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Optimizing Power Electronics with Artificial Intelligence Methods

  • Contents

Overview: This article provides an overview of how artificial intelligence methods, including expert systems, fuzzy logic, metaheuristic techniques, and machine learning, can optimize power electronics systems.

 

Artificial intelligence methods refer to a set of techniques and algorithms that enable machines to perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making. 

Artificial Intelligence Methods

Advances in artificial intelligence are likely to yield substantial benefits for power electronics. Artificial intelligence methods can be broadly divided into expert systems, fuzzy logic, metaheuristic techniques, and machine learning.

 

figure 1

 
Figure 1. Sankey diagram of artificial intelligence methods and applications in each phase of the life-cycle of power electronic systems. Image used courtesy of IEEE Transactions on Power Electronics

Expert System

The oldest artificial intelligence technique that has been successfully used in industrial applications is the expert system. The expert system is essentially a database that incorporates the expert information into a catalog of Boolean logic that serves as the foundation for simulating the IF-THEN logic rules used by human brains to reason. An intelligent system that simulates the inference process using the database answers the why-and-how questions. The database comprises simulation data, facts, and claims or field expert knowledge. It can be updated continuously. 

 

It is important to note that the utilization data in Figure 1 shows that expert system applications are as low as 0.9%. The expert system lacks universality since it is typically built on system principles and norms, which are closely related to the system of interest. It only applies to well-defined domains with reliable expert rules. Because of the quick growth of computer platforms, advanced artificial intelligence with improved inference and approximation skills, such as fuzzy logic and machine learning, can perform expert system functions.

 

Fuzzy Logic

Fuzzy logic, which extends Boolean logic into multivalued conditions, is a rule-based approach similar to expert systems. To deal with system uncertainties and noisy measurements, fuzzy logic is the perfect instrument. 

 

Fuzzification is first carried out using fuzzy sets made up of many membership functions with a range of 0-1 rather than using the exact input crisp value directly. The inference step then aggregates the fuzzy input signals using fuzzy rules. The inference result undergoes defuzzification by considering the level of fulfillment that produces a crisp value. To complete the nonlinear mapping between the input and output, the crisp value is modified in a fuzzy space using precisely constructed principles. 

 

Components of Fuzzy Logic

In most applications, the four basic components of a fuzzy logic method are fuzzification, rule inference, knowledge base, and defuzzification. First, fuzzification is applied to the input of linguistic variables with membership functions such as triangular, trapezoidal, Gaussian, bell-shaped, singleton, and other custom-made shapes. Second, the inference module combines the signals following IF-THEN fuzzy rules drawn from expert experience and stored in the knowledge base. Third, defuzzification of the output signal is carried out.

 

Metaheuristic Methods

Once the optimization objective for a given application has been stated, the best solution can be found using either a deterministic programming method (such as linear or quadratic programming) or a nondeterministic programming method, such as the metaheuristic method. In deterministic programming techniques, their complexity in calculating the gradient and Hessian matrices makes them difficult to use in most optimization problems in power electronics. 

 

For various optimization tasks, metaheuristic approaches act as a general end-to-end tool that requires less specialized knowledge and is effective and scalable. The development of metaheuristic algorithms frequently draws inspiration from biological evolution, as seen in the genetic algorithm (GA) that uses the natural selection process and the ant colony optimization (ACO) algorithm that mimics ants in looking for an effective food path. Trial-and-error is a method that promotes the search for the ideal response. 

 

Metaheuristic Techniques and Methods

The metaheuristic techniques fall into two categories: trajectory-based techniques (tabu search method, simulated annealing method, etc.) and population-based techniques (GA, particle swarm optimization (PSO), ACO, differential evolution, immunity algorithm (IA), etc.). 

 

Trajectory-Based Techniques

For the trajectory-based techniques, there is only one candidate solution included in each exploration step, and it develops into another solution by a set of rules. The standard and effectiveness of the rule largely determine the approach's effectiveness. As a result, for nonconvex optimization tasks, the ultimate solution is frequently a local rather than a global solution, and the convergence speed of trajectory-based approaches is typically slow. 

 

Population-Based Technique

The population-based methods generate a large number of candidate solutions at random. To enhance the quality of the population in the current generation, these candidate solutions are either varied (e.g., crossover in the GA) or incorporated and replaced with fresh candidate solutions at each iterative exploration. As a result, the population's suitability is gradually increasing to get closer to the ideal solution. They are more effective than trajectory-based approaches regarding convergence speed and global searching ability and are particularly helpful for multiple optimization tasks. 

 

However, population-based approaches have a heavier computing requirement. For online application scenarios where effectiveness and speed are crucial, this difficulty must be considered. A list of power electronics-related metaheuristic techniques, together with their benefits and drawbacks, is presented in Table I. In terms of several crucial characteristics, such as implementation ease, global convergence, convergence speed, and parallelism, these metaheuristic algorithms are qualitatively compared.

 

figure 2

Most optimization issues in power electronics are resolved using population-based approaches due to their significant advantages. Table 1 shows various population-based techniques with enhanced versions for power electronics optimization problems. They are created and enhanced using various biological influences. 

 

Several other recently developed approaches, such as biogeography-based optimization, the crow search algorithm, grey wolf optimization, the firefly optimization algorithm, the bee algorithm, the colonial competitive algorithm, teaching-learning-based optimization, etc., have also been used on a limited scale in addition to the earlier, widely used metaheuristic methods.

It is important to note that choosing the optimal strategy is a difficult task that depends on the application. As indicated in Figure 2, the two most common metaheuristic techniques used in power electronics are GA and PSO. They serve as the foundation and models, respectively, for evolutionary algorithms and swarm intelligence algorithms, upon which numerous variants are built. Practitioners can pick the method based on its superiority, as shown in Table I.

 
Figure 2. Usage statistics of population-based metaheuristic methods in the optimization of power electronics Image used courtesy of IEEE Transactions on Power Electronics

 

Machine Learning 

Machine learning is intended to automatically identify patterns and principles through experience gained from either data collection or interactions through trial and error. It is divided into three categories for use in power electronics: supervised learning, unsupervised learning, and reinforcement learning (RL).

 

Summarizing the Key Points

  • Artificial intelligence methods have the potential to revolutionize power electronics by improving system efficiency, reliability, and performance.
  • Expert systems can be used to diagnose faults in power electronics systems and provide recommendations for repair or replacement.
  • Fuzzy logic can improve the accuracy of power electronics control systems by accounting for uncertainty and imprecision in sensor data.
  • Metaheuristic techniques, such as genetic algorithms and particle swarm optimization, can be used to optimize power electronics systems by searching for the best combination of design parameters.
  • Machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, can automatically identify patterns and principles in power electronics data and improve system performance over time.

 

References

Zhao, S., Blaabjerg, F., & Wang, H. (2021, April). An Overview of Artificial Intelligence Applications for Power Electronics. IEEE Transactions on Power Electronics, 36(4), 4633–4658. https://doi.org/10.1109/tpel.2020.3024914



Rakesh Kumar, Ph.D.

Rakesh Kumar holds a Ph.D. in electrical engineering, specializing in power electronics. He is a Senior Member of the IEEE Power Electronics Society, Class of 2021. He writes high-quality, long-form technical articles for global B2B semiconductor brands. Feel free to reach out to him at rakesh.a@ieee.org! Checkout his complete portfolio @muckrack.com/rakesh-kumar-phd | @linkedin.com/in/rakesh-kumar-phd

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