The PhasorNet project will help energy companies process real-time streaming data with an eye to preventing major blackouts.
One of humankind’s most ardent wishes is to anticipate disaster and avert it. While many of our efforts to foresee future problems and correct them fail for lack of timely information, at least one system — the power grid — lends itself to a technology solution that can identify disturbances before they become widespread.
An Open Collaborative Research project at IBM Research called PhasorNet has set out to help energy grid operators take corrective measures ahead of time to prevent blackouts and other system disturbances.
PhasorNet, a real-time monitoring system that uses an IBM stream computing analytics framework to assess data from grid sensors called Phasor Measurement Units (PMU), began as a research collaboration between a team at IBM Research – India, led by Deva P. Seetharam, and IIT Madras and IIT Kharagpur, two of India’s foremost technical institutes.
With India coming to grips with an energy system whose grid all but collapsed in August 2012 — leaving some 700 million people without electrical power for several days — PhasorNet has the potential to help bring greater predictability and reliability to the country’s energy infrastructure.
India’s electrical grid is struggling to supply the country’s transportation, healthcare and manufacturing infrastructure with enough power to meet an annual 9 percent growth target. But by year’s end, India’s Gross Domestic Product (GDP) is expected to reach only 5.5 percent.
Writing about India’s “grinding energy shortage,” The Wall Street Journal predicted two months before the system-wide blackout that India’s energy “insecurity” would be the “largest constraint on the economy, one of alarming proportions.”
India’s grid gets a safety (Phasor)Net
To help address what The Economist called “the wider crisis in India’s power sector,” IBM’s Seetharam and his team use PhasorNet’s PMU sensors to continuously monitor the electrical waves on an electricity grid, and then share the resulting measurements with utility companies and regulatory bodies.
The collaboration with IIT Kharagpur is responsible for the real-time collection of data. The collaboration with IIT Madras focuses on a communications system that delivers data from many PMUs to a local data concentrator, ultimately feeding data into various analytical applications.
As the PhasorNet team wrote in Stream Computing-Based Synchrophasor Application for Power Grids, the emerging stream computing paradigm is “not only capable of dealing with [a] high volume of streaming data, but [it] also enables the extraction of new insights from data in real time.” To this end, PhasorNet also ensures that applications are reconfigurable and scalable — key requirements for a highly responsive grid system.
Building a team of stream computing researchers
“Our goal from the start with this new PhasorNet technology was to create an open research component so that the larger grid community would benefit,” Seetharam said. “A professor from IIT Madras spent time with us in Bangalore to understand the issues involved with networking, and then I visited IIT Kharagpur to focus on PMUs and data collection – where we have worked with interns from both institutes.”
In fact, Kaushik Das, an expert in power systems at IBM Research – India, started as an intern and stayed on to continue working on PhasorNet. Post internship, Kaushik and other new IBMers have extended their research into other related areas:
- They have focused on where to place the relatively expensive PMUs — at transmission/distribution substations — to keep costs down.
- They have performed Transient Stability Analysis tests to determine the source of severe system disturbances, such as the grid splitting into several sub-systems that cannot initiate corrective or preventive controls.
- They have instituted estimation tests that contribute to the core of real-time synchrophasor applications.
Simulating a connected environment
Yet one more issue remains to be addressed by this or another OCR project: Setting up a network test bed between IIT Madras and IIT Kharagpur, and the IBM Research labs in Delhi and Bangalore.
“We would have liked to know how to connect these four stations with a real grid,” Seetharam said. “We wanted to study the data latency – how much time it takes for a packet of data to get from one designated point to another. But because of IBM India’s network policies, we could not receive data from external parties into the IBM system. The IITs had issues as well about opening firewall ports to receive data from us.”
Even so, researchers worked with IIT Madras to create a simulated environment, which could delay data packets and create other network delays. In short, the simulator looked as if Phasor Net was connected over a real network experiencing real network issues.
Although no real-time computational framework that scales well and runs numerous parallel applications yet exists for power grids, Seetharam points out that comparable systems already exist in the financial services industry.
“We know it’s possible for a vast, distributed system to make rapid-fire decisions based on large volumes of ever-incoming data,” Seetharam says. “We’re going to create an energy monitoring system that responds just as quickly and as sensitively as a financial trading system.”