Case Studies

Industrial monitoring for renewable energy

Microland enabled the collection of data from geographically dispersed windfarms for asset performance monitoring for a Fortune 500 company 

Microland’s IIoT team engineered and developed a robust Edge network that enabled the collection and monitoring of asset data.

▲Improved analytical insights

▲Improved control over power output

▼Reduced MTTD (mean time to detect)


The client is a US-based Fortune 500 conglomerate with global operations and revenue of over $10 billion.

Business Goal

The client’s renewable energy business has 1,500+ windfarms with varied specifications from multiple OEMs. The client wanted to track the windfarms to ensure that power generation commitments from each was being met. The requirement, therefore, was to collect data from the assets for analysis. The challenge was to build an industrial scale network that would have the capacity to acquire data from the different data sources at each windfarm.

Microland Solutions

Microland developed the client’s IIoT Edge network, built the protocol tunnel and configured the protocol converter for data acquisition.

This solution collects and aggregates data from the site assets, SCADA, Historian and VPN systems. It then centralizes the data and converts it using the OPC standard for management. Apart from this, Microland also manages the network across the 1,500+ windfarms. The engagement has delivered:

  • New site network builds, integration and project management
  • New B2B VPN tunneling
  • Remote management, operations, and maintenance support for industrial scale network infrastructure
  • Managed services including coordination and enablement of issue resolution between IT, customer, account managers, other solution vendors
Business Outcomes

Microland’s IIoT and network management expertise have delivered the target results. The client is able to:

  • Aggregate data for analytical insights resulting in better control over power output
  • Centralize industrial monitoring and control
  • Increase operational efficiency by reducing MTTD (mean time to detect)
  • Ensure the highest levels of uptime for the Edge network and assets
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