Experimental implementation of predictive indicators for configuring a real-time hybrid simulation

Lin, Fangshu; Maghareh, Amin; Dyke, Shirley J.; Lü, Xilin

doi:10.1016/j.engstruct.2015.07.040

Cited by 19 publications

(20 citation statements)

References 29 publications

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“…The tracking performance can be quantified using the time domain signals of the gap g = z − z . In literature one finds the relative root-mean-square (RMS) error e rel,track , the maximum tracking error (MTE) e MTE,track and the tracking peak e peak,track error [8,[25][26][27]:…”

Section: Accuracy Measuresmentioning

confidence: 99%

“…In an RTHS setup, there are multiple sources of errors that can lead to a loss of test fidelity or even instability of the test. According to [6][7][8], errors can be classified into systematic (epistemic) and random (aleatoric) errors. The latter comprise noise in the displacement or force measurement as well as truncation in electrical signals at the analog to digital conversion [8].…”

Section: Introductionmentioning

confidence: 99%

“…According to [6][7][8], errors can be classified into systematic (epistemic) and random (aleatoric) errors. The latter comprise noise in the displacement or force measurement as well as truncation in electrical signals at the analog to digital conversion [8]. Even though noise has a low amplitude, its high frequency content can excite higher modes in lightly damped structural systems [6].…”

Section: Introductionmentioning

confidence: 99%

“…Rather, the susceptibility of an RTHS test to these errors depends i.a. on the synchronization at the interface, the partitioning of the dynamical system (splitting ratio of mass and stiffness into numerical/experimental part), the RTHS test's fastest eigenfrequency and the amount of damping in the system [8,15]. This means that it depends on these mentioned factors whether a certain amount of error (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Insam

Rixen

2021

Exp Tech

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Real-Time Hybrid Substructure (RTHS) testing is a commonly used method to investigate the dynamical influence of a component on a mechanical system. In RTHS, a part of the dynamical system is tested experimentally, while the remaining structure is simulated numerically in a co-simulation. There are several error sources in the RTHS loop that distort the test outcome. To investigate the reliability of the test, the fidelity of the test must be quantified. In many engineering applications, however, there is no reference solution available to which the test outcome can be validated against. This work reviews currently existing accuracy measures used in RTHS. Furthermore, using Artificial Neural Networks (ANN) to predict the fidelity of the RTHS test outcome when no reference solution is available is proposed. Appropriate input features for the network, such as dynamic properties of the system and existing error indicators, are discussed. ANN training was performed on a data set from a virtual RTHS (vRTHS) simulation of a dynamical system with contact. The training process was successful, meaning that the correlation between the ANN prediction and the true fidelity value was > 99 %. Then, the network was applied to data of experimental RTHS tests of the same dynamical system and achieved a correlation of 98 %, which proves that the relation found by the ANN captured the relation between the chosen input features and the error measure. The application of the trained ANN to data from a linear vRTHS test revealed that further improvement of the network and the choice of input features is necessary. This work suggests that ANNs could be a meaningful tool to predict the fidelity of the RTHS test outcome in the absence of a reference solution, especially if more data from different RTHS tests were aggregated to train them.

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Section: Accuracy Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Insam

Rixen

2021

Exp Tech

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“…[139]- [150] PMU and Synchrophasor Vector Processors (SVPs) [17], [36], [40], [61], [69], [71], [72], [115], [136], [137] SIL [38] CIL-CHIL [42], [81], [41] Power systems and ICT simulation environment (EPOCHS) [49], [71] Real-Time Hybrid simulator [124], [128], [151] Matlab Real-Time Workshop [44], [45], [59], [90], [68] Real-Time extension of the Virtual Test Bed (VTB-RT) [52], [67], [113], [114], [105] Speedgoat technology [151] Network monitoring and control based on real-time wavelet transform. [47] Time division multi-access (TDMA) for precision in simulation of power converters.…”

Section: Real-time Simulation Challenges and New Trends For Protectiomentioning

confidence: 99%

Current Status and Future Trends in Protection, Control and Communications Testing in Electrical Grids using Real - time Simulation

Gómez-Luna¹,

Candelo-Becerra²,

Sáenz³

2018

JESTR

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In recent times, real-time simulation has been used to validate the testing and design of protection, control, and communications systems prior to their commissioning in the field, which has improved the reliability and security of the commissioning processes for electrical systems. This paper reviews the state-of-the-art and future trends in protection, control, and communications testing of electrical grids using real-time simulation. In addition, a summary is presented in which all the works and technologies mentioned are listed with the purpose of identifying new challenges in the proposed topic. Finally, future trends related to the use of real-time simulation for evaluating protection, control, and communications schemes are presented. Real-time simulation is very useful due to the increasing complexity of modern electrical power systems, therefore, it is necessary to use this kind of tool to facilitate field testing, avoid unnecessary work, and reduce costs for clients.

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Real‐time hybrid simulation of a space substructure based on high‐strength steel composite Y‐eccentrically braced frames

Yan

et al. 2021

Struct Control Health Monit

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Summary Real‐time hybrid simulation (RTHS) is a novel structural seismic test method that has good application prospects. The applicability of RTHS for large‐scale space frame structures is further investigated in this study based on high‐strength steel composite Y‐eccentrically braced frames (Y‐HSS‐EBFs). First, an RTHS system was established, which included OpenSees, OpenFresco, an xPC‐Target, and an electrohydraulic servo control system. An adaptive feedforward delay compensator based on displacement prediction was introduced. Then, a shaking table test model of a three‐story Y‐HSS‐EBF was taken as the prototype, the third‐story frame with the strongest nonlinear performance was taken as the experimental substructure, and the remaining two stories were taken as the numerical substructure in order to perform the RTHS. The results of the RTHS were compared with those obtained in a purely numerical model and a shaking table test, and the stability of the RTHS system was analyzed. The results indicate that the RTHS results are in good agreement with the numerical simulation results and that RTHS can be used to obtain results similar to the ones obtained using the shaking table. In the typical seismic testing frequencies and the mass distribution ranges of the substructures, the RTHS system displayed good stability and reliability and was able to effectively evaluate the seismic performance of large‐scale space frame structures.

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Experimental implementation of predictive indicators for configuring a real-time hybrid simulation

Cited by 19 publications

References 29 publications

Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Fidelity Assessment of Real-Time Hybrid Substructure Testing: a Review and the Application of Artificial Neural Networks

Current Status and Future Trends in Protection, Control and Communications Testing in Electrical Grids using Real - time Simulation

Real‐time hybrid simulation of a space substructure based on high‐strength steel composite Y‐eccentrically braced frames

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