The evolution of the power grid, its reliance on decentralized and inverter-based technologies, plus increasing demand for electrification of new types of loads are introducing real challenges to grid stability. Grid operators must adapt, requiring more extensive, granular, and timely data to enable analytics for improved efficiency and development of proactive mitigation strategies.

The balance of cyber-risk and reward is often between on premise and cloud-based fleet analytics depending on the customer.  Often seen as locked down on-premise systems, versus a system-learning cloud enabled architecture which we know can enable potential real time pro-active maintenance strategies. But at Powerside we believe both systems have merits and indeed can co-exist.

Maintaining reliability during black sky days and resiliency during blue sky days is relevant now more than ever in the rapidly changing grid. Moving from a reactive operation to a proactive operation is a key initiative in maintaining and improving grid resilience for grid operators.

So, let's explore how to evolve and navigate a path to effective use of the data we have, securely and effectively to identify and improve our grid edge using the best of both these PQ solutions.

Power quality is defined as the influence that voltage and current anomalies have on end-use equipment. Good power quality enables an optimal level of electrical health, providing assurance for operational stability and equipment efficiency. In contrast, poor power quality occurs when a disturbance interferes with the normal operation of equipment or the electrical system and involves deviations from a generated sine wave at the fundamental frequency. Disturbances such as voltage sags, voltage swells, harmonics, high frequency transients, and imbalance are examples of poor power quality. Later, we provide case study examples on the effects these power quality issues can have on equipment and systems.

Power quality monitoring devices provide high fidelity information that allows the user to uncover issues that often go unseen by traditional power metering systems. Typically, power quality monitors adhere to an international standard on how the measurements are taken, the most common being IEC 61000-4-30 Ed3 [3]. Metering parameters, albeit accurate, have lower sampling rates and cannot pick up the short-duration transients or distortions that often plague modern inverter-based networks. We can use Smart Metering data for trending and general power consumption, but the insight is inadequate for determining root cause and analysis.

Utilities typically install PQ monitors to provide visibility and data on grid conditions without having to physically be on-site to collect the measurements. The cost of running reactive, temporary PQ campaigns is relatively high and provides (by definition), only limited data visibility in a dynamic grid environment.  Historically, permanent PQ analyzers have been installed at feeder sources or substations, but more recently this has been extended downline to critical customers (data centers, hospitals, renewable energy sites, etc.), generation sites, and grid edge locations.

In order to level the vendor playing field and improve data visibility – standards have been created by utility customers for PQ data formats and file transfer standards across multiple monitoring hardware devices.  In particular PQDiff files (Power Quality Data Interchange format) and COMTRADE (Common Format for Transient Data Exchange) are used in common on-premise platforms to manage and consolidate this data.  One of our industry challenges, however, is not the availability of data, it's the shortage of timely and actionable analytics of the data.

A key and powerful PQDMS – (Power Quality Data Management System) is the PQView4 platform; able to pull display and analyze data from multiple sensors in the network – and this is now part of Powersides PQ offering.  Add to this its Fault location capability, compliance reporting and remote access – this brings a powerful suite of tools for not only visibility but useful analytics within the customers' network.  Our vision is to add to it analytics capability and secure accessibility outside the network.

Many of the best cloud analytic suites developed for Power Quality (including our own QubeScan platform) provide excellent fleet management and enhanced, secure Power Quality trend analytic capability but sadly are also limited to their associated OEM hardware.  This is where PQView can help.

Finally bridging the gap between a common multi-instrument file transfer capability and hosting a secure, powerful (and accessible) fleet analytics capability in real time to pro-actively assess changing transient conditions and predict faults without compromise to the security of the system is available.

 From this visibility, utilities are able to see baseline conditions, allowing them to set notifications for when the system is performing outside of normal operating conditions. This can be brought directly to expertise familiar with power quality issues, capable of driving further action if required.

Power quality is also becoming more common for compliance verification (e.g., IEEE 519-2022 [1] for harmonics control). This enhanced visibility saves time with diagnosing grid issues, the detection of changing trends, and overall grid design limitations.

For larger system deployments, attempting to routinely inspect each power quality monitoring site for potential issues is very time consuming. This is where automatic analysis and event notification becomes a necessity for utilities. Automation allows for Power Quality Engineers to effectively utilize their time by focusing on critical issues and events, rather than manually filtering through data. These actions include compliance verification, event frequency tracking, and sustained power quality issues (e.g., voltage imbalance is sustained above 2% for a predetermined amount of time).

The justification for secure, real time pro-active accessibility of PQ data to a utility network must be supported by real examples and system benefits.  Simply adding data visibility without actionable insights is not a viable solution.  So, what have we seen with an enabled high-resolution system with analytics capability? Examples below are well documented elsewhere in papers and customer testimonials:

A Utility using primary metering VTs interfacing with our class A PQ monitor set the HF Impulse trigger to a sensitive threshold. Over time, the electrical behavior of the site changed, with a high volume of intermittent HF impulse events observed over a brief period, as shown in Figure 1. The voltage signature was consistently low in magnitude, oscillatory in nature, and on the same phase. One site suffered a catastrophic failure on the voltage transformer as a result. A recurring event pattern was recognized within the Utility, at a second site (Figure 2) and subsequently triggered proactive replacement of the voltage transformer equipment with similar behavior avoiding major loss costs. Proper analysis of the trended incidence in Figure 3 allows a progression learning model to normalize urgency and development of the issue.

Harmonics are generated by non-linear loads that are prevalent in today’s grid, and source examples include pulse rectifiers and variable frequency drives on the load side and inverter-based resources on the generation side. PCC compliance is not routine in many cases and proper analysis only carried out following issues or complaints by the parties.

A residential customer complaint was reported to the local utility about malfunctioning lighting, GFCIs, and intermittent appliance issues, notably on sunny days. The configuration of the system can be observed in the one-line diagram shown in Figure 4. The Utility visited the site, confirmed the reported behavior, and consulted with the Power Quality Engineers to further investigate the issue. A permanently mounted power quality monitor installed at the point of interconnection (POI) in the solar farm had stored the power quality data needed to immediately diagnose the issue and run an IEEE 519 harmonics compliance report.

During the investigation, it was noted that the long-term flicker (P_lt) and short-term flicker (P_st) were complying with the levels specified by IEEE 1453, and there were no voltage variation disturbances such as voltage sags. When observing the IEEE 519 harmonics report, there were no violations associated with higher order harmonics. Conducted emissions (Supraharmonics) values were however observed, although beyond current industry standards. The 3.4kHz, 10kHz, and 12kHz noise observed was steady state until the solar was isolated, while the 142kHz appears to be an intermittent distortion that occurred throughout the day, after solar was isolated from the feeder. The steady state high frequency distortion observed was determined to be due to the inverters utilized at the solar farm and suspected to be multiples of the inverter’s switching frequency. The Utility isolated the solar farm to troubleshoot the issue, which can be observed when the high frequency distortion abruptly stopped in the conducted emissions graphs. Upon isolating the solar from the problem site, the symptoms from the problematic harmonic distortion subsided. Regarding preventative action, the Utility consulted with the inverter manufacturer to address the issue and resolved it by pulse shifting the switching frequency at the problematic bands. This in turn, cancelled out the higher magnitudes of distortion that caused the symptoms observed.

As power system infrastructure and equipment continues to age and evolve, there are opportunities to get ahead of developing electrical problems with advanced tools that provide actionable, proactive information.  High fidelity Class A power quality monitors are most powerful when combined with other PQ devices and a world class analytics capability.  Interconnection and real time analytics enable actionable insights to be managed in real time by Power Quality experts across our industry in a pro-active grid.   Continuous fleet monitoring and compliance reporting at critical infrastructure locations provide actionable data to help develop mitigation strategies before unforeseen issues develop into costly outages.  The risk reward balance, however, often lies in our choices as to where and how we host this data and how we communicate; on site or within the cloud.  Cloud networking each PQ device seprately may present too large a risk for some clients but combining secure comms to an established and secure on-premise suite can also significantly reduce the access profile and bring enormous analytics capability improvement.   Powerside are unique in holding the key to a tailored, flexible secure solution providing the best of both worlds.

As we look forward as an industry, more high-fidelity data provided in an intuitive and timely manner is critical to understanding grid health. These are essential factors in executing the reliability and resiliency initiatives that match dynamic, ever-changing grid conditions. Are you ready?

As Vice President of Marketing, Tom Richardson leads the Powerside Go To Market Strategy and positioning for Power Quality Analysis and Correction Solutions. He has several decades of experience in the Energy and Transport Sectors globally since starting out in the UK in Power Equipment Design with Alstom. Having led multiple teams across design, commercial projects and field installation within large power plant OEMs, transportation sectors, renewables and control system manufacturing, Tom brings a broad band of international experience to the Team

This article was originally published in the June 2025 issue of the Transformer Technology magazine, which you can access here.

To download the PDF version of this article, click here.