Requirements for a modern PQ and DFR monitoring system: PQ monitoring case study in Portugal

Abstract

There are many manufacturers around the world providing power quality (PQ) monitoring and digital fault recording (DFR) solutions. Different monitoring requirements, monitoring practices, and new standardization make for a diverse range of available solutions. The first section of the paper will summarize the requirements and challenges of a modern PQ and DFR monitoring system. The second section will be a case study about the Qualitrol and Qenergia' systems implemented in Portugal, particularly at EDP and REN; it will show what has been done. Until IEC 61000-4-30 [1] was introduced in 2003 many power quality parameters were not well defined leading to situations where different instruments produced different results. In 2008 Edition 2 was introduced and Edition 3 is now in progress. As well as considering 61000-4-30, this paper will summarize the key requirements of the new upcoming IEC 62586 [2] product standard for power quality monitoring devices. In the second section of the paper we will present how to manage grid measuring points whose characteristics change over time, rather than just manage a fleet of measuring instruments. We will discuss the way to make compatible different types of measurement devices, access different data sources, handle the standards changes both in the limit values and in the way values are calculated and validate PQ and event data. Different ways of representing power quality information will also be addressed. Presenting information and not just raw data from the monitoring system is essential for efficient management of reports for regulatory and third party use. The Qualitrol and Qenergia's systems at EDP and REN have been developed to suit existing communication infra structure, automatic and manual data retrieval and integrate in a single software analysis package, in a multi-vendor way, already existing older PQ devices together with new instruments. Combining PQ and DFR data also leads to efficiencies when collecting and analyzing results.