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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Guruwacharya, Nischal Bhujel, Niranjan Tamrakar, Ujjwol Rauniyar, Manisha Subedi, Sunil Berg, Sterling E. Hansen, Timothy M. Tonkoski, Reinaldo South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA

ISBN: (纸本)9781728155081

A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.

关键词： Converter-dominated electric power systems data-driven modeling grid-connected converters system identification

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Effective wind speed estimation based on a data-driven model of wind turbine tower deflection

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2022年第2期236卷 795-810页

作者： Nasrabad, Vahid Saberi Hajnayeb, Ali Sun, Qiao Univ Calgary Mech & Mfg Engn Calgary AB Canada Shahid Chamran Univ Ahvaz Mech Engn Dept Ahvaz Iran

The deflection of the wind turbine tower can provide us with rich information about the effective wind speed. In this paper, a new method for effective wind speed estimation is proposed based on tower deflection. The tower vibration model is derived and a subspace identification method is used to identify the model parameters. An online estimator of aerodynamic thrust force based on the identified tower model is designed and then implemented using a Kalman filter together with a recursive least squares algorithm. The estimated aerodynamic thrust force is then used as an input to a neural network estimator, which is trained to invert the aerodynamic thrust force equation and estimate the effective wind speed. In order to show the performance of the proposed estimator, the estimated thrust force and wind speed are compared and verified with a third-party simulation data of a 1.5 MW wind turbine. The comparison shows close agreement between their values.

关键词： Effective wind speed wind turbine control data-driven modeling wind speed estimation Subspace identification Kalman filter neural networks

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Learning event-triggered control based on evolving data-driven fuzzy granular models

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第5期32卷 2805-2827页

作者： Cordovil Jr, Luiz A. Q. Coutinho, Pedro H. S. Bessa, Iury Peixoto, Marcia L. C. Palhares, Reinaldo Martinez Univ Fed Minas Gerais Grad Program Elect Engn Belo Horizonte MG Brazil Univ Fed Amazonas Dept Elect Manaus Amazonas Brazil Univ Fed Minas Gerais Dept Elect Engn Av Antonio Carlos 6627 BR-31270901 Belo Horizonte MG Brazil

This article proposes a data-stream-driven event-triggered control strategy using evolving fuzzy models learned by granulation of input-output samples of nonlinear systems with unknown time-varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event-triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event-triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event-triggered learning mechanism is employed to determine the instants in which the event-triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event-triggered fuzzy control algorithm.

关键词： data-driven modeling evolving fuzzy systems fuzzy granular computing learning event-triggered control

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On data-driven Self-Calibration for IoT-Based Gas Concentration Monitoring Systems

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IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13848-13861页

作者： You, Yang You, Kai Chen, Hao Oechtering, Tobias J. KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

In this article, data-driven self-calibration algorithms for the Internet of Things-based gas concentration monitoring systems embedded with low-cost gas sensors are designed. The measurement errors are assumed to be caused by imperfect compensation for the variation of sensor component behavior. Specifically, the calibration procedure for the nondispersive infrared CO2 sensors is developed, for which the temperature dependency is the most dominant drift source. For a single sensor, the hidden Markov model is used to characterize the statistical relationship between different quantities introduced by the physical model that builds on the Beer-Lambert law. For the calibration in the Internet of Things-based system, sensors first transmit their belief functions of the true gas concentration level to the cloud. Then, the cloud fusion center computes a fused belief function according to certain rules. This belief function is then used as reference for calibrating the sensors. To deal with the case where belief functions highly conflict with each other, a Wasserstein distance-based weighted average belief function fusion approach is first proposed as a networked calibration algorithm. To achieve more long-term stable calibration results, the networked calibration problem is further formulated as a partially observed Markov decision process (MDP) problem, and the calibration strategies are derived in a sequential manner. Correspondingly, the deep Q-network approach is applied as a computationally efficient method to solve the proposed MDP problem. The performance of practical designs of the proposed self-calibration algorithms is finally illustrated in numerical experiments utilizing real data.

关键词： Belief function fusion data-driven modeling deep reinforcement learning (DRL) Internet of Things (IoT) nondispersive infrared (NDIR) gas sensor calibration partially observed Markov decision process (POMDP)

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A multiphase information fusion strategy for data-driven quality prediction of industrial batch processes

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INFORMATION SCIENCES 2022年 608卷 81-95页

作者： Sun, Yan-Ning Qin, Wei Xu, Hong-Wei Tan, Run-Zhi Zhang, Zhan-Luo Shi, Wen -Tian Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Artificial Intelligence Shanghai 200240 Peoples R China China Mobile Shanghai ICT Co Ltd Shanghai 200120 Peoples R China

As one of the most important modes of industrial production, the batch process often involves complex and continuous physicochemical reactions, making it challenging to pro-duce identical products between different batch runs even under the same working condi-tion. Optimization and parameter adjustments depend mainly on a good quality prediction model. However, this industrial process has "3M" characteristics of multiple process vari-ables, multiple production phases, and multiple quality indicators, which bring consider-able challenges to the accuracy and robustness of the model. This study proposes a multiphase information fusion strategy for data-driven quality prediction of industrial batch processes. Firstly, aiming for real-world industrial datasets with different sampling frequencies, two types of state variables are summarized, and the multiphase-based cumu-lative quality model is developed. Secondly, information theory with copula entropy is employed to characterize the association relationships between each state variable and the set of multiple quality indicators;thus, phase-specific critical variables are selected by ranking copula entropy. Lastly, a stacking multiway random forest algorithm is pro-posed to develop the prediction relationship between phase-specific critical variables and multiple quality indicators. Experiments on a real-world industrial dataset have demonstrated that the proposed method has better accuracy and stronger robustness than previous baseline methods.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Industrial batch processes data-driven modeling Information fusion Quality prediction Information theory Ensemble learning

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data-driven Identification of Multiple Local Nonlinear Attachments Installed on a Single Primary Structure 40th

Data-Driven Identification of Multiple Local Nonlinear Attac...

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40th Conference and Exposition on Structural Dynamics (IMAC)

作者： Singh, Aryan Moore, Keegan J. Univ Nebraska Lincoln Dept Mech & Mat Engn Lincoln NE 68588 USA

ISBN: (纸本)9783031040863;9783031040856

The goal of this study is to find mathematical models for the dynamics of multiple local nonlinear attachments on a single primary structure. We focus on the application of the characteristic nonlinear system identification (CNSI) method to multiple attachments and attachments tuned higher than the primary structure's first linear mode. The characteristic nonlinear system identification method is a data-driven method for building mathematical models of nonlinear attachments. To produce representative mathematical models, the method only requires the transient experimental response measurements, the general frequency content, and the mass of the attachments. A two-story tower with a nonlinear attachment installed on each floor in two configurations is used to demonstrate the applicability of the CNSI method for identifying multiple nonlinear attachments. The attachments are tuned to interact with the tower's first mode in the first configuration. The linear stiffness of the attachment on the first floor is increased in the second configuration so that it interacts with the second mode rather than the first mode. We numerically integrate the models and compare the resulting displacements with experimental measurements to validate the CNSI method's success and strength.

关键词： Nonlinear dynamics data-driven modeling data-driven identification System identification Nonlinear energy sink

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Inference Attack and Privacy Security of data-driven Industrial Process Monitoring Systems 12

Inference Attack and Privacy Security of Data-driven Industr...

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IEEE 12th data driven Control and Learning Systems Conference (DDCLS)

作者： Zhang, Xinmin Zhang, Xuerui Song, Zhihuan Ren, Qinyuan Wei, Chihang Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou 311121 Peoples R China

ISBN: (纸本)9798350321050

In modern industry, data-driven process monitoring systems (PMS), as the initial defense line of industrial control system security, have been widely used in all walks of life. However, the privacy security of the data-driven PMS itself has rarely or never received serious attention. Once the data-driven PMS suffers from intrusion and malicious attacks, it will not only interfere with the normal operation of the industrial control system, but also lead to the disclosure of industrial confidential and privacy information and major economic losses. To handle this issue, this work proposes a novel pioneering study on the inference attack and privacy security problem in the data-driven PMS. Firstly, the potential attack and privacy violation risks of data-driven PMS are investigated. Second, a novel industrial inference attack and privacy security benchmark on data-driven PMS is presented, in which a series of membership inference attack and defense experiments are designed and conducted. Third, we provided a detailed discussion about which member reasoning attacks are the most potential threats to the data-driven PMS and which defense technologies are most suitable for mitigating the attack. The experimental results will provide researchers and practitioners with a new perspective when designing a novel data-driven PMS with more robust and privacy protection performance.

关键词： Inference Attack data Privacy Security Process Monitoring System data-driven modeling Membership Inference Attack

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Optimizing Building Heating Efficiency: A data-driven Approach for Cost and Energy Savings 18

Optimizing Building Heating Efficiency: A Data-driven Approa...

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18th IEEE Sensors Applications Symposium (SAS)

作者： Banihabib, Reyhaneh Fadnes, Fredrik Skaug Assadi, Mohsen Univ Stavanger Dept Energy & Petr Technol Stavanger Norway Norconsult AS Dept Energy & Smart Technol Oslo Norway

ISBN: (纸本)9798350323078

This study presents a data-driven modeling approach to enhance the heating control of buildings, aiming to address the rising energy costs and growing concerns about climate change. Two distinct models based on artificial neural networks (ANNs) are employed to estimate the cost of different control scenarios and their impact on the office space temperature, using operational data from a municipal building in Norway equipped with electric and thermal energy meters. The proposed method utilizes advanced tools built based on the data from the building and weather forecasts for predictive control, which can be readily accessed for practical implementation. The study demonstrates the potential of AI methods when combined with building data to lower costs and improve efficiency. A 7% reduction in power consumption and cost over working days of a week were achieved through the optimization of the building heating system.

关键词： Building heating efficiency data-driven modeling predictive control cost reduction artificial neural networks

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data-driven Approach to Transactive Energy Systems with Commercial Buildings

Data-Driven Approach to Transactive Energy Systems with Comm...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Ramesh, Meghana Xie, Jing McDermott, Thomas E. Mukherjee, Monish Diedesch, Michael Bose, Anjan Pacific Northwest Natl Lab Richland WA 99354 USA Avista Corp Spokane WA USA Washington State Univ Pullman WA USA

ISBN: (纸本)9781665464413

A microgrid with solar, storage, and responsive load resources has been implemented and tested on an urban academic campus. Through modeling and simulation, a consensus transactive energy mechanism has been implemented, with each resource participating as a virtual battery. Most owners of large buildings do not have the information and expertise to develop and validate suitable models of their buildings using available tools. To mitigate this adoption barrier, a data -driven building model has been implemented and validated. It uses 5 -minute weather data, 10-second feeder data, 3 -second revenue meter data, energy audit information, and a load reduction test conducted by the building owner. The model tracks daily and seasonal variations, along with changes in grid voltage and feeder load.

关键词： Buildings data-driven modeling deep learning energy storage distributed power generation transactive energy

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data-driven Structure Identification of Takagi Sugeno Fuzzy Models Using a Bounded Error Approach

Data-Driven Structure Identification of Takagi Sugeno Fuzzy ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Wittich, Felix Kroll, Andreas Univ Kassel Dept Measurement & Control Kassel Germany

ISBN: (纸本)9798350332285

Takagi Sugeno fuzzy models are a widely used model class for data driven identification of nonlinear systems. Besides good approximation quality, the local model approach allows for good interpretability and the utilization of linear control strategies in dynamic models. However a major challenge is the identification of the model structure, i.e. determining the number of local models and the partitioning parameters. A new method for identification of Takagi Sugeno fuzzy models inspired by bounded error methods is presented. The method finds partitions of the data that are defined by bounding linear inequalities and therefore identifies locally linear system behavior. The error bound as a tuning parameter is used to adjust the compromise between model accuracy and sparsity. The presentation of the method is complemented by two case studies.

关键词： Fuzzy modeling Bounded Error Structure Identification Partitioning data-driven modeling

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