Evolution of knowledge mining from data in power systems: The Big Data Analytics breakthrough
Xavier Domínguez, Alvaro Prado, Pablo Arboleyá, Vladimir Terzija
Abstract
This paper presents an overview of the evolution of knowledge extraction from power systems data since 1980’s up to date. As the existing literature in this application domain is vast and has exponentially grown over the last years, this work remarks the key relevant milestones and contributions that may allow readers to concisely capture the foundations and evolution on which the modern Big Data Analytics (BDA) framework is deployed in this field. Here, it is covered from the first Artificial Intelligent solutions that relied on rule-based expert systems, passing through the usage of Data Mining techniques and arriving to the BDA unfolding, including its trends and prospects in the power industry. Due to exponential increase of metering and communication infrastructure deployment, as well as the impact of distributed generation, one of the stages in the power delivery process that has been particularly revolutionized by the BDA breakthrough is the distribution sector. For this context, the latest and most noteworthy perspectives and experiences are also addresses to highlight the relevance and applicability of knowledge extraction from big data in future power distribution networks.