A genetic-based algorithm for optimal estimation of input–output curve parameters of thermal power plants

Al-Kandari, A.M.; El-Naggar, K. M.

doi:10.1007/s00202-006-0047-x

Cited by 16 publications

(33 citation statements)

References 12 publications

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“…Among these methods, Genetic Algorithm (GA) [13], Particle Swarm Optimization (PSO) [15,16] and Artificial Bee Colony (ABC) [23] algorithm have been applied to the chosen problem. The heuristic techniques outperform the mathematical methods but their solution quality is sensitive to the algorithmic controlling parameters like population size and number of generations.…”

Section: Existing Parameters Estimation Procedures For Ed Problemmentioning

confidence: 99%

Improved parameters for economic dispatch problems by teaching learning optimization

Subramanian

Ganesan

2015

International Journal of Electrical Power & Energy Systems

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Section: Existing Parameters Estimation Procedures For Ed Problemmentioning

confidence: 99%

Improved parameters for economic dispatch problems by teaching learning optimization

Subramanian

Ganesan

2015

International Journal of Electrical Power & Energy Systems

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“…The estimated parameters produced by the ABC algorithm and GA [21] are given in Table 4. In Table 5, the actual fuel cost data [21] for each unit, estimated fuel cost data obtained from the ABC algorithm and GA [21], and percentage error values in the estimation process are presented. As seen in Table 5, the percentage errors produced by both algorithms for all of the generating units except for the unit with 165 MW are under 1%.…”

Section: Test Casementioning

confidence: 99%

“…The ABC algorithm is used to find the optimal parameter estimation of the formulated optimization problem. The results obtained from the ABC algorithm are compared to the results reported in [7] and [21]. The comparison shows that the ABC algorithm produces better solutions than GA, PSO, and LSE algorithms in the solution of fuel cost curve parameter estimation in thermal power systems.…”

Section: Introductionmentioning

confidence: 99%

“…PSO and the GA are well known and widely used optimization algorithms in the field of power systems. El-Kantari and El-Naggar [21] used the GA and El-Naggar et al [7] used the PSO method for estimating the parameters of thermal power plants' fuel cost function. Alrashidi et al [22] proposed the PSO method to estimate the fuel cost function parameters of thermal power plants with valve point effects.…”

Section: Introductionmentioning

confidence: 99%

“…Section 4 presents the experimental study results of the simulation and compares the results obtained from the ABC algorithm and the techniques reported in [7] and [21] for case studies of the estimation problem. Finally, the conclusion is illustrated in Section 5.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of fuel cost curve parameters for thermal power plants using the ABC algorithm

Sönmez¹

2013

Turk J Elec Eng & Comp Sci

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Abstract:The solution accuracy of economic dispatch problems is associated with the accuracy of the fuel cost curve parameters. Therefore, updating of these parameters is a very important issue to further improve the final accuracy of economic dispatch problems. Estimating the parameters of the fuel cost curve may be the best solution for this issue. This paper presents an application of the artificial bee colony (ABC) algorithm to estimate the fuel cost curve parameters of thermal power plants. In the estimation problem, 1st-, 2nd-, and 3rd-order fuel cost functions are used, and the estimation problem is formulated as an optimization one. The ABC algorithm is used to solve this optimization problem by minimizing the total error in the estimated parameters. In this study, in order to evaluate the performance of the ABC algorithm, it is tested on 3 different cases that have 3 different fuel cost types, such as coal, oil, and gas. The results obtained from the proposed method are compared with the genetic algorithm, particle swarm optimization, and least square error methods reported previously in the literature. The results show that the ABC algorithm is stronger than the others at solving such a problem.

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Parameter estimation using a modified whale optimization algorithm for input‐output curves of thermal and hydro power plants

Secui

Hora

Bendea

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a modified whale optimization (MWO) algorithm for solving the problem of parameters estimation for input‐output curves of thermal and hydro generating units that operates in power plants. In order to improve the MWO algorithm, a chaotic component based on logistic map is added. This new algorithm is called chaotic MWO (CMWO). Both MWO and CMWO algorithms have kept the structure of original whale optimization (WO) algorithm, but some characteristics regarding how the solutions positions are updated inside CMWO and MWO phases were modified. Estimation of curve parameters is an optimization problem. Its objective is to minimize the sum of absolute errors between actual values and estimated values. The efficiency of CMWO, MWO, and WO algorithms is tested either on polynomial models or on models that use nonsmooth functions. The results of CMWO algorithm are then compared with the ones obtained by other recently published algorithms.

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A genetic-based algorithm for optimal estimation of input–output curve parameters of thermal power plants

Cited by 16 publications

References 12 publications

Improved parameters for economic dispatch problems by teaching learning optimization

Improved parameters for economic dispatch problems by teaching learning optimization

Estimation of fuel cost curve parameters for thermal power plants using the ABC algorithm

Parameter estimation using a modified whale optimization algorithm for input‐output curves of thermal and hydro power plants

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