# A Review of Smart Grid Anomaly Detection Approaches
## 1 Introduction  
This study aims to examine the many solutions developed for detecting anomalies in Smart Grids (SGs). Specifically, it focuses on those solutions that employ artificial intelligence techniques or approaches. The objective is to gain insight into their practicality and highlight the prevailing trends in this area.  
Digital technologies are being integrated into the foundational framework of the electrical grid. The driving forces behind this include the integration of renewable energy sources, ensuring the security of energy supply, and enhancing infrastructure operation and maintenance efficiency [1].  
Smart grids face the following research challenges: (1) how to get a more accurate prediction method; (2) how to find optimal programming when an online learning task is performed; and (3) how to learn multiple tasks in a more automatic way [2]. As the complexity of the systems that support the operation of the power grids increases, there is also a growing need for a shift in the paradigm of network management from reactive toproactive, and this can be achieved using advanced monitoring tools, data analysis, and predictive methods [3].  
Future smart grids will require systems that can monitor, predict, schedule, learn, and make consumption decisions in real time that are highly associated with external weather conditions and energy output [2,4].  
The relevance of detecting, localizing, and predicting abnormal energy network events or behaviors extends to customers, network operators, service providers, and regulatory bodies. The anomaly detection solutions are applied to the four components of an SG: generation, transportation, distribution, and consumption of electric energy. Therefore, the detection of anomalies is an important part of the challenges facing new smart grids.  
An anomaly is a modification of the expected behavior of the system, and three types are considered: specific, context, and collective anomalies. In specific anomalies, an individual event instance may be considered anomalous compared to the rest of the dataset [5].  
Context anomalies start with the notion that behavior and context are separate: the same conduct may not be regarded as an anomaly if it occurs in a different context. For example, anomalous occurrences scheduled at specific times and days of the year may not activate an anomaly detection mechanism because they are already expected [6,7].  
Collective anomalies refer to instances where abnormal behavior cannot be identified by examining each event in isolation but rather by considering them as part of a collection of events [6,7].  
The SG progressively plays a significant role in critical and industrial infrastructures, especially with the industry 4.0 revolution. They have become more dependent on connectivity by supporting novel communication and remote-control functionalities that expand the risks of cyber-attacks [8]. This has promoted a growing interest in studies on anomaly detection.  
The physical and cybernetic infrastructures that make up an SG may present unexpected behaviors in the face of cyber-attacks, natural disasters, meteorological phenomena, events coming from the network operation itself, physical damage to network components, deviation from normal operating parameters, and overloads [9].  
In this context, SGs must be able to resist anomalous events, detect them, mitigate them, and provide support for the development of a capacity to restore service or behavior quickly and safely.