White Papers

How independent Weibull MLE certification, aligned with the AER’s 2024 Asset Replacement Planning guidance, contributed to the evidentiary strength of regulatory proposals across Victorian electricity distribution network submissions.

Victorian electricity network service providers are engaged in one of the most consequential regulatory processes in recent memory. The Australian Energy Regulator (AER) has reviewed the combined revenue and capital expenditure proposals of five Victorian distributors, AusNet Services, Jemena, Citipower, Powercor, and United Energy, for the five-year regulatory control period commencing 2026.

The AER’s draft determination trimmed the distributors’ collective capex claims by approximately $3.7 billion, with the contested quantum approaching $2.9 billion once the revised proposals were filed. Central to the regulatory debate is the quality and defensibility of probabilistic asset replacement expenditure (REPEX) modelling, and, in particular, the statistical validity of the Weibull-based Probability of Failure (PoF) functions that underpin each distributor’s replacement case.

The Mantua Group (TMG) provided independent Weibull Analysis certification and expert advisory services to selected network service providers engaged in this regulatory process. Our work, conducted in alignment with the AER’s 2024 Asset Replacement Planning (ARP) Practice Note and the AER REPEX Model Framework, strengthened the statistical foundation of REPEX submissions across key asset classes, including distribution transformers, substation power transformers, and overhead conductors. This white paper describes the regulatory context, the methodology we applied, and the outcomes attributable to TMG’s involvement.

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Statistical Rigour in the Regulatory Arena: Weibull Analysis Certification and the Victorian AER REPEX Challenge

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Applying Maximum Likelihood Estimation to Left-Truncated and Right-Censored Lifetime Data

This white paper presents a rigorous statistical methodology for analyzing transmission line asset reliability using Weibull distribution analysis. The approach specifically addresses the analytical challenges inherent in utility asset data: left-truncated observations from legacy system migrations and informatively right-censored data from inspection-driven replacement programs.

A study of approximately 15,000 transmission structures across an 11,000-kilometer network demonstrates the methodology’s practical application. By employing Maximum Likelihood Estimation (MLE) rather than ordinary least squares regression, the analysis produces unbiased parameter estimates even under complex censoring conditions that would render traditional approaches unreliable.

Key findings reveal that unique classification of assets exhibit similar but different reliability characteristics, with characteristic lives (η) of 60 and 75 years, respectively, and shape parameters (β) indicating wear-out failure modes in both populations.

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Weibull Statistical Analysis for Transmission Asset Reliability

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Applying AS 1170.5 Draft Decay Modeling to Predict Functional and Catastrophic Failure Thresholds

This white paper presents an engineering methodology for predicting wood pole deterioration using decay progression models aligned with AS 1170.5 Draft, the Australian standard. The approach integrates field-measured decay rates from test stake programs with pole geometry to establish both functional failure (FF) and catastrophic failure (CF) thresholds for transmission structures.

A critical finding is that failure timing is highly dependent on original pole diameter, not simply pole age. Analysis demonstrates that functional failure for minimum-diameter poles may occur 50 years before maximum-diameter poles of the same age and treatment cohort, fundamentally changing how asset managers should prioritize inspections and replacements.

The methodology incorporates treatment effects for both Pressure Impregnated (PI) and Natural Round (NR) timber, accounting for preservative type, retention levels, and the progression of decay through sapwood, outer heartwood, and inner heartwood (corewood) zones.

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Wood Decay Engineering or Transmission Pole Assessment 

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Using Conditional Unreliability to Justify Capital Expenditure Forecasts

This white paper presents a mathematically rigorous methodology for forecasting transmission asset replacement volumes across regulatory planning periods. The approach applies conditional unreliability analysis to convert Weibull distribution parameters into defensible, period-specific replacement projections suitable for regulatory capital expenditure submissions.

Traditional forecasting methods based on historical replacement rates or simple age-based rules fail to account for the non-linear nature of asset deterioration and the evolving age profile of in-service populations. Conditional unreliability
directly addresses these limitations by answering the essential question: ‘Given that this asset has survived to its current age, what is the probability it will require replacement within the next regulatory period?’

The methodology produces both aggregate replacement volumes for capital planning and individual structure probability assessments for tactical prioritization, providing a united analytical framework that serves multiple asset management functions.

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Regulatory Period Replacement Forecasting for Transmission Assets

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Balancing Inspection Costs Against Failure Consequences Using Risk-Based Optimization Methods

This white paper presents a cost-optimization methodology for determining inspection intervals for transmission line wood pole structures. The approach balances three competing economic factors: inspection costs, planned replacement costs, and unplanned failure consequences—to identify the inspection frequency that minimizes total cost of ownership while maintaining acceptable reliability levels.

Rather than applying uniform inspection schedules across all structures, the methodology calculates individually tailored intervals based on each structure’s age, probability of failure, and consequence of failure. Results typically range
from 6-month intervals for high-risk aged structures to 10-year intervals for low-risk newer assets, with most transmission lines optimizing between 1.5 and 4 years.

Implementation produces measurable cost savings by concentrating inspection resources on structures that provide the greatest risk-reduction value, while avoiding unnecessary inspections of assets with negligible near-term failure
probability.

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Cost-Optimized Inspection Intervals for Transmission Structures

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