Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks

Abstract: Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%–110% of $P_{n}$ ) and distribution network voltage levels (±10% of $U_{n}$ ). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

Fully fuzzified linear programming, solution and duality

Abstract: In this paper, we propose a two-phase approach to find the optimal solutions of a class of fuzzy linear programming problems called fully fuzzified linear programming (FFLP), where all decision parameters and variables are fuzzy numbers. Our approach is constructed on the basis of comparison of mean and standard deviation of fuzzy numbers. In this approach, the first phase maximizes the possibilistic mean value of fuzzy objective function and obtains a set of feasible solutions. The second phase minimizes the standard deviation of the original fuzzy objective function, by considering all basic feasible solutions obtained at the end of the first phase. The advantage of the proposed approach is its simplicity in programming and computation. Moreover, we also generalize the concept of linear programming duality and extend the duality as well as the weak duality theory to FFLP.