Analyzing a wind turbine system: From simulation to formal verification

Seceleanu, Cristina; Johansson, Morgan; Suryadevara, Jagadish; Sapienza, Gaetana; Seceleanu, Tiberiu; Ellevseth, Stein Erik; Pettersson, Paul

doi:10.1016/j.scico.2016.09.007

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…8. 19. We perform the simulation in UPPAAL-SMC to obtain the error trace of CE, which is illustrated in [1] ∨in str [1]∨in acc [1]∨in constSpeed [1]) ∧ (in Str [2]∨in str [2]) =⇒…”

Section: Experiments and Verificationmentioning

confidence: 99%

“…In the context of EAST-ADL, efforts on the integration of EAST-ADL and formal techniques based on timing constraints were investigated in several works [14][15][16], which are however, limited to the executional aspects of system functions without addressing energy-aware behaviors. Kang [17], Goknil et al [18] and Seceleanu et al [19] defined the execution semantics of both the controller and the environment of industrial systems in CCSL [20] which are also given as mapping to UP-PAAL models amenable to model checking. In contrast to our current work, those approaches lack precise stochastic annotations specifying continuous dynamics in particular regarding different energy consumption rates during execution.…”

Section: Related Workmentioning

confidence: 99%

“…8.17 UPPAAL-SMC STAs for End-to-End timing constraints verification 8.18 UPPAAL-SMC STAs for End-to-End timing constraints verification 8. 19 CE in SDV: In (b), leadVe.y equals to followVe.y) from 0 to 8s, which indicates that the two vehicles run in the same lane. In (a), signType becomes "turn left (4)" at 8s and the lead vehicle begins to turn left.…”

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confidence: 99%

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Formal Analysis of Non-functional Properties for a Cooperative Automotive System

Kang,

Huang,

2018

Preprint

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Modeling and analysis of nonfunctional requirements is crucial in automotive systems. EAST-ADL is an architectural language dedicated to safety-critical automotive system design. We have previously modified EAST-ADL to include energy constraints and transformed energy-aware timed (ET) behaviors modeled in SIMULINK/ STATEFLOW into UPPAAL models amenable to formal verification. Previous work is extended in this paper by including support for SIMULINK DESIGN VERIFIER (SDV), i.e., the ET constraints are translated into proof objective models that can be verified using SDV. Furthermore, probabilistic extension of EAST-ADL constraints is defined and the semantics of the extended constraints is translated into verifiable UPPAAL models with stochastic semantics for formal verification. A set of mapping rules are proposed to facilitate the guarantee of translation. Verification & Validation are performed on the extended timing and energy constraints using SDV and UPPAAL-SMC. Our approach is demonstrated on a cooperative automotive system case study.

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Section: Experiments and Verificationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Formal Analysis of Non-functional Properties for a Cooperative Automotive System

Kang,

Huang,

2018

Preprint

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“…The latest literature research shows that it is also possible to evaluate the results of power electronic converter operation with the use of formal methods used previously by universities mainly in the fields of computer engineering and automation control systems. Formal methods have also been successfully used in the industry, for example in nuclear power plants [24][25][26], wind turbine systems [27] or automotive company SCANIA [28]. A performance verification method must prove that the control algorithm can handle any system disturbances and keep the deviations of the controlled variables from the references at a minimum.…”

Section: Introductionmentioning

confidence: 99%

Overview of Control Algorithm Verification Methods in Power Electronics Systems

et al. 2021

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The paper presents the existing verification methods for control algorithms in power electronics systems, including the application of model checking techniques. In the industry, the most frequently used verification methods are simulations and experiments; however, they have to be performed manually and do not give a 100% confidence that the system will operate correctly in all situations. Here we show the recent advancements in verification and performance assessment of power electronics systems with the usage of formal methods. Symbolic model checking can be used to achieve a guarantee that the system satisfies user-defined requirements, while statistical model checking combines simulation and statistical methods to gain statistically valid results that predict the behavior with high confidence. Both methods can be applied automatically before physical realization of the power electronics systems, so that any errors, incorrect assumptions or unforeseen situations are detected as early as possible. An additional functionality of verification with the use of formal methods is to check the converter operation in terms of reliability in various system operating conditions. It is possible to verify the distribution and uniformity of occurrence in time of the number of transistor switching, transistor conduction times for various current levels, etc. The information obtained in this way can be used to optimize control algorithms in terms of reliability in power electronics. The article provides an overview of various verification methods with an emphasis on statistical model checking. The basic functionalities of the methods, their construction, and their properties are indicated.

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“…However, several studies have addressed the extraction of PWA models through hybrid systems identification (Vaezi and Izadian, 2015; Vašak et al, 2011; Zhao et al, 2015). Hybrid systems models of WT have proven effectiveness for verification (Kekatos et al, 2017; Seceleanu et al, 2017). They also can serve as a reliable and systematic framework for modelling and integrating the cyber layer of WT (Moness and Moustafa, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hybrid modelling and predictive control of utility-scale variable-speed variable-pitch wind turbines

Moness

Moustafa

2020

Transactions of the Institute of Measurement and Control

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Wind energy has proven to be the highest reliable source of renewables due to the maturity of the technology. Wind turbine (WT) systems are complex systems with undergoing development process that requires innovative methods of design and control. In this paper, WTs are studied through hybrid systems framework. Hybrid models of WT are extracted with representable dynamics from nonlinear complex design code. The WT model is formulated into a mixed-logical dynamical model (MLD) and a piecewise affine (PWA) model. Then, a receding horizon control strategy is applied to WT hybrid model resulting in a hybrid model predictive control (HMPC) with mixed-integer programming (MIP) problem. The performance of the proposed controller is compared against the baseline controller within a simulation environment for the National Renewable Energy Laboratory (NREL) 5MW benchmark WT as a case study. The analysis and investigation of HMPC highlight its capability as a potential tool for exploiting the control objectives of WT systems.

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Analyzing a wind turbine system: From simulation to formal verification

Cited by 15 publications

References 10 publications

Formal Analysis of Non-functional Properties for a Cooperative Automotive System

Formal Analysis of Non-functional Properties for a Cooperative Automotive System

Overview of Control Algorithm Verification Methods in Power Electronics Systems

Hybrid modelling and predictive control of utility-scale variable-speed variable-pitch wind turbines

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