Identifying Power Quality Issues in LV Distribution Grid by Using Data from Smart Meters : Exploring possibilities of machine learning algorithms

Abstract: Since there is a significant potential to supervise the low voltage network with the assistance of the end-customer smart meters, Vattenfall Eldistribution AB wants to take advantage of such data. Therefore, this project’s overall goal is to investigate how some specific grid disturbances could be detected in certain meter data types. There is a plethora of event data from several different grid areas with their own unique set of customers and power flow. Furthermore, the project aims to propose a detection method within the smart meter’s capability and explore the possibility of using smart meter data to identify the grid’s state. The literature study suggested that the machine learning approaches k-means and SVM were suggested to be used within this study’s scope. Several supervised and unsupervised machine learning algorithms have been identified and applied to power quality issues in various ways. Furthermore, each approach had four cases applied as well to broaden the analysis. After conducting the study, the project results show that smart meter data indeed has the potential to be used in machine learning methods to identify weak grids. However, the study shows that the information gained from smart meter data in its current state alone is not enough to distinguish weak grids from strong grids. For instance, the current data could complement grid data, such as loop impedance and topology data. Future work could include using the same machine learning methods on higher dimensions input data to separate the data points. One way to diversify the data could be to include data describing grid topology and data from PQ-meters. Furthermore, it will be possible to continuously monitor the low voltage grid conditions with future smart meters. In turn, this may give a better insight into how the voltage levels behave for weak and strong grids, respectively.