Abstract
The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.
Highlights
The concept of the smart grid is transitioning the traditional electric power grid from an electromechanically controlled system to an electronically controlled network.According to the US Department of Energy’s Smart Grid System Report [1], the smart grid systems consist of information management, control technologies, digitally based sensing, communication technologies, and field devices that function to coordinate multiple electric processes
The remainder of this section discusses the applications of artificial intelligence (AI) techniques to (1) load forecasting, which is further divided into short-term load forecasting, mid-term load forecasting, and long-term load forecasting; (2) power grid stability assessments, which contain transient stability assessments, frequency stability assessments, small-signal stability assessments, and voltage stability assessments; (3) faults detection; and (4) smart grid security
Can be classified into three levels [72]: (1) short-term load forecasting (LF) (STLF), which predicts the load from minutes to hours; (2) mid-term LF (MTLF), which predicts the load from hours to weeks; and (3) long-term LF (LTLF), which predicts the load for years
Summary
The concept of the smart grid is transitioning the traditional electric power grid from an electromechanically controlled system to an electronically controlled network. Some of the related problem space in smart grids include load forecasting (LF), power grid stability assessment, fault detection (FD), and smart grid security. These key elements are allowing massive amounts of high-dimensional and multitype data to be collected about the electric power grid operations. The authors recognize that one article cannot provide a comprehensive review of all the AI techniques for smart grid applications in load forecasting, power grid stability assessment, faults detection, and security problems; this survey paper presents some present AI applications in some of the areas not covered by these existing reviews, discusses some challenges of applying AI to smart grid problems, and highlights some future potential applications of AI techniques to the smart grid.
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