Multi area economic dispatch using particle swarm optimization

Multi-area Economic Dispatch (MAED) is an important issue in power system operation and generation which the main aim is to achieve minimal cost. In previous paper, the consideration is only on single area economic dispatch. However, this cannot represent power generation as an overall on transmission network. Particle Swarm Optimization (PSO) is used to find optimum cost of generation by considering the constraints such as tie-line limit, area power balance and transmission line losses. In this paper, the algorithm with respect to predicted load demand is tested on a two area network with three set of test data consists of 4 Units, 6 Units and 40 Units system. The proposed methodology to solve MAED problem begins with finding range of area power demands for each area by incorporating the tie line limits. Area with cheaper fuel cost will be selected to export power to area with high demand. In order to design this algorithm, the assumption are no losses in tie-line and fix amount of power flow through the tie-line. Comparison were performed with respect to Genetic Algorithm (GA) and PSO for solving the MAED problem in practical power system. PSO has shown a better result than GA for all the three case studies

Clustering as an example of optimizing arbitrarily chosen objective functions

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison

PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding

Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization

A high fidelity simulation using a PC based Trick framework has been developed for Johnson Space Center's Morpheus test bed flight vehicle. There is an iterative development loop of refining and testing the hardware, refining the software, comparing the software simulation to hardware performance and adjusting either or both the hardware and the simulation to extract the best performance from the hardware as well as the most realistic representation of the hardware from the software. A Particle Swarm Optimization (PSO) based technique has been developed that increases speed and accuracy of the iterative development cycle. Parameters in software can be automatically tuned to make the simulation match real world subsystem data from test flights. Special considerations for scale, linearity, discontinuities, can be all but ignored with this technique, allowing fast turnaround both for simulation tune up to match hardware changes as well as during the test and validation phase to help identify hardware issues. Software models with insufficient control authority to match hardware test data can be immediately identified and using this technique requires very little to no specialized knowledge of optimization, freeing model developers to concentrate on spacecraft engineering. Integration of the PSO into the Morpheus development cycle will be discussed as well as a case study highlighting the tool's effectiveness

CFD Based Qualification of Mixing Efficiency of Stirred Vessels

In this work, we focus on the most crucial units in a chemical technology, the chemical reactors. Using a commercially available CFD software package, COMSOL Multiphysics, 3D mathematical models of a batch reactor with different impeller geometries have been investigated. The reasonable agreement between the experimental and simulation results indicates the validity of the developed CFD model. The effect of the impeller design, e. g. number of blades on the mixing efficiency is evaluated based on the simulation studies. The proposed measure to determine the energy efficiency of mixing (i. e. mixing index) is based on the calculated velocity field and energy usage. The information about the homogeneity of the mixed phase in the system can be extracted from the developed velocity field. Hence, we proposed histograms of velocity fluctuations on a logarithmic scale as an efficient tool to measure the achieved homogeneity of the phase in case of different impellers and rotational speeds

A PARAMETER IDENTIFICATION APPROACH OF A PEM FUEL CELL STACK USING PARTICLE SWARM OPTIMIZATION

ABSTRACT The fear of fossil fuels depletion as well as the constantly increasing pollution rates motivated most of today's engineers and researchers towards focusing on renewable energies and their applications. Fuel Cells are one of the green technologies that are being explored extensively around the world. The work of this paper was done on the 3kW ElectraGen TM fuel cell system under study for domestic use in the United Arab Emirates (UAE). Several experiments were conducted at different operating points and relatively high ambient temperatures. The experimental I/V characteristics of the system are matched by identifying 13 different modeling parameters using basic fitting. The obtained model is then further optimized using Particle Swarm Optimization (PSO). The resulting model is validated experimentally and was found to highly resemble the system's I/V characteristics yielding less than 1.5 V H ∞ norm of the error