Effect of smart meter data collection frequency in an early detection of shorter-duration voltage anomalies in smart grids

Abstract

Smart grids are required to respond quickly and efficiently to any types of electrical incidents. This is possible only if advanced monitoring and decision support tools are available for the operators to collect and analyze the real-time data from the entire power system. Sensors and smart meters become necessary for an effective smart grid operation, as they can play a significant role in measuring system parameters such as the temperature of the transmission line, power outages and power usage. These sensors allow communication between the generation side and user side to ensure full network observability. Hence, the volume of data which needs to be analyzed for more reliable electrical services has been increased. Upon the analysis of all the data collected, useful insights can be gained to bring benefits to the utilities, consumers and the related organizations. In this project, the effect of smart meter data collection frequencies on the early detection of short-duration voltage anomalies has been investigated. An anomaly detection algorithm that analyzes the voltages collected from smart meters is developed. The proposed anomaly detector checks the normality of the voltage data collected from the smart meters installed at the residential loads. To test the effectiveness of the frequent-and-large smart meter data, a smart grid model was simulated in MATLAB/Simulink and the model was tested under three different operating conditions. The efficacy of the anomaly detector with the smart meter data collected at different data collection intervals was compared under the three operating conditions. The investigation of the smart meter data clearly shows that the shorter-duration anomalies can be detected effectively with more frequent data and hence it reveals the potential of smart meters in the early detection of short-duration anomalies in smart grids.