scholarly journals Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

2021 ◽  
Vol 13 (24) ◽  
pp. 13709
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Dhanasekar Ravikumar ◽  
Vijayaraja Loganathan ◽  
Mohammed H. Alsharif ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Objective Functions ◽  
Distributed Generations ◽  
Multi Objective ◽  
Metaheuristic Optimization

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals Developing a Hybrid Approach Based on Analytical and Metaheuristic Optimization Algorithms for the Optimization of Renewable DG Allocation Considering Various Types of Loads

2021 ◽  
Vol 13 (8) ◽  
pp. 4447
Author(s):  
Amal A. Mohamed ◽  
Salah Kamel ◽  
Ali Selim ◽  
Tahir Khurshaid ◽  
Sang-Bong Rhee
Keyword(s):  
Optimal Allocation ◽  
Optimization Technique ◽  
Hybrid Approach ◽  
Optimization Techniques ◽  
Constant Current ◽  
Hybrid Technique ◽  
Voltage Profile ◽  
Analytical Technique ◽  
Metaheuristic Optimization ◽  
Different Types

The optimal location of renewable distributed generations (DGs) into a radial distribution system (RDS) has attracted major concerns from power system researchers in the present years. The main target of DG integration is to improve the overall system performance by minimizing power losses and improving the voltage profile. Hence, this paper proposed a hybrid approach between an analytical and metaheuristic optimization technique for the optimal allocation of DG in RDS, considering different types of load. A simple analytical technique was developed in order to determine the sizes of different and multiple DGs, and a new efficient metaheuristic technique known as the Salp Swarm Algorithm (SSA) was suggested in order to choose the best buses in the system, proportionate to the sizes determined by the analytical technique, in order to obtain the minimum losses and the best voltage profile. To verify the power of the proposed hybrid technique on the incorporation of the DGs in RDS, it was applied to different types of static loads; constant power (CP), constant impedance (CZ), and constant current (CI). The performance of the proposed algorithm was validated using two standards RDSs—IEEE 33-bus and IEEE 69-bus systems—and was compared with other optimization techniques.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

A Strategy for Multi-Point Compressor Blading Using Multi-Objective Optimization

2012 ◽  
Author(s):  
Alireza Fathi ◽  
Abdollah Shadaram ◽  
Mohammad Alizadeh
Keyword(s):  
Incidence Angle ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Design System ◽  
Design Calculation ◽  
Blade Design ◽  
Objective Functions ◽  
Multi Objective ◽  
First Case ◽  
Through Flow

This paper introduces a framework to perform a multi-objective multipoint aerodynamic optimization for an axial compressor blade. This framework considers through-flow design requirements and mechanical and manufacturing constraints. Typically, components of a blade design system include geometry generation tools, optimization algorithms, flow solvers, and objective functions. In particular, optimization algorithms and objective functions are tuned to reduce blade design calculation cost and to match designed blade performance to the through flow design criteria and mechanical and manufacturing constrains. In the present study, geometry parameters of blade are classified to three categories. For each category, a distinct optimization loop is applied. In outer loop, Gradient-based optimization techniques are used to optimize parameters of the second category and a two-dimensional compressible viscous flow code is used to simulate the cascade fluid flow. Surface curvature optimization is carried out in inner loop, and its objective function is defined by integrating the normalized curvature and curvature slope. The genetic algorithm is used to optimize the parameters in the interior loop. To highlight the capabilities of the design method and to develop design know-how, an initial profile is optimized with three different design philosophies. The highest performance improvement in the first case is 15% reduction in loss at design incidence angle. In the second case, 16.5% increase in allowable incidence angle range, improves blade’s performance at off design conditions.

Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

2012 ◽  
pp. 179-200 ◽  
Author(s):  
Cristiane G. Taroco ◽  
Eduardo G. Carrano ◽  
Oriane M. Neto
Keyword(s):  
Power Distribution ◽  
Distribution Systems ◽  
Search Algorithm ◽  
Efficient Solutions ◽  
Optimization Techniques ◽  
Nsga Ii ◽  
Robust Solutions ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Reliability And Robustness

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

scholarly journals Evaluation of Hunting-Based Optimizers for a Quadrotor Sliding Mode Flight Controller

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 22
Author(s):  
Josenalde Oliveira ◽  
Paulo Moura Oliveira ◽  
José Boaventura-Cunha ◽  
Tatiana Pinho
Keyword(s):  
Control Systems ◽  
Sliding Mode ◽  
Tracking Error ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Linear Control Systems ◽  
Classical Particle ◽  
Grey Wolf Optimizer ◽  
Local Optima ◽  
Chattering Effect

The design of Multi-Input Multi-Output nonlinear control systems for a quadrotor can be a difficult task. Nature inspired optimization techniques can greatly improve the design of non-linear control systems. Two recently proposed hunting-based swarm intelligence inspired techniques are the Grey Wolf Optimizer (GWO) and the Ant Lion Optimizer (ALO). This paper proposes the use of both GWO and ALO techniques to design a Sliding Mode Control (SMC) flight system for tracking improvement of altitude and attitude in a quadrotor dynamic model. SMC is a nonlinear technique which requires that its strictly coupled parameters related to continuous and discontinuous components be correctly adjusted for proper operation. This requires minimizing the tracking error while keeping the chattering effect and control signal magnitude within suitable limits. The performance achieved with both GWO and ALO, considering realistic disturbed flight scenarios are presented and compared to the classical Particle Swarm Optimization (PSO) algorithm. Simulated results are presented showing that GWO and ALO outperformed PSO in terms of precise tracking, for ideal and disturbed conditions. It is shown that the higher stochastic nature of these hunting-based algorithms provided more confidence in local optima avoidance, suggesting feasibility of getting a more precise tracking for practical use.

scholarly journals Multi-objective Informative Frequency Band Selection Based on Negentropy-induced Grey Wolf Optimizer for Fault Diagnosis of Rolling Element Bearings

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1845 ◽  
Author(s):  
Xiaohui Gu ◽  
Shaopu Yang ◽  
Yongqiang Liu ◽  
Rujiang Hao ◽  
Zechao Liu
Keyword(s):  
Frequency Band ◽  
Background Noise ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Rolling Element Bearings ◽  
Grey Wolf ◽  
Objective Functions ◽  
Multi Objective ◽  
Rolling Element ◽  
Envelope Spectrum

Informative frequency band (IFB) selection is a challenging task in envelope analysis for the localized fault detection of rolling element bearings. In previous studies, it was often conducted with a single indicator, such as kurtosis, etc., to guide the automatic selection. However, in some cases, it is difficult for that to fully depict and balance the fault characters from impulsiveness and cyclostationarity of the repetitive transients. To solve this problem, a novel negentropy-induced multi-objective optimized wavelet filter is proposed in this paper. The wavelet parameters are determined by a grey wolf optimizer with two independent objective functions i.e., maximizing the negentropy of squared envelope and squared envelope spectrum to capture impulsiveness and cyclostationarity, respectively. Subsequently, the average negentropy is utilized in identifying the IFB from the obtained Pareto set, which are non-dominated by other solutions to balance the impulsive and cyclostationary features and eliminate the background noise. Two cases of real vibration signals with slight bearing faults are applied in order to evaluate the performance of the proposed methodology, and the results demonstrate its effectiveness over some fast and optimal filtering methods. In addition, its stability in tracking the IFB is also tested by a case of condition monitoring data sets.

Optimal Channel Assignment Algorithm for Least Interfered Wireless Mesh Networks

2014 ◽  
Vol 6 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Tarik Mountassir ◽  
Bouchaib Nassereddine ◽  
Abdelkrim Haqiq ◽  
Samir Bennani
Keyword(s):  
Wireless Mesh Networks ◽  
Channel Assignment ◽  
Mesh Networks ◽  
Optimization Technique ◽  
Objective Functions ◽  
Multi Objective ◽  
Interference Level ◽  
Wireless Mesh ◽  
Assignment Algorithm ◽  
Particle Swarm Optimization Technique

Unlike most of proposed solutions that usually consider the overall throughput as the main optimization, Channel Assignment in Wireless Mesh Networks has to ensure connectivity, minimize interference level and guarantee an acceptable throughput. This problem must be solved taking into account all the parameters that influence the output of the proposed algorithm. In this paper, the authors propose an efficient multi-objective optimization model that, simultaneously, optimizes two conflicting objective functions in order to assign channel to radio interfaces subject to connectivity, interference and bandwidth requirements. Then they use the Multi-Objective Particle Swarm Optimization Technique to resolve this problem and provide a non-dominated set of near optimal solutions.

scholarly journals A BBO/PSO based Hybrid Technique for Distribution System Feeder Reconfiguration

2019 ◽  
Vol 9 (2) ◽  
pp. 1156-1159
Keyword(s):  
Radial Distribution ◽  
Distribution System ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Cooperative Strategy ◽  
Optimal Position ◽  
Radial Distribution System ◽  
Optimum Position ◽  
New Feature

A cooperative strategy to reconfigure the feeder network by maximizing the location and volume of the distribution generator (DG) in the power system was addressed in this report. The new feature of the proposed method is the integrated output of the Biography Based Optimization (BBO) and PSO techniques. The above methods are the optimization techniques used to configure the radial distribution system for the optimal position and capacities of the DG. For determining the optimum position and strength of the DG, the BBO algorithm includes radial distribution network voltage, actual and reactive energy. The input parameters of BBO are classified into sub settings here and are allowed as the optimization of the PSO algorithm. The PSO synthesizes the problem and uses sub-parameters to create the sub-solution. The method of BBO migration and mutation is used to determine the optimal position and ability of DG for the sub solution of PSO. The cooperative strategy introduced is then applied on the system MATLAB / Simulink, and the usefulness is evaluated using BBO and PSO techniques. The findings of the analysis demonstrate the strength of the solution suggested and affirm its capacity for resolving the problem.

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