Papers discussing DFA Technology
Sound commercial and operational responses now to hand from a timely innovation
With increasing regulatory scrutiny, New Zealand distribution lines businesses are now facing a time of budgetry austerity and increasing pressure to improve asset management knowledge and practice.
A newly-released but long-standing technology called Distribution Fault Anticipation (‘DFA’) is being offered to the Industry under a unique companion implementation and consulting package.
Unlike any other technology in concept or contribution, the DFA implementation model offers innovations that address a great many of the commercial challenges facing the distribution lines industry. The technology provide an effective means to identify developing faults and manage asset degradation throughout the asset lifecycle, thus providing lines companies and Asset Managers a clear step change to a new phase in the evolution of Asset Management practice globally.
New to New Zealand, the adoption of DFA presents an opportunity to progress from the reactive corrective maintenance practices that have been traditionally employed to enable preventative maintenance on MV lines to avoid unplanned outages. Providing the unique ability to anticipate many faults before they have an impact on customers and feeder reliability, DFA/HiZ offers the following advantages and benefits:
- Improved safety for staff and the public.
- Improved efficiency (reduced cost) of field line repairs (reduced OPEX).
- Faster response to line faults by field crew (increased network availability).
- Network risk reduction – especially bushfire and electrocution risk.
- Increased asset life and improved asset management decision-making (more targeted and effective Capex investment).
- Improved quality of supply.
- Improved customer satisfaction.
- Improved senior management regulatory reporting ability.
- Demonstrable evidence of appropriate, measured and targeted investment based on pin-point determination of asset fitness within the asset lifecycle curve.
Implementation of DFA/HiZ provides a range of other accessible logged data and information plus many of the features of a digital fault recorder providing additional functionality and capability to analyse customer complaints and distribution feeder performance
Electric distribution utilities generally operate circuits in a reactive mode, responding and making repairs after outages occur. They perform periodic maintenance on certain equipment, such as capacitor banks, but most apparatus (e.g., connectors, insulators, service transformers) are numerous, long-lived, and geographically dispersed, making inspection resource-intensive. It would be preferable to make repairs proactively before outages occur, but utilities lack information that enables this. Recent “smart grid” technologies restore service more rapidly after outages occur yet remain reactive.
Working closely with the Electric Power Research Institute (EPRI), Texas A&M University researchers collected and analyzed an extensive library of high-fidelity current and voltage waveform data from more than 70 in-service circuits. They discovered waveform signatures caused by nascent failures of line apparatus and in the process validated the notion that apparatus often deteriorate over time before failing. As a result, researchers demonstrated numerous cases where detecting incipient failures enabled utilities to avoid outages. In some cases, utilities also were able to schedule corrective actions during normal working hours and in favorable weather, rather than responding to outages in adverse conditions (e.g., storms, nights), thereby improving efficiency and crew safety.
Researchers have developed algorithms to characterize circuit health and events based on high-fidelity data digitized by substation-installed devices. On-line algorithms deliver real-time information to improve awareness of circuit conditions, thereby enabling improved reliability and operations. This enables a move away from reactive operations and toward condition-based approaches.
Multiple electric distribution utilities have participated in multi-year trials of the technology. Their experience includes detection of incipient failures, improved response to vague customer problems (e.g. flickering lights, lights out), and prevention of faults. This paper discusses the technology and relies on case studies illustrative of events on real circuits and of how personnel can use improved system awareness to better manage assets and improve operations.
This paper introduces a new and unique technology for the improved management of MV lines. The technology has recently completed a one year programme of review by a local asset management consulting company and is now in its rollout phase in the Australasian region using an innovative life-time support continued-relevance business case model.
Known as Distribution Fault Anticipation [“DFA”] technology it provides a quantum advance for MV line management by providing the user with information to assist the identification of faults even before they cause interruptions. DFA has been demonstrated to detect a wide variety of developing line failures, pre-failures, and other events in real time, providing timely warnings to the asset owner to enable preventative actions.
An important outcome of this technology is that it can determine, characterise, and notify the occurrence of developing issues prior to these being known to the asset owner by conventional means.
Developed collaboratively over some 20 years at the request of the USA power industry and tested over hundreds of feeder-years of service, the techniques employed are such that they operate with extremely low false positive outcomes even in high noise environments, unlike other concepts employing defined level-based triggers. Importantly, the technology is inherently adaptable and remotely-upgradable as refinements are implemented.
The web-based DFA technology reports findings quickly in a simple-to-read code of characterised issues, along with associated timings and other information to determine the nominal type of fault and the location of the reported matter. Reports allow a clear understanding of the issue(s) immediately. Such details on likely fault and location have proven a major boon to DFA users in enhancing the safety and efficiency of standard operational responses, as well as offering a significant contribution to improved customer service delivery, improved regulated line performance statistics, and lowered operational costs.
The paper reviews the technology and assesses its contribution, both in the context of regulatory content and fundamentals of distribution Line Company operational and asset management objectives.
Application of Waveform Analytics for Improved Situational Awareness of Electric Distribution Feeders
Over the past several years, distribution utilities have invested heavily in installations of “smart-meter” advanced metering initiative (AMI) systems. Among the anticipated benefits of these systems, utilities with smart-meter deployments are generally able to quickly assess which portions of their systems are operating normally and which customers are experiencing an outage. Projects at multiple utilities have focused on integrating AMI information, along with traditional supervisory control and data acquisition data sources, into utility distribution management systems to improve situational awareness on distribution feeders. Despite the clear benefits each of these systems offer, their ability to provide utilities with broad awareness of events affecting the health and status of the distribution system is limited, and often reactive in nature. This paper presents never-before published cases observed in real-world field trials, detailing how integration of waveform analytics into utility operational practice leads to improved situational awareness.
Line capacitors are used ubiquitously for voltage regulation, power factor correction, and reactive power management on distribution circuits. Utility companies spend considerable money installing and maintaining these banks. Capacitor banks are widely known for experiencing internal short circuits, fuse operations, and other failure modes.
As part of ongoing projects at Texas A&M University, researchers have documented multiple, real-world failures and other improper behavior of line capacitors. They also have documented the unique electrical current and voltage waveforms signatures these failures produce, as measurable from conventional, substation-based current and potential transformers. The resulting database contains many examples of failures detectable by conventional utility inspection and testing practices, but also many examples of failures not detectable by those conventional methods. Some failure modes can have consequences more deleterious to system health than simple loss of voltage support, voltage balance, or reactive power support. Based on the field experience and library of electrical signatures, researchers have developed a system known as Distribution Fault Anticipation (DFA) technology, which detects and warns utilities about a variety of line apparatus failures and pre-failures, including those involving capacitors, thereby enabling system condition awareness and condition-based maintenance for circuit apparatus, including capacitor banks.
This paper presents selected case studies from operational circuits, and describes the benefit of using DFA technology to detect capacitor problems in their pre-failure state, enabling timely repair.
AVO NZ and Lord Power Equipment is planning to market Distribution Fault Anticipation (DFA) technology and has requested Terry Krieg, a Senior Affiliate of Lord Consulting to undertake an evaluation and an assessment of the suitability of the technology for Australasia using publicly available information sources.
The DFA technology is based on 30 years of research initially via EPRI1 in the US and more than 10 years of practical field trials involving more than 10 US power utilities. In that time, an extensive database of experience in distribution faults has been collected.
The DFA technology uses waveform analysis and pattern recognition of system events to determine precursor indications of hardware failure or interference with the distribution line from flora and fauna. The aim is that these indications of early contact with plants and animals may be detectable before the fault can result in damage to line hardware resulting in a protection operation (line outage), equipment damage or a fire start.
Automated Power System Waveform Analytics for Improved Visibility, Situational Awareness, and Operational Efficiency
The proliferation of “smart” devices on distribution feeders in the past decade has resulted in a deluge of data. Most utilities recognize that recorded waveforms and other data contain information that could enable them to operate more effectively. In practice, however, most utilities find themselves confronted with an intimidating mountain of data, without tools or expertise to differentiate the important data from the pedestrian. Much data analysis continues to be performed manually, off-line, in response to specific perceived problems. That can provide forensic value, if the utility has the requisite expertise and the time necessary to identify and interpret the relevant data, but it provides little real-time system visibility or situational awareness that would enable operational improvements.
For multiple years, supported by EPRI and EPRI-member utility companies, Texas A&M researchers have used sensitive, high-fidelity waveform recorders to collect data from scores of feeders, using technology readily achievable with modern electronics. This has created the most comprehensive extant database of waveforms of incipient failures and feeder events. Based on that database and experience, they developed sophisticated waveform analytics and reporting methods. Dubbed distribution fault anticipation (DFA) technology, the system acquires high-fidelity waveforms from conventional CTs and PTs and then uses automated processes to apply analytics to those waveforms and thus report events and conditions. This provides personnel with real-time visibility of feeder events and conditions, including incipient failures. This newfound visibility, or awareness, enables improved reliability, improved operational efficiency, and true condition-based maintenance.
This paper explains general DFA concepts and then uses selected case studies to illustrate concrete operational benefits to utility companies.
Advanced Monitoring of Low-voltage Secondary Networks for the Detection and Mitigation of Arcing Faults
Low-voltage secondary networks represent a particular power-system topology deployed in load-dense urban environments requiring ultra-high reliability. These grid networks have a high degree of interconnectivity that provides multiple, redundant paths to loads and, consequently, also to faults. For nearly a century, arcing faults on grid networks have been a well-known problem, creating smoke, fires, and explosions, collectively known as manhole events, as well as disrupting normal service. Network arcing faults draw intermittent current of relatively low level that seldom operates conventional protection devices. They generally remain undetected until manhole events or other operational problems cause their discovery. Though discussion of arcing faults appears in secondary network literature as early as the 1920’s, as late as the early 2000’s, most experts have considered them “an industry problem that presently has no available solution.”
Texas A&M University has partnered with the Consolidated Edison Company of New York (Con Edison) on a project to detect, locate, and ultimately mitigate arcing faults on secondary networks. Researchers instrumented a single secondary network with thirty high-speed, high-fidelity data collection devices, as well as one functionally identical device on a primary feeder serving that secondary network. Project results show that arcing faults 1) occur more frequently than previously understood, 2) can persist for long periods, 3) can recur multiple times over a period of days or weeks, interspersed with quiescent periods of hours, days, or longer, and 4) can be detected by direct network monitoring and also by monitoring medium-voltage primary feeders serving the secondary network.
This paper presents selected examples observed on operational secondary networks during a nominal two-year period. During this period, thousands of distinct arcing events were recorded and analyzed, with many events simultaneously recorded at multiple network points and the primary-feeder point. Preliminary results suggest multi-point monitoring of secondary networks may enable identification and location of incipient arcing conditions before they rise to the level of a public safety hazard.
Automated Waveform-Based Analytics for Enhanced Reliability, Power Quality, and Operational Efficiency
The past decade has witnessed numerous technological innovations to improve various aspects of operational efficiency and reliability. Upon the occurrence of an outage on a distribution feeder, for example, automated schemes can perform switching to localize the outage and restore service to as many customers as possible, often within seconds to minutes. Other systems use data-mining analytics to discover patterns and problems identifiable from AMI (advanced metering infrastructure) databases.
Research at Texas A&M University applies analytical algorithms at a finer level of detail than do AMI-based data-mining analytics. Specifically this research recognizes that electrical waveforms, as measured from conventional current and potential transformers (CTs and PTs), reflect load and other activity occurring on the power system and that, therefore, these waveforms hold the potential for providing the utility with heightened awareness of feeder operations and conditions.
Texas A&M’s efforts in this area began in the late 1990’s, at which time they focused specifically on anticipating, or predicting, future faults. Long-term instrumentation of 70 feeders at 11 utility companies created a large database of waveforms representing a wide variety of activity on feeders. The process also demonstrated that waveforms reflect a wide variety of valuable feeder information, not just incipient failures. As an example, as has been recognized for decades for protective relaying purposes, a conventional overcurrent fault produces specific variations in measured currents and voltages, and a switched capacitor bank produces specific variations that differ markedly from those of the overcurrent fault. Other project findings document, for the first time, that anomalies such as failing in-line switches or clamps also produce specific variations in waveforms. Algorithms capable of detecting and differentiating specific types of variations provide valuable situational awareness and intelligence that can help utilities proactively avoid certain faults and respond more intelligently and efficiently to faults and other system anomalies.
Certain smart grid technologies can reduce the number of customers affected by prolonged outages, and thereby increase reliability through automated switching to restore service. Such technologies are useful, but reactive in nature, performing their function only after a fault occurs and an outage has been detected. They must presume that nonfaulted feeder sections and alternative feeders are healthy and capable of carrying increased power flow. Research at Texas A&M University has demonstrated that sophisticated, automated real-time analysis of feeder electrical waveforms can be used to predict failures and assess the health of distribution lines and line apparatus. Reliability can be substantially improved by detecting, locating, and repairing incipient failures before catastrophic failure, often before an outage occurs. Requirements for data and computation are substantially greater than for devices like digital relays and power-quality meters, but feasible with modern electronics. This paper provides selected examples of failures that have been predicted by intelligent distribution fault anticipation (DFA) algorithms. The data requirements and processing analysis to detect these failures are discussed. The problems related to full-scale deployment of the proposed system in a utility-wide application are presented. The authors use experience gained from their long-term research to propose concepts for overcoming these impediments.