M H Refan, A Dameshghi, M Kamarzarrin,
Volume 9, Issue 4 (12-2013)
Abstract
Differential base station sometimes is not capable of sending correction information for minutes, due to radio interference or loss of signals. To overcome the degradation caused by the loss of Differential Global Positioning System (DGPS) Pseudo-Range Correction (PRC), predictions of PRC is possible. In this paper, the Support Vector Machine (SVM) and Genetic Algorithms (GAs) will be incorporated for predicting DGPS PRC information. The Genetic Algorithm is employed to feature subset selection. Online training for real-time prediction of the PRC enhances the continuity of service on the differential correction signals and therefore improves the positioning accuracy in Real Time DGPS. Given a set of data received from low cost GPS module, the GASVM can predict the PRC precisely when the PRC signal is lost for a short period of time. This method which is introduced for the first time for prediction of PRC is compared to other recently published methods. The experiments show that the total RMS prediction error of GASVM is less than 0.06m for on step and 0.16m for 10 second ahead cases
A. Boukaroura, L. Slimani, T. Bouktir,
Volume 16, Issue 3 (9-2020)
Abstract
The progression towards smart grids, integrating renewable energy resources, has increased the integration of distributed generators (DGs) into power distribution networks. However, several economic and technical challenges can result from the unsuitable incorporation of DGs in existing distribution networks. Therefore, optimal placement and sizing of DGs are of paramount importance to improve the performance of distribution systems in terms of power loss reduction, voltage profile, and voltage stability enhancement. This paper proposes a methodology based on Dragonfly Optimization Algorithm (DA) for optimal allocation and sizing of DG units in distribution networks to minimize power losses considering variations of load demand profile. Load variations are represented as lower and upper bounds around base levels. Efficiency of the proposed method is demonstrated on IEEE 33-bus and IEEE 69-bus radial distribution test networks. The results show the performance of this method over other existing methods in the literature.
P. Paliwal,
Volume 18, Issue 1 (3-2022)
Abstract
The determination of a suitable technology combination for an isolated micro-grid (IMG) based on hybrid renewable energy resources (HRES) is a challenging task. The intermittent behavior of RES leads to an adverse impact on system reliability and thus complicates the planning process. This paper proposes a two-fold approach to provide a suitably designed HRES-IMG. Firstly, a reliability-constrained formulation based on load index of reliability (LIR) is developed with an objective to achieve a minimum levelized cost of energy (LCOE). Multi-state modeling of HRES-IMG is carried out based on hardware availability of generating units and uncertainties due to meteorological conditions. Modeling of battery storage units is realized using a multi-state probabilistic battery storage model. Secondly, an efficient optimization technique using a decentralized multi-agent-based approach is applied for obtaining high-quality solutions. The butterfly-PSO is embodied in a multi-agent (MA) framework. The enhanced version, MA-BFPSO is used to determine optimum sizing and technology combinations. Three different technology combinations have been investigated. The combination complying with LIR criterion and least LCOE is chosen as the optimal technology mix. The optimization is carried out using classic PSO, BF-PSO, and, MA-BFPSO and obtained results are compared. Further, in order to add a dimension in system planning, the effect of uncertainty in load demand has also been analyzed. The study is conducted for an HRES-IMG situated in Jaisalmer, India. The technology combination comprising of solar, wind, and battery storage yields the least LCOE of 0.2051 $/kWh with a very low value of LIR (0.08%). A reduction in generator size by 53.8% and LCOE by 16.5% is obtained with MABFPSO in comparison with classic PSO. The results evidently demonstrate that MA-BFPSO offers better solutions as compared to PSO and BF-PSO.
Mohamad Almas Prakasa, Mohamad Idam Fuadi, Muhammad Ruswandi Djalal, Imam Robandi, Dimas Fajar Uman Putra,
Volume 20, Issue 3 (9-2024)
Abstract
The unbalanced load distribution in the electrical distribution network caused crucial power losses. This condition occurs in one of the electrical distribution networks, 20 kV Tarahan Substation, Province of Bandar Lampung, Indonesia. This condition can be maintained using optimal reconfiguration with the integration of Distributed Generation (DG) based on Renewable Energy (RE). This study demonstrates the optimal reconfiguration of the 20 kV Tarahan Substation with the integration of the Photovoltaic (PV) and Battery Energy Storage System (BESS). The reconfiguration process is optimized by using the Firefly Algorithm (FA). This process is conducted in the 24-hour simulation with various load profiles. The optimal reconfiguration is investigated in two scenarios based on without and with DG integration. The optimal configuration with more balanced load distribution conducted by FA reduces the power losses by up to 31.39% and 32.38% in without and with DG integration, respectively. Besides that, the DG integration improves the lowest voltage bus in the electrical distribution network from 0.95 p.u to 0.97 p.u.
