Optimizing the Operation of Distributed Generation Using Genetic Algorithms

2013 ◽  
Vol 732-733 ◽  
pp. 691-696
Author(s):  
Jun Du ◽  
Dong Yan Zhao ◽  
Yu Bo Wang
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Distributed Generation ◽  
Profit Maximization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Matrix Operations ◽  
Time Period ◽  
Loop Matrix

By the heuristic algorithm and particle swarm optimization algorithm combining hybrid particle swarm algorithm proposed combination of heuristic search and stochastic optimization,stochastic optimization process using a spanning tree and the loop matrix operations combined to ensure the system topology constraints to improve the efficiency of solution. The analysis shows that the proposed method calculation speed,easy to converge to the global optimal solution. Development of distributed generation units, along with concerns raised over the security of supply has prompted many customers to consider the installation of their own local capacity for generating electricity (and heat). This paper proposes a methodology for optimizing the operation of a portfolio of distributed units, based on profit maximization using genetic algorithms. The method is tested on a set of distributed units, demonstrating the ability to find good solutions in an acceptable time period.

Distribution System Restoration Based on Hybrid Particle Swarm

2013 ◽  
Vol 732-733 ◽  
pp. 662-668
Author(s):  
Li Zhi Tang ◽  
Jun Du ◽  
Kun Peng Xu ◽  
Xue Qing Qi
Keyword(s):  
Stochastic Optimization ◽  
Distribution System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Particle ◽  
Matrix Operations ◽  
Global Optimal ◽  
Loop Matrix ◽  
Network Fault

By the heuristic algorithm and particle swarm optimization algorithm combining hybrid particle swarm algorithm proposed combination of heuristic search and stochastic optimization,stochastic optimization process using a spanning tree and the loop matrix operations combined to ensure the system topology constraints to improve the efficiency of solution. The analysis shows that the proposed method calculation speed,easy to converge to the global optimal solution. It can effectively solve the problem of distribution network fault recovery.

scholarly journals An Enhanced Quantum-Behaved Particle Swarm Algorithm for Reactive Power Optimization considering Distributed Generation Penetration

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Runhai Jiao ◽  
Bo Li ◽  
Yuancheng Li ◽  
Lingzhi Zhu
Keyword(s):  
Distributed Generation ◽  
Power Loss ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Active Power ◽  
Particle Swarm Algorithm ◽  
Reactive Power Optimization ◽  
Distributed Generations ◽  
Better Than

This paper puts forward a novel particle swarm optimization algorithm with quantum behavior (QPSO) to solve reactive power optimization in power system with distributed generation. Moreover, differential evolution (DE) operators are applied to enhance the algorithm (DQPSO). This paper focuses on the minimization of active power loss, respectively, and uses QPSO and DQPSO to determine terminal voltage of generators, and ratio of transformers, switching group number of capacitors to achieve optimal reactive power flow. The proposed algorithms are validated through three IEEE standard examples. Comparing the results obtained from QPSO and DQPSO with those obtained from PSO, we find that our algorithms are more likely to get the global optimal solution and have a better convergence. What is more, DQPSO is better than QPSO. Furthermore, with the integration of distributed generation, active power loss has decreased significantly. Specifically, PV distributed generations can suppress voltage fluctuation better than PQ distributed generations.

Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization

2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimal Location ◽  
Particle Swarm Algorithm ◽  
Power Losses ◽  
Negative Effects ◽  
Swarm Optimization ◽  
Distributed Generations

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

Diagnosis for Vibration Fault of Steam Turbine Based on Modified Particle Swarm Optimization Support Vector Machine

2011 ◽  
Vol 128-129 ◽  
pp. 113-116 ◽  
Author(s):  
Zhi Biao Shi ◽  
Quan Gang Song ◽  
Ming Zhao Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

Due to the influence of artificial factor and slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a modified particle swarm optimization support vector machine (MPSO-SVM). A Steam turbine vibration fault diagnosis model was established and the failure data was used in fault diagnosis. The results of application show the model can get automatic optimization about the related parameters of support vector machine and achieve the ideal optimal solution globally. MPSO-SVM strategy is feasible and effective compared with traditional particle swarm optimization support vector machine (PSO-SVM) and genetic algorithm support vector machine (GA-SVM).

The Research of Particle Swarm Algorithm Based on Heuristic Rules for the Layout of Airplane Equipment Cabin

2013 ◽  
Vol 300-301 ◽  
pp. 659-663
Author(s):  
Xiao Jian Han ◽  
Xiang Fang Ding ◽  
Chun Xiao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Three Dimensions ◽  
Small Scale ◽  
Particle Swarm Algorithm ◽  
Heuristic Rules ◽  
Layout Problem ◽  
Swarm Optimization ◽  
Np Problem

How to get the most optimal solution of equipment layout in the aircraft cabin of the limited space is a completely NP problem. The problem is abstracted as three dimensions （3D） layout problem. A co-evolutionary particle swarm optimization with heuristic rules is presented. The cabin is decomposed into several small-scale layout problems. The co-evolutionary framework is adopted, and particle swarm optimization (PSO) and heuristic roles for layout are integrated to solve this problem. Finally, an example is used to verify the feasibility and effectiveness of the algorithm.

The Application of Quantum-Behaved Particle Swarm Algorithm in Intelligent Test Paper Generation

2013 ◽  
Vol 694-697 ◽  
pp. 2378-2382 ◽  
Author(s):  
Xin Ran Li
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Aggregation ◽  
Particle Swarm Algorithm ◽  
Test Paper ◽  
Inertia Weight ◽  
Local Optimal Solution ◽  
Swarm Algorithm ◽  
Low Efficiency

Aiming at solving the low efficiency and low quality of the existing test paper generation algorithm, this paper proposes an improved particle swarm algorithm, a new algorithm for intelligent test paper generation. Firstly, the paper conducts mathematically modeling based on item response theory. Secondly, in the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Finally, simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

Optimization of Neural Tree Based on an Improved Quantum Particle Swarm Optimization

2013 ◽  
Vol 475-476 ◽  
pp. 956-959 ◽  
Author(s):  
Hao Teng ◽  
Shu Hui Liu ◽  
Yue Hui Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Local Optimal Solution

In the model of flexible neural tree (FNT), parameters are usually optimized by particle swarm optimization algorithm (PSO). Because PSO has many shortcomings such as being easily trapped in local optimal solution and so on, an improved algorithm based on quantum-behaved particle swarm optimization (QPSO) is presented. It is combined with the factor of speed, gather and disturbance, so as to be used to optimize the parameters of FNT. This paper applies the improved quantum particle swarm optimization algorithm to the neural tree, and compares it with the standard particle swarm algorithm in the optimization of FNT. The result shows that the proposed algorithm is with a better expression, thus improves the performance of the FNT.

scholarly journals Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 262
Author(s):  
Tianhua Zheng ◽  
Jiabin Wang ◽  
Yuxiang Cai
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Swarm Algorithm ◽  
Hybrid Particle ◽  
Algorithm Efficiency ◽  
Parallel Hybrid ◽  
Swarm Algorithm ◽  
Mass Assembly ◽  
Scheduling Plan ◽  
Algorithm Parallelization

In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency.

Multivariant Optimization Algorithm for the 0-1 Knapsack Problem

2014 ◽  
Vol 556-562 ◽  
pp. 3514-3518
Author(s):  
Lan Juan Liu ◽  
Bao Lei Li ◽  
Qin Hu Zhang ◽  
Dan Jv Lv ◽  
Xin Ling Shi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Heuristic Algorithm ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Global Search ◽  
Particle Swarm Algorithm ◽  
Data Sets ◽  
Better Than

In this paper, a novel heuristic algorithm named Multivariant Optimization Algorithm (MOA) is presented to solve the 0-1 Knapsack Problem (KP). In MOA, multivariant search groups (locate and global search groups) execute the global exploration and local exploitation iteratively to locate the optimal solution automatically. The presented algorithm has been compared with Genetic Algorithm (GA) and Particle swarm algorithm (PSO) based on five data sets, results show that the optimization of MOA is better than GA and PSO when the dimension of problem is high.

Sign in / Sign up

Export Citation Format

Share Document