Multi-objective for optimal placement and sizing DG units in reducing loss of power and enhancing voltage profile using BPSO-SLFA

Abstract: Algorithms are used to optimize both single and multi-objective system limits. This research aimed to detect the optimal location and size of the DGs, which can significantly minimize power loss and improve the stability of the voltage. The research uses binary particle swarm optimization and shuffled frog leap (BPSO-SLFA) algorithms for simulation and testing of an optimal power flow (OPF) on 33 and 69 bus radial distribution system. The result shows that the algorithms give better DG allocation and minimizes the power losses but at the nascent stage of advancement. The power losses associated with the system have significantly reduced up to 31.8244kW using multi-DGs reconfiguration placement. The outcomes are established to verify the potency of the recommend algorithm to minimize losses, general improvement in voltage profiles and cost saving for various distribution system. However, the proposed methodology can be used as a reliable method in DG settings and sizing in distribution network system which produce better outputs rather than hybrid grey wolf optimization (GWO) and hybrid big bang big crunch

An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning

Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction layer of the deep Boltzmann network and deploy deep learning training model as the source model. The sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.publishedVersio

Improved distributed generations models and its optimal placement and sizing in distribution system with application in smart grid

Ph.D.Abstract: Please refer to full text to view abstract

Optimization techniques applied for optimal planning and integration of renewable energy sources based on distributed generation: Recent trends

Numerous potential advantages to the requirements and effectiveness of the supplied electricity can be accomplished by the installation of distributed generation units. In order to take full advantage of these benefits, it is essential to position the Distributed Generation (DG) units in appropriate locations. Otherwise, their installation may have an adverse effect on the quality of energy and system operation. Several optimization techniques have been created over the years to optimize distributed generation integration. Optimization techniques are therefore constantly changing and have been the main attention of many fresh types of research lately. This article evaluates cutting-edge techniques of optimizing the issue of positioning and sizing distributed generation units from renewable energy sources based on recent papers that have already been applied to distribution system optimization. Furthermore, this article pointed out the environmental, economic, technological and regulatory drivers that lead to a rapid interest in the DG system based on renewable sources. A summary of popular meta-heuristic optimization tools discussed in table form with merits and demerits to increase fresh prospective paths to multi-approach that have not yet been studied

An Enhanced P&O MPPT Algorithm With Concise Search Area for Grid-Tied PV Systems

Due to improved efficiency of solar photovoltaic (PV) systems, this article proposes a modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm. The MPO algorithm in question adopts a tracking approach that divides the power-voltage curve into four operational regions based on the estimated open-circuit voltage. Additionally, this algorithm enhances the maximum power point (MPP) tracking method by reducing unnecessary step-size calculations, focusing only on a 10% Section of the power-voltage curve that contains the MPP. Consequently, the two regions located far from the MPP, below 90% of the power-voltage range, utilize a large fixed step-size to ensure swift tracking speed. Furthermore, in the regions close to the MPP, the remaining areas employ a similar tracking strategy as the adaptive P&O algorithm, aiming to achieve minimal steady-state oscillations around the optimal MPP. The performance of the proposed MPO algorithm is demonstrated by validating it against sinusoidal, ramp irradiance, and one-day (10 hr.) irradiance profiles using MATLAB/SIMULINK. The simulation results confirm that the proposed algorithm outperforms recently published techniques in terms of convergence speed, achieving the shortest time of 15 ms, and slightly higher tracking efficiency of the PV system under uniform irradiation, reaching 99.8%