As a leading electronic components sales company, we delve into the intricate realm where artificial intelligence (AI) converges with power electronic systems. This article explores the pivotal role of AI in advancing the performance and reliability of power electronic systems, shedding light on the challenges, opportunities, and benefits that accompany these innovative approaches.
I. Unraveling the Tapestry: AI Approaches Across Power System Life-Cycles
Power electronic systems undergo three distinctive life-cycle phases: design, control, and maintenance. Each phase involves complex tasks, from optimization to real-time control, and AI emerges as a transformative force capable of enhancing these processes.
Ⅰ. AI Approaches For Design: The Art of Optimization
Designing components like the heatsink of a converter system demands meticulous consideration of variables such as weight, volume, and pattern. This optimization task finds its ally in metaheuristic approaches, unleashing the power of AI for iterative trial-and-error processes. Unlike real-time control, design optimization can afford a more deliberate pace, making algorithm speed less critical. The suboptimal heatsink designs often prove to be sufficient, making precision and interpretability secondary considerations.
Ⅱ. AI Approaches For Control: Real-Time Precision
The control phase demands a different set of priorities. Here, algorithm speed and accuracy take center stage. AI facilitates adaptive updating in real-time, addressing control errors like voltage and current discrepancies. Interpretability becomes crucial, ensuring the stability of the intelligent controller. The online adjustment nature of the controller eliminates the need for preparing datasets for model training.
Ⅲ. AI Approaches For Maintenance: Navigating Slow Decay
Maintenance tasks, particularly predicting Remaining Useful Life (RUL) of switching devices, require a slow algorithm speed. The gradual breakdown of devices allows for a manageable decision-making period. Computational effort is minimal, and the degradation model for RUL prediction can be developed offline and modified online. Dataset quality becomes paramount in this phase, impacting the accuracy of RUL predictions. Interpretability of findings, especially with uncertainty, becomes a key consideration.
Ⅳ. AI in Power Electronic Systems: Challenges and Features
Using AI in power electronics presents distinctive challenges and features. Despite the vast promise of AI, real-world applications in power electrical systems remain limited. The article advocates for further research to distinguish AI's practical advantages over traditional methods. Key challenges include the adoption of AI, necessitating clarity on its advantages from an industrial perspective.
Ⅴ. Combined AI Use: A Synchronized Symphony
The article explores the potential of using AI across all stages of the life-cycle—design, control, and maintenance. This combined usage unlocks flexible functional interactions, simplifying procedures and optimizing overall performance. It facilitates seamless data flow management between electrical and other fields, promoting a holistic approach.
Features and Challenges in AI Implementation:
Merging of Multilevel Information: Robust power electronic systems vital for safety benefit from the fusion of various models and information sources. Combining data at different levels reduces flaws, enhancing system stability.
Computation-Light AI: Overcoming the computing power limitations of power electronic systems, computation-light AI algorithms offer performance comparable to complex deep learning methods at a cost-effective implementation.
Data-Light AI: Acknowledging the dataset limitations in power electronics, the focus shifts to AI algorithms that require fewer datasets, ensuring satisfactory performance even with limited data.
Explainable AI: Addressing the "black box" nature of AI algorithms, emphasizing the need for transparency in algorithms for better reliability, sensitivity analysis, and quantification of uncertainty.
Data Privacy: Recognizing the importance of data privacy, the article suggests a cooperative learning approach for AI algorithms in power electronics, aligning with evolving data privacy laws.
Database on Power Electronics: Advocating for the establishment of a standard data and knowledge base for power electronics, providing large datasets for model training, benchmarking algorithms, and accelerating application development.
Ⅵ. Key Takeaways: Navigating the AI-Powered Horizon
In summary, artificial intelligence holds immense potential to elevate the performance and reliability of power electronic systems. Its application spans the entire life-cycle, with computation-light and data-light AI algorithms offering promising solutions to address unique challenges in the domain. Embracing explainable AI and prioritizing data privacy are crucial steps towards ensuring the widespread adoption of AI-based solutions in power electronic systems.
Ⅶ. Reference
Zhao, Shuai, Frede Blaabjerg, and Huai Wang. “An Overview of Artificial Intelligence Applications for Power Electronics.” IEEE Transactions on Power Electronics 36, no. 4 (April 2021): 4633–58. Link.
In the rapidly evolving landscape where technology and power converge, our commitment as an electronic components sales company is to navigate these challenges and harness the transformative potential of artificial intelligence.