Artificial Intelligence based Station Protection Concept for Medium Voltage Microgrids

Hubana, Tarik

doi:10.1109/infoteh48170.2020.9066305

Cited by 11 publications

(1 citation statement)

References 17 publications

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“…To develop the stability, operation, and reliability of a microgrid an alternative robust solution scheme is discovered based on the concepts of artificial intelligence (AI) that includes machine learning to control the operation of big-scale data in power systems (conventional grids, microgrids, and smart grids) [16], [17], [18], [19]. The important properties of advanced protection systems should have a quick response, simplicity in nature, flexibility, and controllability in all complicated configurations.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Data Processing Method for Artificial Neural Network ANN Based Micro grid Protection Model

Baidya

Nandi

2023

EAE

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Effective fault detection and isolation technologies are very necessary for uninterrupted power supply and for making a flexible protection scheme. Almost all protection schemes in the power system are based on data exchange among protection units through a strong communication structure. Thus, it is important to deal with a large amount of data. Artificial Intelligence (AI) is one of the key factors in this regard. AI has several sections and Artificial Neural Network (ANN) is one of them. It is suggested to implement the ANN-based models while working with big data. The existing protection models are facing difficulties while trying to deal with big data. Thus ANN-based approaches have come into the front line in advanced power system networks. The performance of the ANN model is depending on the training of the data set. Hence in this work, we are focusing on preparing the data to provide input in the ANN model. The principal component analysis (PCA) method is applied here for reduced the dimension of a large number of data sets. The new data set is used to run the k-means clustering algorithm. It is shown that the clustering is more accurate with the processed data set by PCA. Therefore, the prepared data set is used to run the ANN model that has a smaller size with higher information and minimum computational time. This study shows the data preparation part to train the ANN model.

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