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This study provides a project-based learning case study of the design of data acquisition module for the greenhouse system. The project-based learning environment is provided to the final year undergraduate engineering students where the students try to develop a prototype of a greenhouse system equipped with various sensors and actuators. The learning target is to develop a fully functional laboratory prototype of the greenhouse with sensors and actuators and perform data acquisition and data logging operations in the greenhouse. In addition to the development of hardware prototype, the students are encouraged to learn the theory and modeling aspects behind the problem. The result of the project-based learning approach has been encouraging since the students were able to design the working prototype of the greenhouse with basic control functionalities. [ABSTRACT FROM AUTHOR]

This article offers an islanding detection approach based on dual-tree complex discrete wavelet transform (DTCDWT) and extreme gradient boosting method (XGBoost). Initially, the DTCDWT is used to extract the feature vector from collected negative sequence voltage and current signals at the respective distributed energy resource ends. Afterward, the nonlinear synthetic minority oversampling technique (SMOTE)-based XGBoost is used to classify the islanding (I) and nonislanding (NI) events with the following characteristics: high accuracy, good sensitivity, and faster speed. Here, the SMOTE is used as an offline prefeature processing approach to reduce the unbalancing/uncertainty of “I” and “NI” classes and helps the intelligent classifier to impart a fair classification result. This scheme is found to be effective for the detection of I and NI conditions, satisfying the IEEE islanding detection standard. Additionally, the obtained simulation results are compared with other existing works to show its usefulness. [ABSTRACT FROM AUTHOR]