Automatically Detecting and Correcting Errors in Power Quality Monitoring Data

Dependable power quality (PQ) monitoring is crucial for evaluating the impact of smart grid developments. Monitoring schemes may need to cover a relatively large network area, yet must be conducted in a cost-effectivemanner. Real-time communications may not be available to observe the status of a monitoring scheme or to provide time synchronization and, therefore, undetected errors may be present in the data collected. This paper describes a process for automatically detecting and correcting errors in PQ monitoring data, which has been applied in an actual smart grid project. It is demonstrated how to: unambiguously recover from various device installation errors; enforce time synchronization between multiple monitoring devices and other events by correlation of measured frequency trends; and efficiently visualize PQ data without causing visual distortion, even when some data values are missing. This process is designed to be applied retrospectively to maximize the useful data obtained from a network PQ monitoring scheme, before quantitative analysis is performed. This work therefore ensures that insights gained from the analysis of the data-and subsequent network operation or planning decisions-are also valid. A case study of a U.K. smart grid project, involving wide-scale distribution system PQ monitoring, demonstrates the effectiveness of these contributions. All source code used for this paper is available for reuse.