Flicker Detection with Optimized Continuous Point-On-Wave (CPOW) Monitoring and Data Visualization

Traditional power quality (PQ) monitors record waveform data only when preset threshold limits are exceeded, capturing short-term recordings based on significant changes. While this approach improves efficiency in data storage and analysis, it poses challenges: improperly set thresholds may either overlook anomalies or generate excessive data, complicating a PQ investigation. Continuous Point-On-Wave (CPOW) monitoring offers a comprehensive solution by capturing every waveform cycle, ensuring no critical event is missed. The challenge lies in efficiently managing and extracting meaningful insights from the vast volume of data—over five million 60Hz cycles per day. This presentation introduces a streamlined method, supported by a real-world PQ investigation, demonstrating how CPOW successfully detected and analyzed waveform anomalies that conventional PQ monitors failed to capture.

Limitations of Flicker as an RMS voltage variation phenomenon

Flicker has traditionally been defined and evaluated as an RMS voltage variation phenomenon, a framework rooted in the pre–solid-state lighting era and tied to the thermal and visual response of incandescent filaments to voltage fluctuations. However, with the widespread adoption of LED technology, the mechanisms driving perceived flicker have expanded beyond RMS variations to include point-on-wave effects and harmonic distortion. This presentation examines a real-world field investigation into reports of “flickering” LED lights, where distortion and point-on-wave behavior were identified as key contributors. Despite measured Pst and Plt values falling within current IEEE guidelines, the investigation highlights the limitations of existing flicker metrics and underscores the need for updated methodologies to accurately assess flicker in modern solid-state lighting applications.

Continuous Waveform Recording Advances Power Quality Awareness

The reliable supply of electricity with high power quality is essential for the continuous and efficient operation of data centers, industrials, and manufacturing facilities. AI training algorithms, electric furnaces, EV charging, power electronics, and other non-linear loads are driving a growing need to better understand power systems within these facilities to optimize performance, quickly detect equipment failures, and mitigate the impact of these facilities on the larger smart grid. This presentation shares recent observations from a three-phase manufacturing facility 14.4 ksps continuous point-on-wave timestamped voltage and current waveform measurements. Post-analysis of the data provides additional troubleshooting information, allowing analysis of any disturbance that occurs on the power system. This presentation demonstrates how power quality metrics, software calculated incremental quantities signals, software derived PMU, load profile data fundamentals, harmonics, power, rms, and symmetrical measurements can be derived in real-time (calculated) directly from the 14.4 ksps continuous waveform measurements. The purpose of the presentation is to demonstrate the value of continuous waveform recording for power quality monitoring and analysis.

Monitoring the Grid with High Resolution Distribution POW Data

Comparing Standard Measurement Algorithms for Detection of Subsynchronous Power Swings

Power grid stability and reliability increasingly depend on accurate, real-time data across the distribution network. Traditionally, all grid sensing has been performed by devices owned, operated, and maintained by utilities. However, growing grid complexity—driven by distributed energy resources (DERs), inverter-based resources (IBRs), storage, and electric vehicles—is creating an urgent need for broader observability: higher spatial and temporal resolution, deeper situational context, and faster response. At the same time, new non-utility sensing sources—ranging from co-located infrastructure to third-party assets and sophisticated behind-the-meter (BTM) systems—offer opportunities to expand observability without utilities needing to own or maintain the sensors or their communications. This shift opens the door to a new paradigm: Sensor Data as a Service (SDaaS). By unbundling sensing from ownership, SDaaS can deliver critical attributes such as location, time, integrity, event detection, and secure transport, augmenting utility PMU, DFR, and SCADA data. This presentation will explore real-world implementations of point-on-wave (PoW) sensing in distribution power grids, showing how third-party data has detected frequency excursions, harmonics, arcing, misbehavior of equipment, and phase imbalances. Additionally, we introduce a new medium-voltage PoW current sensor being designed to capture power flow direction and current harmonics—especially relevant to monitoring the dynamic impacts of IBRs. This work is supported by the U.S. Department of Energy's Office of Electricity.

Monitoring the Grid with High Resolution Distribution POW Data

The expansion of data centers used for artificial intelligence (AI) training has raised concerns about their potential impact on the grid. One area of concern is subsynchronous power swings caused by large parallel computations. This presentation analyzes a toy model with several measurement algorithms, incuding phasor measurement units, flicker, RMS calculation, and time-domain measurements. The presentation also compares the frequency responses of the various algorithms

Impact assessment of geomagnetic induced current neutral blocking devices with power electronics sources on distance relay for 230 kV transmission lines

Electrical grids increased the integration of power electronics sources onto transmission lines. In addition, recent reports show a significant rise in geomagnetic storms occurring in 2024. These geomagnetic storms can affect transmission lines by inducing electrical currents within them, potentially causing power outages due to over-loaded power transformers. Geomagnetic induced current neutral blocking devices (GIC-NBDs) are capacitors on the ground of wye power transformers, to avoid damage caused by geomagnetic storms. The integration of distance relays and transmission lines with power electronics sources and GIC-NBDs needs to be studied to observe if GIC-NBDs and power electronics sources could adversely affect operation of the electrical grid. In this analysis, the effect of 2 MW power electronics sources and GIC-NBDs on distance relays is assessed for different 230 kV transmission line lengths in radial and nonradial power systems. The simulations measured the apparent impedances, with different electrical faults. Results for a typical 2,650 μF GIC-NBD application were presented on impedance plots, and the distance relay model operations were assessed. The tests for radial and nonradial power systems showed the behavior of the distance elements and source impedance ratios, assessing the effect of power electronics sources and GIC-NBDs on 230 kV transmission lines.

Enhancing Distribution Grid Visibility through Third-Party Sensing and Sensor Data as a Service (SDaaS)

Power grid stability and reliability increasingly depend on accurate, real-time data across the distribution network. Traditionally, all grid sensing has been performed by devices owned, operated, and maintained by utilities. However, growing grid complexity—driven by distributed energy resources (DERs), inverter-based resources (IBRs), storage, and electric vehicles—is creating an urgent need for broader observability: higher spatial and temporal resolution, deeper situational context, and faster response. At the same time, new non-utility sensing sources—ranging from co-located infrastructure to third-party assets and sophisticated behind-the-meter (BTM) systems—offer opportunities to expand observability without utilities needing to own or maintain the sensors or their communications. This shift opens the door to a new paradigm: Sensor Data as a Service (SDaaS). By unbundling sensing from ownership, SDaaS can deliver critical attributes such as location, time, integrity, event detection, and secure transport, augmenting utility PMU, DFR, and SCADA data. This presentation will explore real-world implementations of point-on-wave (PoW) sensing in distribution power grids, showing how third-party data has detected frequency excursions, harmonics, arcing, misbehavior of equipment, and phase imbalances. Additionally, we introduce a new medium-voltage PoW current sensor being designed to capture power flow direction and current harmonics—especially relevant to monitoring the dynamic impacts of IBRs. This work is supported by the U.S. Department of Energy's Office of Electricity.

Oscillation Detection and Source Location Based on Synchronized Point-on-Wave Measurements

The widespread deployment of inverter-based resources (IBRs) has led to high-frequency oscillations in power systems, challenging conventional phasor-based detection due to bandwidth and filtering limitations. This work proposes a frequency-domain approach for oscillation detection and localization using synchronized Point-on-Wave (PoW) measurements. Powered by high-resolution data captured by Universal Grid Analyzers (UGAs)—developed by the University of Tennessee (UTK)—the method identifies control-induced and resonance-driven oscillations extracts sideband signatures. The detection algorithm scans for symmetric or asymmetric sidebands around the 60 Hz carrier and ranks modes by spectral magnitude. Detection and localization are performed by the Forced Oscillation Localization Tool (FOLT), co-developed by UTK and EPRI, which correlates spectral data across nodes and uses grid topology to pinpoint sources. Validation on the 2021 Kauai Island event—where a 19.5 Hz oscillation was captured by UGA—revealed sidebands at 40 and 80 Hz, consistent with PLL dynamics. The result demonstrates the framework’s effectiveness for oscillation monitoring under high IBR penetration.

Using IEEE 2664 for Streaming Point on Wave Data

With the increase in substation devices capable of recording point on wave records and the increasing availability of network connectivity in the field, utilities have shifted to retrieving data remotely via network connections. This presentation introduces a variation of the IEEE 2664 Protocol applied to transfer point on wave (POW) records.

Point-on-Wave-Based Measurement Infrastructure Health Analytics

This presentation introduces a toll to monitor infrastructure health using point-on-wave analytics. A collection of analytics has been integrated into an opensource data analytic platform to detect known waveform patterns that would indicate certain identifiable disturbance causes, including hardware malfunction and external factors such as lightning. The results are then used to better inform and prepare engineers to address equipment issues such as meter failures, CT/PT failures and other commonly undetected issues.

Data Analytics Pipeline

Data Analytics are critical to the quality and resiliency of the modern grid. Technical drivers, business drivers, regulatory drivers, and political drivers are all hastening the deployment of sensor technology effectively creating a digital twin. The sensor deployments require the use of a modern data analytics pipeline to provide actionable information in a timely manner. This presentation will provide an overview of pipelines that support analytics while laying the foundation for artificial intelligence and machine learning.