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Common Mode and Differential Mode: Definition, Cause, and Elimination
Published in Richard Lee Ozenbaugh, Timothy M. Pullen, EMI Filter Design, 2017
Richard Lee Ozenbaugh, Timothy M. Pullen
An ideal transformer is a notional perfect circuit element that transfers electrical energy between primary and secondary windings by the action of perfect magnetic coupling. The ideal transformer will only transfer alternating, differential-mode current. Common-mode current will not be transferred because it results in a zero potential difference across the transformer windings and therefore does not generate any magnetic field in the transformer windings. Any real transformer will have a small, but nonzero capacitance linking primary to secondary windings as shown in Figure 3.2. The capacitance is a result of the physical spacing and the presence of a dielectric between the windings. The size of this interwinding capacitance may be reduced by increasing the separation between the windings, and by using a low-permittivity material to fill the space between the windings. For common-mode current, the parasitic capacitance provides a path across the transformer, the impedance of which is dependent on the magnitude of the capacitance and the signal frequency. In some cases, this common-mode current will have different magnitudes, depending on the parasitic inductance and capacitance, and may also contribute to the differential-mode noise. With no external current path from input to output (i.e., the converter is driving an isolated load that has very little capacitance back to the input), the common-mode current is largely contained within the converter and flows through the stray capacitance from the input to the output, therefore causing no further problems.
Calculus in action
Published in W. Bolton, Mathematics for Engineering, 2012
The circuit element called a capacitor is essentially just a pair of parallel conducting plates separated by an insulator. When a voltage is applied across a capacitor then one of the plates becomes negatively charged and the other positively charged as a result of charge flowing on to one of the plates and leaving the other plate (Figure 22.8). The amount of charge gained by one plate is the same as that lost by the other plate. It is found that the amount of charge q on a capacitor plate is directly proportional to the potential difference v between the plates. Hence we can write q ∞ v and so q=Cv
Self-Repeating Robotic Arm
Published in Kaushik Kumar, Sridhar B. Babu, Industrial Automation and Robotics, 2023
B. Nagamani, N. Subadra, Sathvik Parasa, Hari Sarada, Ashrith Gadeela
A resistor is a passive electrical component with two terminals that employs electrical resistance to operate as a circuit element. Resistors are used in electronic circuits to control current flow, alter signal levels, divide voltages, bias active devices, and terminate transmission lines, among other things.
Exponential stabilisation analysis of a class of delayed inertial memristive neural networks
Published in International Journal of Control, 2022
Jiemei Zhao, Zhuoyi Zhang, Dongliang Yang
In Chua (1971), the memristor was first predicted by Prof. Chua due to symmetry arguments. It is regarded as the fourth basic circuit element besides resistor, capacitor and inductor. The actual physical device of memristor was founded by HP laboratories by using a two-terminal titanium dioxide () to describe memristive characteristics (Strukov et al., 2008). A memristor has two different states, that is, high resistance () and low resistance (), respectively, which depends upon the current flowing into it (Zhang et al., 2015). A notable characteristic of the memristor is that it exhibits a pinched hysteresis loop just as neurons in a human brain do. Hence, utilising memristors to replace resistors, memristive neural networks, as a kind of novel neural networks model, have been designed. Memristive neural networks have been successfully applied in many scientific areas including image encryption and associative memory (Wen et al., 2015). Inertial items were the critical instruments to generate bifurcation and chaos and were applied in the circuits with inductances implementing the axon of sepia or membrane of a hair firstly (Angelaki & Correia, 1991). By incorporating the inertial term into memristive neural networks, inertial memristive neural networks (IMNNs) are proposed. IMNNs are a class of systems about the second-order derivative of states. The model of IMMNs has been constructed in Guo et al. (2018), Rakkiyappan et al. (2016), Sheng et al. (2020), Xiao et al. (2017), and Zhang and Zeng (2018). The studies of IMMNs are necessary and significant for both theoretical and potentially practical applications. Currently, many significant developments on IMNNs have been reported. The problems of stability and stabilisation for IMNNs with mixed time delays were investigated in Liu et al. (2020), Zhang and Zeng (2018), Zhang et al. (2018), and Zhang et al. (2017). The dissipativity or passivity analysis for IMNNs with time-varying delays was studied in Tu et al. (2017) and Xiao et al. (2017). The synchronisation problem of time delays IMNNs was studied in Guo et al. (2018), Gong et al. (2018), He et al. (2021), Wan and Jian (2018), and Yao et al. (2020).