Grid monitoring
where it’s most needed

We partner with government agencies, funders, power providers, and researchers to provide accurate, timely data about energy systems. Our custom sensors, analytics, and expertise help customers better focus their investments into grid infrastructure.

Our process

What does working with us look like?

We work with partners on incremental projects, or large-scale deployments. Projects usually follow four steps.

We build local teams to deploy our custom sensors in residents’ homes, and in key locations for accurate data collection.

A sensor being installed in a home.Team members preparing for a deployment.

Our work

Sensor deployment projects

For the past 5 years we’ve been refining our methodology and deploying it to real effect in diverse contexts in Sub-Saharan Africa.

See all projects→
A view of a street with power lines overhead in Accra, Ghana.
Active
Accra, Ghana

Monitoring and evaluating the Ghana Power Compact

Streets with power lines overhead in rural Kenya.
Kenya

Measuring the effects of donor conditionality on power quality in electricity access projects

A road where a series of utility poles with solar panels are mounted.
Kampala, Uganda

Illuminating power inequity across informal and formal urban communities

Why it matters

Better data means better outcomes for everyone

Clearer operational visibility

With our sensors deployed, utility providers can better understand where the grid is weakest and respond accordingly.

Improved long-term planning

Government agencies, funders, power providers, and researchers can use our data to better prioritize investments and evaluate real program outcomes.

Better service for citizens

At the end of the day, people get better service when the infrastructure they rely on is monitored.

More about our projects

We regularly publish behind-the-scenes details of our work on our blog.

Read our blog→
Avatar for Mohini Bariya
Mohini Bariya

What Can Voltages Tell Us About the Structure of the Grid?

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.
Avatar for Mohini BariyaAvatar for Molly HickmanAvatar for Genevieve Flaspohler

From Measurements to KPIs: Estimating SAIDI at nLine

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.
Avatar for Margaret OderoAvatar for Mohini Bariya
Margaret Odero and Mohini Bariya

A Clustering Algorithm for Power Outage Detection

nLine installs power sensors at outlets in homes, small businesses, and social infrastructure. How do we estimate the extent of a grid outage from individual sensor reports? And in the real world, where sensors can be unplugged or prepaid credit can run out, how do we separate real grid outages from false outage reports?

Get in touch

We’re open to new partnerships, or sharing more with people interested in our work.

Contact Us→