Artificial Intelligence Techniques in Smart Grid: A Survey

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.

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

“…Firstly, the intelligent switching devices (reclosers, sectionalizers and load break switches) work collectively, with robust IoT-sensor networks and applications. Unlike the previous model [14], where switching devices do not require a communication link or any form of computerization. The essence of this proposed model is that if a fault occurs in real-time, the smart switching devices isolate the neighbourhood in which the fault occurred and keep on delivering energy to other regions while AI sensors monitor and learn the pattern of failure or causes of the fault via its ML algorithms for future references.…”

“…The previous plans in this sector witnessed the initiation of automated meter reading (AMR) schemes in the distribution network. AMR allows the utility companies to read the utilization data, warnings/alarms, and status from customers premises equipment remotely [13], [14]. The report in [15], indicate that the smart grid comprises FIGURE 3.…”

“…What makes the next-generation smart grid unique from the classical smart grid system is the incorporation of disruptive techniques and technologies. Their relevance and application cut across subdomains of the next-generation grid architecture which includes the AI radio system (though for 6G), network control centre powered by AI, intelligent substation and feeders, intelligent sensor and sensor hubs, smart distribution transmission, and smart power generation just to mention but a few [14], [32]. As a cyberphysical system, the next-generation smart grid is internetoriented, hence it is exposed to attacks.…”

“…As a cyberphysical system, the next-generation smart grid is internetoriented, hence it is exposed to attacks. To take care of cyber and other malicious attacks, a machine learning approach is deployed which will be discussed in detail in the course of the study [6], [14]. Apart from securing the grid, the other reasons for deploying AI/ML for the proposed next-generation smart grid are human inaccuracy, building an automated, robust, and autonomous system that is a self-learning system that can make a decision based on statistics and information available from current and previous experience.…”

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

“…The integration of ML and SG leads to numerous benefits like responding to consumer in a quickest time period, predicting consumer requirements and analysing consumer behavior etc. [12]. The ML algorithms are also used for optimizing load between industries.…”