scholarly journals Fault Location and Restoration of Microgrids via Particle Swarm Optimization

2021 ◽  
Vol 11 (15) ◽  
pp. 7036
Author(s):  
Wei-Chen Lin ◽  
Wei-Tzer Huang ◽  
Kai-Chao Yao ◽  
Hong-Ting Chen ◽  
Chun-Chiang Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Fault Location ◽  
Optimal Algorithm ◽  
Flow Analysis ◽  
Particle Swarm ◽  
Test System ◽  
Swarm Optimization ◽  
Error Matrix ◽  
Location Algorithm

This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of a fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations was utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve fault location and restoration problems in MGs.

scholarly journals Particle Swam Optimization Based Fault Location and Restoration Approach in Microgrids

2019 ◽  
Author(s):  
Wei-Tzer Huang ◽  
Kai-Chao Yao ◽  
Feng-Ying Wang ◽  
Chun-Chiang Ma ◽  
Hong-Ting Chen ◽  
...  
Keyword(s):  
Power Flow ◽  
Fault Location ◽  
Optimal Algorithm ◽  
Flow Analysis ◽  
Test System ◽  
Error Matrix ◽  
Network Connection ◽  
Voltage Variation ◽  
Location Algorithm ◽  
Bus Voltage

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

scholarly journals Particle Swarm Optimization with Various Inertia Weight Variants for Optimal Power Flow Solution

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Prabha Umapathy ◽  
C. Venkataseshaiah ◽  
M. Senthil Arumugam
Keyword(s):  
Particle Swarm Optimization ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Optimal Power ◽  
The Impact

This paper proposes an efficient method to solve the optimal power flow problem in power systems using Particle Swarm Optimization (PSO). The objective of the proposed method is to find the steady-state operating point which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow, and voltage. Three different inertia weights, a constant inertia weight (CIW), a time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated for each of the method individually. It is observed that the PSO algorithm with the proposed inertia weight yields better results, both in terms of optimal solution and faster convergence. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The algorithm is computationally faster, in terms of the number of load flows executed, and provides better results than other heuristic techniques.

scholarly journals Using Particle Swarm Optimization, Genetic Algorithm, Honey Bee mating Optimization and Shuffle Frog Leaping Algorithm for solving OPF Problem with their Comparison

2015 ◽  
Vol 15 (3) ◽  
pp. 445
Author(s):  
Sajjad Ahmadnia ◽  
Ehsan Tafehi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Honey Bee ◽  
Power Flow ◽  
Particle Swarm ◽  
Test System ◽  
Mathematical Methods ◽  
Swarm Optimization ◽  
Shuffle Frog Leaping Algorithm ◽  
Honey Bee Mating Optimization

Today using evolutionary programing for solving complex, nonlinear mathematical problems like optimum power flow is commonly in use. These types of problems are naturally nonlinear and the conventional mathematical methods aren’t powerful enough for achieving the desirable results. In this study an Optimum Power Flow problem solved by means of minimization of fuel costs for IEEE 30 buses test system by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO) and Shuffle Frog Leaping Algorithm (SFLA), these algorithms has been used in MATLAB medium with help of MATHPOWER to achieving more precise results and comparing these results with the other proposed results in other published papers.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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