Measurement of synchronous machine parameters using Kalman filter based fuzzy logic estimator

Influence of Coal Gangue Volume Mixing Ratio on the System Contact Response When Multiple Coal Gangue Particles Impacting the Metal Plate and the Study of Coal Gangue Mixing Ratio Recognition Based on the Metal Plate Contact Response and the Multi-Information Fusion

“…Kalman filter [65][66][67][68][69] is a widely used filtering method, it can obtain the system states or real signals by processing the input and observed signals which contains the noise. From Figures 16 (a) -(f), after the metal plate is impacted by coal gangue, the displacement, velocity, acceleration and each part of energy of the metal plate all present the fluctuating curves, which conceal some characteristics of the contact responses.…”

On‐line parameter identification of power plant characteristics based on phasor measurement unit recorded data using differential evolution and bat inspired algorithm

“…This algorithm is introduced in the next section. Normally, in meta-heuristic optimisation algorithms, the objective function is minimised but here we need to maximise the objective function defined by (13). The value of the objective function is between 0 and 1, where the maximum value is only obtained for a perfect match between measured and modelled output signal.…”

“…Therefore, EKF is not an optimal estimator since the measurement and the state transition model are both non-linear [7]. As stated in [13], the estimation results of EKF are slightly less accurate than sensitivity analysis. UKF method has been proposed to overcome the demerits of EKF.…”

Metaheuristic linear modeling technique for estimating the excitation current of a synchronous motor

“…Thus, they have been commonly used in the power factor correction task [1][2][3]. The wide variety of applications of SMs as reactive power compensators makes it necessary to achieve a fast and reliable parameter modeling system design [4][5][6][7]. The usage of SMs in industrial applications leads to a poor power factor.…”

“…The relationships among the SM parameters are mostly complex and nonlinear [5,[12][13][14][15]. Researchers have suggested artificial intelligence (AI)-based nonlinear modeling techniques, such as proportional plus integral plus derivative [16], pulse width modulation [17][18][19][20], fuzzy logic [2,3], Kalman filter-based methods [7,15,21], artificial neural networks (ANNs) [22,23], particle swarm optimization (in real-time applications) [24], intuitive k-nearest neighbor (k-NN) estimator and genetic algorithm (GA) [5,25], and adaptive ANNs [4] for modeling the parameters and/or predicting the excitation current of SMs and permanent magnet synchronous machines. The modeling of SM parameters using modern AI-based methods for excitation current estimation was realized in recently published studies [4,5,17].…”