μPMU-based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

IET Smart Grid

Dusmanta Kumar Mohanta¹  Soham Dutta²  Makrand Sing Kushwah²  Pradip Kumar Sadhu²  Maddikara Jaya Bharata Reddy³ 

[1] BIT;IIT (ISM);NIT;

关键词: power grids;    phasor measurement;    distributed power generation;    solar power stations;    feature extraction;    signal classification;    random processes;    spectral analysis;    μpmu-based intelligent island detection;    grid resilience enhancement;    climatic change;    modern grid complexity;    grid resiliency;    quick island detection scheme;    reliable unintentional island detection scheme;    microphasor measurement units;    inadvertent island detection scheme;    intelligent μpmu;    nondetection zone;    detection time;    extreme grid power outage;    unintentional island;    distributed generations;    solar generator bus;    feature extraction;    spectral kurtosis;    random forest classifier;    control methodology;    zero nondetection zone;    analytical hierarchical approach;    software fault-tree analysis;    time 20.0 ms;   

DOI  :  10.1049/iet-stg.2019.0161

来源: DOAJ

【 摘 要 】

With the increased climatic change and modern grid complexity, extreme grid power outage events caused by natural calamity and human interruptions have led to an urgency to enhance the grid resiliency. Microgrids (MGs) have proved to be a concrete solution to these situations. However, these events are quite uncertain, leading to the unintentional island of MGs that has adverse effects. Thus, as a first step toward increasing grid resiliency with MG, informing the distributed generations about the unintentional island is a critical task. Hence, there is a need to develop a quick and reliable unintentional island detection scheme. Micro phasor measurement units (μPMUs) are becoming popular in MG. Given this, this study proposes an inadvertent island detection scheme in an MG using an intelligent μPMU. With the μPMU, the voltage at solar generator bus is measured, three features are extracted through spectral kurtosis and random forest classifier is employed for island detection. After island detection, a control methodology is proposed to circumvent the post-effects. The method has zero non-detection zone, 99.83% accuracy and a detection time of 20 ms. The reliability of the algorithm is ascertained using the analytical hierarchical approach and software fault-tree analysis.

【 授权许可】

Unknown