کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
5001253 1460870 2017 8 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
Capacitor and passive filter placement in distribution systems by nondominated sorting genetic algorithm-II
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Capacitor and passive filter placement in distribution systems by nondominated sorting genetic algorithm-II
چکیده انگلیسی


- Our study determines the number, placement, control, and parameters of the compensators.
- We unified the compensation of reactive power and harmonics under a single problem.
- We also consider the constraints of power quality and overstress of the capacitors.
- We employed the NSGA-II multi-objective optimization algorithm to solve this problem.

The optimization of passive filters in distribution systems has been addressed through different approaches. In general, these approaches can be classified as single-objective and multi-objective formulations. The single-objective formulations normally try to determine the least costly filters that ensure compliance with the relevant power quality standards. In multi-objective approaches, other goals are added. In general, most studies consider the reactive power of filters at a fundamental frequency to be equal to a previously determined magnitude, and the optimization is devoted to calculate the other parameters of the filters that are required to minimize the distortion indices of the network. In the present approach, the capacitor placement and passive filter placement problems are considered as a unified problem in which a set of passive compensators (capacitors and/or tuned filters) that allow to obtain the maximum annual saving in cost and maximum improvement of the power quality of the circuit are determined. In this study, the annual saving is calculated as the equivalent present value of the compensation project to simultaneously account for the benefits of the reactive power compensation and the cost of investment in the compensators. Although many studies have solved the multi-objective problem by minimizing a single function comprising several subobjectives, this study employs the nondominated sorting genetic algorithm for the optimization of several objective functions. The present approach is tested with two example circuits from literature.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Electric Power Systems Research - Volume 143, February 2017, Pages 482-489
نویسندگان
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