Abstract
This paper summarizes the literature on detection of islanding resulting from distributed generating capabilities in a power distribution system, with emphasis on the rural distribution systems. It is important to understand the legacy technology and equipment in the rural distribution electrical environment due to the growth of power electronics and the potential for adding the new generations of intelligent sensors. The survey identified four areas needing further research: 1. Robustness in the presence of distribution grid disturbances; 2. the future role of artificial intelligence in the islanding application; 3. more realistic standard tests for the emerging electrical environment; 4. smarter sensors. In addition, this paper presents a synchro-phasor-based islanding detection approach based on a wireless sensor network developed by the University of Texas at Austin. Initial test results in a control hardware-in-the-loop (CHIL) simulation environment suggest the effectiveness of the developed method.
Highlights
Like the preponderance of distribution systems, rural networks have historically been radial networks
The major focus of the survey is to understand the key challenges in islanding detection for high distributed energy resources (DERs) penetration grid and to identify the major practical solutions
The smarter sensor technology is adopted by the team to improve the islanding detection performance
Summary
Like the preponderance of distribution systems, rural networks have historically been radial networks. The increasingly critical role played by electricity coupled with the large geographic vulnerability of the distribution system requires system operators to have better situational awareness and better control on their systems. The decreasing cost of distributed energy resources (DERs) provides more incentive for the consumers to install DERs and provide electric power locally. Earlier research has suggested that the operation state of equipment on a distribution feeder can be predicted based on the system model and data captured from the substation and smart meters [1]. While this approach is appealing, it is slow due to meter sampling intervals. This survey was conducted to support the development of such sensor systems, those with the ability to identify unintentional islanding
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