Statistical inference for visualization of large utility power distribution systems

Abstract

Electrical variable visualization has been widely applied to report the performance and effectiveness of novel devices and strategies in utility power distribution systems. Many graphical alternatives are useful to demonstrate critical characteristics of distribution systems such as voltage regulation or power flow. This visualization of electrical variables can also be an effective approach to analyze, compare and evaluate large-scale systems. However, there is a lack of generalized visualization strategies oriented to perform electrical validations of smart grid strategies in large distribution systems. In this paper, we show that the proposed probabilistic density evolution is a powerful resource for long-term time-sequential simulations. Examinations with an IEEE 8500 node test feeder shows that the proposed approach increases the circuit situational awareness and reduces the validation time. To illustrate this methodology, the dynamic voltage condition was simulated and analyzed to recognize the global effect of voltage regulating equipment. The results show an accurate and convenient support that can be interpreted at first glance. The proper use of long-term field measurements and short time-step simulations is a robust method for future grid research, such as designing an optimum operation of intelligent devices or diagnosing electrical interoperability issues in complex grids.