This study investigates the installation of multiple droop-controlled distributed generator (DG) sites in an Autonomous Microgrid (AMG) to mitigate power losses. The methodology employs a differential evolution algorithm integrated with a modified backward-forward sweep load flow method to optimise the DG sizing and positioning. Tested on an IEEE 33-bus AMG, the results show a significant reduction in power losses with multiple DG placements, highlighting the potential to improve microgrid performance.
PL
Przedstawiono analizę umiejscowienia generatorów rozproszonych (DG) w izolowanej mikrosieci (AMG), z regulowanym statyzmem oraz z rozwiniętą strukturą hierarchiczną w celu redukcji strat mocy. Metoda wykorzystuje algorytm ewolucji różnicowej zintegrowany z modyfikowaną metodą rozpływu mocy w celu optymalizacji rozmiaru i pozycjonowania DG. Przetestowane na 33-węzłowej mikrosieci testowej IEEE wyniki ukazują znaczącą redukcję strat mocy dzięki optymalnym lokalizacjom DG, wskazują na potencjał poprawy wydajności pracy mikrosieci.
2
Dostęp do pełnego tekstu na zewnętrznej witrynie WWW
Przedstawiono analizę umiejscowienia generatorów rozproszonych (DG) w izolowanej mikrosieci (AMG), z regulowanym statyzmem oraz z rozwiniętą strukturą hierarchiczną w celu redukcji strat mocy. Metoda wykorzystuje algorytm ewolucji różnicowej zintegrowany z modyfikowaną metodą rozpływu mocy w celu optymalizacji rozmiaru i pozycjonowania DG. Przetestowane na 33-węzłowej mikrosieci testowej IEEE wyniki ukazują znaczącą redukcję strat mocy dzięki optymalnym lokalizacjom DG, wskazują na potencjał poprawy wydajności pracy mikrosieci.
EN
This study investigates the installation of multiple droop-controlled distributed generator (DG) sites in an Autonomous Microgrid (AMG) to mitigate power losses. The methodology employs a differential evolution algorithm integrated with a modified backward-forward sweep load flow method to optimise the DG sizing and positioning. Tested on an IEEE 33-bus AMG, the results show a significant reduction in power losses with multiple DG placements, highlighting the potential to improve microgrid performance.
3
Dostęp do pełnego tekstu na zewnętrznej witrynie WWW
Postseismic global positioning system (GPS) time series are of fundamental importance for investigating the physical mechanisms of postseismic deformations, as well as the construction and maintenance of terrestrial reference frames. Particularly, methods for constructing accurate ftting models for such time series are critical. Based on the physical features of postseismic deformation models, we propose a new algorithm that combines the strengths of the Levenberg–Marquardt (LM) and dif ferential evolution (DE) algorithms, that is, the LM+DE algorithm. In this algorithm, the parameters are initialised by the constrained DE algorithm; the fnal parameters of the postseismic model are then solved by the LM algorithm. To validate the proposed method, DE, LM, and LM+DE were compared using synthetic and observational data from the 2011 Tohoku Earthquake. For all tests based on synthetic data, the LM+DE algorithm consistently converged to the global solution and the residual is small, regardless of how the independent parameter was varied. In the 2011 Tohoku earthquake, the parameters calculated by the LM+DE algorithm matched consistently for the global solution with a 100% passing rate after constraints were provided for the ratios of the initial relaxation time parameters. In contrast, the LM and DE algorithms individually achieved passing rates of only 22% and 1%, respectively. These results demonstrate that the proposed LM+DE algorithm efectively solves the initial estimate problem in the ftting of nonlinear postseismic models, and also ensures that the fts are mathematically optimal and consistent with physical reality.
Short-term traffic estimations have a significant influence in terms of effectively controlling vehicle traffic. In this study, short-term traffic forecasting models have been developed based on different approaches. Seasonal autoregressive integrated moving average (SARIMA), artificial bee colony (ABC) and differential evolution (DE) algorithms are the techniques used in the optimization of models, which have been developed by using observation data for the D-200 highway in Turkey. 80% of the data were used for training, with the remaining data used for testing. The performances of the models were illustrated with mean absolute errors (MAEs), mean absolute percentage errors (MAPEs), the coefficient of determination (R2) and the root-mean-square errors (RMSEs). It is understood that all the models provided consistent and useful results when the developed models were compared with the statistical results. In the models created separately for two lanes, the R2 values of the models were calculated to be approximately 92% for the right lane, which is generally used by heavy vehicles, and 88% for the left lane, which is used by less traffic. Based on the MAE and RMSE values, the model developed by the ABC algorithm gave the lowest error and showed more effective performance than the other approaches. Thus, the ABC model showed that it is appropriate for use on other highways in Turkey.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.