Bharat Light & Power: Enhancing Industrial Productivity and Asset Health with Google Cloud

Bharat Light & Power Pvt Ltd (BLP) is one of the leading renewable energy generation and technology companies in India. It builds, owns, and operates utility-scale renewable energy power plants to generate clean energy through wind and solar. BLP provides distributed solar solutions to large commercial and industrial consumers in India, and, through its AI-based IoT platform, Orion, delivers monitoring, reporting, and predictive intelligence solutions across industries all over the globe. BLP leverages big data, the cloud, mobility, and advanced analytics to help customers build smarter operations. The company aims to improve productivity, reliability, quality, and sustainability across a range of industries and sectors, including automotive, energy, steel, manufacturing, ports, supply chains, infrastructure, and rail.

Bharat Light & Power Pvt Ltd (BLP), a leading renewable energy generation and technology company in India, faced significant challenges in ensuring the availability of wind turbines that powered utility-scale wind power generation across the country. The failure of even the smallest component could shut down an entire wind turbine, compromising efficient operation. For instance, a $5,000 battery failure could shut down a $2.5 million piece of equipment. As the business expanded, it began to extend beyond its core 'AI for industry' mission. It recruited an IoT team to help factory owners and operators enable the programmable logic controllers (PLC) and supervisory control and data acquisition (SCADA) systems that run disparate equipment and machines to communicate with each other and provide usable data and insights. However, BLP's initial cloud service provider could not provide an architecture that made sense for an industry-focused solution.

BLP implemented an AI and Internet of Things (IoT) project, using hundreds of tags from its wind turbines to capture and send machine behavior and performance data to a control center. This data triggered an 18-month project to create AI and machine learning algorithms that would enable engineers to predict component failures in wind turbines, improve generation, and adjust maintenance and replacement schedules accordingly. BLP found Google Cloud provided the best fit for its needs for a range of reasons, including the ease of use of Google Cloud services, high-quality database services, high availability, fast response times, and the power to run the platform’s AI and visual analytics. BLP established as its key objective a 20% reduction in costs over its previous cloud service and availability levels exceeding 99.9%. The business completed the deployment in June 2019 following discussions and input from Google Cloud’s engineering and architecture experts prior to and during the first stage of the project.

• With Google Cloud, BLP is delivering projects with a range of benefits to customers. For one of the largest electrical companies in India, BLP has enabled comparison of production line performance by capturing data from PLC and SCADA systems and extracting it to Google Cloud for processing, analysis, and reporting. For another customer, BLP has deployed an IoT system that has saved them about $200,000 in energy costs over six months. BLP is also providing visual analytics to help one of the largest ports in the world detect when workers are not wearing helmets or safety equipment. Furthermore, BLP is helping companies improve supply chains and factories keep track of tooling through Bluetooth low-energy technologies. With Google Cloud, BLP is now ideally placed to execute its business strategy of helping customers improve productivity and increase growth, control and reduce costs, and enhance quality and safety.

• Supports monitoring, reporting, and analytics for 2,000 wind turbines generating energy across several countries

• Increases manufacturing productivity by at least 5% and reduces costs by 10%

• Processes data from 8 million data points in 578 turbines and 200,000 data points in 4,000 sensors per day