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Communication assisted fuzzy based adaptive protective relaying scheme for microgrid

Chaitanya B. K., Soni A. K., Yadav A.

Journal of Power Technologies

2018

Vol. 98, nr 1

57--69

This study proposes a communication assisted fuzzy based adaptive protective relaying scheme for fault detection, fault classification and faulty phase identification of microgrid along with a solution to isolate the microgrid from the utility grid by disconnecting the static-switch. Any fault in the utility grid causes the microgrid to be isolated from the utility grid whereas if there is a fault in the microgrid it continues to operate with the utility grid. An adaptive fuzzy inference system has been developed using a separate fuzzy rule base for the two modes of operation of microgrid, i.e. islanded mode or grid connected mode. The Central Grid Status Communication System (CGSCU) is considered which monitors the status of PCC and sends a command signal to the relays so that the relay settings are updated with new rules for any transition in the mode of the microgrid. The fundamental phasor amplitude and zero sequence component of current signals are used as input features, fault detection, fault classification and faulty phase identification. A standard microgrid model IEC 61850-7-420 was simulated using MATLAB/SIMULINK. The proposed method is tested for all types of faults by varying fault parameters and also for dynamic situations such as connection/disconnection of DGs and loads. The test results substantiate the effectiveness of the method.

A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system

Prasad M., Liu Y.-T., Li D.-L., Lin C. -T., Shah R. R., Kaiwartya O. P.

Journal of Artificial Intelligence and Soft Computing Research

2017

Vol. 7, No. 1

33--46

A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of TakagiSugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within eachother. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.