New grid intelligence applications are shifting the industry paradigm by doing things that have never been possible before. A good example is Distribution Fault Anticipation (DFA), a method for detecting incipient faults or failures in order to prevent service interruptions. Texas A&M University (TAMU:DFA) is a leader in this innovative application for improved grid analysis and operations.
Conventional grid operations are based on: (1) planning and constructing a distribution grid that is as robust as it is economically and technically feasible, (2) implementing system-wide, time-based preventative maintenance programs to minimize equipment failures, and (3) detecting, locating and eliminating causes of service outages in order to restore service as quickly as possible. DFA, on the other hand, involves sophisticated sensing and analytics to detect problems that can be corrected before they cause a service interruption.
An electronic device automatically samples and analyzes outputs of potential transformers and current transformers on distribution feeders to detect low level transients or distortions that are generally too fast and/or subtle to be sensed by SCADA, protective relays or fault recorders. The sensing is done by a device not unlike a phasor measurement unit which samples the outputs often enough (e.g., hundreds to thousands of times a cycle) to obtain a waveform of sufficient high definition that low level or very fast perturbations can be detected and analyzed.
This involves way too much data to be transmitted continuously to the utility, much less to be reviewed and analyzed. Instead, the DFA monitor does the analysis on the spot to match waveform disturbances with a growing library of patterns for known causes of service interruptions (e.g., vegetation intermittently contacting lines, loose cable clamp, malfunctioning equipment, improper equipment settings, UG cable insulation / conductor degradation, conductor contamination arcing, etc.). These are then alarmed and the event data log is transmitted so that utility operations can investigate and perhaps mitigate the causes before a service interruption occurs.
As the electric grid continues to decentralize with more energy production, storage and management being deployed at the distribution edges of the grid, this kind of distributed intelligence will be even more important and valuable for reliability, security, quality of service, efficiency and economy.