To combine the advantages of both model-driven and data-driven methods, this paper proposes a model-data-hybrid-driven (MDHD) method to diagnose open-switch faults in power converters. This idea is based on the explicit analytical model of converters and the learning capability of artificial neural network (ANN). The process of the method is divided into two parts: offline model analysis and learning, and online fault diagnosis. For both parts, model-driven and data-driven are combined. With the model information and data-based learning capability, a fast diagnosis for various operating conditions can be achieved without high computation burden, tricky threshold selection and complex rulemaking. This can greatly contribute to the practical application. The open-switch fault diagnosis in a two-level three-phase converter is studied for method validation. For this converter, an ANN is trained with two input elements, seven output elements, and two neurons in the hidden layer. Experimental results are given to demonstrate good performance.

Modular multilevel converter (MMC) has attracted much attention for years due to its good performance in harmonics reduction and efficiency improvement. Model predictive control (MPC) based controllers are widely adopted for MMC because the control design is straightforward and different control objectives can be simply implemented in a cost function. However, the computational burden of MPC imposes limitations in the control implementation of MMC because of many possible switching states. To solve this, we design machine learning (ML) based controllers for MMC based on the data collection from the MPC algorithm. The ML models are trained to emulate the MPC controllers which can effectively reduce the computation burden of real-time control since the trained models are built with simple math functions that are not correlated with the complexity of the MPC algorithm. The ML method applied in this study is a neural network (NN) and there are two types of establishing ML controllers: NN regression and NN pattern recognition. Both are trained using the sampled data and tested in a real-time MMC system. A comparison of experimental results shows that NN regression has a much better control performance and lower computation burden than the NN pattern recognition.

The integration of artificial intelligence (AI) techniques in power converter-based systems has the potential to revolutionize the way these systems are optimized and controlled. With the rapid advancements in AI and machine learning technologies, this paper presents the analysis and evaluation of these powerful tools as well as in computational capabilities of microprocessors that control the converter. This paper provides an overview of AI-based controllers, with a focus on online/offline supervised, unsupervised, and reinforcement-trained controllers. These controllers can be used to create surrogates for inner control loops, complete power converter controllers, and external supervisory or energy management control. The benefits of using AI-based controllers are discussed. AI-based controllers reduce the need for complex mathematical modeling and enable near-optimal real-time operation via computational efficiency. This can lead to increased efficiency, reliability, and scalability of power converter-based systems. By using physics-informed methods, a deeper understanding of the underlying physical processes in power converters can be achieved and the control performance can be made more robust. Finally, by using data-driven methods, the vast amounts of data generated by power converter-based systems can be leveraged to analyze the behavior of the surrounding system and thereby forming the basis for adaptive control. This paper discusses several other potential disruptive impacts that AI could have on a wide variety of power converter-based systems.

The H-bridge (HB) converters are essential components in various power typologies, offering dual energy flow, increasing power density, and enhancing extensibility. For the HB converters, the dead zone plays a crucial role in avoiding power semiconductors against shoot-through phenomenon in the phase leg of an HB converter. However, it also negatively affects the quality of the output ac currents, impacting system performance. This letter introduces a simplified digital logic-based scheme that effectively compensates for the dead zone in HB converters. Initially, the scheme examines the repercussions of dead zones in HB converter. Subsequently, it adjusts pulsewidth modulation signal waveforms by either delaying the leading edge or the lagging edge for the dead-zone compensation, irrespective of the control and modulation methods employed. Furthermore, extensibility of the proposed scheme to other power converters, such as a neutral-point-clamped converter is discussed. The effectiveness of the proposed scheme is validated by experimental results.

The capacitor voltage ripples of the modular multilevel converter (MMC) are increased under unbalanced grid fault conditions. Since high capacitor voltage ripples deteriorate their lifetimes and may even cause tripping of the MMC system, it is important to restrict them. To this end, it is well known that injecting double fundamental frequency circulating currents can reduce the capacitor voltage ripples. However, finding a proper circulating current reference to achieve desired ripples analytically is complicated. This paper proposes an alternative method to quickly calculate the proper circulating current references without analytical computations, which is achieved by an artificial neural network (ANN) trained to approximate the relationship between circulating current references and capacitor voltage ripples. The training data is firstly extracted from a detailed simulation model of the MMC. Afterwards, the ANN is trained by the input-output data to obtain the mapping relationship, which is then used to derive the desired circulating current references. Both the simulation and the experimental results verify the practicability of the proposed method, where the operating region can be extended 30% at a minimum in all testing conditions.

This article proposes a novel application method, inverse application of artificial intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of artificial neural network is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multilevel converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility, and practicality of IAAI.