Data analytics can help utilities develop more effective distribution operations through better supporting automation and control systems.
Although data analytics do not control real-time operations systems, they can support control systems based on the data collected from the new sensors being rolled out. For example, using information collected from distribution systems and sensors, analytics can help utilities with feeder voltage optimization, distribution load analysis, and distribution model load allocations. Utilities can also use real-time information from control systems to improve asset management strategies based on the increased visibility the data provides on asset utilization.
Responding to Data is Key
Existing distribution applications, such as distribution SCADA and outage management—while still vital—are generally unable to respond to the challenges presented by the influx in volume of data. They cannot predict and respond to many energy system problems before they cause outages. They cannot handle—much less analyze and act on—new, relevant data coming from distribution sensors.
This is where data analytics can play a valuable role by leveraging the data gathered from sensors to help utilities pinpoint trouble areas in the systems. Measuring currents and voltages across the system may indicate potential trouble spots. Although the practice of synchronizing the measurements at different points in the system is not new, data analytics can be used to perform the synchronization as well as use the information gathered to perform additional analysis on the system.
Data analytics can help utility operations gain more value from distribution automation by pulling together information from additional systems to better inform load forecasts and identify factors that could affect the quality of energy delivery.
Every advance in energy system management rests on information management. As those advances accumulate, utilities must gather, validate, store, and analyze ever-increasing data volumes. Data integration within a centralized data warehouse will help utilities tackle the challenge.
From Real Time to Near Future
Real-time analysis will allow engineers and operators to shift from static operations, based upon predicted scenarios, to actively monitor and control the distribution system. This shift will make it possible to make decisions and control equipment and devices based upon complete information from the system as it exists at the moment, or even shortly before. This shift will further facilitate decision making allowing utilities to know, with confidence, when unforeseen conditions develop.
It’s no longer enough to calculate the real-time condition of the distribution system. The ability to estimate the condition of the system in the immediate future is paramount to improving efficiency, reliability, and stability of the system. And proactive management of controllable elements is required to meet operational goals. Given the challenges faced by today’s distribution operations teams, it’s definitely time for distribution automation to meet data analytics, don’t you think?