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Artificial intelligence (AI) applications for power electronic systems

Artificial intelligence (AI) applications for power electronic systems


Intelligence Electronic Device
Artificial intelligence


Artificial intelligence (AI) has emerged as a powerful tool for design, simulation, control, estimation, fault diagnostics, and fault-tolerant control of power electronic systems. AI is particularly important in modern smart grid and renewable energy systems (RESs) in addition to its applications in traditional power electronics and motor drives area. The three distinctive life-cycle phases, design, control, and maintenance, are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration. The applications of four categories of AI are discussed, which are expert system, fuzzy logic, metaheuristic method, and machine learning. More than 500 publications have been reviewed to identify the common understandings, practical implementation challenges, and research opportunities in the application of AI for power electronics.

An example of AI in power electronics:

here are some examples of AI applications in power electronics:

  1. Short-term load forecasting: AI can predict the load on a power system in the short term, which helps in efficient power management.

  2. Predictive maintenance: AI can predict when resources might fail and schedule maintenance before costly repairs are needed1.

  3. Control of voltage, frequency, stability, and power flow: AI can help in controlling various parameters of a power system for efficient operation.

  4. Control of a fuel cell and thermal power plant: AI can optimize the operation of fuel cells and thermal power plants1.

  5. Automation for restoration management, fault analysis, and network security: AI can automate various tasks in power electronics, such as restoration management, fault analysis, and network security.

  6. Silicon Carbide (SiC) based JFETs and MOSFETs: AI has proven to be an efficient method of designing, controlling, and sustaining power electrical systems.

These are just a few examples. The application of AI in power electronics is vast and continues to grow with advancements in technology.


How AI helps in fault analysis?


AI helps in fault analysis in power electronics by using deep learning algorithms and neural networks. These algorithms can learn to differentiate between parts and find anomalies compared to what the system ‘expects’ to see in the data.

AI models can completely or almost completely eliminate the need for human personnel in asset fault detection. They can also provide analysis results faster, allowing maintenance crews to act faster and avoid additional costs related to late repairs.

In summary, AI can significantly improve the reliability and efficiency of power electronic systems by providing early fault detection and predictive maintenance. It’s a powerful tool that continues to advance and evolve in the field of power electronics.


Summary:

  • AI for power electronics: The document introduces the applications of artificial intelligence (AI) for power electronic systems, which are devices that convert and control electric power. AI can help with design, simulation, control, estimation, fault diagnostics, and fault-tolerant control of these systems.

  • AI in smart grid and RESs: The document emphasizes the importance of AI in modern smart grid and renewable energy systems (RESs), which are systems that integrate renewable energy sources, such as solar and wind, with the power grid. AI can help with optimization, classification, regression, and data structure exploration of these systems.

  • AI categories and methods: The document discusses four categories of AI methods that are used for power electronics: expert system, fuzzy logic, metaheuristic method, and machine learning. Expert system is a rule-based system that mimics human experts. Fuzzy logic is a logic system that deals with uncertainty and imprecision. Metaheuristic method is a search technique that finds optimal or near-optimal solutions. Machine learning is a data-driven technique that learns from data and improves with experience.

  • Literature review and challenges: The document reviews more than 500 publications that apply AI methods to power electronics and identifies the common understandings, practical implementation challenges, and research opportunities in this field. Some of the challenges include data quality, computational complexity, hardware compatibility, and system robustness.

Looking to learn more about this?


  1. Online Courses: There are several online platforms offering courses on power electronics. For instance, Coursera offers an “Introduction to Power Electronics” course1. This course introduces the basic concepts of switched-mode converter circuits for controlling and converting electrical power with high efficiency1.

  2. Books and Reading Materials: There are countless books available that can help you gain knowledge about power electronics2. You can also read about the basics of power electronics on various websites3.

  3. Tutorials: Online tutorials can be a great way to learn power electronics. For example, TutorialsPoint offers a tutorial on power electronics4.

  4. College or University Programs: If you’re interested in a more formal education, you might consider enrolling in a college or university program that offers a degree in electrical engineering with a focus on power electronics2.

Remember, the key to learning is consistency. So, choose the method that suits you best and stick with it. Happy learning!


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