions. Pressure drop increases and minimum fluidization velocity decreases with increasing the ash deposition in the bed. The model was also simulated for 2, 4, and 6 vol% of ash at a temperature of 800ºC. The minimum fluidization velocity was decreased in all the cases. The particle species concentration shows the ash particles start to segregate at the minimum fluidization condition and are totally separated at higher velocities. The bubble behavior of the bed is not effected by ash deposition.
{"title":"Experimental and computational study on the effect of ash deposition on fluid dynamic behavior in a bubbling fluidized bed gasifier","authors":"R. Thapa, S. Thapa, Rajan Jaiswal, N. C. Furuvik, Britt M. E. Moldestad","doi":"10.3384/ecp20170170","DOIUrl":"https://doi.org/10.3384/ecp20170170","url":null,"abstract":"The effect of ash deposition on fluid dynamic behavior in a fluidized bed gasification reactor has been studied using experimental and computational methods. The experiments were carried out using sand particles as bed material and air as a fluidizing agent. A 3D computational model has been developed for a bubbling fluidized bed gasification reactor. First, the model was simulated using only sand particles and air. The results are compared with the experimental results. The comparison shows good agreement between the two sets of the results. The model was further used to study the effect of ash accumulation on the fluid dynamic properties of a biomass gasification reactor. The bed material was mixed with 2 and 4vol% of ash and simulated in cold conditions. Pressure drop increases and minimum fluidization velocity decreases with increasing the ash deposition in the bed. The model was also simulated for 2, 4, and 6 vol% of ash at a temperature of 800ºC. The minimum fluidization velocity was decreased in all the cases. The particle species concentration shows the ash particles start to segregate at the minimum fluidization condition and are totally separated at higher velocities. The bubble behavior of the bed is not effected by ash deposition.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115617538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A large fraction of the world’s energy production is used for HVAC in buildings. It is therefore important to develop improved strategies for the efficient use of energy in buildings. Storage of intermittent energy production is important; storage as hot water in water tanks is the most common way to store energy in private homes/ smaller apartment complexes. Finding good models for building thermal behavior is an important part of developing building energy management systems (BEMS) that are capable of reducing energy consumption for space heating through model predictive control (MPC). In this paper, previous models of temperature dynamics in hot water tanks are considered, and a simple well mixed tank model is compared with a model describing a more realistic stratified temperature distribution. Two models are fitted to experimental data from a hot water tank. Description of temperature stratification requires a distributed model, but a relatively low order discretized model suffices to describe the important effect while simultaneously being useful for BEMS. A suitable hot water tank model in combination with weather forecast enables temperature estimation and prediction in MPC, and allows for finding a suitable water temperature at minimal energy consumption.
{"title":"Models for control of thermal energy in buildings","authors":"Casper Amandus Johansen, B. Lie, Nils-Olav Skeie","doi":"10.3384/ecp2017059","DOIUrl":"https://doi.org/10.3384/ecp2017059","url":null,"abstract":"A large fraction of the world’s energy production is used for HVAC in buildings. It is therefore important to develop improved strategies for the efficient use of energy in buildings. Storage of intermittent energy production is important; storage as hot water in water tanks is the most common way to store energy in private homes/ smaller apartment complexes. Finding good models for building thermal behavior is an important part of developing building energy management systems (BEMS) that are capable of reducing energy consumption for space heating through model predictive control (MPC). In this paper, previous models of temperature dynamics in hot water tanks are considered, and a simple well mixed tank model is compared with a model describing a more realistic stratified temperature distribution. Two models are fitted to experimental data from a hot water tank. Description of temperature stratification requires a distributed model, but a relatively low order discretized model suffices to describe the important effect while simultaneously being useful for BEMS. A suitable hot water tank model in combination with weather forecast enables temperature estimation and prediction in MPC, and allows for finding a suitable water temperature at minimal energy consumption.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114155155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With access to fast computers and efficient machine learning tools, it is of interest to use machine learning to develop surrogate models from complex physics-based models. Next, a hybrid model is a combination model where a data driven model is built to describe the difference between an imperfect physics-based/surrogate model and experimental data. Availability of Big Data makes it possible to gradually improve on a hybrid model as more data become available. In this paper, an overview is given of relevant ideas from model approximation/data driven models for dynamic systems, and machine learning via artificial neural networks. To illustrate how the ideas can be implemented in practice, a simple introduction to package Flux for language Julia is given. Several types of surrogate models are developed for a simple, illustrative system. Finally, the development of a hybrid model is illustrated. Emphasis is put on ideas related to Digital Twins for control.
{"title":"Surrogate and Hybrid Models for Control","authors":"B. Lie","doi":"10.3384/ecp201701","DOIUrl":"https://doi.org/10.3384/ecp201701","url":null,"abstract":"With access to fast computers and efficient machine learning tools, it is of interest to use machine learning to develop surrogate models from complex physics-based models. Next, a hybrid model is a combination model where a data driven model is built to describe the difference between an imperfect physics-based/surrogate model and experimental data. Availability of Big Data makes it possible to gradually improve on a hybrid model as more data become available. In this paper, an overview is given of relevant ideas from model approximation/data driven models for dynamic systems, and machine learning via artificial neural networks. To illustrate how the ideas can be implemented in practice, a simple introduction to package Flux for language Julia is given. Several types of surrogate models are developed for a simple, illustrative system. Finally, the development of a hybrid model is illustrated. Emphasis is put on ideas related to Digital Twins for control.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114483724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In smart building environments, both office and residential buildings, it is important to have some information about the use and occupation. Today this is normally solved by a fixed time schedule meaning the occupants must adapt to the system, not the other way around. This paper discuss the usage of a top hat probability models, based on a four weeks history from inexpensive sensor devices, for prediction of the occupation in the next week. The model was divided into seven groups, one group for each of day of the week. A software system, based on several modules, was developed. One module was used to record the information from the motion sensors and stored the data as historical data. One module was used to create the model, and another module was used to prediction of occupation for the next days, up to a week. The models are working satisfactory as long as the behavior patterns are similar for the training and prediction period. However, the models are sensitive to changes in the daily behavior pattern of the occupants, like holidays or taking a day off.
{"title":"Building occupation modelling using motion sensor data","authors":"Nils-Olav Skeie, Jørund Martinsen","doi":"10.3384/ecp2017043","DOIUrl":"https://doi.org/10.3384/ecp2017043","url":null,"abstract":"In smart building environments, both office and residential buildings, it is important to have some information about the use and occupation. Today this is normally solved by a fixed time schedule meaning the occupants must adapt to the system, not the other way around. This paper discuss the usage of a top hat probability models, based on a four weeks history from inexpensive sensor devices, for prediction of the occupation in the next week. The model was divided into seven groups, one group for each of day of the week. A software system, based on several modules, was developed. One module was used to record the information from the motion sensors and stored the data as historical data. One module was used to create the model, and another module was used to prediction of occupation for the next days, up to a week. The models are working satisfactory as long as the behavior patterns are similar for the training and prediction period. However, the models are sensitive to changes in the daily behavior pattern of the occupants, like holidays or taking a day off.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117292245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Halstensen, J. Lundberg, Per Ivan Januschas, Hans-Petter Halvorsen
MSG Production is a company specializing in automated washing, de-icing, anti-icing and inspection of commercial passenger aircrafts. It is critically important that the viscosity of the anti-icing fluid is according to specifications. This study investigates if a combination of acoustic/vibrational measurements on the spraying nozzle of the system and multivariate regression modelling provides reliable viscosity estimates can be used for real time monitoring. The estimated viscosity based on independent test data show promising results for real time monitoring with a root mean square error of prediction of 278 [cP] within the valid range of the model which is 1900-8400 [cP].
{"title":"On-line Monitoring of Viscous Properties of Anti-icing Fluid Based","authors":"M. Halstensen, J. Lundberg, Per Ivan Januschas, Hans-Petter Halvorsen","doi":"10.3384/ecp2017026","DOIUrl":"https://doi.org/10.3384/ecp2017026","url":null,"abstract":"MSG Production is a company specializing in automated washing, de-icing, anti-icing and inspection of commercial passenger aircrafts. It is critically important that the viscosity of the anti-icing fluid is according to specifications. This study investigates if a combination of acoustic/vibrational measurements on the spraying nozzle of the system and multivariate regression modelling provides reliable viscosity estimates can be used for real time monitoring. The estimated viscosity based on independent test data show promising results for real time monitoring with a root mean square error of prediction of 278 [cP] within the valid range of the model which is 1900-8400 [cP].","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121383345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Torbjörn Trostén, H. Mosskull, D. Jansson, M. Azaza, E. Dahlquist
In this paper several discontinuous pulse width modulation methods (DPWM) are compared with space-vector pulse width modulation (SV-PWM) method. The comparisons are done based on measurements of motor magnetic noise and total drive losses for inverter switching frequencies from 500Hz to 4000Hz. It is concluded that using SV-PWM it is possible to reach lower magnetic noise on the traction motor without increasing the total losses significantly.
{"title":"Comparison and analysis of magnetic noise and drive losses using different PWM methods (1165)","authors":"Torbjörn Trostén, H. Mosskull, D. Jansson, M. Azaza, E. Dahlquist","doi":"10.3384/ecp2017032","DOIUrl":"https://doi.org/10.3384/ecp2017032","url":null,"abstract":"In this paper several discontinuous pulse width modulation methods (DPWM) are compared with space-vector pulse width modulation (SV-PWM) method. The comparisons are done based on measurements of motor magnetic noise and total drive losses for inverter switching frequencies from 500Hz to 4000Hz. It is concluded that using SV-PWM it is possible to reach lower magnetic noise on the traction motor without increasing the total losses significantly.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131232755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Building Energy Management systems can reduce energy consumption for space heating in existing buildings, by utilising Model Predictive Control. In such applications, good models of building thermal behaviour is important. A popular method for creating such models is creating Thermal networks, based cognitively on naive physical information about the building thermal behaviour. Such models have lumped parameters which must be calibrated from measured temperatures and weather conditions. Since the parameters are calibrated, it is important to study the identifiability of the parameters, prior to analysing them as physical constants derived from the building structure. By utilising a statistically founded parameter estimation method based on maximising the likelihood function, identifiability analysis can be performed using the Profile Likelihood method. In this paper, the effect of different sensor locations with respect to the buildings physical properties is studied by utilising likelihood profiles for identifiability analysis. The extended 2D profile likelihood method is used to compute two-dimensional profiles which allows diagnosing parameter inter-dependence, in addition to analysing the identifiability. The 2D profiles are compared with confidence regions computed based on the Hessian.
{"title":"Sensor placement and parameter identi?ability in grey-box models of building thermal behaviour","authors":"O. M. Brastein, Roshan Sharma, Nils-Olav Skeie","doi":"10.3384/ecp2017051","DOIUrl":"https://doi.org/10.3384/ecp2017051","url":null,"abstract":"Building Energy Management systems can reduce energy consumption for space heating in existing buildings, by utilising Model Predictive Control. In such applications, good models of building thermal behaviour is important. A popular method for creating such models is creating Thermal networks, based cognitively on naive physical information about the building thermal behaviour. Such models have lumped parameters which must be calibrated from measured temperatures and weather conditions. Since the parameters are calibrated, it is important to study the identifiability of the parameters, prior to analysing them as physical constants derived from the building structure. By utilising a statistically founded parameter estimation method based on maximising the likelihood function, identifiability analysis can be performed using the Profile Likelihood method. In this paper, the effect of different sensor locations with respect to the buildings physical properties is studied by utilising likelihood profiles for identifiability analysis. The extended 2D profile likelihood method is used to compute two-dimensional profiles which allows diagnosing parameter inter-dependence, in addition to analysing the identifiability. The 2D profiles are compared with confidence regions computed based on the Hessian.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125622556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In control theory for dynamic systems, the information about observability and controllability of states plays a key role to evaluate the possibility to observe states from outputs, and use inputs to move states to a desired position, respectively. Th automatic determination of observability and controllability is possible, in particular for linear models where typically observability and controllability grami-ans are considered. In the case of large scale systems, e.g., complex models of regional energy systems, standard analysis becomes challenging. For large scale systems, structural analysis based on directed graphs is an interesting alternative: structural observability (or: controlla-bility) is a necessary requirement for actual observability (or: controllability). Directed graphs can be set up directly for linear models, but can also be extracted from nonlinear models. Modelica is a suitable language for describing large scale models, but does not support graph algorithms. One possibility is to integrate the Modelica model into a language supporting graph algorithms, e.g., Julia: this integration can be done using package OMJulia which works with the free tool OpenModelica. OMJulia does not give direct access to the nonlinear model in Modelica, but a linear model approximation can be extracted and used for setting up the system graph. In this study, an experimental implementation of automated structural analysis is done in Julia using the LightGraphs.jl package. As an example, this structural analysis is tested on hydropower models of different complexity that are modelled in OpenModelica using our in-house hydropower Modelica library — OpenHPL, where different models for hydropower systems are assembled.
{"title":"Structural analysis in Julia for dynamic systems in OpenModelica","authors":"Liubomyr Vytvytskyi, B. Lie","doi":"10.3384/ecp2017017","DOIUrl":"https://doi.org/10.3384/ecp2017017","url":null,"abstract":"In control theory for dynamic systems, the information about observability and controllability of states plays a key role to evaluate the possibility to observe states from outputs, and use inputs to move states to a desired position, respectively. Th automatic determination of observability and controllability is possible, in particular for linear models where typically observability and controllability grami-ans are considered. In the case of large scale systems, e.g., complex models of regional energy systems, standard analysis becomes challenging. For large scale systems, structural analysis based on directed graphs is an interesting alternative: structural observability (or: controlla-bility) is a necessary requirement for actual observability (or: controllability). Directed graphs can be set up directly for linear models, but can also be extracted from nonlinear models. Modelica is a suitable language for describing large scale models, but does not support graph algorithms. One possibility is to integrate the Modelica model into a language supporting graph algorithms, e.g., Julia: this integration can be done using package OMJulia which works with the free tool OpenModelica. OMJulia does not give direct access to the nonlinear model in Modelica, but a linear model approximation can be extracted and used for setting up the system graph. In this study, an experimental implementation of automated structural analysis is done in Julia using the LightGraphs.jl package. As an example, this structural analysis is tested on hydropower models of different complexity that are modelled in OpenModelica using our in-house hydropower Modelica library — OpenHPL, where different models for hydropower systems are assembled.","PeriodicalId":179867,"journal":{"name":"Proceedings of The 60th SIMS Conference on Simulation and Modelling SIMS 2019, August 12-16, Västerås, Sweden","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129801479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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