Conclusions and outlook using AI in power systems

In summary, this book has focused on applying AI technology to deal with a series of challenges caused by the integration of renewable energy. Among them, we first overview the development trend of renewable energy in the power system and introduce several important AI technologies in Chapter 1. After that, Chapters 2-8 provide some examples of utilizing AI methods to address some issues like fault diagnosis, power electronic reliability, dual active bridge modulation, voltage control of an active distributed network, energy management, and stability-oriented control. Specifically, Chapter 2 analyzes the characteristics of electric machine faults, and applies the few-shot learning method and Gaussian-process-enabled method to do fault diagnosis. In Chapter 3, deep learning methods are utilized for power electronic monitoring, optimization, and control. Moreover, to improve energy conversion efficiency, Chapter 4 explores reinforcement learning-enabled modulation strategies for dual active bridges. In Chapter 5, the voltage fluctuation in an active distributed network caused by the integration of photovoltaic (PV) and wind turbine (WT) is considered, and a reinforcement learning multi-agent deep reinforcement learning (DRL) algorithm is used to solve it. Chapters 6 and 7 apply the DRL method for the energy management of hybrid energy systems and microgrids, respectively. Chapter 8 pays attention to the small signal stability of renewable energy systems, and the DRL-enabled controllers are designed to enhance the small signal stability of the systems.Overall, the integration of renewable energy will contribute to reducing carbon emissions to alleviate the environmental energy crisis, but it increases the nonlinearity and uncertainty in the power system, which makes the operation, management, and control of such a system more difficult. Especially, most conventional approaches use physical model-based methods, which highly depend on accurate system parameters and topology. However, due to the large-scale, high-order, and nonlinear time-varying characteristics of the actual power grid, it is difficult to establish a full-scale, detailed model. With the use of phasor measurement units (PMU), a large amount of electricity data with high volumes, mutual correlations, and complex structures can be observed and saved, which can be applied for the extraction of experience and further used for the operation and planning of the power system with a renewable energy system. In this way, the disadvantages of the physical model-based method can be overcome. To this end, data-driven methods are proposed. Among them, the AI method can learn from historical data to construct an adaptive agent to deal with the newly encountered conditions. In recent years, AI has achieved rapid development in optimization and control problems, around power electronics, active distribution networks, and the main power network. In this context, this book has demonstrated some state-of-the-art applications of AI technology for renewable energy systems, and power electronics. The results of these studies indicate that AI can better deal with the uncertainties and intermittency of complex systems, which makes it demonstrate better control and optimization performance using renewable energy systems.