GWO-based estimation of input-output parameters of thermal power plants

Abstract

The fuel cost curve of thermal generators was very important in the calculation of economic dispatch and optimal power flow. Temperature and aging could make changes to fuel cost curve so curve estimation need to be done periodically. The accuracy of the curve parameters estimation strongly affected the calculation of the dispatch. This paper aims to estimate the fuel cost curve parameters by using the grey wolf optimizer method. The problem of curve parameter estimation was made as an optimization problem. The objective function to be minimized was the total number of absolute error or the difference between the actual value and the estimated value of the fuel cost function. The estimated values of parameter that produce the smallest total absolute error were the values of final solution. The simulation results showed that parameter estimation using gray wolf optimizer method further minimized the value of objective function. By using three models of fuel cost curve and given test data, parameter estimation using grey wolf optimizer method produced the better estimation results than those estimation results obtained using least square error, particle swarm optimization, genetic algorithm, artificial bee colony and cuckoo search methods.

Online 3D path planning for Tri-copter drone using GWO-IBA algorithm

“…Delta wolves depend on the beta and alpha, but they command on the omega, and the rest of the wolf group count as omega. In this optimization method (GWO), the optimal solutions are leading by alpha, beta, and delta [16], [17]. They lead residue wolves (omega) to the optimal point in the searching area, as shown in Figure 2.…”

Transient response mitigation using type-2 fuzzy controller optimized by grey wolf optimizer in converter high voltage direct current

“…Genetic algorithm OM based on sliding-mode scheme is used to enhance the dynamic stability of source voltage converter HVDC in considering wide range operating conditions [50]. Bacterial foraging OM is applied in capacitor-run motor [51], and grey wolf optimizer (GWO) is developed to estimate input-output parameters power plant [52] and to compute the optimal shape and size in design of 800 kV transformer of HVDC system [53].…”

Transient response improvement of direct current using supplementary control based on ANFIS for rectifier in HVDC

“…Artificial intelligent application is very attractive research and it is implemented in some fields such as: Genetic algorithm to optimize robustness and efficiency of micro-grid DC solar-system structures [22], firefly algorithm to optimize PID parameter of automatic generation control in multi-area [23]. Grey wolf optimizer (GWO) is used to optimize power flow wind farm integrated to PS [24], to optimize power allocation to realize plug-and-play capability of micro-grid system [25], to minimize operating cost and active power loss by optimizing reactive power generation [26] and to estimate input-output parameters of thermal plant [27]. The GWO is also applied on power system stabilizer to damp local oscillation in multimachine [28] and inter-area oscillation in wide-area [29].…”