Electricity distribution wildfire risk management with data quality
Introduction
The risk of wildfire is increasing in New Zealand due to climate change, and there has been an increase in the frequency of significant wildfires over the last five years. Wildfires can cause serious property and infrastructure damage and lead to loss of life.
According to Fire and Emergency New Zealand, annually there are approximately 4,500 wildfires in New Zealand, 98% of which are caused by people. Three percent of wildfires develop into major incidents. Stats NZ found that very high and extreme fire danger days increased at 12 sites across New Zealand between 1997 and 2019.
Meteorologists are predicting that New Zealand’s 2023/24 summer will be particularly hot and dry due to the El Niño climate system. This is expected to expose certain areas of the country to elevated risk of wildfires, with Fire and Emergency New Zealand predicting above average fire potential for the East Coast of both the North and South Islands even during spring.
Electricity distribution businesses face a unique challenge of managing extensive network assets across thousands of square kilometres, in a variety of climatic and geographical conditions. Their overhead networks are often in close proximity to vegetation and communities, which creates the threat of ignition events in the right conditions.
As the climate continues to warm, the risk of wildfires developing will increase, requiring electricity distributors to develop detailed mitigation strategies and plans.
Strategies for Electricity Distributors to Manage Wildfire Risk
The geographic extent and thousands of individual assets comprising electricity distribution networks means that daily or even weekly surveillance of equipment to identify and assess wildfire risks is not practicable. Quantitative models are therefore required to enable the identification and assessment of the most critical risk areas.
Inputs to such models include data and information about the assets, their location, type, installation configuration, age, and condition. The quality of the data must meet acceptability thresholds to ensure the outputs of the models can be relied upon.
The use of quantitative models to support risk management and decision making is a well-established practice in New Zealand electricity distributors. For example, the EEA developed the Asset Health Indicator Guide that provides a common way for the industry to approach the preparation of asset health indicators, which are intended as a strategic tool for asset management governance discussions.
In Australia a range of methodologies for forecasting bushfire risk and modelling risk factors for distribution network service providers have been developed, many of which could be modified for use in New Zealand. Some New Zealand electricity distributors have independently developed models that they are using to manage wildfire risk.
Often the biggest obstacle to producing valuable insights from models is not the modelling approach itself, but the quality of the data held by the organisation. This is because reference models can be developed and continually improved for application by an entire sector, however the ability to apply that model within a specific organisation depends heavily on the format and quality of the data that organisation holds.
Information governance ensures that organisations can realise full value from their data and information assets. In this situation value is the ability to identify and assess the risk of electricity distribution assets causing or being impacted by wildfires.
Managing Wildfire Risk with Data Quality Monitoring
For wildfire risk models to be effective, the data inputs must be of acceptable quality. A structured and systematic approach is needed to provide assurance that the data is fit for purpose, and where issues exist, identify corrective actions to be taken.
The key principle for effective data quality management is that data must be precisely linked to the information requirements of the business. For managing wildfire risk this means that data and information inputs for the selected modelling approach must be identified.
The diagram below provides a way of representing the information requirements for managing bushfire risk within the context of an electricity distributor’s asset management system. A decomposition of the organisation’s business capabilities is identified, from top-level Subject Areas into Capabilities.
Within the “Maintain” capability a Sub-capability “Manage Bushfire Risk” is established. This sub-capability includes the business processes, systems, competencies and data and information the organisation requires to implement its bushfire risk management strategy. Four information requirements for managing bushfire risk are identified.
For each of the Information Requirements a range of Data Quality Requirements have been identified. These Data Quality Requirements represent the specific data inputs to risk models. For each Data Quality Requirement the specific dataset within the organisation’s information systems is located. This creates the precise linkage that is required between the data and information needs of the organisation and the data that is held.
Once these linkages are created, it is possible to evaluate the quality of the data to meet the information requirements. To achieve this, acceptability thresholds for data quality must be established. These thresholds consider the criticality of the data to the modelling approach and the sensitivity of the model to the data.
The thresholds may be translated into database queries that provide the capability to perform programmatic data quality checks at the desired cadence, meaning information users can receive near real-time feedback on data quality. This provides the basis for identifying data quality deficiencies that could compromise the ability of wildfire risk models to properly identify and assess wildfire risks posed by network equipment.
The diagram above shows that data quality issues exist with aged conductor data. This means that the organisation’s understanding of risk in some conductor assets may not be sufficient to implement its bushfire risk management strategy.
Establishing Data Quality Monitoring
Data quality monitoring is a critical business capability to support information governance and asset management. The elevated risk of wildfires makes now an ideal time to consider the strategy to manage this risk on your network.
DataFrame is a proven and rapid means of establishing real time data quality monitoring in the infrastructure sector. In a matter of weeks, data quality monitoring can be established to enable and enhance your wildfire management strategy in time for summer.
Check out DataFrame data health software here.