How to make digital twin for power grid which has data assimilation?5 answersTo create a digital twin for a power grid with data assimilation, a hierarchical architecture integrating real-time simulation is essential. This architecture should leverage technologies like the Internet of things, blockchain, and big data to manage massive grid-related data for planning, construction, and operation. Incorporating digital modeling, intelligent perception, and accurate prediction based on standards like IEC61850 can ensure reliable grid operation. To enhance sensing device integration, a method for embedding perception data into the digital twin is crucial for flexible mapping and improved interaction between the virtual and real components. By constructing a digital twin power grid model encompassing shape, data, business, energy flow, and time dimensions, full-state information perception and interactive mapping can be achieved, facilitating fault diagnosis and enhancing smart grid operations.
What is the overall importance of power electronic converters in modern electrical systems?3 answersPower electronic converters play a crucial role in modern electrical systems. They are used to convert electrical power from one form to another, such as DC-DC, AC-DC, DC-AC, and AC-AC conversions. These converters are essential for power processing applications, including wind power, photovoltaic systems, electric vehicles, microgrids, and DC distribution systems. Improvements in power electronic converters are necessary for more efficient and sustainable transport systems. Power electronic converters are vital for electrical energy conditioning, enabling higher efficiency and better performance in modern industries. The advancement of power electronics has led to smaller and more efficient power converters, benefiting applications that require high power density. Additionally, power converters are crucial for addressing power quality issues in grid-connected systems, especially with the increasing penetration of renewable energy sources. Overall, power electronic converters are of great importance in modern electrical systems due to their diverse applications and their role in improving efficiency, reliability, and power quality.
How can eco-design principles be implemented in future power electronics converters?5 answersEco-design principles can be implemented in future power electronics converters by addressing two major challenges. Firstly, there is a need to adapt existing circularity design criteria to the physical subsystems of power electronics converters. Secondly, the conversion topology design process should integrate a "potential circular" physical structure, which involves incorporating repair, reuse, and remanufacturing methods. To achieve this, it is important to consider the environmental impacts throughout the life cycle of power electronic systems. This includes conducting life cycle assessments to identify hotspots and prioritize eco-design actions. The implementation of a circular economy approach, with maintenance, recycling, and reuse loops, along with modularity and self-diagnostic functions, can also contribute to sustainable design. By integrating these considerations into the early design phases, power electronics engineers can contribute to more efficient and sustainable power systems.
What is data mapping or modelling in digital twin solutions?3 answersData mapping or modeling in digital twin solutions refers to the process of creating a virtual representation of a physical system by emulating its behavior and capturing its data patterns. It involves associating the dynamical process with a digital twin model that mirrors the original process behavior. The modeling process requires the availability of accurate models and a large amount of information to adequately represent the reality being modeled. The goal is to create a high-fidelity digital twin data model that accurately maps the dynamics of the system with reduced complexity. This modeling approach can be applied to various domains, such as fluid flows, power grids, and other physical systems. The digital twin model can be continuously updated with real-time data and calibrated using artificial intelligence techniques, such as deep learning, to improve its fidelity. The mapping function in data modeling is automatically generated based on descriptions of the data models and data items, and can be adjusted based on user feedback.
What are the advantages of using a digital twin instead of a simulation?5 answersUsing a digital twin instead of a simulation offers several advantages. Digital twins provide high fidelity simulations of power systems, allowing for accurate prediction and analysis of system behavior. They enhance prediction accuracy, accelerate analysis speed, improve system reliability, and aid in efficient risk management. Digital twins also allow for the development and debugging of control algorithms, increasing the efficiency, reliability, and safety of control objects. They can be used to design more versatile workplaces and assembly lines, optimizing their workload. Additionally, digital twins can be employed to ensure the stability of mechanical systems with uncertain elements, such as nonlinear backlash. They enable the estimation and monitoring of system parameters in real-time, facilitating the re-design of control strategies to maintain stability and performance. Furthermore, digital twins serve as simulators for training control engineers, providing a risk-free environment for interaction and testing.
How can deep learning be used for power converter control?5 answersDeep learning can be used for power converter control by leveraging its ability to learn optimal control policies. By using deep neural networks, the optimal control policy defined by a complex model predictive formulation can be learned offline, allowing for rapid execution on embedded hardware during online use. This approach has been applied to black-box modeling of dc-dc step-down converters, accurately replicating their behavior without knowledge of the internal structure or parameters. Additionally, deep reinforcement learning methods can be combined with expert experience to improve the learning speed and performance of emergency voltage control in power systems. Historical power control data can also be utilized to accelerate the learning of power control in unexplored environments, resulting in increased learning speed and robustness. Furthermore, deep reinforcement learning techniques with the Actor-Critic architecture have been incorporated into control schemes to stabilize voltage in dc-dc converters feeding constant power loads.