A Support Vector Machine (SVM) based approach for microgrid islanding decision and control is investigated. The IEEE 13-feeder system is modified and serves as the microgrid model connected to Kundur four-machine two-area system that models the main transmission grid. A representative data set is obtained through simulations in MATLAB/Simulink considering multiple typical scenarios with or without a fault. A SVM classifier is designed to identify insecure scenarios with satisfying accuracy. Comparisons between different kernel functions are then carried out, which indicate that linear SVM can be effective for the islanding control. The SVM approach is further compared with a decision tree based approach in terms of training and testing accuracies for the microgrid islanding control problem.
This paper proposes a passive islanding detection technique for microgrid. The proposed technique relies on capturing the underlying signatures of a wide variety of system events on critical system parameters through the utilization of pattern recognition tools for islanding detection in a microgrid. The proposed technique is tested on a microgrid model implemented on IEEE 13-node distribution feeder system under a wide variety of system operating states. Results from test case study have been analyzed to evaluate the effectiveness of the proposed method. Case study results indicate that the proposed method can detect islanding events with high accuracy and reliability.
The present study emphasizes the efficacy of a biosurfactant-producing bacterial strain Klebsiella sp. KOD36 in biodegradation of azo dyes and hexavalent chromium individually and in a simultaneous system. The bacterial strain has exhibited a considerable potential for biodegradation of chromium and azo dyes in single and combination systems (maximum 97%, 94% in an individual and combined system, respectively). Simultaneous aerobic biodegradation of azo dyes and hexavalent chromium (SBAHC) was modeled using machine learning programming, which includes gene expression programming, random forest, support vector regression, and support vector regression-fruit fly optimization algorithm. The correlation coefficient includes the dispersion index, and the Willmott agreement index was employed as statistical metrics to assess the performance of each model separately. In addition, the Taylor diagram was used to further investigate the methods used. The findings of the present study were that the support vector regression-fruitfly optimization algorithm (SVR-FOA) with correlation coefficient (CC) of 0.644, (scattered index) SI of 0.374, and (Willmott’s index of agreement) WI of 0.607 performed better than the autonomous support vector regression (SVR), gene expression programming (GEP), and random forest (RF) methods. In addition, the standalone SVR model with CC of 0.146, SI of 0.473, and WI of 0.408 ranked the second best. In summary, the SBAHC can be accurately estimated using the hybrid SVR-FOA method. In other words, FOA has proven to be a powerful optimization algorithm for increasing the accuracy of the SVR method.
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