ASSET RISK MANAGEMENT Asset Health Framework

ASSET RISK MANAGEMENT Asset Health Framework 15 C O P Y R I G H T 2013 T R A N S P O W E R N E W Z E A L A N D L I M I T E D. A L L R I G H T S R E S E R V E D Preface This document has been prepared to outline the state of development and application of an Asset Health Framework for Grid assets, as at the date of the RCP2 submission in December 2013. Preface This document has been prepared to outline the state of development and application of an Asset Health Framework for Grid assets, as at the date of the RCP2 submission in December 2013. ASSET RISK MANAGEMENT Asset Health Framework.. Page 1 of 19

C O P Y R I G H T 2013 T R A N S P O W E R N E W Z E A L A N D L I M I T E D. A L L R I G H T S R E S E R V E D This document is protected by copyright vested in Transpower New Zealand Limited ( Transpower ). No part of the document may be reproduced or transmitted in any form by any means including, without limitation, electronic, photocopying, recording or otherwise, without the prior written permission of Transpower. No information embodied in the documents which is not already in the public domain shall be communicated in any manner whatsoever to any third party without the prior written consent of Transpower. Any breach of the above obligations may be restrained by legal proceedings seeking remedies including injunctions, damages and costs. ASSET RISK MANAGEMENT Asset Health Framework.

Table of Contents ASSET HEALTH... 1 ASSET RISK MANAGEMENT... 1 1 INTRODUCTION... 1 1.1 Purpose... 1 1.2 Risk Management... 1 2 ASSET HEALTH INDICES... 1 3 AHI MODELS... 2 3.1 AHI Categories... 2 4 APPLICATIONS... 3 4.1 Relationship to Service Performance... 3 5 FORECASTING ASSET HEALTH... 3 6 FUTURE IMPROVEMENTS... 4 6.1 Extensions to Other Fleets... 4 6.2 Refinements to Current Models... 5 APPENDIX A: AHI FORECAST SCENARIOS... 7 APPENDIX B: POWER TRANSFORMERS... 8 APPENDIX C: CIRCUIT BREAKERS... 10 APPENDIX D: TRANSMISSION LINES... 11 ASSET RISK MANAGEMENT Asset Health Framework.

Decreasing Asset Health BR02 1 INTRODUCTION 1.1 Purpose This document explains the development and application of an asset health framework as part of our overall asset risk management approach. It includes an overview of asset health and describes the different categories of asset health, how we use asset health indices (AHI), and future improvements. Details of our AHI models are set out in the appendices. 1.2 Risk Management Risk management is an important foundation for asset management. Its overall purpose is to understand the cause, effect and likelihood of adverse events and to optimally manage associated risks to an acceptable level. We do not yet have a fully quantified risk assessment framework that can be applied to the management of our assets. As an interim measure, we have sought to reflect the two main determinants (likelihood and consequence) of risk through an integrated framework by using asset health and asset criticality as simplified proxies. 1 We use a combination of asset health and criticality to reflect asset-related risk. This is used to support prioritisation within our planning processes. Our asset health and criticality frameworks have been designed to be used together. An illustration of the concept is shown below. Increasing Criticality Figure 1: Asset Risk Proxy: Asset Health and Criticality 2 ASSET HEALTH INDICES We have developed AHI as an asset management tool that supports the prioritisation of asset management interventions. It serves as a simplified proxy for the likelihood of asset failure. In our model, asset health is measured in years and reflects the remaining life of assets, where remaining life represents the estimated time before an intervention may be required in response to increasing asset risk. When remaining life is zero, it does not mean that failure is necessarily imminent, but does indicate that an intervention is likely to be required and should be investigated. 1 Asset health is used with asset criticality, which acts as a proxy for the consequences of failure (refer BR03 Asset Risk Management Criticality Framework). ASSET RISK MANAGEMENT Asset Health Framework 1

To date AHI models have been developed for three core assets: Transmission lines 2 ; Power transformers; and Outdoor circuit breakers. The above asset classes were selected for AHI because they are discrete identifiable assets with reasonable asset data that will be subject to material expenditure during RCP2 and beyond. We are expanding AHI to cover additional fleets. Based on AHI forecasts we can estimate the required future volume of asset interventions. These are then used to inform our expenditure forecasts. The approach and methodology is still in development and is continuously being improved. 3 AHI MODELS As discussed above, asset health is expressed in terms of remaining life, reflecting the estimated time before an asset intervention is required. 3 The design of the AHI model is based on factors relevant to the particular asset fleet. The factors incorporated into the design of an AHI model may include: the condition of the asset condition degradation models or projections factors that affect the rate of degradation, such as the environment, or the frequency of operation failure and outage rate historic and projected known defects in certain assets or groups of assets issues that limit acceptable lifetime, such as compliance with safety or environmental regulations the age of the asset life expectancy for the asset class. 3.1 AHI Categories The derived remaining life is used to assign an AHI category as set out in Table 1. The basis for the categories is flexible and we have chosen them to align with our regulatory control periods. 2 3 Transmission lines, in this case, includes a number of associated fleets e.g. insulators, foundations, towers etc. Refer to Appendix B for more information. The need to upgrade assets to meet increased load or system fault levels is not considered in our AHI models. ASSET RISK MANAGEMENT Asset Health Framework 2

AHI Category (years) Implied Timing of Intervention Now Due RCP1 0 2 RCP1 2 7 RCP2 7 12 RCP3 12+ Beyond RCP3 Table 1: AHI Categories 4 APPLICATIONS The main applications of AHI include: forecasting the likelihood of failures in our asset fleets identifying assets that may be particularly at risk, and should be prioritised for intervention providing a consistent basis for monitoring asset fleets over time supporting decisions on prioritising investments within and between fleets testing and visualising the future effects of alternative fleet investment scenarios supporting capital investment decisions. 4.1 Relationship to Service Performance As set out in the Service Performance Measures paper 4, we have developed a set of customer-facing service performance measures and targets. To support the delivery of these targets we will monitor the likelihood of asset failure using AHI. This includes seeking health improvements to ensure assets remain available and to manage the likelihood of outages caused by asset failure. Given the ongoing refinement to these AHI models, and that our service performance measures are maturing, it is too early to directly link AHI to either service performance improvements or revenue. 5 FORECASTING ASSET HEALTH To date, AHI have been mainly used to inform replacement and refurbishment decisions. Figure 2 provides an example of how asset health has been used to demonstrate the impacts of an investment scenario for tower foundations refurbishments. The charts compare asset health in 2019/20, following the investment programme, with a do nothing scenario. 4 BR04 Service Performance Measures. ASSET RISK MANAGEMENT Asset Health Framework 3

FOUNDATIONS- ASSET HEALTH (19/20 - PLAN) 12+ YRS (91%) 7-12 YRS (5%) 2-7 YRS (4%) 0-2 YRS (0%) NOW DUE (0%) FOUNDATIONS- ASSET HEALTH (19/20 - DO NOTHING) 12+ YRS (79%) 7-12 YRS (5%) 2-7 YRS (4%) 0-2 YRS (2%) NOW DUE (10%) Figure 2: Example AHI comparison charts intervention versus no intervention When forecast remaining life is zero (indicated by Now Due ), it does not mean that an asset is about to fail. Instead, it indicates that an asset intervention is likely to be required and should be investigated. The relationships between AHI, intervention strategies and expenditure are set out in the relevant fleet strategies. An overview of the method used to develop these forecast scenarios is included in Appendix A. 6 FUTURE IMPROVEMENTS The design of the AHI models is still at an early stage. We expect that these models will be continually refined as our asset management approach improves, and we obtain more consistent and higher-quality condition data to support the models. Additional improvements will include extending the use of AHI to other fleets and further embedding the models and their outputs into our planning and asset information systems. We may ultimately be able to make a formal linkage between AHI and service performance measures that have recently been developed. This would improve our ability to directly link investment scenarios with future service performance. The following sections provide further detail on our plans for the extension and improvement of our AHI models. 6.1 Extensions to Other Fleets We are working to extend the coverage of AHI to further fleets, including: Disconnectors and earth switches: we are currently reviewing our asset management approach for disconnectors and earth switches. The improvement plan will include updating our asset intervention criteria for these assets. This work may enable the development of an AHI model for this class of equipment. This work is planned for early 2014. Power cables: we are undertaking a pilot investigation into partial discharge mapping to assess the internal condition of a power cable. There is potential for this new condition information to be incorporated into an AHI model in 2015. Conductor asset health: the development of an AHI model for conductors is at a relatively early stage in its development, and is based on expected life for each conductor type by corrosion zone. We will continue to refine and develop this model throughout RCP2, using more detailed inspections such as close aerial inspections, Cormon testing and laboratory inspections of samples. ASSET RISK MANAGEMENT Asset Health Framework 4

6.2 Refinements to Current Models Transmission Lines AHI models We are planning the following improvements to our transmission line AHI models. Degradation curves: For tower steel, insulators, and attachment points, we plan to further enhance the degradation curves used as the basis for corrosion and the geographic zones in which structures are located. For grillages, we plan to ensure the modelling includes lessons learned from the refurbishment programme in relation to observed degradation in various soil types, moisture contents, land use, and so on. Asset data/knowledge: while good asset attribute and condition data is available for most sites, some fields (such as type test reports) may be incomplete. Data quality and completeness will be reviewed, cleansed and augmented as required to ensure a high-quality dataset is maintained. AC Stations AHI models We are planning the following improvements to our AC Stations AHI models: Condition assessment granularity: to improve the granularity of substation condition assessment, we are planning to adopt the 0-100 scoring system (as used in transmission lines). This will enable a more accurate assessment of condition, enabling improved predictions of future condition and remaining life. Tools and training: improved tools and training for assessing condition will be considered for substation staff. It has also been recommended that testing regimes be refined. Testing regimes should be reviewed to include tests that most directly indicate asset deterioration. Results will record the condition before and after any work. This would test the effectiveness of maintenance intervals, and provide better estimates of expected long-term deterioration. Condition ageing : there can be long maintenance/inspection intervals for assets. If a condition score was within acceptable limits at the time recorded, it may now have deteriorated, perhaps significantly. It is therefore desirable to age the older condition score or test results to obtain a predicted/estimated condition or test score as of today or a future date. Increased granularity and condition degradation curves will be developed to allow systematic ageing of AC Stations condition assessment data. Data processes: data processes must be capable of ensuring that work done to change the condition of an asset (such as transformer rust removal) is recorded, together with the resulting change in condition assessment. Transformer through-faults: data about transient disturbances is not sufficient at present to determine how many through-faults a transformer has suffered and their magnitudes. New data sources are being investigated to enable this information to be collected in future. Transformer AHI: We are a member of the CIGRE power transformer committee, which is currently discussing guidelines for developing an asset health approach for power transformers. As part of this discussion, we will be disclosing information, along with peer utilities, on asset data, failure rates and performance issues to the CIGRE study group. Once the guidelines are completed, we will assess their applicability and may adopt them. CBRM Study: we plan to undertake a pilot investigation of a full Condition-Based Risk Management (CBRM) model, with an initial focus on outdoor circuit breakers. ASSET RISK MANAGEMENT Asset Health Framework 5

APPENDICES ASSET RISK MANAGEMENT Asset Health Framework 6

APPENDIX A: AHI FORECAST SCENARIOS The asset health forecast is prepared by taking a snapshot of the current population for the asset fleet from asset databases, along with the investment programme as at the date of the snapshot. A remaining life is assigned to each asset in the current population based on the factors in the AHI model that are relevant to the particular fleet. The remaining life can be calculated for any point in time between the date of the extract and the end of RCP2 or other significant milestone. The model must be refreshed using updated condition assessment results, test data, and latest failure history each time a new snapshot is produced. If an annual update process is followed, each asset typically loses one year of remaining life every elapsed calendar year, unless it is replaced or refurbished in which case a revised remaining life year is assigned, based on the date of replacement plus the nominal expected life for the asset type. Forecasts of asset health can be prepared for various periods, and with various investment scenarios. A typical forecast will compare the asset health resulting from a do nothing scenario, with the asset health resulting from a defined investment scenario. Do nothing scenario Do nothing in this case means no investment in a defined planning period. For a base case scenario of do nothing in a defined period such as RCP2, the first step in forecasting asset health is to generate the population of assets expected to be in service at the start of the period. This forecast population is derived from the current extract, together with adjustments for the changes in the asset base that will result from investments planned to occur between the date of the extract and the start of RCP2. For the scenario of no investment in the RCP2 period, the expected population at the start of the period will then be retained and aged by calculating the remaining life for each asset at the start of RCP2, and decreasing this by one year for each year throughout RCP2. 5 Defined investment scenario Preparing the forecast of asset health for a defined investment scenario also commences with the predicted population of assets at the start of RCP2 period, as used in the no investment scenario. However, in this case, a number of the existing assets will be replaced or refurbished during the period, as set out in the proposed investment plan. For these assets, their remaining life will be reset based on the year of replacement and the nominal expected operating life for that asset type. All other assets are aged as in the no investment scenario. This can be repeated for a number of different investment scenarios. 5 Additional assets could be added to the model to reflect forecast changes in the fleet arising from system growth, such as from new Grid Exit Points (GXPs). However, in general, these would not have a material effect on the asset health forecast for the RCP2 period. At this stage, new assets forecast to be added for system growth have not been included in the model. ASSET RISK MANAGEMENT Asset Health Framework 7

APPENDIX B: POWER TRANSFORMERS Our power transformer population base is small and diverse compared to our international peers and this makes it difficult to confidently calculate AHI entirely using statistics (for example, industry standardised asset condition scores and forecast failure rates or hazard functions). We have therefore concentrated on factors that have affected the performance or led to major failures in our fleet, over the past 30 years of operating experience. For each of these characteristics, we have undertaken a simple statistical or engineering judgement approach to inform AHI. These characteristics and their corresponding remaining life adjustments and commentary are set out in Table 2. Factor Adjustment to remaining life Commentary Base life Major overhaul Winding design or manufacturing defects Transformer components Poor external condition Manufactured <1992 Base life of 60 years Manufactured >1992 Base life of 70 years Single phase units that have undergone major overhaul increase of 10 years Specific makes and models reduction of 15 years Mechanically ganged tap changers decrease of 10 years Tap changer generic design defects decrease of 10 years Bushing generic design defects decrease of 5 years Poor external condition decrease of 5 years We have improved our transformer design specification since 1992. We expect these transformers to have a longer base life than those manufactured before 1992. The base life of transformers manufactured before 1992 is established based on optimal replacement age (analysis detailed in the fleet strategy). We have completed a programme of major overhaul on most of our single phase transformer fleet. These overhauled transformers have not yet had a major failure or intervention and we expect them to have an extended life as a result. We experienced a high rate of failures of a particular type of 220 kv interconnecting transformer. The cause of these failures has been attributed to generic winding design defects. The remaining life adjustment is based on an engineering statistical analysis comparing the specific makes/models with the rest of the fleet. Components such as tap changers and bushings contribute to a significant portion of failures and unplanned outages. We have identified specific makes and models of these transformer components and have assigned the respective remaining life adjustments based on simplistic engineering analysis/judgement. Technically, poor external condition is not a driver for replacement; but it is a useful proxy for wholeof-life costs (such as increased maintenance cost or environmental oil leak costs). Poor internal condition High moisture content decrease of 10 years High DGA/Furans decrease of 10 years Table 2: AHI Categories Moisture content, DGA and Furans readings are an indication of failure risk. Other factors, characteristics and analysis that were considered but not included in the model were single-phase banks versus three-phase banks, winding design, and failure functions. ASSET RISK MANAGEMENT Asset Health Framework 8

Single-phase banks versus three-phase banks: the model already incorporates a number of the characteristics of our single-phase transformers that lead to poor asset health. We have not yet determined that a further adjustment factor is warranted that would specifically penalise single-phase transformers compared with three-phase transformers. Winding design: adjustments have only been applied to one type of transformer, as data on other makes/models was not conclusive. Failure functions: our population base is too small and we have too many unique/bespoke transformers to develop a statistically confident hazard or failure function for each characteristic. ASSET RISK MANAGEMENT Asset Health Framework 9

No of CB Operation counts BR02 APPENDIX C: CIRCUIT BREAKERS The AHI model for outdoor circuit breakers is mainly based on fleet wide performance issues we have encountered in the past. The model first takes into account the initial life expectancy of a circuit breaker depending on its interrupter type. The life expectancy has been based on our historic experience and analysis: SF 6 type 35 years bulk oil types 45 years other interrupter types 40 years. Most transmission circuit breakers undergo relatively few operations, and are unlikely to reach the limits of their mechanical endurance before they are replaced for another reason. However, some circuit breakers operate frequently, such as those switching capacitor banks. Circuit breakers that undergo a large number of operations will eventually deteriorate. To model this deterioration, we forecast the number of circuit breaker operations. If this exceeds the operation count limit before the expiry of the initial life expectancy (such as in the case of frequently operated circuit breakers), then the time to reach the operation count limit takes precedence (see Figure 3). 10000 9000 Example of End of Life Calculation based on No of CB Operations Manufacturer recommended operation count limit 8000 7000 6000 5000 4000 3000 2000 1000 Base Life 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 Year End of Life Figure 3: Example of End of Life Calculation based on number of circuit breaker operations In addition, the following factors are applied when determining circuit breaker asset health: historical performance versus expectations leak-prone SF 6 models and whether these have been refurbished whether or not it is a minimum oil circuit breaker corrosion zones for bulk oil circuit breakers sites whether a model is orphan (less than 5 units of that model left in service). ASSET RISK MANAGEMENT Asset Health Framework 10

CA CODE BR02 APPENDIX D: TRANSMISSION LINES Introduction This appendix sets out the current AHI models for our transmission lines assets, including: towers - tower steel - attachment points tower paint foundations grillages poles insulators dampers and spacers AHI Inputs Three sources of data are used to calculate transmission line AHI: MMS/MAXIMO: information used from our asset management information systems includes: - condition assessment data - environmental factors such as the corrosion zone - material/construction of asset - installation/commissioning date. Asset knowledge: typical asset knowledge includes expected asset life and intervention point (typically a condition assessment score at which we will intervene). Degradation path: typical degradation paths have been derived for specific assets. Example degradation curves for tower steel are shown in Figure 4. TOWER STEEL DEGRADATION RATE CURVES 100 EXTREME 80 60 GALVANISING DEGRADING VERY SEVERE SEVERE 40 20 0 REPLACEMENT REQUIRED 0 20 40 60 80 100 AGE (YEARS) RUSTING STARTING TOWER CRUMBLING Figure 4: Example Degradation Curves for Tower Steel MODERATE LOW BENIGN ASSET RISK MANAGEMENT Asset Health Framework 11

Calculations AHI for transmission lines uses the inputs described above and performs a series of calculations to estimate remaining life. In general the calculation evaluates the following two results: implied age at inspection, and implied age at present day Implied Age at Inspection: This uses a combination of CA data and degradation path to arrive at an implied age. This can be different to actual age and is arrived at by finding an implied age based on actual asset condition. Implied Age at Present Day: The Implied Age at Inspection is aged by adding the elapsed years since the last condition assessment. This value can then be converted to an Estimated CA at Present Day based on the degradation path. AHI Outputs The final calculation generally uses the following equation to arrive at a figure for AHI: AHI = [Expected Asset Life] - [Implied Age at Present Day] A typical AHI calculation for transmission lines is shown in Figure 5. Inputs Calculation Output MMS / Maximo [Body Major Steel Cond], [Body Minor Steel Cond] Average of ([Body Major Steel Cond], [Body Minor Steel Cond]) KEY Data CA Score Calculation Degradation Curves Corrosion Zone Degradation Curve Data Derive [Implied Age (@ Inspection)] from degradation curve Result [Implied Age (@ Inspection)] [Years Since Inspection] [Implied Age (@ Inspection)] + [Years Since Inspection] [Implied Age (12/13)] Asset Knowledge [Expected Life] [Expected Life] [Implied Age (12/13)] Remaining Life AHI Figure 5: AHI Calculation for Tower Steel (example) ASSET RISK MANAGEMENT Asset Health Framework 12

Transmission Line Fleets Table 3 summarises the key inputs, calculations, and inputs for the calculation of AHI for each transmission line fleet. Fleet Inputs Calculation Output Tower Tower Steel MMS/MAXIMO: Body Major Steel Condition score Body Minor Steel Condition score Date of Condition Assessment Corrosion Zone. Degradation Path (Figure 4) Asset Knowledge: Typical life expectancies for unpainted towers for the six corrosion zones. Condition Assessment score: Average of Body Major Steel Condition and Body Minor Steel Condition. This is done to provide a single value for tower steel condition. Implied Age at Inspection: Derived using the Condition Assessment score and the appropriate degradation curve. Implied Age at Present Day [AHI (Remaining Life)] = [Expected Asset Life] - [Implied Age at Present Day] Tower Attachment Points MMS/MAXIMO: Attach Condition score Date of Condition Assessment Corrosion Zone. Implied Age at Inspection: Derived using the CA Score and the appropriate degradation curve. Implied Age at Present Day [AHI (Remaining Life)] = [Expected Asset Life] - [Implied Age at Present Day] Degradation Path (identical to tower steel) Asset Knowledge: Typical life expectancies for attachment points for the six corrosion zones. ASSET RISK MANAGEMENT Asset Health Framework 13

Fleet Inputs Calculation Output Paint MMS/MAXIMO: Date of Condition Assessment Corrosion Zone Last Paint Date Tower Steel CA. Unpainted towers: Years to Optimal First Paint (from new) Implied Age at Present Day Painted towers: [AHI (Remaining Life)] = [Years until first paint or repaint] Asset Knowledge: Repaint period for each corrosion zone Optimal condition code for tower painting. Years until repaint (based on last paint date) Foundation & Grillage MMS/MAXIMO: Condition score Grillages Non-grillages Date of Condition Assessment. Implied Age at Inspection: Derived using the CA Score and the appropriate degradation rate. Implied Age at Present Day [AHI (Remaining Life)] = [Expected Asset Life] - [Implied Age at Present Day] Degradation Path: Straight line based on historic degradation. Asset Knowledge: Expected asset life is allocated to each foundation based on observed life and typical condition degradation rates for each foundation type. ASSET RISK MANAGEMENT Asset Health Framework 14

Fleet Inputs Calculation Output Poles MMS/MAXIMO: Condition score Date of Condition Assessment. Degradation Path: Implied Age at Inspection: Derived using the CA Score and the appropriate degradation rate. Implied Age at Present Day [AHI (Remaining Life)] = [Expected Asset Life] - [Implied Age at Present Day] Straight line based on historic degradation. Asset Knowledge: Expected asset life is allocated to each pole based on observed life and typical condition degradation rates for each foundation type. Insulator MMS/MAXIMO: Insulator (insulator/hot/cold) Condition score and Date of condition Assessment Insulator Material and Type (Suspension/Strain) Corrosion Zone Year of Manufacture/Commissioned Date. Degradation Path Degradation curves (glass/porcelain) Straight line degradation (composite). Asset Knowledge: Condition Assessment score: Minimum of the three (insulator/hot end/cold end) Condition Assessment scores. Implied Age at Inspection: Glass/porcelain only: derived using the Condition Assessment score and the appropriate degradation rate. Implied Age at Present Day [AHI (Remaining Life)] = [Expected Asset Life] - [Implied Age at Present Day] Typical life expectancies for insulators for the six corrosion zones. ASSET RISK MANAGEMENT Asset Health Framework 15

Fleet Inputs Calculation Output Dampers and spacers MMS/MAXIMO: Damper/spacer Condition score and Date of Condition Assessment. Corrosion Zone. Year of Manufacture/Commissioned Date. Degradation Path Degradation curves. Asset Knowledge: Typical life expectancies for dampers/spacers for the six corrosion zones. Table 3: Transmission Line AHI Inputs, Calculations, and Outputs Implied Age at Inspection: Derived using the Condition Assessment score and the appropriate degradation rate. Implied Age at Present Day [AHI (Remaining Life)] = [Expected Asset Life] - [Implied Age at Present Day] ASSET RISK MANAGEMENT Asset Health Framework 16