1. Executive Summary
TransGrid operates network infrastructure assets with high bushfire, safety, and electricity network reliability-related failure risks. As consequences of failure are high, running to failure is not a viable option for critical assets, the requirement for replacements must be based on a conditional probability of failure. This presents a challenge to justifying capital investment to regulators and consumers with minimal ‘failure data’ available, and decisions must be informed by data-driven asset health analysis.
TransGrid addressed this challenge by augmenting our analytics using conditional field data and transforming this into risk based asset information from which asset managers make optimal asset replacement decisions to the benefit of stakeholders.
2. Description of Project
2.1 Identification of Need
“To provide value TransGrid must as far as possible quantify risk in its decisions based translating field inspection data into risk based decisions that benefit the consumer.”TransGrid Asset Management Policy
TransGrid manages a high voltage electricity network with over 13,000 km of transmission lines, 109 substations; the associated monitoring equipment contains around 40,000 SCADA and 10,000 on-line condition-monitoring points. This network is critical to the safe and reliable transmission of electricity to consumers and industry customers at the lowest cost as articulated in its Asset Management Policy. Pivotal to achieving this objective is ensuring that investment decisions are prudent and rely on data-driven analytics with adequate quantification of risks. This approach informs TransGrid’s regulatory capital expenditure proposals every five years that aim to balance cost, risk, and performance to the benefit of consumers.
Based on its experience in obtaining data, and feedback from the regulator during the previous revenue reset determination, TransGrid identified the need to improve the collection and analysis of data, so that it can improve the business outcome by better quantifying and balancing its decisions. The Asset Management group identified the following weaknesses and resolutions needed to address them:
|Information was not being properly treated as an asset
|Create an asset-risk focussed team within Asset Management specifically responsible for facilitating the balance of cost, risk, and performance of assets at TransGrid based on information.
|Lack of Quantified Asset Condition Data
|Develop Maintenance Plans that incorporate the collection of asset condition information. Implementation of a field inspection tool and collection database that digitises ‘asset condition’ databased on specified criteria. Revise existing health indices and automate importing and creation of asset condition reports.
|Failure models required quantified data to determine probability of Failure, Likelihood of Consequence, and Probability of Consequence.
|Improve the use of the existing reliability analysis tool for creating statistical failure models on which to base population analysis.
|Information tools were inadequate for forecasting asset condition and risk.
|Implementation of an Asset Analytics Tool that consolidates asset nameplate, condition, and risk information into a single portal. The system must also facilitate optimised capital decisions in terms of project selection and timing of such.
2.2 Create a Dedicated Team to Handle Asset Information as an Asset
Figure 1 – TransGrid’s Asset Data to Decision Cycle
ISO55000 stipulates, “An asset is an item, thing or entity that has potential or actual value to an organization.” TransGrid in 2017 realised that beside its physical assets, asset information is the core of data driven decisions on assets. To ensure consistency in handling information and assessing risk, we:
- Updated the scope of the Asset Management System to identify Asset Information as an Asset Class
- Created a specific team in the Asset Management group with an ‘Asset Manager’ to coordinate asset information consistently.
- Developed an Asset Information Strategy. Central to this strategy was developing knowledge that answers questions asset managers ask through systematic collection and conversion of field data to information. Figure 1 shows this fundamental cycle that underpins our CRP based decisions.
2.3 Gather and Manage Quality Asset Information
An emphasis was placed on obtaining quality asset information. As an example:
- Previously, tower inspections were done from ground level. This was changed to a detailed climbing inspection that uses 10 levels of condition, based on specified levels of degradation or corrosion.
- Corrective maintenance programs were enhanced with greater asset manager oversight and inclusion of failure modes to better classify corrective actions on assets.
- Asset condition inspections were collected in the TransGrid Asset Information Management (AIM) system. This smartphone based field tool digitised most of the asset inspection forms.
- Introduction of aerial imagery to replace aerial patrols – significantly enhancing both the quality and depth of the condition data of assets
2.4 Embed Process for Transforming Data to Decisions
In order to make informed decisions, TransGrid is required to transform stakeholder and network requirements into programs of work that maintain system integrity and reliability. It was not clear in the previous revenue reset how this process occurred. In order to ensure that this process was coordinated, a ‘SIPOC’ style model was developed that clearly delineates how the transformation was to occur.
Figure 2 shows a summarised model of the transformation process to develop capital and operating programs of work.
Figure 2 – Information Transformation Process
2.5 Improved integration through an Asset Analytics and Insight Tool (AAIT)
The TransGrid Asset Risk Assessment Methodology quantifies risk as a function of Consequence of Failure, Likelihood of Consequence, and Probability of Failure, as shown in Figure 3:
Figure 3 – Assessment of Risk
As assets age, their condition deteriorates, and the probability of failure increases. In-service failures lead to potential network consequences in terms of safety and reliability, as well as bringing about financial and reputational costs to the business. In order to manage our network risk effectively, the optimal timing of TransGrid’s investment in asset replacements and refurbishments is paramount. Investing too early in an asset’s lifecycle lowers the return on investments for the consumer and stakeholders, while investing too late leads to increased in-service failures and operating spend.
A balance needs to be struck to balance capital investment against asset risk growth and network operational spend. This was achieved by:
Development of Asset Heath Indices
Asset health indices were developed on the asset condition data collected from the field. Previously this was held in static spreadsheets that relied on a snapshot of AIM data. TransGrid digitised the health indices to be updated routinely as AIM data is processed, allowing asset condition to be automated and available in investment analytics in close to real time. This greatly increases the capability for Asset Managers, allowing them to focus more time on network risk management initiatives and analysis, rather than data processing.
Consolidation of network risk
In the previous regulatory period, asset risk was consolidated and managed by asset type and asset class (i.e. by Substation, Digital Infrastructure and Transmission Lines). By aggregating the individual asset risks all the way up to network level, TransGrid is now able to visualise the risk contributions of all the assets on the network and the risk breakdown by key categories such as safety, reliability, and environment (bushfire). This also allows TransGrid the capability to prioritise investment spend across the asset classes to best manage network risk and deliver the best value for investments to stakeholders.
Integrating project cost estimates.
Project cost information held in a Project Management system. By creating an integration point with the AAIT, the latest project cost information is brought in and the current investment metrics (NPV, IRR and optimal timing) are updated automatically.
Development of the AAIT to consolidate this information and data into decisions.
The most significant benefit from the enhancements above has been its consolidation through the Asset Analytics and Insights Tool. This tool links condition and risk data through common asset registers allowing Asset Managers to analyse and prioritise high-risk assets for replacement and visualise the impact of these investments on the forecasted network risk. Together with the integration of project costs directly from the PPM, this allows the re-prioritisation of projects to occur dynamically to fit budgetary constraints.
In addition, the introduction of asset lifecycle strategies into the AAIT, which mirror the Asset Maintenance Strategies, replacement, and rehabilitation events are forecast into the future based on projected risk. Asset replacements are also fed back to the asset register so that the replaced asset risk is reset, allowing for a network risk forecast up to 50 years in the future. Forecast events will then form the foundation of proposed investment in future regulatory periods. Figure 4 diagrammatically shows the transformation of the data and information into knowledge and value-add analytics within the AAIT.
Figure 4 – Asset Analytics and Insights Transformation
2.6 Benefits of Solution
Reduced cost to consumers through optimised decisions:
- Optimal timing of investment to balance portfolio risk, cost, and benefit
- Managing optimal network risk, through right-sizing of investments
Optimised ongoing OPEX through Life Cycle Costing:
- Managing assets at end of life
- Balance unplanned maintenance expenditure versus replacement
- Defer or remove any planned maintenance on assets to be replaced – the risks of such actions understood
Optimised Network Safety Outcomes:
- Aggregated network risk
- Projected Do-nothing network risk versus risk mitigation from investments
- Offset for un-modelled risk
- Ability to model different investment scenarios for risk mitigation
- Network risk index to track current investments in RP2
Reduced Effort in Analysis (Efficiency):
- Integrated information systems allow the efficient analysis of capital requirements further reducing operational costs.
- Provides AM with summary view of condition, risk and cost of assets as well as asset class and network.
- Bridges the gap between technical and financial views – provide linkages between assets and projects
2.7 Program and Project Management
2.7.1 Project Management
This project was managed by undertaking the above projects under the sponsorship of the Head of Asset Management with overall coordination by the Asset Analytics and Insights Manager as shown in Figure 5
Figure 5 – Project Management Structure
3. Stakeholder Engagement
“… Asset management decisions and strategies will deliver value add, will be quantitatively derived to support optimal performance of our assets.”Head of Asset Management – Asset Management Group Vision
Head of Asset Management:
The Head of Asset Management was instrumental in leading focus on information and the need to improve the ability to make data driven decisions. The sponsor:
- Provided leadership to the Asset Management group
- Engaged the business to get approval for the organisational changes to make information an asset.
Asset Analytics and Insight Manager
The AAI Manager is required to provide overall management of the transformation of data to information and ensure the appropriate IT systems are available to support information collection. This required stakeholder engagement with:
- Asset Managers to ensure collection of asset data
- IT groups to ensure that the relevant Enterprise Asset Management and digital field collection capabilities were in place.
- Risk managers and corporate strategy groups to ensure that the final information would meet the requirements of external stakeholders to demonstrate sound capital program development.
- The Head of Asset Management and TransGrid senior management, to report on the progress of data collection, system development, and outcome decisions on capital requirements.
Asset Managers were provided direction by the Asset Analytics group to gather data to improve information on:
- Identifying the asset condition data relevant for the asset types.
- Developing the appropriate Asset Health information for the assets and collection methods. For example, corrosion degradation failure modes for transmission assets versus relay model obsolescence inputs for digital assets.
- Managing the collection of information by requirements communicated through Asset Management Plans (the ‘Maintenance Plan’)
- Engage with delivery functions to monitor progress of maintenance plans activities and ensure they are completed as required.
Development of a consolidated view of assets, associated associate health, and life cycle costs is providing the information required for TransGrid’s Asset Managers to take actions that optimise cost, risk, and performance. This is achieved through replacements, refurbishments, or optimised maintenance activities and is a significant step change in TransGrid’s asset management journey toward excellence. Table 1 shows the TransGrid’s asset management objectives improved or optimised through this project.
Table 1 – Alignment of Asset Management Objectives
|Asset Management Objective
|Manage Network Safety Risk
|Create an efficient high performing network
|Manage assets efficiently without compromising security holder and consumer value.