Simultaneous stabilization of multimachine power systems via genetic algorithms (original) (raw)
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Genetic Algorithm Aided Design of Power System Stabilizers in a Multimachine Power Systemd
Proc. of the Ninth International Middle East Power Systems Conference, MEPCON'2003, 2003
This paper presents a Genetic Algorithm for Power System Stabilizers (GAPSS) design in a multimachine power system. The proposed GAPSS is to find the best location and optimal parameters of the power system stabilizers (PSS) in order to improve the dynamic and transient stability of a power system over a wide range of operating conditions. The objective function allows the selection of the stabilizer parameters to shift critical closed loop eigenvalues to the lefthand side the complex S plane. Simulation studies are performed to demonstrate the effectiveness of the GAPSS under different operating conditions and disturbances.
Optimal tuning of multi-machine Power System Stabilizer parameters using Genetic-Algorithm
2010 International Conference on Power System Technology, 2010
The problem of dynamic stability of power system has challenged power system engineering since over three decades now. In a generator, the electromechanical coupling between the rotor and the rest of the system causes it to behave in a manner similar to a spring mass damper system, which exhibits an oscillatory behavior around the equilibrium state, following any disturbance, such as change in loads, change in transmission line parameters, fluctuations in the output of turbine and fault etc. Optimal tuning of multi machine Power System Stabilizers (PSSs) using genetic algorithm is presented in this paper. Selecting the parameters of power system stabilizers which simultaneously stabilize system oscillations is converted to a simple optimization problem that is solved by a genetic algorithm. The advantage of GA technique for tuning the PSS parameters is that it is independent of the complexity of performance index considered. The efficiency of the proposed method has been tested on two cases of multi-machine systems include 3-machine 9 buses system and 10-machine 39 buses New England system. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and at the same time guarantees a robust acceptable performance over a wide range of operating and system condition.
Tuning of power system stabilizers using genetic algorithms
Electric Power Systems Research, 1996
Several techniques exist for developing optimal controllers. This paper investigates the tuning of power system stabilizers (PSS) using genetic algorithms (GA). A digital simulation of a linearized model of a single-machine infinite bus power system at some operating point is used in conjunction with the genetic algorithm optimization process. The integral of the square of the error and the time-multiplied absolute value of the error performance indices are considered in the search for the optimal PSS parameters. In order to have good damping characteristics over a wide range of operating conditions, the PSS parameters are optimized off-line for a selected set of grid points in the real power (P)-reactive power (Q) domain. The optimal settings thus obtained can then be stored and retrieved on-line to update the PSS parameters based on measurements of the generator real and reactive power. Time domain simulations of the system with GA-tuned PSS show the improved dynamic performance under widely varying load conditions.
Multimachine Power System Stabilizer Design Based on Evolutionary Algorithm
2009
This paper discusses the design of multimachine power system stabilizers based on three evolutionary algorithm techniques, namely: Genetic Algorithm (GA), Population Based Incremental Learning (PBIL) and the Breeder Genetic Algorithm (BGA) with adaptive mutation. The three PSSs are designed using eigenvalues analysis, whereby the lowest damped ratio is maximized. A comparison is done to determine which type of algorithm gives better results. Theoretically, the BGA and the PBIL based power system stabilizers perform better than the GA based power system stabilizer. The three PSSs (based on BGA, PBIL and GA) are tested against the Conventional Power System Stabilizer (CPSS), to verify their effectiveness. A four machine theoretical system is used in the simulations. Time domain simulations are presented to show that PBIL and BGA based PSSs perform better than the GA based PSS. However, the PSSs based on the evolutionary algorithms perform better than the CPSS.
A Genetic-Based Power System Stabilizer
Electric Machines & Power Systems, 1998
A genetic local search (GLS) algorithm for optimal design of multimachine power system stabilizers (PSSs) is presented in this paper. The proposed approach hybridizes the genetic algorithm (GA) with a heuristic local search in order to combine their strengths and overcome their shortcomings. The potential of the proposed approach for optimal parameter settings of the widely used conventional lead±lag PSSs has been investigated. Unlike the conventional optimization techniques, the proposed approach is robust to the initial guess. The performance of the proposed GLS-based PSS (GLSPSS) under different disturbances, loading conditions, and system con®gurations is investigated for different multimachine power systems. Eigenvalue analysis and simulation results show the effectiveness and robustness of the proposed GLSPSS to damp out local as well as interarea modes of oscillations and work effectively over a wide range of loading conditions and system con®gurations. q
A Novel Power System Stabilization Technique using Advanced Genetic Algorithm Optimization Approach
Bonfring
In order to deal with wide range of operating environment and disturbance, Power System Stabilizers (PSS) should be developed with appropriate stabilization signals. Recently, stabilizing control techniques for the multimachine power system with the help of intelligent methods have been developed. The main aim for the stability analysis of the power system is because of the importance of the power systems in the present world. Moreover, industries do not encourage the controller design if power system stability is not significant. In order to handle the above mentioned problems, intelligent approaches are used. The optimal sequential design for multi-machine power systems is very vital and many techniques are widely used to deal with control signals in power system. Most widely used optimization technique is Genetic Algorithm (GA). But, GA takes more time in optimization and lack in accuracy. To overcome the above mentioned issues, this paper uses Non-Dominated Ranked Genetic Algorithm (NRGA) for optimization. Simulation results suggest that the proposed stabilization approach is better when compared to the conventional techniques.
This paper presents a procedure to coordinated design of PSSs and SVCs in a multimachine power system. The aims of the proposed method are to find the best location and the optimal parameters of these compensators in order to improve the steady state and transient performances and also to increase the system damping over a wide range of operating conditions. The objective function of the GA allows the selection of the PSSs and SVCs to shift critical closed loop eigenvalues to the left-hand side in the complex s-plane. The multimachine power system considered in this study consists of nine buses, three generating units (steam, hydro and nuclear) and three static loads. Digital simulation studies show that the proposed design procedure provides good damping for the power system at different operating conditions, and moreover improves steady-state and transient performance of the system.
Optimization of Power System Stabilizer by Genetic Algorithm
IFAC Proceedings Volumes, 2005
Power system stabilizers (PSS) play an important role in damping of power system oscillations. An intensive research activity has been devoted to design of their structure and optimal setting of their parameters. In this paper the simultaneous optimization of multiloop PSS and automatic voltage regulator parameters (AVR) by means of genetic algorithm is proposed. Using an example of the 259 MVA turbogenerator excitation system in the nuclear power plant Mochovce (Slovak Republic) it is shown that the genetic algorithms are able to find the optimal parameters of excitation system so that the requirements on terminal voltage performances as well as on damping of active power oscillations are satisfied.
Power system stabilizer tuning in multi machine electric power systems
Power System Stabilizers (PSS) are used to generate supplementary damping control signals for the excitation system in order to damp the Low Frequency Oscillations (LFO) of the electric power system. The PSS is usually designed based on classical control approaches but this Conventional PSS (CPSS) has some problems. The CPSS is usually designed based on a linear model of the plant for a particular operating point. However, power systems are inherently nonlinear and the operating point frequently changes. Therefore, CPSS performance may deteriorate under variations that result from nonlinear and time-variant characteristics of the controlled plant. In this paper, to develop a highperformance PSS for a wide range of operating conditions, meta-heuristic optimization methods such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are used for tuning PSS parameters. The proposed optimization methods are evaluated against each other at a multi machine electric power system considering different loading conditions. The simulation results clearly indicate the effectiveness and validity of the proposed methods.
Enhancement of multi-machine power system stability by using Power System Stabilizer
International Journal of Advance Research, Ideas and Innovations in Technology, 2019
In this paper, we are going to deal about the enhancement of system stability, by using Eigen-values we can say how system stability is varying. We can use different devices like Facts controllers, Power system stabilizers, and automatic voltage regulators to improve system stability. Power system stabilizer had been used for improving two machine three bus system stability. We have different linear and nonlinear methods to design controller parameters for power system stabilizers. Genetic algorithm technique had been used to design PSS for two machine three bus system.