Positive Sequence Current Phase-Based Improved Reverse-Power Protection and a PV Hosting Capacity Assessment Method for Spot Networks

Zhang, Zhihua; Wang, Kun; Zhao, Qianpeng; Tian, Yongtao; Li, Lianghuan

doi:10.1109/access.2020.2977323

Cited by 4 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. [5,6], harmonics [7], and potential damage to protection schemes [8,9]. These phenomena highlight the need for a careful approach to the integration of DERs, necessitating an extensive overview and in-depth understanding of hosting capacity (HC).…”

Section: Introductionmentioning

confidence: 99%

“…The challenges arising from DER integration include overvoltage [1, 2] at distribution feeder endpoints, reverse power flow [3, 4] when the net load turns negative, overcurrent [5, 6], harmonics [7], and potential damage to protection schemes [8, 9]. These phenomena highlight the need for a careful approach to the integration of DERs, necessitating an extensive overview and in‐depth understanding of hosting capacity (HC).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning based hosting capacity determination methodology for low voltage distribution networks

Qammar,

Arshad,

Miller

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

For the past few years, the addition of renewable energy sources has been on the rise, but the unregulated addition of these sources can cause severe harm to the grid. Therefore, it is necessary to have a predefined limit for a grid, beyond which no further addition of renewables should be allowed without reinforcement. That limit is called the hosting capacity (HC), which is addressed in the literature by search‐based methods with heavy computational burdens. This manuscript first presents a framework to find the HC for a grid. The same framework is then applied to 503 different simulated but realistic LV distribution feeders in Finland to find their HCs and the most effective network parameters in defining a network specific HC. Next, different machine learning models, that is, Decision Tree (DT), Random Forest (RF), Linear Regression (LR), K nearest neighbours (KNN), Logistic Regression, and Support Vector Machine (SVM) are implemented on the generated data. For the classification case, the accuracy values for logistic regression, KNN, and SVM were 0.89, 0.84, and 0.81, respectively. The findings demonstrate that the developed machine learning based technique will enable distribution network operators in finding the HC without applying any deterministic or probabilistic approaches.

show abstract