Power Quality Insights and Fault Analysis in Distribution Networks With Optimally Placed D-PMUs
Creators: name: Anguswamy, Manoj Prabhakar
Publisher: name: RMIT University
Year: 2025
Subjects: classificationCode: 400907
subject: Industrial electronics
classificationCode: 400905
subject: Electronic instrumentation
classificationCode: 400805
subject: Electrical energy transmission, networks and systems

description: The escalating integration of renewables and advanced power electronics into the electrical grid has introduced greater uncertainty into distribution networks. This uncertainty stems from the stochastic characteristics of distributed energy resources (DERs) and the emergence of bidirectional power flows, leading to previously unencountered demand dynamics and increased susceptibility to instability as the distribution network transitions from passive to active distribution networks (ADNs).

This paradigm shift poses significant challenges for distribution system operators (DSOs), necessitating the adoption of real-time, synchronized monitoring, control, and protection mechanisms to enhance existing supervisory control and data acquisition (SCADA) systems. This thesis proposes the use of distribution-level phasor measurement units (D-PMUs or µPMUs) as a solution to these challenges. D-PMUs offer advantages over traditional transmission-level PMU units in cost, accuracy, and speed, making them suitable for unbalanced ADNs. However, due to the sheer scale of ADNs, the economic feasibility of deploying these instruments on a large scale and the benefit to ADN applications are unknown.

In response to these challenges and central to this thesis, is the development of an optimal D-PMU placement technique explicitly tailored for the unique requirements of ADNs. By considering the unique distribution network attributes (UDNA), a novel optimal placement method using deterministic and meta-heuristic optimization techniques that considers all the uncertainties as constraints for ADNs is developed. With this state-of-the-art placement method, it guarantees real-time situational awareness under all operational conditions and cost-effective implementation through a new usable zero-injection phase strings (UZIP Strings) reduction method, which lowers the number of D-PMUs deployed while maintaining global observability.

Then, based on the developed placement method, an advanced machine learning-based (ML) power quality disturbance (PQD) classification method is created, utilizing discrete wavelet transform (DWT) combined with a cubic multi-class support vector machine (CMSVM). This unique part of the research incorporates a decade’s worth of high-quality continuous waveform power quality (PQ) data from the Australian power network. Additionally, it integrates misclassification cost (MC) and cost-sensitive classification theory to enhance robustness in traditional ML network models.

Lastly, by leveraging the high-resolution symmetrical components from optimally placed D-PMUs, an innovative holistic machine learning-based approach for fault detection, classification, and localization (FDCL) has been developed that meets fault ride-through (FRT) requirements. This proposed hybrid ML model combines feed-forward neural networks (FFNN) and ensemble bagged trees (EBT) classifiers and is complemented by a coordinated two-ended fault localization strategy.

This research highlights the transformative effect of D-PMU technology in enhancing smart grid applications, thereby fostering a more stable and dependable grid. It signifies enhanced operational efficiency in PQ monitoring, grid stability, and asset management domains. Moreover, the proposed fault analysis approach leads to financial savings, quicker repairs, reduced system downtime, prevent cascading events and extensive network failures. It also provides DSOs with an effective array of tools to meet the demands of contemporary, renewable-energy-intensive smart grids. These findings push the boundaries in real-time PQ monitoring and fault analysis in ADNs and establish a foundation for future D-PMU-driven innovations and applications through optimal placement.