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

Abstract

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.

Improved Gradient-Based Optimizer for Modelling Thermal and Hydropower Plants

“…Additionally, attained SAE of models 1-9 using GBO, IGBO, and other approaches are written in Tables 6-14, respectively. Comparisons of IGBO with other employed approaches in the literature, NR [19], LES [20], PSO [20], ABC [21], DE [22], CS [23], RMO [24], TLBO [26], CSA [27], WOA [28], reveal that IGBO has the minimum SAE. e SAE of GBO are the same as those of IGBO in models 1-3, which have linear, quadratic, and cubic equations; that is, they are either linear or low nonlinear, while IGBO produces SAEs with fewer values in models 4-9 which have high nonlinearity.…”

“…Relationships of HPP are modelled using models 8 and 9. We use the top and bottom bounds for the parameters of models of TPP and HPP in [28].…”

“…e metaheuristic optimization techniques surpass the mathematical ones as per their fast convergence, robustness, and avoidance trapping in regional optimal points. Recently, many metaheuristic algorithms are deployed for the parameter extraction of input-output curves of power plants like particle swarm optimization (PSO) [20], least error square (LES) [20], artificial bee colony (ABC) [21], differential evolution (DE) [22], cuckoo search (CS) [23], radial movement optimization (RMO) [24], genetic algorithm [25], teaching learning-based optimization (TLBO) [26], crow search algorithm (CSA) [27], and whale optimization algorithm (WOA) [28].…”

Energy Efficient QoS Aware Cluster Based Multihop Routing Protocol for WSN

“…The GFL algorithm is based on the movement of groundwater from recharging regions to discharging regions stimulating the concept of Darcy's law. A major idea of GFL algorithm is the flow of ground water is mainly directed by Darcy's law [25]. Earlier works explored that the velocity of the groundwater flows highly based on height difference and gap in position.…”

A New Method for Axis Adjustment of the Hydro-generator Unit Using Machine Learning

“…In terms of axial adjustment orientation search, literature [19][20] proposed an improved adaptation evaluation method, it put forward the concept of stability and instability parameter sets, and it applied to the stability search strategy, which effectively improved the local search capability of the model algorithm and it ensured the stability and accuracy of the hydro-generator set model. The literature [21][22][23][24] proposed a prediction method based on radial basis function (RBF) interpolation, empirical modal decomposition (EMD), approximate entropy, artificial neural network, and grey theory for the degradation of hydro-generator unit characteristic parameters. A nonlinear prediction model for the degradation trend of hydro generator unit operating parameters was proposed in the literature [25][26][27][28][29].…”