Optimal Parameters of Static Synchronous Series Compensator (SSSC) Connected to a Power System

The objective of this project is to develop a Static Synchronous Series Compensator (SSSC) controller with the purpose to control the power flow in the transmission lines. The optimal parameters of this controller are sized using an optimization technique so that the transmission line losses can be minimized. SSSC is a part of Flexible AC Transmission System (FACTS) technology that has the ability to control the interrelated parameters that govern the operation of transmission systems. The optimization of the parameters of SSSC is formed as an optimization problem with the objective of minimizing the transmission loss in the power system network. Particle Swarm Optimization (PSO) technique is used to solve the problem and the Newton-Raphson method for power flow is modified to consider the insertion of SSSC in the network. The proposed method is applied using MATLAB and tested on IEEE 14-bus system to observe the voltage profile and the transmission loss of the power system network. This report also covers basic principles and operation of SSSC, the power flow model and PSO technique. The result and outcome of the project are included as well as the recommendation for future work

Complete Identification of a Dynamic Fractional Order System Under Non-ideal Conditions Using Fractional Differintegral Definitions

This contribution deals with identification of fractional-order dynamical systems. System identification, which refers to estimation of process parameters, is a necessity in control theory. Real processes are usually of fractional order as opposed to the ideal integral order models. A simple and elegant scheme of estimating the parameters for such a fractional order process is proposed. This method employs fractional calculus theory to find equations relating the parameters that are to be estimated, and then estimates the process parameters after solving the simultaneous equations. The data used for the calculations are intentionally corrupted to simulate real-life conditions. Results show that the proposed scheme offers a very high degree of accuracy even for erroneous data.Comment: 16th IEEE International Conference on Advanced Computing and Communication, 200

Swarm Intelligence and Evolutionary Approaches for Reactive Power and Voltage Control

This paper presents a comparison of swarm intelligence and evolutionary techniques based approaches for minimization of system losses and improvement of voltage profiles in a power network. Efficient distribution of reactive power in an electric network can be achieved by adjusting the excitation on generators, the on-load tap changer positions of transformers, and proper switching of discrete portions of inductors or capacitors. This is a mixed integer non-linear optimization problem where metaheuristics techniques have proven suitable for providing optimal solutions. Four algorithms explored in this paper include differential evolution (DE), particle swarm optimization (PSO), a hybrid combination of DE and PSO, and a mutated PSO (MPSO) algorithm. The effectiveness of these algorithms is evaluated based on their solution quality and convergence characteristic. Simulation studies on the Nigerian power system show that a PSO based solution is more effective than a DE approach in reducing real power losses while keeping the voltage profiles within acceptable limits. The results also show that MPSO allows for further reduction of the real power losses while maintaining a satisfactory voltage profile

Reduction method with system analysis for multiobjective optimization-based design

An approach for reducing the number of variables and constraints, which is combined with System Analysis Equations (SAE), for multiobjective optimization-based design is presented. In order to develop a simplified analysis model, the SAE is computed outside an optimization loop and then approximated for use by an operator. Two examples are presented to demonstrate the approach

ZASTOSOWANIE ALGORTYMU SELEKCJI KLONALNEJ DO SYNTEZY REGULATORA PID SYSTEMÓW MIMO W PRZEMYŚLE PETROCHEMICZNYM

This paper presents the results of the Smart technologies application to the synthesis of MIMO-systems in oil and gas industry. In particular, there is considered a multidimensional multiply connected system for gas distillation process control through a distillation column with regulators configured on the basis of Smart-technologies – clonal selection algorithm (CLONALG) of an artificial immune system (AIS).W artykule przedstawiono wyniki zastosowania inteligentnych technologii do syntezy systemów MIMO w przemyśle petrochemicznym. W szczególności rozważany jest wielowymiarowy układ sterowania procesem destylacji gazu w kolumnie destylacyjnej z regulatorami skonfigurowanymi na podstawie tzw. inteligentnych technologii – algorytmu doboru klonów (CLONALG) sztucznego układu immunolgicznego (AIS)

Characterization of vectorization strategies for recursive algorithms

A successful architectural trend in parallelism is the emphasis on data parallelism with SIMD hardware. Since SIMD extensions on commodity processors tend to require relatively little extra hardware, executing a SIMD instruction is essentially free from a power perspective, making vector computation an attractive target for parallelism. SIMD instructions are designed to accelerate the performance of applications such as motion video, real-time physics and graphics. Such applications perform repetitive operations on large arrays of numbers. While the key idea is to parallelize significant portions of data that get operated by several sequential instructions into a single instruction, not every application can be parallelized automatically. Regular applications with dense matrices and arrays are easier to vectorize compared to irregular applications that involve pointer based data structures like trees and graphs. Programmers are burdened with the arduous task of manually tuning such applications for better performance. One such class of applications are recursive programs. While they are not traditional serial instruction sequences, they follow a serialized pattern in their control flow graph and exhibit dependencies. They can be visualized to be directed trees data structures. Vectorizing recursive applications with SIMD hardware cannot be achieved by using the existing intrinsic directly because of the nature of these algorithms. In this dissertation, we argue that, for an important subset of recursive programs which arise in many domains, there exists general techniques to efficiently vectorize the program to operate on SIMD architecture. Recursive algorithms are very popular in graph problems, tree traversal algorithms, gaming applications et al. While multi-core and GPU implementation of such algorithms have been explored, methods to execute them efficiently on vector units like SIMD and AVX have not been explored. We investigate techniques for work generation and efficient vectorization to enable vectorization in recursion. We further implement a generic tree model that allows us to guarantee lower bounds on its utilization efficiency