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Date: July 13, 2016 Authors: Bert Taube, Paul Leufkens, Jim Weik, Jesse Dill

WHITEPAPER

Proactive Transmission and Distribution Asset Management Utilizing Advanced Data Management and Predictive Analytics

Reference to part of this report which may lead to misinterpretation is not permissible.

No. Date Reason for Issue Prepared by Verified by Approved by

1 2016-07-13 First issue Bert Taube

Paul Leufkens

Jim Weik

Jesse Dill

Jesse Dill Jesse Dill

Date: July 2016

Prepared by DNV GL - Software

© DNV GL AS. All rights reserved

This publication or parts thereof may not be reproduced or transmitted in any form or by any means, including copying or recording, without the prior written consent of DNV GL.

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Table of Contents

1 ABSTRACT ..................................................................................................................... 2

2 KEYWORDS .................................................................................................................... 2

3 EXECUTIVE SUMMARY ..................................................................................................... 3

4 ADVANCED FIELD TESTING & ONLINE MONITORING METHODOLOGIES FOR T&D ASSET MANAGEMENT & OPTIMIZATION ....................................................................................... 4

Asset Diagnostic Categories 4

Examples of Non-Intrusive Asset Diagnostics 5

5 DATA MANAGEMENT & ANALYTICS SOLUTIONS FOR T&D ASSET MANAGEMENT & OPTIMIZATION ............................................................................................................... 8

Risk-Based Maintenance 9

6 MAXIMIZING THE VALUE OF ASSET MANAGEMENT & OPTIMIZATION THROUGH ADVANCED DATA MANAGEMENT AND PREDICTIVE & PRESCRIPTIVE ANALYTICS .................. 12

The Transformation from Condition to Risk Based Asset Management 12

Embrace Data Analytics 12

Where are Utility Data Analytics Today? 13

Utility Big Data Capabilities to Increase Value from Utility Data Analytics 14

7 PROACTIVE ASSET MANAGEMENT & OPTIMIZATION DRIVEN BY PREDICTIVE & PRESCRIPTIVE ANALYTICS IN COMBINATION WITH ADVANCED DATA MANAGEMENT, FIELD TESTING AND ONLINE MONITORING METHODOLOGIES ........................................... 18

Risk-Based Maintenance – Case Study 18

8 REFERENCES .................................................................................................................. 1


1 ABSTRACT

This paper will merge the concepts of asset field testing and online monitoring with asset criticality-health-

risk (CHR). The goal for that is to design and deploy predictive top-down and bottom-up asset management

(AM) and optimization programs for power transmission and distribution. It will show how such programs

can be enhanced with scalable situational awareness (SA) enabled through, data driven software capabilities

such as advanced predictive and prescriptive analytics and big data processing. This development will drive

next-generation asset management & optimization with informed, event-driven and real-time decision-

making.

2 KEYWORDS

Predictive Asset Management & Optimization, Asset Field Testing and Online Monitoring Methodologies,

Distributed Energy Resources (DER), Energy Storage Systems (ESS), Asset Criticality-Health-Risk (CHR),

Asset Management Top-Down and Bottom-up Strategy, Asset Data Management & Analytics, Big Data, Asset

Data Driven Scalable Situational Awareness, Predictive Data, Test and Online Monitoring Driven Asset

Maintenance


3 EXECUTIVE SUMMARY

Utilities work continuously to leverage their assets. They are challenged to grow earnings even when they do

not have the corresponding revenue growth. For this there are no standards, only best practices. And

everything is performed under the strict and severe supervision of a public commission while at the mercy of

local circumstances and considerable history. As a result, questions come up: “What field testing can be

done to predict asset lifetime and support a maintenance methodology? How can a testing program be put

together to ensure an outcome of solutions and real data leading to more accurate conclusions about the

remaining lifetime of components and necessary efforts and investments into maintenance?”

Asset management is the name of the game. It maximizes the lifetime of the assets, prevents outages and

other disturbances from happening, and optimizes the maintenance effectiveness and efficiency. NERC

compliance represents only a minimum requirement in asset management. In addition, utilities get new

responsibilities such as safely and securely integrating and operating new distributed energy resources (DER)

composed of renewable sources as well as energy storage systems (ESS) including the necessary power

electronics devices that monitor and control these systems. This all happens while there is still so much

uncertainty about lifetime performance and efficiency of these new disruptive technologies and how they

combine with traditional generation as well as the existing T&D infrastructure. In addition to that, storms

such as Katrina and Sandy have challenged utilities to provide a proper response and demonstrate grid

resilience under abnormal weather conditions. All too often such catastrophic events are claimed to be an

act of God while in many cases weather-related outages can be avoided by applying a tight quality

assurance system to the equipment that is impacted and under distress.

Besides DER, utilities are also faced with a number of new and innovative software technologies to deal with

an exponentially growing variety of networked data sources. Wide-area situational awareness enabled by

better data integration and advanced analytics poses opportunities, but a substantial problem is that the

current utility workforce has not been trained for that. There is huge upside potential leveraging these

innovative software technologies that bring powerful capabilities such as big data processing as well as

predictive and prescriptive analytics. This will hugely impact the effectiveness and efficiency of asset

management and will change the way it is done. Real-time automation enabling event-driven informed

decision making in asset operation and maintenance is at our fingertips. The necessary hardware and

software technologies are available today. The challenge is to integrate them into the existing information

systems infrastructure such that reliable and effective grid operation and maintenance are guaranteed at the

same time.


4 ADVANCED FIELD TESTING & ONLINE MONITORING

METHODOLOGIES FOR T&D ASSET MANAGEMENT & OPTIMIZATION

What should be the role of testing of aged asset components? Refurbishment and retrofit are a viable

alternative to investments in new equipment, once a sample test of the refurbished asset demonstrates the

capability for starting a new life. In quite a few cases, experience shows that “vintage” equipment far

exceeds its projected lifetime because at the time of its design, much more margin was included than

nowadays. Also, intelligent use of temporary overloading practices (e.g. dynamic loading of cables and lines)

can be considered as an AM solution.

The first part of AM, acquiring new material, is largely covered by global industry standards, manufacturers’

type-tests, and effective commissioning tests. The reliability of the assets during usage depends upon their

age, conditions at the moment of purchase, specific wear and tear, weather circumstances at their location,

and the maintenance in the field. So far, field testing mainly consists of oil measurement for transformers,

some lubricating and mechanical maintenance, and condition checks on critical assets.

Condition monitoring and advanced maintenance strategies further reinforce reliability. Reliability surveys on

aged components, such as the one recently carried out by Cigré on HV switchgear (Cigre, Oct 2012) and

power transformers can provide major input on failure modes at advanced age and thus help to prioritize

maintenance targets.

The general problem is that both in transmission and distribution there is no real opportunity to take assets

out of service for a condition check. There are too many, it is too costly, the objects and connections are too

critical in their function, the traditional condition check is not sufficiently forward looking, and with

traditional means the economics are not proven.

Asset Diagnostic Categories

The adjectives intrusive/non-intrusive and invasive/non-invasive are commonly used in technical

literature ,the CIGRE working group WG A3.32 recommends using non-intrusive in the context of electrical

equipment because it is more specific and refers to the fact that there is no intrusion into the system.

In medicine, non-intrusive procedures are well defined and known as having clear advantages over other

procedures, as they eventually respect the fundamental principle “first do not harm.” Adopting this to the

domain of electricity is not straightforward. There are two major criteria to classify an asset diagnostic

method as non-intrusive:

1. How the integrity of the asset itself could be potentially affected by the diagnostics and

2. How the grid is affected by the diagnostics.

CIGRE working group WG A3.32 proposes to consider the usefulness of a diagnostic method as its cost

effectiveness, a comparison of its value (benefits versus cost). The value of a diagnostic method is

expressed in terms of condition indicators and the potential diagnosis which one can get using it. The cost of

a diagnostic method equals the total of expenses and effort needed to be able to apply it. WG A3.32

provides guidelines for evaluating value and cost in order to help grid operators appreciate non-intrusive

diagnostic methods.


Examples of Non-Intrusive Asset Diagnostics

Examples of non-intrusive asset diagnostics are manyfold as asset management and optimization develops

further. The introduction of sensor and measurement components in existing assets as well as in new asset

solutions enjoys growing popularity due to the increasing expectations and possibilities from data driven

approaches to unveil significant value with new and innovative utility business models.

Non-Intrusive Diagnostics for MV and HV Switchgear

MV and HV switchgears are composed of highly costly circuit breakers and represent an important asset

solution category in power delivery. No surprise that CIGRE WG A3.32 has established particular focus on

this asset class. More than a hundred diagnostic methods, mostly non-intrusive, have been identified. The

methods generate a multitude of condition indicators using diagnostic tests, diagnostic measurements and

sensing, signal processing, data analysis as well as soft- and firmware.

The following (Figure 4.1) illustrates the distribution of the different types of diagnostic methods (non-

intrusive, minimally-intrusive, intrusive). For further detail, please see Uzelac, Pater, Heinrich (CIGRE 2016).

Figure 4.1 – Distribution of Diagnostic Methods for each Intrusion and Voltage Category of

Switchgear

As can clearly be seen, there are a vast majority of non- and minimally intrusive diagnostic methods (95%)

that can be used for proper high- and medium voltage switchgear diagnostics without intrusion during power

delivery service. This implies the possibility to apply data driven analytics to test and identify major

indicators of asset health without service interruption. As a result, the asset conditions can be permanently

monitored and analytics applied in real-time.

69%

26%

5%

Distribution of Number of Diagnostic Methods for each Intrusion Category

Non-Intrusive

MinimallyIntrusive

StronglyIntrusive

26%

28%

46%

Distribution of Number of Diagnostic Methods for each Voltage Category

MediumVoltage

High Voltage

Medium + HighVoltage


Figure 4.2 – Typical setup of a Smart Cable Guard sensor placed around the earth leads of a three phase XLPE MV power cable in a substation.

Non-Intrusive Diagnostics with Smart Cables

New test methodologies offer solutions for a part of the problems. A good example is the Smart Cable Guard

which as an approach has also proven to be useful for other asset types in addition to cables. It is an

instrument to monitor underground power cable systems while the cable is in service (on-line).

It uses two inductive sensors around the cable ends and synchronized fast

communication to a central data acquisition system (Figure 4.2 and 4.3).

SCG’s ability to locate weak spots and to create an on-line PD map has

resulted in many interesting cases of avoided faults, showing its ability to

reduce the system average interruption duration as well as its frequency. On

top of that the collected information describes the health condition at all

cable points to support the correctness of the maintenance strategy.

Non-Intrusive Diagnostics with Smart Wires

Another good example is Smart Wire’s distributed PowerLine Guardian

technology (see Figure 4.4). The device, similar to a current transformer

with on-board computing and cellular connectivity, is mounted directly on

the conductor near the transmission structures. It adds impedance as

needed to “choke” the flow of electrons through overloaded lines and redirect

it to other

transmission

corridors. The

technology represents part of an evolving grid

optimization toolkit to help utilities alleviate

congestion, improve network utilization, manage

changing generation profiles and maintain reliable

electric service. In addition to the previously

mentioned direct operational benefits, the device

collects fast data to describe the dynamic electric

profile of the overhead lines and adjacent Figure 4.4 – PowerLine Guardian technology for power flow control on high voltage line

Figure 4.3 – Typical setup

of a Smart Cable Guard sensor placed around the earth leads of a three phase XLPE MV power cable in a substation.


components. This technology provides another valuable source for a new class of asset monitoring

information acquired in real-time with the assets in service. It can be leveraged to improve asset

management for overhead transmission lines and related asset components including the monitoring of

DER-related impact on more flexible power conduits for an increasingly solar- and wind- powered grid.

In addition to the PowerLine Guardian device, Smart Wires also developed the PowerLine Router. Its

objective is to directly increase the throughput of underutilized transmission lines, just as the larger and

more capital-intensive flexible AC transmission systems (FACTS) but at much lower cost. The router affects

digital power controls on the transmission grid just as similar devices from companies such as GridCo and

Varentec perform on distribution grids (see Figure 4.5).

Interestingly, all the new monitoring and indicative

signals available from these different technologies now

turn out to be a challenge for traditional data acquisition

systems due to lack of standard interoperability. If this

problem can be solved through adequate design and

integration of data acquisition, communication and

collection solutions to feed existing and new utility

information systems it will result in valuable

contributions to better asset health and predictive

maintenance strategies.

In addition to the well-known and still emerging

advanced metering and synchrophasor infrastructures,

the above new and innovative solutions are available to measure, monitor and control specific points and

areas of the power delivery network. These technologies provide access to fast regional data in the second

and millisecond range, system frequencies where capture of information is not supported by the currently

available and deployed AMI communication systems. While the hard and firmware products available from

various vendors represent valuable options for utilities to improve monitoring and control at the grid edge

(e.g. secondary feeder side of power distribution infrastructures) the development of larger centralized big

data management and analytics solutions fed by the massive amount of newly available data from a wider

range of data points is still in its infancy. This is by and large due to the fact that wide-area communication

technologies to transport all this data over larger distances to central data center locations (i.e. data is

moved to and processed at the utility head-end where the main utility information systems are located) has

not yet sufficiently matured to justify its costs and support for the needed real-time, event driven data

solutions. In addition, today’s trend is clearly toward more distributed grid intelligence with decentralized

grid monitoring and control options. This not only avoids extra time and cost of data transportation but also

enables distributed real-time, event driven monitoring and control performance as expected from the

growing number of intelligent nodes in the transformation toward a more intelligent and smarter power grid.

Nevertheless, an integrated centralized asset data management and analytics solution will be a critical part

of the overall concept of distributed intelligence to enable and manage the single version of the asset data

truth.

The integration of renewable energy sources and energy storage systems currently provides utilities with

new concerns. The first question is what requirements to establish for a product to be purchased particularly

when it represents a first generation development. Today, there are no or inadequate standards available to

Figure 4.5 – ENGO device for decentralized

sensing, monitoring and control of grid edge


do so. As a consequence, utilities must make difficult technology choices given the lack of opportunity to

find proof of performance. Another critical aspect is the necessary interoperability between the new

components as there is no or little validation. For instance, it is not yet clear whether the best choice for

storage is lithium or flow batteries. Testing technology needs to develop aligned with the technology

evolution itself. However, this is often not the case. In addition, the multi MW size of renewable installations

makes field testing a technically and financially challenging option due to necessary investments in high

power installations.

Part of the solution to the above problems can be found in a so-called “telescope” approach which is based

on the principle to test as much as possible on the smallest scale and work up in size up to in modules

wherever an option exists. This way, only reliability testing of integrated modules is necessary. Two

considerations are to be made. One is that proper functioning of power electronics is heavily related to the

interaction within the immediate grid vicinity. Power flow ripples and electromagnetic surges can produce

responses depending on the specific circuit in which the inverter is positioned. This condition can only be

tested at a specific location and at various circuit loading conditions. The second problem is that proper

functioning of inverters in the grid is highly impacted by their controls and software. This represents again a

local interaction with the grid. As a result, the development of adequate test methodology is critical.

5 DATA MANAGEMENT & ANALYTICS SOLUTIONS FOR T&D ASSET

MANAGEMENT & OPTIMIZATION

When properly applied, a mature, predictive asset management strategy works and provides

numerous benefits to implementing organizations. Chief among these benefits, it maximizes the

value of physical assets to the company’s bottom line. This means back office systems working in

continuity and complement to accurate and critical field work such as inspections and

maintenance.

To develop this type of predictive asset management program, a company must understand what asset

management is and how to get the most out of it. Asset management treats the company and all of its

assets holistically. Asset management is both a top-down and bottom-up endeavor. It is a top-down process

because for asset management to work there has to be a philosophical shift and change leadership at the

top levels. Departments and divisions that used to focus solely on maintaining equipment in their territory

will need to start looking at assets as parts in a company-wide system (Figure 5.1).

It is also a bottom-up system, in so far as equipment data is of paramount importance. To implement an

effective, evolving asset management program, a utility will need to identify and evaluate each maintainable

asset and then develop a comprehensive maintenance strategy to increase the reliability and maximize the

performance results of that asset. Field personnel must be engaged and involved.

Second, asset management brings information from diverse sources (nameplate data, online monitoring

information, conditional information including periodic diagnostic test results, repair activities and so forth)

into one locus of information. All analysis and decisions are derived from this master data set. Having a

current, normalized data source helps eliminate ‘turf wars’ between departments and allows a utility to make

financial decisions based on current, accurate data.


Third, a mature asset management program monitors equipment health (H) and determines a device’s

criticality (C) to the overall performance of the company. By combining criticality and health, a utility can

evaluate the risk (R) to the organization’s operation, represented by a given piece of equipment.

Using the CHR approach, a utility can effectively identify which devices should be temporarily but

purposefully ignored, which should be maintained, and where and when replacements are required. This

cuts down on unnecessary maintenance and predicts capital expenditures to where they are needed and

most beneficial.

Fourth, asset management provides flexibility, so categories of devices can be evaluated based on individual

corporate situations and goals. A category might be as broad as all oil-filled reclosers or as specific as

substation transformers made by a specific manufacturer in the 1960’s. A category can also include all

devices on a critical transmission line. As more equipment data is collected, it will become easier to identify

trends and, therefore, target equipment groups with similar characteristics and levels of importance.

An asset management system can only be truly considered a predictive maintenance program

when health and criticality can be quantified and used to determine when to ignore, maintain or

replace a given device.

Risk-Based Maintenance

Risk-based maintenance (RbM) has many guises and comes in many forms. The bottom line is this:

Maintenance programs move from being reactive to being proactive. The focus shifts from preventing

Figure 5.1 - Vertical Enterprise Asset Reliability System conceptual map


failures to predicting what the optimal maintenance schedules are – when maintenance work is most cost

effective. This may seem like a minor difference, but it has powerful ramifications.

To begin, criticality is now included in the decision making process. This is vitally important. Using criticality,

work can be prioritized based on the impact to the corporation upon a specific asset’s failure. Through the

monitoring of operational stress and measuring key electrical and mechanical parameters utilities can

identify when a device crosses a performance threshold that would negatively impact grid operations.

RbM, which is necessary to support organizational reliability goals, is only enabled by a robust predictive

maintenance (PdM) system which allows utilities to identify those assets, which if they fail, have the highest

impact to the enterprise. PdM uses all available equipment health data. As a result, there has to be one

comprehensive, trustworthy source of data. All decisions are made based on this common source of truth.

The Advantages

Predictive maintenance is the most efficient and effective way to schedule maintenance. It also maximizes

the value of diagnostic and monitoring data which produce the most reliable results. This includes the high

volume of data collected from diverse sources, like Smart Grid technologies, such as Smart Meters, or IoT

devices, such as new online monitoring sensors.

PdM allows a utility to view the company as a single entity, without separating goals by department (e.g.

Operations, IT, Budgeting, Financial). By using PdM, a utility can develop risk-based maintenance plans.

Maintenance triggers can be created and alerts sent to allow just-in-time maintenance.

The Disadvantages

Moving from a condition-based to a predictive maintenance approach requires a philosophical shift in the

way everyone in the utility thinks about equipment and the purpose of maintenance. For example, line

workers normally change out oil-filled reclosers every three years. Before PdM or RbM, they thought they

were maintaining the lines. With PdM, they should be thinking, ‘I am ensuring the revenue stream from the

customers on this line, by maintaining or improving this line’s reliability.’ Substation crews might find that

the normally scheduled outage in the spring has been cancelled, because the risk of equipment failure is low

and the loss of revenue does not justify shutting down the substation.

Depending on the previous maintenance system, a PdM system may or may not require training. It may or

may not require the integration of new monitoring systems to get data into a central data storehouse. If

various departments and divisions were used to working autonomously, there may be some resistance to

sharing data and giving up decision making power. However, the cost savings, improved reliability, and

increased organizational efficiency make overcoming these challenges worthwhile and critical to continued

organizational growth and success.

Once a PdM system is in place, a utility can develop a risk and condition-based maintenance system, adding

more sources of data and fine-tuning work and capital expenditure plans, to meet corporate goals.


Figure 5.2 – Large Substation infrastructure requires better analytic and maintenance tools than historical time based methods can provide.


6 MAXIMIZING THE VALUE OF ASSET MANAGEMENT &

OPTIMIZATION THROUGH ADVANCED DATA MANAGEMENT AND PREDICTIVE & PRESCRIPTIVE ANALYTICS

The Transformation from Condition to Risk Based Asset Management

As elaborated in the previous sections, the current objective of utilities is to move from reactive condition-

based to proactive risk-based asset management. In order to do so, utilities need to introduce the concept

of asset criticality as illustrated in Figure 6.1.

But what does this

transformation mean

from the perspective of

innovative data solutions

driven by capabilities

such as advanced

analytics or big data?

While reactive,

condition-based asset

management is driven

by the actual asset

health identified through

field testing and asset

online monitoring.

Proactive risk-based

asset management

introduces the concept

of asset criticality in

addition to asset health to also weigh in the impact and importance of each asset on the overall performance

of the utility enterprise. This new predictive approach not only needs to introduce the advanced concepts of

predictive and prescriptive analytics in order to identify and perform forward-looking maintenance strategies,

it also requires far more granularity to move from the asset class to the individual asset level, which

essentially requires big data capabilities to allow for the necessary scalability and flexibility to handle both

top-down and bottom-up asset management.

Embrace Data Analytics

Electrical utilities are in the process of moving into the data analytics business. This is the result of several

global forces – one being the proliferation of less expensive electronic monitoring technologies and the

speed and availability of communications systems.

Also, everyone wants to have the ‘smartest’ grid possible. As a result, an unprecedented amount of raw data

is being collected by utilities each day. On the one hand, all that data creates a real opportunity for utilities

to better monitor and understand how a device or system is operating. On the other hand, converting that

sea of data into actionable information can be a daunting task. Therefore, it is imperative to have an asset

management system that can handle, integrate, and verify the data to maximize its value.

Figure 0 – Transforming from reactive to pro-active Asset Management


A major strength of a mature asset management system is the ability to bring all the data into one ‘store

house’ and develop algorithms that can analyze the data and predict which devices should be ignored,

maintained, or replaced.

Where are Utility Data Analytics Today?

At this point, most utilities are still in the first information-based phase of descriptive and diagnostic

analytics. In other words the data sets are used to answer questions such as “What happened?” or “Why did

it happen?” while some utilities won’t even use the data for exploring those vital concerns. The following,

Figure 6.2, (Gartner’s value curve) addresses that.

Only few utilities leverage

available datasets to

design optimizing

predictive and prescriptive

analytics solutions that

address questions such as

“What will happen?” or

“How can we make it

happen?” which is not

surprising given the

increasingly difficult

nature of the problems as

well as the need for more

advanced data scientists,

which utilities do not

usually have in their own

workforce. While those

would still be available

from top consulting firms, utilities are also mandated to protect the privacy of their customers as well as the

cyber security of their infrastructure. That makes it difficult for them to provide the collected data to

external parties and have those perform the necessary data discovery as well as the development and

deployment of the desired data analytics. There is still plenty to do in order to achieve true value from the

collected data at all levels of difficulty. Unlike the scenario anticipated by many analysts in the last few years,

utilities by in large are still in the first phase where:

data is only collected without specific objectives (‘Yikes! - we have a lot of data’)

data is stored, secured and made available (data fortress)

data is used in basic reporting to deliver information about what happened with limited data

representation and without intuitive explanations (basic reporting)

data is feeding simple dashboards using dynamic data representation to answer the question “What

happened?” in a more intuitive manner (business intelligence)

Figure ‎6.2 – Analytics Capabilities Framework


While the above types of value extracted from data are certainly helpful to increase situational awareness at

some enterprise levels they do not support comprehensive analysis that leads to closed-loop automation

with elements such as actionable triggers and real-time decision making.

Tomorrow’s utility data analytics will execute on real-time and near real-time data. It will be predictive and

prescriptive in nature to warrant the necessary modeling and planning based on historic data. And it will

drive business transformation where business process change is initiated by analytics-derived information.

Utility Big Data Capabilities to Increase Value from Utility Data Analytics

The concept of big data has been around for more than a decade. Its potential to transform the

effectiveness, efficiency, and profitability of virtually any enterprise has been well documented. Yet, despite

the concept of big data being well-defined, and the general enormity of its opportunity well-understood, the

means to effectively leverage big data and realize its promised benefits still eludes many.

Big data’s remaining challenge that prevents the realization of these benefits comes in two parts. The first is

to understand that the true purpose of leveraging big data is to take action - to make more accurate

decisions, more quickly. We call this situational awareness, an idea that is quite self-explanatory. Regardless

of industry or environment, situational awareness means having an understanding of what you need to know,

have control of, and conduct analysis for in real-time to identify anomalies in normal patterns or behaviors

that can affect the outcome of a business or process. If you have these things, making the right decision in

the right amount of time in any context becomes much easier.

Achieving situational awareness used to be much easier because data volumes were smaller, and new data

was created at a slower rate, which meant our worlds were defined by a much smaller amount of

information. But new data is now created at an exponential rate, and therefore any data management and

analysis system that is built to provide situational awareness today must also be able to do so tomorrow.

Thus, the imperative for any enterprise is to create systems that manage big data and provide scalable

situational awareness.

The utilities industry is in particular need of scalable situational awareness so that it can realize benefits for

a wide range of important functions that are critical for enabling smart grid paradigms. Scalable situational

awareness for utilities means knowing where power is needed, and where it can be taken from, to keep the

grid stable. When power flow is not well understood, the resulting consequences can quite literally leave

utilities and their customers in the dark: a fitting-though-ironic analogy considering the goal of awareness.

Utilities can learn much about how to achieve scalable situational awareness from other industries, most

notably building management and telecommunications, which have learned to deal well with big data’s

complexity and scale.

The utility industry’s time scales vary over 15 orders of magnitude due to the unique diversity of sensors

and critical business processes, and often at much faster intervals than other industries, which, when trying

to create scalable situational awareness, impacts all five V’s of the industry’s big data pressures.

Analyzing huge volumes of data that span multiple orders of timescale magnitude falls short of traditional

data management technologies’ abilities. Traditional methods of data management, such as relational

databases (RDB) or time-serialized databases, may not have the capability to capture the causal effects of

years or decades of events that may occur in a millisecond or microsecond range, and therefore cannot

meet the real-time smart grids’ scalable situational awareness needs. Additionally, such an array of devices


and processes create an especially-wide variety of data types and formats that must be considered when

making any decision, and thus for enabling scalable situational awareness. The following figure (Figure 6.3)

summarizes the complexity of utility big data use cases.

A typical utility asset infrastructure is composed of thousands of networked asset components which result

in petabytes of rich and linked grid asset data with deep inheritances (Figure 6.4).

The datasets are not only large in volume but also vary substantially due to the variety in data types and

several orders of magnitude in terms of sample rates. There is a spectrum of data velocity, variety, validity

and veracity. In addition, the base of data generating technology is growing at an exponential rate. Taking

Figure 6.3 – Illustration of the Utility Big Data Problem

Figure 6.4 – Definition of the Utility Big Data Problem


all that into account it can only be concluded that a predictive and prescriptive asset management and

optimization problem for a complete utility enterprise asset infrastructure with asset monitoring, control and

maintenance at the individual component level would greatly benefit from big data management and

analytics capabilities.

Routine maintenance and repairs to power lines and other grid infrastructure account for a substantial

portion of utilities’ ongoing costs. With a sophisticated data management system that enables advanced

analytics, fault locations can be identified more precisely and characterized even before a truck is sent to fix

it. This can also allow utilities to determine if a truck and crew are needed to fix a problem at all, resulting in

immediate cost savings. Given what has been laid out in previous sections it should be clear that a

comprehensive predictive asset management and optimization solution should leverage big data capabilities

to utilize the power of asset information at the individual as well as collective level. It should take advantage

of advanced parallel computing capabilities (grid and cluster) as well as virtualization and cloud

infrastructure. The utility asset infrastructure evolves more and more into a network of networks. To monitor,

control, model and simulate this infrastructure cannot be done without advanced big data engines to

leverage top-down and bottom-up approaches, representing a trend that will continue in general and is

essential for predictive asset management and optimization.

Data Analytics Systems Requirements for Scalable Situational Awareness in Utility Asset

Networks

The underlying data management and analytics solutions required to provide scalable situational awareness

for intelligent utility asset networks must have five key characteristics: flexibility, interoperability through

connectivity, a control network, it must use open, standards-based data management technologies, and it

must support scalable data analysis.

Flexibility - Unlike many industries, power delivery is notoriously variable, with daily, weekly, and annual

variations due to variability in customer load, generation dispatch, delivery system outages, and other

reasons. This variability has challenged the industry to discern patterns that can be used to identify

abnormal conditions and anomalies that spur critical decisions-making processes. Advanced object-oriented

database technologies can deal with data that looks at voltage and current rate data just as easily as any

other type of data from any other industry. By embedding a variety of different data object models to

capture the different energy data types, as well as corresponding sample rates, object-oriented

programming allows for an integrated data management and analytics concept. It creates the necessary

flexibility to deal with the challenging characteristics of big energy data in real-time. Fast and reliable data

retrieval, suitable data formats for data analysis, one object-oriented programming language (for DDL and

DML), connectivity between objects without application code, direct use and storage of object identities, and

advanced, as well as traditional data management, features merged together represent critical values of a

fully-integrated object-oriented data management and analytics solution. This is what gives you the

situational awareness that is needed for utilities: understanding the immediate value of making a decision to

solve an abnormality in normal data patterns within a relevant time frame.

Interoperability and Connectivity – The intelligent utility asset network of the future will be a massive

collection of devices, sensors, actuators, and systems, all of them creating ever-larger data volumes and

ever greater analytics complexity. In this form, this will be a hugely complex network that must have full

accessibility of all these devices and sensors. Central to enabling this is Internet connectivity, something


again that Versant’s technologies have proven highly capable of by managing data and analysis for many

global telecommunications service providers.

Control Network - Not only is collecting all of the asset data that asset sensors and devices produce a

challenge, but all of these devices must be fully communicative, interconnected, and critically controllable.

The decisions made based on having full situational awareness must be rapidly translated in to the

functioning grid, which, like enabling interoperability, requires a single, cohesive control system enabled

heavily through Internet connectivity.

Open, Standards-Based Data Management Systems – A network as complex, variable, and fast-moving

as the intelligent asset grid requires billions of devices, sensors, and machines. It is impossible to expect

that any one data management technology vendors’ systems will be used across every grid application and

scenario. But more to the point, smart grids will be integral to the everyday life of billions of people, so as

new technologies are developed and adopted over time the smart grid must be able to adjust and change

the data management systems to meet new requirements. To enable this, utilities must leverage open

system architectures across five specific areas to permit ease of adoption and avoid costly vendor lock-in:

Network Infrastructure: Includes protocol, routers, media type, IT connectivity, etc.

Control Devices: Heavily-utilized devices that produce, consume, and manipulate data, as well as

control and monitor the energy grid network.

Network Management and Diagnostic Tools: Enable configuration, commission, and

maintenance for the system.

Human-Machine Interface (HMI): Includes the visualization tools through which users and

managers obtain a view into the system, including both PC software and instrumentation panels.

Enterprise/IT Level Interface: Connects the control network into the data network. No gateways

other than open systems standards-based routers and IT-based data exchange mechanisms are

used.

A critical sixth factor is the data management system itself, which must also be considered part of this open

standards-based architecture. The DB represents the configuration database for the complete network of the

grid, storing the configuration profile data of every device participating in the open, fully interoperable and

integrated control network, and enabling effective communication and control between them all.

Scalable Data Analysis - Utilities will face immense data volume increases over the next several decades,

making the job of ensuring the validity and veracity of data analysis ever harder. Open architectures and

data management technologies will play a pivotal role in enabling data analysis that scales to these new

volume demands. These systems must not only be capable of dynamically scaling to account for and

manage increased data complexity, but also sheer volume as new types of devices are deployed on the grid

network.


7 PROACTIVE ASSET MANAGEMENT & OPTIMIZATION DRIVEN BY

PREDICTIVE & PRESCRIPTIVE ANALYTICS IN COMBINATION WITH ADVANCED DATA MANAGEMENT, FIELD TESTING AND

ONLINE MONITORING METHODOLOGIES

Today, asset management is one of the most critical components of the utility business model. The

identification of asset health is instrumental in the approach. It is driven by asset field testing as well as

asset online monitoring. While field testing has only limited possibilities of application online monitoring

becomes more and more important as the asset infrastructure can remain in-service.

While current asset management is reactive in nature for most utilities, the newly available data streams

from asset online monitoring offer tremendous opportunity for development and deployment of more

advanced proactive predictive and prescriptive analytics solutions supported by capabilities such as big data

engines and advanced computing. As a result, top-down and bottom-up concepts can be applied to asset

management going from the asset class to the individual asset level, the predictive and prescriptive concept

embraced by asset criticality and risk can be integrated in the asset management approach to move from a

reactive to a proactive asset management, situational awareness in the asset infrastructure becomes more

and more real-time and event driven, and informed decisions can be taken without excessive delay.

One of the key elements in this transformation toward a more proactive and data driven asset management

is a properly defined asset management system software which can model the asset infrastructure, identify

bottlenecks, and act where needed. If a utility is collecting more data, it only makes sense to put that data

to use in as many ways as possible to maximize ROI. The most obvious use is to evaluate the criticality,

health and risk of individual devices. Engineers can use standard industry evaluation criteria, such as

performing maintenance on breakers after ‘X’ number of operations or when a single event had a fault

current above ‘Y.’ With the right asset management system, utilities can also create their own evaluation

criteria quite easily.

Risk-Based Maintenance – Case Study

The following case study demonstrates risk-based maintenance leveraging a study titled “Evaluating oil-filled

Circuit Breakers using CHR Criteria” that can be found in [5]. In this study,engineers at a large investor-

owned utility (IOU) identified the most important risk factors associated with the failure of oil-filled circuit

breakers. They created an algorithm to calculate the chance of failure and rated each of its approximately

20,000 oil-filled circuit breakers in the following four areas:

1. Overstress (A)

2. High maintenance (B)

3. Bushing type (C)

4. Manufacturer (D)

In each category, every breaker was given a score of ‘0’ through ‘3’. The higher the score, the greater the

concern. For example, certain bushing types had a history of failure, so that any breaker with that type of

bushing automatically received a score of ‘3’ for “Bushing Type.”


Also, historical data showed that overstressed breakers were at significantly greater risk of failure. This was

addressed by creating an algorithm which weighted in the “Overstress” criterion by a factor of 6.

A final score (0…3) was calculated for each breaker using the following algorithm:

𝐹𝑖𝑛𝑎𝑙 𝑆𝑐𝑜𝑟𝑒 =[6𝐴 + 𝐵 + 𝐶 + 𝐷]

9

Based on the calculated final score the following recommended maintenance activity was triggered for every

breaker:

𝑀𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝐴𝑐𝑡𝑖𝑜𝑛 = {𝑵𝒐 𝑨𝒄𝒕𝒊𝒐𝒏: 𝐹𝑖𝑛𝑎𝑙 𝑆𝑐𝑜𝑟𝑒 = 0 𝑜𝑟 1

𝑪𝒍𝒐𝒔𝒆 𝑴𝒐𝒏𝒊𝒕𝒐𝒓𝒊𝒏𝒈: 𝐹𝑖𝑛𝑎𝑙 𝑆𝑐𝑜𝑟𝑒 = 2𝑹𝒆𝒑𝒍𝒂𝒄𝒆 𝑩𝒓𝒆𝒂𝒌𝒆𝒓: 𝐹𝑖𝑛𝑎𝑙 𝑆𝑐𝑜𝑟𝑒 = 3

As a result of this evaluation, the utility scheduled the replacement of 800 of its oil-filled breakers (4%) over

a ten year period. Roughly 1,400 breakers (7%) were monitored more closely. About 89% of the breakers

did not require any action. The following figure 11 illustrates the percentage split of the identified

maintenance actions:

Figure 7.1 – Oil-Filled Circuit Breaker CHR Results

By using the CHR approach, the utility identified where the greatest risk existed and took action to reduce it.

This capability represents one of the benefits of a robust AM system.

Also, predictive and prescriptive maintenance systems have the capability to determine and set thresholds

that trigger maintenance (or replacement) to reduce the risk of failure. For example, a transformer can be

operated under heavy-load conditions for a long time without suffering undue damage. But, if a transformer

is overheated once, its life span can be reduced to essentially zero. Preventing a transformer from crossing

the threshold (from ‘hot’ to ‘too hot’) can mean the difference between regular maintenance and potential

replacement.

In addition, moving to a predictive/prescriptive or reliability-centered maintenance system is to use CHR to

optimize non-operational aspects of the corporation. This can include required reports on reliability metrics

(SAIDI, SAIFI, MAIDI, MAIFI) and on regulatory compliance.


Asset management systems can provide a host of benefits to utilities wanting to capitalize on their data

systems and maximize asset health and reliability. Asset management, when systematically applied:

Collects and analyzes available data and uses it to make informed decisions about the conditions of

the equipment.

Identifies and schedules necessary maintenance on the most critical assets, while reducing or

eliminating unnecessary work.

Removes device, personnel and system risk by eliminating unnecessary maintenance and inspection

work.

Determines the most cost-effective capital replacement plan.

Provides regulatory compliance information and reporting capabilities.

Improves reliability by managing system risk, thereby improving customer satisfaction and

increasing revenue.

8 REFERENCES

1. “A Case for Best of Breed Technical Asset Management and Predictive Maintenance Utility Software –

A Solution for Engineering Operations and Asset Management”, White Paper 2013, Digital

Inspections.

2. “Asset Management of T&D Equipment and Integration of Renewables Needs Advanced Field Testing

Methodology”, Paul Leufkens, February 2016.

3. “Data Correlation – Effectively Combining Grid Data with Public Data and Social Media Data to

Maximize Forecasting Accuracy,” T. Borst and P. Myrseth, DNV GL Presentation, 2016.

4. “Flexibility in Wind Power Interconnection Utilizing Scalable Power Flow Control,” P. Jennings, F.

Kreikebaum, and J. Ham. CIGRE Grid of the Future Symposium, 2015.

5. “Fundamentals of CIM for Big Data Integration and Interoperability,” S.Pantea, N. Petrovic and I.

Kuijlaars, Presentation, Grid Analytics Europe, April 2016.

6. “Growing an Asset Management Program – Steps to Take and Advantages along the Way”, White

Paper 2014, DNV GL AS.

7. “Leveraging Big Data and Real-Time Analytics to achieve Situational Awareness for Smart Grids”.

White Paper 2012, Versant.

8. “Overview of Non-intrusive Condition Assessment of T&D Switchgear,” N. Uzelac, R. Pater and C.

Heinrich, Paper AS-101, CIGRE Symposium, 2016.

9. “Smart Cable Guard – A Tool For On-Line Monitoring And Location of PD’S AND Faults In MV Cables

– Its Application And Business Case”, Fred Steennis at al., Paper 1044. Cired 23rd Conference on

Electricity Distribution, June 2015.

10. Cigre report 510: Final Report of the 2004 – 2007 international Enquiry on Reliability of High Voltage

Equipment Part 2 - Reliability of High Voltage SF6 Circuit Breakers – Cigré Working Group A3.06 -

October 2012

About the Authors

Bert has spent more than 20 years with technology and consulting companies such as DNV GL, Siemens, General Electric, Versant and Supertex creating, leading and delivering projects for high-voltage power transmission and electric transportation networks, industrial manufacturing as well as big data analytics and automation software to

serve large-scale, mission-critical infrastructures. He earned a Masters and Ph.D. in Technical Cybernetics and Automation from the University of Rostock and an MBA from the Kellogg School of Management at

Northwestern University.

Bert Taube Contact Info: [email protected] 408 307 4424

Paul Leufkens, President of the consulting firm Power Projects Leufkens, has more than 20 years of experience in the power sector. He has worked internationally in Business Development and Leadership for consulting and testing companies, including 13 years with KEMA in the Netherlands as well as in Chalfont, PA. Previously, Paul directed product development for the T&D cable industry and witchgear manufacturing. He holds a MS EE

degree from Delft Technical University in the Netherlands.

Paul Leufkens

Contact Info:

[email protected]

267 963 8812

Jim Weik is Regional Sales Manager for DNV GL Software’s Electric Grid product center. For the past six years, he has managed sales of asset management solutions for electric utilities in North America. He has over

30 years experience in sales management of engineered solutions with 17 years experience in Asia. He holds an undergraduate degree in Mechanical Engineering from Washington University in St. Louis and an MBA from

Webster University in St. Louis.

Jim Weik Contact Info: [email protected] 541.752.7233 x 76115

Jesse Dill is the Global Marketing Manager for DNV GL Software’s Electric

Grid product center. He manages digital campaigns and outreach designed to help electric utilities adapt their business processes and systems to meet the challenges of the modern power market. He has over a decade of

business consulting and marketing experience, with 4+ years in the electric utility industry. He holds an undergraduate degree in Business Management as well as an MBA from Oregon State University.

Jesse Dill Contact Info: [email protected] 541 752 7233 x 76114

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