Conference Paper

A Genetic Algorithm Solution to the Governor-Turbine Dynamic Model Identification in Multi-Machine Power Systems

Student Member, IEEE, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: )
DOI: 10.1109/CDC.2005.1582336 Conference: Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on
Source: IEEE Xplore


Speed governors are key elements in the dynamic performance of electric power systems. Therefore, accurate governor models are of great importance in simulating and investigating the power system transient phenomena. Model parameters of such devices are, however, usually unavailable or inaccurate, especially when old generators are involved. Most methods for speed governor parameter estimation are based on measurements of frequency and active power variations during transient operation. This paper proposes a genetic algorithm based optimization technique for parameter estimation, which makes use of such measurements. The proposed methodology uses a real-coded genetic algorithm. The paper estimates the parameters of all system generators simultaneously, instead of every machine independently, which is fully in line with the interest to treat the electric power system as a whole and study its comprehensive behaviour. Moreover, the methodology is not model-dependent and, therefore, it is readily applicable to a variety of model types and for many different test procedures. The proposed methodology is applied to the electric power system of Crete and the results demonstrate the feasibility and practicality of this approach.

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Available from: Nikos D. Hatziargyriou, Oct 07, 2015
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    • "Studies on parameter identification methods of steam turbine governor for different simulation cycles can be found in [6] [7] [8] [9] [10] [11]. The fixed oil motive opening and closing time constants are adopted in the existing literatures and power system simulation software settings [9] [10] [11] [12], but they are difficult to be applied to the nonlinear characteristics of some thermal power plant actuator valve's fully open or close test. As for the nonlinear valve of hydroturbine, certain existing literature uses several sets of proportional integral derivative (PID) parameters to meet the different operating conditions [13]. "
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    ABSTRACT: The governor actuators in some heat-engine plants have nonlinear valves. This nonlinearity of valves may lead to the inaccuracy of the opening and closing time constants calculated based on the whole segment fully open and fully close experimental test curves of the valve. An improved mathematical model of the turbine governor actuator is proposed to reflect the nonlinearity of the valve, in which the main and auxiliary piecewise opening and closing time constants instead of the fixed oil motive opening and closing time constants are adopted to describe the characteristics of the actuator. The main opening and closing time constants are obtained from the linear segments of the whole fully open and close curves. The parameters of proportional integral derivative (PID) controller are identified based on the small disturbance experimental tests of the valve. Then the auxiliary opening and closing time constants and the piecewise opening and closing valve points are determined by the fully open/close experimental tests. Several testing functions are selected to compare genetic algorithm and particle swarm optimization algorithm (GA-PSO) with other basic intelligence algorithms. The effectiveness of the piecewise linear model and its parameters are validated by practical power plant case studies.
    Journal of Applied Mathematics 01/2015; 2015:1-9. DOI:10.1155/2015/709272 · 0.72 Impact Factor
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    • "In real representation, since parameters do not change during crossover, but are just recombined differently (except for the arithmetical crossover), the only way of affecting their values is by the mutation operator. Moreover, the mutation probabilities used are greater than the ones in binary representation and may reach up to 5% [7] "
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    ABSTRACT: This paper suggests the techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase induction machine using genetic algorithm. The parameter estimation procedure is based on the steady-state phase current versus slip and input power versus slip characteristics. The propose estimation algorithm is of non-linear kind based on selection in genetic algorithm. The machine parameters are obtained as the solution of a minimization of objective function by genetic algorithm. Simulation shows good performance of the propose procedures.
    Journal of Electrical Engineering and Technology 01/2009; 4(3). DOI:10.5370/JEET.2009.4.3.360 · 0.53 Impact Factor
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    ABSTRACT: The system frequency of a synchronous power system varies with the imbalance of energy supplied and the electrical energy consumed. When large generating blocks are lost, the system undergoes a frequency swing relative to the size of the loss. Limits imposed on the magnitude of frequency deviation† prevent system collapse. Operation of frequency responsive plant to control frequency, results in lower machine efficiencies. Changes to the generation mix on the British transmission system have occurred in the past ten years, when the response requirement was last reviewed. Future increased levels of wind turbines‡ will alter the operational characteristics of the system and warrant investigation. A process to optimise the response requirements while maintaining statutory limits on frequency deviation has been identified. The method requires suitable load and generator models to replicate transmission system performance. A value to substitute for current load sensitivity to frequency has been presented from empirical studies. Traditional coal fired generator models have been improved with additional functions to provide a comparable response with existing units. A novel combined cycle gas turbine model using fundamental equations and control blocks has also been developed. A doubly fed induction generator model, based on existing literature, has been introduced for representing wind turbine behaviour in system response studies. Validation of individual models and the complete system against historic loss events has established confidence in the method. A review of the current system with the dynamic model showed that current primary response requirements are inadequate. The secondary response requirements generally show a slight reduction in the holding levels. Simulations including extra wind generation have shown that there is potential to reduce the primary response requirement in the future. The secondary response requirements are maintained with added wind farms.
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