Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

The large-scale application of voltage-source converters (VSCs) to power grids may negatively impact their stability. Stability analysis methods based on impedance modeling offer a theoretical framework to analyze and address this issue. However, under conditions of uncertainty regarding the internal structure and parameters of VSCs, existing impedance measurement techniques using frequency scanning are limited in terms of data acquisition and accuracy. In practice, the power output demand for VSCs in grid-connected operation tends to vary, making it challenging for frequency scanning schemes to account for changes in operating conditions. To address this challenge, this article proposes a sequence impedance prediction solution based on a bidirectional long short-term memory method, which requires only limited data to accomplish high-precision prediction of the overall impedance of VSCs under multiple operating points. Unlike the existing $dq$-based impedance prediction schemes, the sequence impedance method will greatly simplify the prediction process, and the physical meaning is clear. In addition, this article applies trained model to a small-scale VSC model through transfer learning for experimental validation. The process saves computational resources while maintaining prediction accuracy and demonstrates the adaptability of the proposed model.

Frequency instability poses a significant challenge to the overall stability of islanded microgrid systems. Deep reinforcement learning (DRL)-based intelligent control strategies are drawing considerable attention for their ability to operate without the need for previous system dynamics information and the capacity for autonomous learning. This paper proposes an intelligent frequency secondary compensation solution that divides the traditional secondary frequency control into two layers. The first layer is based on a PID controller and the second layer is an intelligent controller based on DRL. To address the typically extensive training durations associated with DRL controllers, this paper integrates transfer learning, which significantly expedites the training process. This scheme improves control accuracy and reduces computational redundancy. Simulation tests are executed on an islanded microgrid with four distributed generators and an IEEE 13-bus system is utilized for further validation. Finally, the proposed method is validated on the OPAL-RT real-time test platform. The results demonstrate the superior performance of the proposed method.