Maximum current estimation considering power gating

Two infinitely long parallel conductors of arbitrary cross section connected to a voltage source form a loop. If the source voltage depends on time, then due to induction there is no constant current density in the loop conductors. It is only recently that a method has been published for accurately calculating current density in a group of long parallel conductors. The method has thus far been applied to the calculation of steady-state current density in a loop connected to a sinusoidal voltage source. In the present article, the method is used for an accurate calculation of transient current using transient current density. The transient current is analysed when connecting and short-circuiting the sources of sinusoidal, constant and sawtooth voltages. For circular cross section conductors, the dependences of maximum current density, maximum current and the time of achieving steady state on the source frequency, the distance of the conductors and their resistivity when connecting the source of sinusoidal voltage are examined.

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Automatic Power System Restoration With Inrush Current Estimation For Industrial Facility

2021 IEEE/IAS 57th Industrial and Commercial Power Systems Technical Conference (I&CPS) ◽

10.1109/icps51807.2021.9416636 ◽

2021 ◽

Author(s):

Anusha Papasani ◽

Kaynat Zia ◽

Wei-Jen Lee

Keyword(s):

Power System ◽

Inrush Current ◽

Industrial Facility ◽

Power System Restoration ◽

Current Estimation ◽

System Restoration

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Sensorless LC Filter Implementation for Permanent Magnet Machine Drive Using Observer-Based Voltage and Current Estimation

Sensors ◽

10.3390/s21113596 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3596

Author(s):

Chia-Ming Liang ◽

Yi-Jen Lin ◽

Jyun-You Chen ◽

Guan-Ren Chen ◽

Shih-Chin Yang

Keyword(s):

Permanent Magnet ◽

High Speed ◽

Pulse Width Modulation ◽

Output Current ◽

Current Harmonics ◽

Pwm Inverter ◽

Current Estimation ◽

Filter Output ◽

Lc Filter ◽

Parameter Error

For pulse width modulation (PWM) inverter drives, an LC filter can cascade to a permanent magnet (PM) machine at inverter output to reduce PWM-reflected current harmonics. Because the LC filter causes resonance, the filter output current and voltage are required for the sensorless field-oriented control (FOC) drive. However, existing sensors and inverters are typically integrated inside commercial closed-form drives; it is not possible for these drives to obtain additional filter output signals. To resolve this integration issue, this paper proposes a sensorless LC filter state estimation using only the drive inside current sensors. The design principle of the LC filter is first introduced to remove PWM current harmonics. A dual-observer is then proposed to estimate the filter output current and voltage for the sensorless FOC drive. Compared to conventional model-based estimation, the proposed dual-observer demonstrates robust estimation performance under parameter error. The capacitor parameter error shows a negligible influence on the proposed observer estimation. The filter inductance error only affects the capacitor current estimation at high speed. The performance of the sensorless FOC drive using the proposed dual-observer is comparable to the same drive using external sensors for filter voltage and current measurement. All experiments are verified by a PM machine with only 130 μH phase inductance.

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A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Sensors ◽

10.3390/s21124068 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4068

Author(s):

Zheshuo Zhang ◽

Jie Zhang ◽

Jiawen Dai ◽

Bangji Zhang ◽

Hengmin Qi

Keyword(s):

Unscented Kalman Filter ◽

Time Estimation ◽

Estimation Algorithm ◽

Estimation Methods ◽

Fusion Algorithm ◽

State Variable ◽

Current Estimation ◽

Real Time Estimation ◽

And Control ◽

Independent Parameters

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

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