Independent auditors of the world’s critical infrastructure

Nigeria, the largest economy in Africa, has ambitious plans to reform its power sector and increase electrification. One piece of this strategy is the federal government’s Energizing Economies Initiative (EEI), which aims to provide electricity solutions to small and medium-sized enterprises (SMEs) in “economic clusters” such as markets, shopping complexes, and agricultural/industrial clusters. In many developing regions, SMEs in the informal sector play a major role in the economy. Nigeria is no exception: these enterprises employ millions of people. Providing affordable and reliable electricity to these marketplaces is important for continued economic growth.

Knowing the structure of the grid—how lines interconnect and what phases loads are on—is vital for efficient grid maintenance and operations, informing applications ranging from fault localization to phase balancing. Yet, grid structures, especially in distribution, can change over time and are often poorly known. This blog starts to explore how nLine’s voltage data could be used to infer grid structure, with a vision toward eventually providing such insight to utilities.

How can we estimate SAIDI (System Average Interruption Duration Index)—the average power outage duration experienced across all customers served—from PowerWatch sensor measurements of only a subset of customers? This post describes nLine’s statistical approach to estimate SAIDI from such a dataset. The nLine method has several favorable statistical properties that make it well-suited to calculating SAIDI in the real world where data is generally limited, which traditional SAIDI calculations neglect to consider.