Parameter Tuning for dynamic Digital Twin of Generation Unit in Power Grid

Abstract The control performance of the control system directly affects the running performance of the product. In order to solve the problem that the dynamics characteristics of mechanical systems are affected by the performance degradation of the controller, a digital twin-driven proportion integration differentiation (PID) controller tuning method for dynamics is proposed. In this paper, first, the structure and operation mechanism of the digital twin model for PID controller tuning are described. Using the advantages of virtual real mapping and data fusion of the digital twin model, combined with the online identification of the controlled object model, the problems of real-time feedback of an actual control effect of the controller and the unreal virtual model of the control system caused by time-varying working conditions are effectively solved, and the closed-loop self-tuning of PID controller is realized. At the same time, intelligent optimization algorithm is integrated to improve the efficiency and accuracy of PID controller parameter tuning. Second, the modeling method of the digital twin model is described from three aspects of physical prototyping, twin service system, and virtual prototyping. Finally, the controller tuning for gear transmission stability is taken as an example to verify the practicability of the proposed method.

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A Practical Approach to Construct a Digital Twin of a Power Grid using Harmonic Spectra

10.1109/upec50034.2021.9548199 ◽

2021 ◽

Author(s):

Hui Cai ◽

Xinya Song ◽

Yuelin Zeng ◽

Teng Jiang ◽

Steffen Schlegel ◽

...

Keyword(s):

Power Grid ◽

Practical Approach ◽

Digital Twin

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POWER GRID PLANNING FRAMEWORK AND APPLICATION PROSPECTS BASED ON DIGITAL TWIN

10.1049/icp.2021.2260 ◽

2021 ◽

Author(s):

Wenjuan Niu ◽

Chen Wu ◽

Guiyuan Xue ◽

Jian Tan ◽

Ciwei Gao ◽

...

Keyword(s):

Power Grid ◽

Digital Twin ◽

Planning Framework ◽

Power Grid Planning ◽

Grid Planning ◽

Application Prospects

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Research on Intelligent Monitoring and Maintenance Technology of Substation Based on Digital Twin

Journal of Physics Conference Series ◽

10.1088/1742-6596/2136/1/012029 ◽

2021 ◽

Vol 2136 (1) ◽

pp. 012029

Author(s):

He Wang ◽

Peng Lin ◽

Zhansheng Hou ◽

Shijun Sun

Keyword(s):

Life Cycle ◽

Fault Diagnosis ◽

Power Grid ◽

Intelligent Monitoring ◽

Digital Twin ◽

Full Life Cycle ◽

Operation And Maintenance ◽

Test Operation ◽

Digital Substation ◽

Full Life

Abstract The design, research and development, test, operation and maintenance of power grid substation equipment are relatively independent. Related problems in each link cannot be comprehensively managed from the perspective of the full life cycle of equipment. Operation and maintenance personnel need to face data monitoring, network monitoring, equipment monitoring, business system monitoring, fault diagnosis and repair and other business scenarios. There are many types of information, complex association relationship, abstract and inefficient operation and maintenance information, “man-machine-object” can not be two-way cooperation and interaction, fault diagnosis and troubleshooting difficulties. Digital substation intelligent monitoring operational digital twin applications, digital intelligent substation operations provide operational monitoring service ability, realize the grid digital substation twin modeling and environment reconstruction, false or true merged superposition, two-way coordinated interaction, twin intelligence operations and panoramic visual monitoring, field devices for power grid operation maintenance, resource scheduling, and construction planning to provide guidance, Fully guarantee the safe and reliable operation of power grid equipment, realize the full life cycle management of power grid equipment, and improve the level of intelligence, visualization and digitalization of power grid.

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Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

Energies ◽

10.3390/en13061407 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1407 ◽

Cited By ~ 2

Author(s):

João Vitor Leme ◽

Wallace Casaca ◽

Marilaine Colnago ◽

Maurício Araújo Dias

Keyword(s):

Energy Demand ◽

Power Grid ◽

Parameter Tuning ◽

Gradient Boosting ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

Related Data ◽

Electricity Cost ◽

Forecasting Errors

The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.

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