In the heart of Denmark, researchers are tackling a growing challenge for power grids worldwide: aging underground cable systems. Mohammad Reza Shadi, a researcher at the SDU Center for Energy Informatics at the University of Southern Denmark, has developed a novel approach to predictive maintenance that could significantly reduce costs and improve reliability for energy providers. His work, recently published in the English-language journal “IEEE Access,” combines advanced statistical modeling and simulation to address a critical gap in asset management strategies.
The problem is clear: as underground cable systems age, their failure rates increase, posing risks to the reliable operation of power grids. This is particularly pressing as societies increasingly electrify in response to climate change. “Without proper asset management methods, the grid may not adequately support this ongoing transition,” Shadi explains. His research aims to bridge this gap by integrating detailed predictive analytics with maintenance strategies that balance reliability and cost.
Shadi’s method employs a Neural Weibull Proportional Hazard model, designed to handle common data deficiencies like truncation and censoring. This is coupled with a Bayesian nested Monte Carlo simulation to address the uncertainties inherent in probabilistic reliability models. The approach was tested on a real distribution system in Denmark, yielding impressive results. The optimal replacement strategy identified by the model could reduce outage costs by up to 6.16 million DKK and decrease the System Average Interruption Duration Index (SAIDI) by more than 50% compared to alternative strategies.
One of the most compelling aspects of this research is its potential to reshape maintenance budgets. Shadi’s sensitivity analysis revealed that operators could reduce their annual budget for cable replacements by up to 50%, incurring only minor additional costs and a modest increase in peak SAIDI. “This shows that with the right tools, operators can make significant savings without compromising reliability,” Shadi notes.
The innovation lies in the combination of the Neural Weibull Proportional Hazard model with Bayesian Monte Carlo simulation. This approach not only handles incomplete records but also quantifies uncertainty, guiding cost-effective maintenance decisions. As the energy sector continues to evolve, such tools will be crucial in ensuring the reliable and efficient operation of power grids.
Shadi’s work is a testament to the power of advanced analytics in transforming traditional industries. By integrating cutting-edge statistical methods with practical maintenance strategies, he has demonstrated a way forward for asset management in the energy sector. As the world grapples with the challenges of electrification and climate change, such innovations will be key to building resilient and efficient power grids.