Voltage Profile Enhancement and Loss Minimization Using Optimal Placement and Sizing of Distributed Generation in Reconfigured Network

Haider, Waseem; Hassan, S Jarjees Ul; Mehdi, Arif; Hussain, Arif; Adjayeng, Gerardo Ondo Micha; Kim, Chul‐Hwan

doi:10.3390/machines9010020

Cited by 73 publications

(41 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to prior researches, the results demonstrated a positive influence on system efficiency. This study found that increasing the DG and capacitor doesn't always yield the best results, despite higher system costs, maintenance, and unit distance for gas delivery [10].…”

Section: Literature Surveymentioning

confidence: 88%

Optimal Allocation of Distributed Generation with Reconfiguration by Genetic Algorithm Using Both Newton Raphson and Gauss Seidel Methods for Power Losses Minimizing

2022

IJIES

View full text Add to dashboard Cite

The power loss in electrical networks is considered unavoidable, because of its inherent resistance, for effective and economical operation; network loss should be reduced to maximum extent. There are two goals for this study, the first one is extracting the optimal size and location of distribution generators (DG) and the optimal reconfiguration with the aim of decreasing power loss and enhance voltage profile, while the second objective is to prove the success of the methods of Newton Raphson (NR) and guess sidle(GS) in the study and analysis of radial distribution networks. In order to reduce power loss, the genetic algorithm (GA) is used to address the network distribution problem. Software package MATPOWER-7 and MATLAB are utilized to simulate a 33-bus testing system for achieving four cases of various Distribution generator (DG) numbers. A comparison with another previous studies done and the conclusion indicated a positive impact on the efficiency of the system, this work support loss reduction and voltage improvement with the lowest Distribution generator (DG) size and prove that the increase of Distribution generator (DG) number not always can give better result though system cost increase, maintenance, along with the units' distance for gas supplying increases. There are 4 cases with distinctive Distribution generator (DG) number applied in this paper and a compassion is done of power losses and voltage profile with references where case4 show better result with voltage profile 0.96 and power losses 59.09. As load flow method the Gauss-Seidel (GS) method used because it is a simple iterative method for solving n number of load flow equations by using iterative method. Where partial derivatives not require. And a Newton-Raphson (NR) method used where it is based on Taylor's series and partial derivatives. The Newton-Raphson (NR) method advantages are less number of iterations needs to reach convergence, takes less computer time when computation cost is less and the convergence is definite. Also it is more accurate, and is insensitive to factors like slack bus selection, regulating transformers. and the number of iterations required in this method is almost independent of the system size. Genetic algorithm (GA) has a good ability to making a global search and explore the search space using their different kind of crossover. Also you can combine it with local search method to increase the exploitation search of the Genetic algorithm. In this paper the introduction part introduced where it is the general part of the paper then the research method proposed in section 3 with details then a Genetic algorithm proposed finally the result with conclusions are proposed.

show abstract

Section: Literature Surveymentioning

confidence: 88%

Optimal Allocation of Distributed Generation with Reconfiguration by Genetic Algorithm Using Both Newton Raphson and Gauss Seidel Methods for Power Losses Minimizing

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…Figure 8 depicts the basic FOPID diagram [51]. The e(s) error signal helps in the generation of the u(s) control output.…”

Section: Fopid Controllermentioning

confidence: 99%

Power Quality Improvement Using Distributed Power Flow Controller with BWO-Based FOPID Controller

Goud

Reddy²,

Bajaj

et al. 2021

Sustainability

View full text Add to dashboard Cite

The integration of hybrid renewable energy sources (HRESs) into the grid is currently being encouraged to meet the increasing demand for electric power and reduce fossil fuels which are causing environmental-related problems. Integration of HRESs into the grid can create some power quality (PQ) problems. To mitigate PQ problems and improve the performance of grid-connected HRESs some flexible devices should be used. This paper presents a distributed power flow controller (DPFC), as a type of flexible device to mitigate some PQ problems, including voltage sag, swell, disruptions, and eliminating the harmonics in a hybrid power system (HPS). The HPS presented in this work comprises a photo voltaic (PV) system, wind turbine (WT) and battery energy storage system (BESS). As a result, black widow optimization (BWO) with DPFC with real and reactive power (DPFC-PQ) is built in this paper to solve the PQ issues in HRES systems. The main aim of the work is to mitigate PQ problems and compensate for load demand in the HRES scheme. The controller used to drive this DPFC-PQ is a fractional-order PID (FOPID) controller optimized by the black widow optimization (BWO) technique. To assess the capability of BWO in fine-tuning the FOPID controller parameters, twelve optimization techniques were presented: P&O, PSO, Cuckoo, GA, GSA, BBO, Whale, ESA, RFA, ASO, and EVORFA. Additionally, a comparison between the FOPID controller and the classical PI controller is introduced. The results showed that the proposed BWO-FOPID controller for DFPC had mitigated the PQ problems in grid-connected HRESs. The system’s performance with the presented BWO-FOPID controller is compared with eleven optimization techniques used to optimize the FOPID controller and also compared with the conventional PI controller. The design of the proposed system is implemented in the MATLAB/Simulink platform and performances were analyzed.

show abstract

“…Dissimilar, the conventional centralized generation, distributed generation (DG) aims a strategy in which a part of the electric power is produced and transmited to clients with small production units allocated near to the burdens [1][2][3][4]. The widespread use of DG not only increases economic efficiency, but also benefits improvements in energy reliability [5]. DG could be operated isolated mode (off grid) or grid connected (on-grid) [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Symmetrical-Asymmetrical Fault Characteristics Analysis within Cogeneration Power Plant in Izmir, Turkey: An Experimental Assessment

Oztura

Polat

2022

Gazi University Journal of Science

View full text Add to dashboard Cite

show abstract

Voltage Profile Enhancement and Loss Minimization Using Optimal Placement and Sizing of Distributed Generation in Reconfigured Network

Cited by 73 publications

References 53 publications

Optimal Allocation of Distributed Generation with Reconfiguration by Genetic Algorithm Using Both Newton Raphson and Gauss Seidel Methods for Power Losses Minimizing

Optimal Allocation of Distributed Generation with Reconfiguration by Genetic Algorithm Using Both Newton Raphson and Gauss Seidel Methods for Power Losses Minimizing

Power Quality Improvement Using Distributed Power Flow Controller with BWO-Based FOPID Controller

A Symmetrical-Asymmetrical Fault Characteristics Analysis within Cogeneration Power Plant in Izmir, Turkey: An Experimental Assessment

Contact Info

Product

Resources

About