A Joint Filter Approach for Reliable Power System State Estimation

A model‐based method for rotor speed estimation of synchronous generators using wide area measurement system

“…A k Here, to evaluate the accuracy of the presented approximation, summation of three random sinusoidal functions including cos(t+0.1), 1.2cos(1.2t+0.2) and 1.1cos(1.4t+0.5) is compared with the approximated function results from Equation (10). This comparison is depicted in Figure 1.…”

“…In this regard, high sampling rate PMUs-60-120 samples/s-can be used for online state estimation purposes. Apart from the methods that use direct measurements of the rotor angle and rotor speed via installing mechanical sensors on the generator [6,7], due to the practical limitations, most of the presented methods use common measurements of PMU for the state estimation purposes [8][9][10][11]. Accordingly, it is required to consider a proper model for the synchronous generator to calculate state variables using the PMU's data.…”

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties

“…The PDF of t -distribution is given by where is the gamma function, is the scale parameter, and is the shape parameter. In addition, to the best of our knowledge, only the symmetrical distributions such as Gaussian model [ 34 , 35 ] and Gaussian mixture models [ 29 ] are widely used to characterize the measurement error for the distribution system state estimation. Therefore, the symmetrical distribution is applied in our proposed algorithm presented later.…”

Robust adaptive filter using fuzzy logic for tightly‐coupled visual inertial odometry navigation system

“… filter does not need to know the prior statistical characteristics of measurement noise [17, 18], and it tries to minimise the effect of the worst possible disturbances on the estimation errors [19, 20]. In [21], extended Kalman filter is proposed to solve the uncertainty of noise statistics and obtained good robustness. However, the suitable disturbance attenuation level of filter, which bounds the influence caused by the system uncertainties, is very hard to select [19, 21].…”

“…In [21], extended Kalman filter is proposed to solve the uncertainty of noise statistics and obtained good robustness. However, the suitable disturbance attenuation level of filter, which bounds the influence caused by the system uncertainties, is very hard to select [19, 21]. The level also has a great influence on the filtering results and may be thought of as a tuning parameter to control the tradeoff between the robust performance and the minimum variance performance [19].…”