scholarly journals Optimal Power Dispatch of Distributed Generators in Direct Current Networks Using a Master–Slave Methodology that Combines the Salp Swarm Algorithm and the Successive Approximation Method

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2837
Author(s):  
Andrés Alfonso Rosales Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Distributed Generation ◽  
Direct Current ◽  
Mathematical Formulation ◽  
Average Power ◽  
Power Losses ◽  
Salp Swarm Algorithm ◽  
Test Systems ◽  
Distributed Generators ◽  
Optimal Power ◽  
Solution Methodology

This paper addresses the Optimal Power Flow (OPF) problem in Direct Current (DC) networks by considering the integration of Distributed Generators (DGs). In order to model said problem, this study employs a mathematical formulation that has, as the objective function, the reduction in power losses associated with energy transport and that considers the set of constraints that compose DC networks in an environment of distributed generation. To solve this mathematical formulation, a master–slave methodology that combines the Salp Swarm Algorithm (SSA) and the Successive Approximations (SA) method was used here. The effectiveness, repeatability, and robustness of the proposed solution methodology was validated using two test systems (the 21- and 69-node systems), five other optimization methods reported in the specialized literature, and three different penetration levels of distributed generation: 20%, 40%, and 60% of the power provided by the slack node in the test systems in an environment with no DGs (base case). All simulations were executed 100 times for each solution methodology in the different test scenarios. The purpose of this was to evaluate the repeatability of the solutions provided by each technique by analyzing their minimum and average power losses and required processing times. The results show that the proposed solution methodology achieved the best trade-off between (minimum and average) power loss reduction and processing time for networks of any size.

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

scholarly journals Adaptive quantum-inspired evolutionary algorithm for optimizing power losses by dynamic load allocation on distributed generators

2019 ◽  
Vol 16 (3) ◽  
pp. 325-357
Author(s):  
Gopisetti Manikanta ◽  
Ashish Mani ◽  
Hemender Singh ◽  
Devendra Chaturvedi
Keyword(s):  
Evolutionary Algorithm ◽  
Dynamic Load ◽  
Time Horizon ◽  
Distribution Network ◽  
Average Power ◽  
Rank Test ◽  
Benchmark Problems ◽  
Power Losses ◽  
Distributed Generators ◽  
Bus System

In this paper, an Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA) has been applied for minimizing the power losses in the distribution network by suitable placement, sizing and subsequent allocation of load on Distributed Generators (DG) for a varying load with a time horizon of twentyfour hours. Many efforts have been reported in the literature to minimize power losses. However, they have mostly used a fixed load, i.e., nonvarying load, whereas it is well known that load in distribution network varies during the day. An investigation was undertaken to find the reduction in power losses on a timevarying load. It has been found that the average power losses for dynamic load allocation on DGs for every hour have a maximum reduction in power loss as compared with other well-known cases in the literature. Optimal location and size of DG is a difficult nonlinear, non-differentiable combinatorial optimization problem. AQiEA is used to find the appropriate location and capacity of DG for a varying load with a time horizon of twenty-four hours to minimize the power losses. AQiEA doesn?t require additional operators like local search and mutation to improve the convergence rate and avoid the premature convergence. A Quantum Rotation inspired Adaptive Crossover operator is used as a variation operator, which is parameter free. The effectiveness of AQiEA is demonstrated on two test bus systems viz., 33 bus system and 69 bus system, which are used as benchmark problems for validating the proposed methodology as well as for comparative testing amongst existing techniques. Wilcoxon signed rank test is also used to demonstrate the effectiveness of AQiEA. The experimental results show that AQiEA has better performance as compared to some existing ?state of art? techniques.

scholarly journals Optimal Location and Sizing of DGs in DC Networks Using a Hybrid Methodology Based on the PPBIL Algorithm and the VSA

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1913
Author(s):  
Luis Fernando Grisales-Noreña ◽  
Oscar Danilo Montoya ◽  
Ricardo Alberto Hincapié-Isaza ◽  
Mauricio Granada Echeverri ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Location Problem ◽  
Search Algorithm ◽  
Optimization Method ◽  
Population Based ◽  
Power Losses ◽  
Test Systems ◽  
Processing Times ◽  
Distributed Generators ◽  
Parallel Version

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

scholarly journals On the Lifetime Estimation of SiC Power MOSFETs for Motor Drive Applications

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 324
Author(s):  
Carmelo Barbagallo ◽  
Santi Agatino Rizzo ◽  
Giacomo Scelba ◽  
Giuseppe Scarcella ◽  
Mario Cacciato
Keyword(s):  
Electric Drive ◽  
Average Power ◽  
Estimation Procedure ◽  
General Purpose ◽  
Operating Conditions ◽  
Power Losses ◽  
Power Devices ◽  
Lifetime Estimation ◽  
Power Mosfets ◽  
Mission Profile

This work presents a step-by-step procedure to estimate the lifetime of discrete SiC power MOSFETs equipping three-phase inverters of electric drives. The stress of each power device when it is subjected to thermal jumps from a few degrees up to about 80 °C was analyzed, starting from the computation of the average power losses and the commitment of the electric drive. A customizable mission profile was considered where, by accounting the working conditions of the drive, the corresponding average power losses and junction temperatures of the SiC MOSFETs composing the inverter can be computed. The tool exploits the Coffin–Manson theory, rainflow counting, and Miner’s rule for the lifetime estimation of the semiconductor power devices. Different operating scenarios were investigated, underlying their impact on the lifetime of SiC MOSFETs devices. The lifetime estimation procedure was realized with the main goal of keeping limited computational efforts, while providing an effective evaluation of the thermal effects. The method enables us to set up any generic mission profile from the electric drive model. This gives us the possibility to compare several operating scenario of the drive and predict the worse operating conditions for power devices. Finally, although the lifetime estimation tool was applied to SiC power MOSFET devices for a general-purpose application, it can be extended to any type of power switch technology.

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