This paper proposes a Particle Swarm Optimization (PSO) based technique for the optimal allocation of Distributed Generation (DG) units in the power systems. In this paper our aim is to decide optimal number, type, size and location of DG units for voltage profile improvement and power loss reduction in distribution network. Two types of DGs are considered and the distribution load flow is used...

In this paper a novel method is presented for robot motion planning with respect to two objectives, the shortest and smoothest path criteria. A Particle Swarm Optimization (PSO) algorithm is employed for global path planning, while the Probabilistic Roadmap method (PRM) is used for obstacle avoidance (local planning). The two objective functions are incorporated in the PSO equations in which th...

To compensate the flaws of single neural network such as low classification precision and long training time as well as weak generalized ability etc, the paper proposes a novel ensemble neural classifier for stream turbine vibration fault diagnosis with improved PSO-based. The method fully utilizes the advantages of PSO, such as fast seeking speed and easily realizing mode etc, the integrated t...

We propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (PSO). In this way, the PSO update equation is treated as the system dynamic in the state space model, while the objective function in optimization problem is designed as the observation/measurement in the state space model. Part...

This study explores a novel application of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) heuristic methods in a hybrid construction on a 4 cylinder medium-duty diesel engine at part-load conditions. The application of the hybrid PSO-GA approach is compared with a basic PSO in the optimization of the control parameters of a diesel engine utilizing high EGR capability, modestly...

This thesis proposes a two-stage optimization for solving unit commitment. In the first stage, Lagrangian Relaxation (LR) method is used to solve unit commitment using a sub-gradient algorithm for updating Lagrange multipliers. In the second stage, Particle Swarm Optimization (PSO) is applied to update Lagrange multipliers in the Lagrangian Relaxation method (PSOLR). Lagrange multipliers soluti...

Original scientific paper In this study a multi-objective formulation for optimal sizing and finally optimal operation of wind farm in distribution systems for maximizing net present worth of system is analysed. The proposed system in this study consists of upstream network i.e. 63 kV / 20 kV substation as main grid and wind turbines as DG to supply load. In this study, total net present worth ...

This paper presents a new hybrid method for the short-term load forecasting in electric power systems based on particle swarm optimization (PSO) and relevance vector machine (RVM). In this method, we firstly develop a type of kernel as the kernel function of the RVM model, and then its parameter is optimized by the PSO, finally the established RVM forecast mode is applied to short-term load for...

Combining nonmonotone trust region algorithms and PSO methods, we propose a mixed method for nonlinear complementarity problems. The iterative formula of k μ is very simple. When the determinant of k k B I λ + is very large or small, the PSO method will obtain a new point which gets us better astringency. Numerical test results show the effectiveness of this algorithm.

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimoda...