Abstract - The paper considers the potential to continuously monitor critical and important power assets in a smart grid structure. It introduces the smart node concept for multi-factorial monitoring and analysis of a single or cluster of assets. The proposed scheme always includes partial-discharge (PD) measurements because PD is an important distress indicator for insulation deterioration of all HV and most MV equipment. An essential aspect of the continuous monitoring paradigm is that an integrated database is generated that shows selected trend- and distress-indicating variables and extreme events in a consolidated time-sequence record. The data can be processed at the smart-node level. The smart database provides a history of asset operating conditions that facilitates optimal maintenance planning, dynamic rating and improved life-span prediction. The application of continuous monitoring in a smart-grid environment is described using Techimp’s PDCheck II (smart node) and TiMS (smart database).

Index Terms — Continuous asset monitoring Electrical supply industry Power system reliability Partial discharges Power system control Smart grids

I. INTRODUCTION MARTgrid developments are underpinned by the developments of microcomputer technology and high-

speed communications. These developments are also improving the feasibility of continuously monitoring the condition of critical and important power-system assets. The challenge is to produce useful information that improves efficiency and asset utilization rather than generating an unmanageable new data set. The paper describes development of continuous asset monitoring using TechimpSpA equipment, that is based on partial discharge monitoring and incorporates a range of other monitored variables. The structure implements local data analysis and a consolidated global database.

II. SMART GRID STRUCTURE There are two pillars underpinning the smart electrical grid.

These are the ‘smart’ microcomputer and the high-speed packet-switched communication network.

P. M. Hart is the technical manager with Jubatus Consulting Pty Ltd (Melbourne Australia e-mail: pmhart@jubatus.net). Jubatus represents TechimpSpA (Bolognia, Italy) in Australia.

The capability of current-generation microcomputers allows

on-site data analysis and storage. The availability of a packet-switched communication networks allows individual devices to be accessed via a common channel. The communication capacity is now substantial so significant data quantities can be transmitted, if required. However, collection of ‘raw data’ into a central database is likely to create an unmanageable information system.

Whilst we are interested in electricity supply, the same technological developments are driving change in other domains. Road-safety engineers are working on the intelligent transport network and hydrologists are developing intelligent irrigation systems. The central tenants are the same in each case. By applying distributed intelligence, some ‘figures of merit’ might be improved and new functionality provided. Significant new challenges arise concerning software quality, vulnerability to failure, security and complexity.

Figure 1 illustrates that different ‘layers’ will exist in a smart grid architecture. Each layer provides particular functionality, which is often of interest to different participants. There will be communication between layers but each layer can operate independently.

Most general-press reporting in Australia concerning the smart grid focuses on smart meters. Naturally, energy consumers are interested in this layer because of cost and demand-management implications. The deployment of smart devices that can be controlled by, or in association with smart meters, has not yet occurred in Australia, although consumer-goods manufacturers are advanced in developing intelligent appliance controllers. Smart devices might be controlled remotely to achieve demand management by shedding non-essential equipment. This is the next logical progression from the smart meter.

The smart grid will have interfaces to other information and communication systems. The business system is specifically identified in Fig. 1 because smart meters communicate directly with it. Other interconnections can be envisaged to other utility business systems (such as gas and water suppliers), so as to provide an integrated interface at the consumer level. The diagram does not explicitly identify the communication channels that must exist between layers and systems. These essential elements are beyond the scope of this paper.

Wide Area Power-Quality Monitoring concerns widespread monitoring of operating conditions (voltage, current,

Continuous Asset Monitoring on the Smart Grid Peter Michael Hart, Member, IEEE

S

978-1-4577-0875-6/11/$26.00@2011 IEEE

temperature, harmonic levels, balance,…) at a large number of nodes – mainly in the distribution system. Power quality monitoring will become increasingly important as distributed generation is added to the grid. Renewable energy sources such as wind, tide and solar generators will, by nature, operate intermittently. This will make management of voltage and frequency challenging. Power–quality monitoring, in particular at the distribution level, will be necessary to allow problem locations to be identified. The Wide Area Monitor level will need to be interfaced with local weather information so that predictions of short-term variations in local generation can be made and accounted for.

This paper focuses on the Continuous Asset Monitoring and Dynamic Ratings layers. By continuously monitoring suitable parameters, the current health of a power asset can be estimated. Real-time dynamic ratings computer programs can be used to predict the capability of the assets to withstand high-level operating conditions with acceptable risk. Continuous long-term monitoring also facilitates development of a history that shows the extreme events that could have affected the safety margins and should improve the accuracy with which the remaining life span of an asset can be estimated.

The Continuous Asset Monitoring and Dynamic Ratings layer considerations are applicable to both the community electricity supply system and to a large industrial / mining facility. Whilst the later does not have some of the lower-layers of the smart grid, the asset utilization and protection considerations are essentially the same, although with fewer nodes.

Fig. 1. The Smart Grid has a layer structure. Different interest groups access data from the different layers.

III. SMART GRID GLOBAL MONITORING CONCEPT The Asset Monitoring layer is primarily of interest to the

reliability and maintenance engineers. The purpose is to maximize the availability of equipment by basing maintenance actions on measured condition or trend indicators. Whilst basic preventative maintenance remains essential, decisions to

replace or refurbishment can be based on continuous (or at least frequent) condition indications.

Fig. 2 illustrates a conceptual life history for a transformer. Ageing occurs due to known mechanisms of material degradation that occur relatively slowly under ‘normal’ operating conditions. Elevated currents and temperatures accelerate ageing. Therefore monitoring of these variables is the first necessary step in controlling premature ageing.

Sudden reduction in safety margin occurs because of severe

short-term ‘abnormal’ conditions. These stress the insulation. Extreme events, such as lighting strikes, short-circuits or abnormal temperature conditions, rather than usual service conditions, cause step-like changes in condition leading eventually to the onset of unreliability.

Deterioration of polymeric insulation that is subject to high electric-field levels can occur due to ‘treeing’ and subsequent void development. Abnormal voltage events result in heating and pressurization of water in natural water channels (micro-voids). This promotes spreading of the defect in a ‘tree’ pattern. Each abnormal voltage event spreads the tree and reduces the inception and extinction voltage for partial discharge generation. Because of this mechanism, it is important for the reliability engineer to know the history of over-voltage events that assets have experienced, particularly in arduous operating environments.

One challenge is to make reasonably accurate assessments of the safety margins that exist based upon measurements made recently. Obviously, a reliable operating history will be useful; the longer the history the better. However, this is often unavailable or at best not readily available. Continuous monitoring together with storage of operating history and extreme-event occurrences offers the prospect of more reliable life-span prediction. This is a major potential feature of the smart grid.

Because semi-permanent memory storage is now inexpensive and reliable, the operating history of an asset can be stored in a smart node (or monitor) at or near the power asset. The data history can be periodically accumulated in a controlled manner via a communication channel, into a central data-base applicable to similar types of assets. Thereby the reliability engineer can obtain a powerful tool for prediction of life-span and the maintenance engineer can base onerous maintenance actions on actual condition. The ‘criticality index’ of each asset can be assessed using a

risk-analysis approach: • Critical equipment: Failure of this equipment stops the

process. On-line condition monitoring is essential.

• Important equipment: Key items that can be covered by redundancies or restricted operating conditions. On-line condition monitoring is justifiable. Periodic condition monitoring may be applied.

• General equipment: Failure of general equipment can be tolerated with some inconvenience. Periodic condition

monitoring is justifiable. This might be done using portable monitors.

Energy supply companies are seeking to achieve N-2

redundancy levels. That is, a large interconnected supply system should be able to operate with the unplanned loss of two important assets. In many cases this will require continuous monitoring of critical and important assets, to allow maintenance decisions to be made based on trend conditions.

Predictive rating programs, such as Real Time Temperature Rating (RTTR), which rely upon continuous condition monitoring, can be used by planners to find an acceptable system configuration that allows the system to operate with tolerable asset risk levels.

The Continuous Monitoring Concept is that critical and important assets should be continuously monitored to determine the operating point, deterioration trends and ‘distress level’. The monitored variables are processed locally and then added to a consolidated time-stamped multi-factorial database that is the operating history of the asset. This database is available to different professional interest groups within the supply organization. The control of the equipment is a separate function.

Fig. 2.Illustration of a power transformer life-history; from CIGRE Working Group 12.18, Ref[3].

The choice of which condition indicators to use in the asset history is based upon knowledge of likely failure modes, the voltage level and the practicality of the various measurements.

There is a wide range of monitoring equipment in the market place that can be applied to assets. The reliability engineer should choose to monitor variables that can provide useful indications of health in three distinct domains:

• Operating variables; which describe the current operating point.

• Trend variables; which identify deterioration and aging. • Distress indicators; which identify serious deterioration

levels, mainly with insulation. Usually the operating point of a major asset is continuously

monitored and the information is reported to the control room

via the SCADA. Out-of-limit conditions generate alarms. None of this will change in the Smart Grid, although the capability of the control system will increase.

Maintenance engineers sometimes monitor trend indicators. These are (for example) winding temperatures and oil contaminants in transformers, dielectric loss factor of insulators and vibration levels in rotating machines. The measurements are often made using portable equipment according to a maintenance schedule. In Australia these variables are rarely monitored continuously and usually monitored spasmodically. Partial discharge level might be classified as a trend indicator, although it is more often used to determine distress level.

Distress indicators show high levels mainly when the insulation (solid, oil or gas) has deteriorated. Examples are partial discharge level and high CH4, C2H2 and C4H6 levels in insulating oils.

Table 1 shows some condition indications for various types of MV and HV equipment. The accuracy of life-expectancy predictions is likely to be improved by careful choices of the trend and distress variables. An essential requirement is that multi-factorial variables are monitored and all the selected variables (condition, trend and distress) are accumulated in a consolidated database. Such a consolidated approach facilitates specialist assessment of the condition of the asset.

The right-hand column in Table 1 shows recommended monitoring variables. Partial discharge monitoring is consistently chosen because it is applicable to all types of insulation on HV and most MV equipment. There has been considerable technological advancement over the past decade with PD sensor development and PD classification and analysis techniques. It is now practical to continuously monitor partial discharges without generating huge data sets. Partial discharges can be effectively analyzed as will be described in a following section.

IV. SMART NODE CONCEPT The smart node concept is that monitoring data is collected,

stored and processed locally. Selected data can be sent to a reporting location where the processed data is incorporated into a history database, Techimp’s PDCheck II is an example of a smart node(Fig.3). This unit can be used for both high-speed and low-speed data collection and on-board analysis. The essential features are: • Three high speed analog channels (each with a 40 MHz

bandwidth); • A high-speed digitizer (100 Mb/s); • Three low-speed analog channels (each with a 5 kHz

bandwidth); • Four digital-input channels; • Substantial memory capability capable of storing 6

months or more operational data; • On-board intelligence that can be programmed to perform

assessments;

• Programmable set-up that facilitates different sampling rates;

• A high speed communication port (fiber ethernet). • A multiplexer can be used to extend the channel capacity.

The PDCheck II (smart node) can be used for various monitoring configurations that are applicable to different types of equipment. The high-frequency channels are used for wide-band partial discharge monitoring. This was the original motivation for its development. The other analog channels are used for continuously monitored variables other than PD. The digital channels are used to record protection relay operation. A multiplexer can be used to increase the number of channels.

Fig. 3.PDCheck II construction

An example of a smart node application to a power-transformer is shown in Fig 4. The partial discharges are collected from the high-voltage bushings using capacitive probe sensors. The insulator dielectric loss factors are calculated by the custom module. The DGA analyzer monitors CO, CH4, H2O and H2 at an oil port.

Fig. 4. Smart-node connections for a power transformer

V. PARTIAL DISCHARGE ASSESSMENT Partial discharges arise from electrical breakdown at the

microscopic level. There are broadly six types, which are: • Voids in solid dielectrics. • Surface discharges. • Tracking discharges.

• Fluid droplets in air (corona). • Free-metal discharges in GIS switch-gear (Ref[2]). • Gas bubbles in oil.

Partial discharges are invariably associated with deterioration of electrical materials. Micro-sparking generates free electrons that will potential change the material characteristics at the discharge site. Localized heating also drives deterioration. The electric field generates partial discharge. As the system operating voltage level increases, the electric field intensity is likely to also increase. Consequently the potential for partial discharge behavior increases with the voltage level of the equipment. The rate at which electrical materials might deteriorate from a pre-existing flaw is likely to be greater for high-voltage than medium-voltage equipment. Furthermore, the level of safety is probably greater on a medium-voltage system than a high-voltage system. Consequently, partial discharge level is a key indicator of insulation distress in high-voltage systems.

On medium-voltage systems higher levels of partial discharges can be accepted because the electric field level is typically lower and the safety margins are greater than on high-voltage systems. However, it is wise to assess the likely cause of partial discharge in any system. As a guide, partial discharge assessment is applicable to equipment with a line-voltage level above 4kV.

Not all partial-discharges are equal! Discharges that occur in solid insulation voids can lead to rapid deterioration whereas corona discharges that occur in open-air systems might cause no deterioration or at worst slow degradation of surface condition. Partial discharges that occur in surface contamination of insulators might not lead to material failure but could indicate a risk of flashover.

Characteristically partial discharges occur during the first- and third-quadrants of the voltage cycle. This occurs because the capacitive components are being charged up to the discharge inception voltage. When discharge occurs the voltage across the void collapses which generates a voltage pulse in the reverse direction.

Partial discharge level may drop before actual failure of insulation. This occurs because of the build-up of surface carbon within the discharge void, which provides a direct conduction path for breakdowns with consequent reduction in the intensity of radiated or transmitted discharges. Consequently, occasional PD measurements might not accurately identify defect growth. Insulation resistance testing is needed to complement partial discharge measurements in this situation.

Surface discharges do arise from contaminating moisture films on insulation surfaces. Surface heating promotes evaporation of moisture which causes the film to break-up. Each break provides a site where micro-arcing can occur. Hence, surface PD level can be sensitive to moisture and humidity levels.

Partial discharge activity is correlated with electric-field

(and hence the voltage) waveform experienced by the insulation. Noise pulses that are un-correlated can be deleted, although noise might be received at phase angles of the voltage waveform at which partial discharges typically occur and are likely to be retained. The first level of assessment is to delete uncorrected noise pulses.

Partial discharge pulses might generate cross talk in other phases by induction or direct transmission via a cable link box. They are also reflected at discontinuities. Therefore pulse clusters can occur repetitively at unexpected phase angles and with unexpected polarity. Commutation events in power- electronic circuits generate pulses at predictable phase angles so these can be gated out. Processes or motors with varying speed will generate disturbances that have time-varying phase position can be eliminated using intelligent signal processing. The second level of assessment is therefore to eliminate power system sources of noise pulses and identify the phase or line-line voltage on which the partial discharge source is located.

The third level of assessment is to determine what device (or location) the pulses are coming from. Pulses might propagate a considerable distance so it is useful to have directional information. Sensors are available that give this. Another useful characteristic is that pulses tend to attenuate and disperse with distance from the source. Loss of high-frequency information with distance indicates movement away from the source. This is particularly significant on a power cable where the source might be up to ~ 1.5 km from the receiver. Time-domain reflectometry is used on distributed systems (cables) to estimate the location of the discharge.

At the fourth level of assessment the type of the partial discharge is identified. Experience shows that partial discharges from different generating mechanisms usually have different pulse shapes. Some testers have developed a classification of pulse shapes. The PD generating mechanism may produce pulses with a characteristic correlation with voltage phase angle, which can allow identification and deletion of pulses from non-dangerous mechanisms, such as corona. Techimp has developed a novel approach (Refs [4], [5]), which is to calculate an ‘equivalent duration’ and an ‘equivalent bandwidth’, which facilitates plotting pulses onto a time-frequency (TF or classification) plane. Pulses from different sources tend to fall onto different areas of the TF plane, as is illustrated in Figure 5. For example, the characteristics of PDs generated by treeing defects are different from those of voids. Voids generate a single discharged each half cycle whereas tree-defects tend to generate multiple discharges that tend to cascade each branch. The rise / fall times and durations of PDs also depends upon the intensity of the local electric field. These characteristics become known with experience and this allows the development of a database of common PD discharge TF parameters.

Fig. 5. Illustration of a TF classification map for a power transformer.

Rather than store the raw pulse information, the smart node can store the processed information. Pulses that fall in uninteresting parts of the plane can be deleted. Thereby the smart node can be programmed to do the basic analysis and only pass on crucial information.

The fifth level of assessment is to identify the location of the source within an asset. This requires a detailed knowledge of the construction details and of the materials that are used inside the asset. It is enhanced by an expert level of knowledge of partial discharge and materials behavior. The relative sizes of the pulses that occur in the first (positive) quadrant of the cycle compared with the third (negative) quadrant can also indicate the nature of the source. This is characteristic of micro-discharges occurring across material boundaries because one material is likely to be a more efficient cathode than the other. At this level of analysis a smart database that provides both the recent and extended continuously-monitored history is very useful. The fifth level of assessment is conducted remotely from the asset.

VI. SMART DATABASE A smart database is an essential element of a global

condition monitoring approach. Without a relational database, the monitored data soon becomes unmanageable. Techimp has developed a database tool (TiMS) that periodically receives and processes the time- and location-stamped summary data from smart nodes. The software runs on a computer at a sub-station or a convenient location. Information is stored in a relational database. A graphical user interface (GUI) is provided that shows the location of PD or other monitored variables during a specified time period. Using this tool, people with operational and maintenance responsibility can easily review the performance of assets in a geographical region. Fig.6 illustrates the concept.

TF Map Entire Pattern

Fig. 6. GUI synoptic screen for the TiMS database. The alarm status for a multi-node cable installation is shown.

VII. CONCLUSIONS The technological developments that drive development of

the Smart Grid, are also making continuous asset monitoring viable and cost-effective. Techimp has developed smart-node and smart-database technology that helps define the continuous-monitoring layer in the smart-grid. A multi-factorial approach to condition monitoring is proposed to give a clear indication of condition trends. The smart-node always includes a partial-discharge measuring capability because PD is an early ‘distress’ indicator for HV and MV equipment. Five levels of PD analysis are identified. The Techimp smart-node is capable of operating to the fourth PD assessment level; which is classification of the likely PD generating mechanism. Thereby the investigation of the condition of an important grid asset can be focused on the potential substantive issues.

VIII. REFERENCES

[1] National Energy Technology Lab (USA), 430.01.03 Electric Power Asset Optimization, March 7 2011, DOE / NTEL contact DE FE 0004001, Consultants, Booz Allen Hamilton.

[2] Girodet, A,. Meijer, S., Smit, J. Development of a Partial Discharge Analysis Method to Assess the Dielectric Quality of GIS, CIGRE Session 2002, paper 15-106.

[3] CIGRE Brochure No. 227, Guide for life management techniques for power transformers, 20 January 2003.

[4] Tozzi, M., Montanari, G.C., Cavillini, A.; Asset management through on-line PD analysis in MV and HV network: a smart-grid approach, CIGRE RECIFE 2011, Paris, paper 86.

[5] Montanari, G,.C. Cavillini, Tozzi, M., Global monitoring: the paradigm for asset management in the smart grid framework, ICEEI 2011, Jakarta

IX. BIOGRAPHY

Peter Hart (M’1975) was born in Melbourne Australia on July 1, 1954. He graduated from the Monash University, Melbourne, with B.E (hons I). M.Eng Sc. and PhD. He has worked as an engineer at Monash University, Telcom Research Labs, PACCAR Australia, Hartwood Consulting and Jubatus Consulting. Dr Hart’s current work activities are focused on forensic engineering assessments of failed

parts and systems. He appears frequently as an expert court witness. His work as technical manager of Jubatus Consulting is concerned with asset monitoring in power supply systems. This is focused on partial discharge analysis and on distributed temperature measurement installations. He is the chairman of an Australian industry association.

Table 1 Potential Operating, Trend and Distress Variables for Assets

* Distributed temperatures can be measured using optical fibre technology. If fibre is not installed, temperatures are made using discrete temperature sensors at selected locations on the asset.

Equipment Type Operating Variables Trend Variables Distress Indicators Recommended continually monitored variables

Power Transformer Voltages Currents Power factor Oil temperature P & Q

Oil contaminants C2H2, CH4, C2H6, CO, H2, O2, sulphur, furans. Moisture, tanδ, PD level winding temperatures. Oil flow and level. Acoustic noise / vibration. Insulation resistance.

High partial- discharge level. High contaminant levels in oil, high tanδ

V, I, P, Q, oil temp, winding temps (if available), CO, CH4, H2O, PD level, tanδ.

Power Cables Terminal currents Terminal voltages

Link box voltages and currents. Distributed temperatures. Partial discharge level. Oil contamination – oil cables

High partial discharge levels. Abnormal link box currents.

V, I, P, Q, oil temp, distributed temperatures* (if available), PD level.

Overhead Power Lines

Terminal currents Terminal voltages

Insulator loss factor. Distributed conductor temperature, tanδ PD level from insulators. Leakage currents.

High partial discharge levels. High tanδ.

V, I, P, Q, PD level and tanδ at selected insulators.

Switchgear Voltages Currents Gas pressure (GIS) Temperatures

Contact thickness. Arc occurrence detection, RF noise PD level, gas contamination.

Contact thickness degradation, PD level, RF noise, gas decomposition products.

V, I, P, Q, PD level, gas pressure.

Generators Voltages. Currents. P & Q. Frequency. Earth current. Cooling water temperatures.

Distributed winding temperatures. Insulation resistances. Radial and axial rotor position, tanδ, magnetic field level, mechanical expansions, coolant contamination.

Partial discharge level. High H2 gas generation. Winding – frame insulation resistances.

Mechanical variables, V, I, P, Q, PD level, H2, coolant temperatures, distributed winding temperatures, tanδ.

Large MV Motors Voltages. Currents. P & Q. Frequency. Earth current.

Distributed winding temperatures. Radial and axial rotor position, tanδ, magnetic field level, rotor bar current wave-shape, mechanical expansions, coolant contamination.

Partial discharge level. High winding temperatures.

Mechanical variables, V, I, P, Q, PD level, distributed winding temperatures, tanδ

Voltage Transformers Voltages. Temperature.

tanδ, PD level, winding temperatures.

High PD level. High leakage current.

V, I, distributed temps (if available), PD level.

Current Transformers Currents. Voltages. Temperature.

Voltage drop, winding temperatures, PD level.

High PD level. High voltage drop. High winding temperatures.

ΔV, winding temperatures, PD level.