Transient stability improvement with unified power flow controller using fuzzy logic and Anfis approach

Ace p.

Order the writing of a tailor-made Computer science Term papers

Free quote online

Thesis Format .pdf

Transient stability improvement with unified power flow controller using fuzzy logic and Anfis approach

Download

Read an extract

Reader
Abstract
Contents
Extract

Abstract

Transient stability is an important aspect in planning and operation of an electric power system. The application of Fuzzy logic and Adaptive Neuro Fuzzy Inference System (ANFIS) approach to power system problems has received increased attention in recent years. This paper investigates the fuzzy logic based Unified Power Flow Control (UPFC) methods namely in-phase voltage control and quadrature voltage control in improving transient stability of power system using energy function method. The potential of the two control methods in transient stability enhancement is identified in Single Machine Infinite Bus System (SMIB) system. UPFC is connected in SMIB system in middle of the transmission line. Results indicate that significant reductions in the transient swings are obtained with UPFC. The result shows that the transient stability margin is substantially improved with in-phase voltage control higher than the quadrature voltage contro1.

Abstract
Introduction
Transient swing analysis
Stability margin
Fuzzy logic based transient stability assessment
1. Fuzzy variables
2. Fuzzy sets associated with the transient stability problem
3. Membership functions
4. In SMIB system without UPFC connection
5. Comparison of results obtained from conventional method with fuzzy logic results
ANFIS based transient stability assessment
1. ANFIS variables
2. MATLAB /ANFIS functions used for transient stability assessment
Results and discussions
Conclusions
References

Get this table of contents for free after login.

Extract

[...] The Fuzzy Logic Approach (FLA) is employed in transient stability problem because it usefulness in reducing the need for complex mathematical models in problem solving and take in to account qualitative information about transient stability that remains unutilized by conventional approaches. The ANFIS is employed in transient stability problem because it toolbox functions constructs a fuzzy inference system (FIS) whose membership function parameters are tuned (adjusted) using either a backpropagation algorithm alone or in combination with a least squares type of method. [...]

[...] To improve the stability margin the controller must be capable of either increasing the critical energy or reducing the acquired transient energy p te tia e eg o n l nr y - 0.5 - 1.5 - rotor angle uncontrolled case vpq variation rho variation Fig 3.1 Potential energy profiles of SMIB system with different UPFC control methods For the selected SMIB system with and without UPFC, transient energy, critical energy and Stability margin are calculated and tabulated in Table 1-3. [...]

[...] The purpose of this paper is to apply Fuzzy logic and ANFIS in the improvement of transient stability with and without UPFC in SMIB system. Fig Implementation of UPFC Transient swing analysis SMIB system used for the analysis is shown in Fig The system data are given in the Appendix. Fig SMIB circuit model. The UPFC is connected at the middle of the transmission line in SMIB system. Swing equation for single-machine infinite bus system (SMIB) is as follows M d 2δ = Pa = Pi Pe dt 2 Where, Pe=ωTa = V2 sin δ =electrical output. [...]

[...] Table 1 shows the Stability Margin of SMIB system without UPFC connection. Stability margin is improved by UPFC connection with the SMIB system Table 2 shows that critical energy can be raised markedly resulting in a significant improvement in the stability margin compared with that of the uncontrolled case. Table3 shows that critical energy is raised to a slightly smaller extent compared with that of in phase voltage control. The improvement in the stability margin is slightly less. Fuzzy Logic and ANFIS Approach has been applied for calculating stability margin [...]

[...] The fuzzy variables associated with the transient stability problem in the SMIB system with UPFC control methods namely in-phase voltage control are: Change in delta, Delta, Vpq, Margin of stability, System stability In SMIB system with UPFC control methods namely in-phase voltage control has only one additional inputs is Vpq Comparing to the SMIB system without UPFC. Vpq is chosen as the fuzzy input variable because its value is increased means simultaneously stability margin is increased. The fuzzy variables associated with the transient stability problem in the SMIB system with UPFC control methods namely Quadrature voltage control are: Change in delta, Delta, Rho Margin of stability, System stability In SMIB system with UPFC control methods namely in-phase voltage control has only one additional inputs is Rho(ρ) Comparing to the SMIB system without UPFC. [...]

pdf