Draw a negative clamping circuit. If the input is a rectangular wave, draw both the input and output waves. 

Updated: 11 months ago
āωāĻ¤ā§āϤāϰāσ
298

Related Question

View All
āωāĻ¤ā§āϤāϰāσ

Thevenin's theorem is a fundamental concept in circuit analysis that simplifies complex linear circuits into a simple equivalent circuit at a pair of terminals. This equivalent circuit consists of a single voltage source (Thevenin Voltage, VTh) in series with a single resistor (Thevenin Resistance, RTh). This simplification is particularly useful for analyzing how a specific load component behaves when connected to the complex circuit.

To determine the Thevenin's equivalent circuit at terminal X-Y, follow these steps:

        
  •         Step 1: Identify the terminals (X-Y) where the Thevenin equivalent circuit is to be determined. Disconnect any load connected across these terminals.     
  •     
  •         Step 2: Calculate the Thevenin Voltage (VTh) or Open-Circuit Voltage (VOC).         
    This is the voltage across the terminals X-Y when the load is disconnected.         
    To find VTh, analyze the circuit with the terminals open. You can use various circuit analysis techniques such as Kirchhoff's Voltage Law (KVL), Kirchhoff's Current Law (KCL), nodal analysis, or mesh analysis.     
  •     
  •         Step 3: Calculate the Thevenin Resistance (RTh) or Equivalent Resistance (REQ).         
    This is the equivalent resistance looking into the terminals X-Y with all independent voltage sources short-circuited (replaced by a wire) and all independent current sources open-circuited (removed from the circuit).         
    If there are dependent sources in the circuit, a test voltage (Vtest) or test current (Itest) source must be applied to the terminals X-Y, and RTh is calculated as Vtest/Itest (or Itest/Vtest if a current source is used). All independent sources are still turned off during this step.         
    For circuits with only independent sources, simplify the resistor network seen from terminals X-Y using series and parallel resistance combinations.     
  •     
  •         Step 4: Construct the Thevenin Equivalent Circuit.         
    Draw the equivalent circuit consisting of the calculated Thevenin Voltage (VTh) source connected in series with the Thevenin Resistance (RTh) at terminals X-Y.     
  •     
  •         Step 5: Reconnect the Load (Optional, for analysis).         
    If there was a load originally connected, reconnect it to the Thevenin equivalent circuit to easily find the voltage across or current through the load using simple Ohm's law.     
Satt AI
Satt AI
4 days ago
720
āωāĻ¤ā§āϤāϰāσ

Maximum Power Transfer Theorem :

In electrical engineering, the maximum power transfer theorem states that, to obtain maximum external power from a power source with internal resistance, the resistance of the load must equal the resistance of the source as viewed from its output terminals.

699
āωāĻ¤ā§āϤāϰāσ

For t<0, the switch is closed.

Capacitor acts as open to DC.

Voltage across the capacitor,

     v(0-)= {(12||4)×V}Ãˇ{(12||4)+6} = 8VFor t=0, the switch is opened,Voltage across the Capacitor cannot change instantaneously,So, v(0-)= v(0) = 8VAt t>0, The capacitor is discharging.Rth = (12||4) = 3 ohmC= 1/6 FTime constant, Π = Rth×C = 0.5 So, v(t) = v(0)e-t/Π  = 8e-t/0.5 =8e-2t V (Ans.)

Md Shoyaeb
Md Shoyaeb
11 months ago
720
āωāĻ¤ā§āϤāϰāσ

āĻāĻ–āĻžāύ⧇ āĻĒā§āϰāĻĻāĻ¤ā§āϤ āĻŦāĻ°ā§āĻ—āĻžāĻ•āĻžāϰ āĻ­ā§‹āĻ˛ā§āĻŸā§‡āϜ āϤāϰāĻ™ā§āĻ— āĻĨ⧇āϕ⧇ āĻ—ā§œ (average) āĻāĻŦāĻ‚ āĻ•āĻžāĻ°ā§āϝāĻ•āϰ (effective) āĻŽāĻžāύ āύāĻŋāĻ°ā§āϪ⧟ āĻ•āϰāĻž āĻšāϞ⧋:

āĻĒā§āϰāĻĻāĻ¤ā§āϤ āϚāĻŋāĻ¤ā§āϰ āĻĨ⧇āϕ⧇ āĻĒāĻžāχ:

        
  • āϏāĻ°ā§āĻŦā§‹āĻšā§āϚ āĻ­ā§‹āĻ˛ā§āĻŸā§‡āϜ, \(V_m = 10 \text{ V}\)
  •     
  • āϤāϰāĻ™ā§āĻ—āϟāĻŋ \(1 \text{ ms}\) āĻāϰ āϜāĻ¨ā§āϝ \(+10 \text{ V}\) āĻāĻŦāĻ‚ \(1 \text{ ms}\) āĻāϰ āϜāĻ¨ā§āϝ \(-10 \text{ V}\) āĻĨāĻžāϕ⧇āĨ¤
  •     
  • āϏ⧁āϤāϰāĻžāĻ‚, āĻĒāĻ°ā§āϝāĻžā§ŸāĻ•āĻžāϞ, \(T = 1 \text{ ms} + 1 \text{ ms} = 2 \text{ ms}\)

ā§§. āĻ—ā§œ āĻŽāĻžāύ (Average Value) āύāĻŋāĻ°ā§āϪ⧟:

āϕ⧋āύ⧋ āĻ­ā§‹āĻ˛ā§āĻŸā§‡āϜ āϤāϰāĻ™ā§āϗ⧇āϰ āĻ—ā§œ āĻŽāĻžāύ āύāĻŋāĻ°ā§āĻŖā§Ÿā§‡āϰ āϏ⧂āĻ¤ā§āϰ āĻšāϞ⧋:

\[ V_{avg} = \frac{1}{T} \int_0^T v(t) dt \]

āϝ⧇āĻšā§‡āϤ⧁ āϤāϰāĻ™ā§āĻ—āϟāĻŋ \(0\) āĻĨ⧇āϕ⧇ \(1 \text{ ms}\) āĻĒāĻ°ā§āϝāĻ¨ā§āϤ \(+10 \text{ V}\) āĻāĻŦāĻ‚ \(1 \text{ ms}\) āĻĨ⧇āϕ⧇ \(2 \text{ ms}\) āĻĒāĻ°ā§āϝāĻ¨ā§āϤ \(-10 \text{ V}\) āĻĨāĻžāϕ⧇, āϤāĻžāχ:

\[ V_{avg} = \frac{1}{2 \times 10^{-3}} \left[ \int_0^{1 \times 10^{-3}} 10 dt + \int_{1 \times 10^{-3}}^{2 \times 10^{-3}} (-10) dt \right] \] \[ V_{avg} = \frac{1}{2 \times 10^{-3}} \left[ [10t]_0^{1 \times 10^{-3}} + [-10t]_{1 \times 10^{-3}}^{2 \times 10^{-3}} \right] \] \[ V_{avg} = \frac{1}{2 \times 10^{-3}} \left[ (10 \times 1 \times 10^{-3} - 0) + (-10 \times 2 \times 10^{-3} - (-10 \times 1 \times 10^{-3})) \right] \] \[ V_{avg} = \frac{1}{2 \times 10^{-3}} \left[ 10 \times 10^{-3} - 20 \times 10^{-3} + 10 \times 10^{-3} \right] \] \[ V_{avg} = \frac{1}{2 \times 10^{-3}} \left[ 0 \right] \] \[ V_{avg} = 0 \text{ V} \]

āϏ⧁āϤāϰāĻžāĻ‚, āĻ­ā§‹āĻ˛ā§āĻŸā§‡āϜ āϤāϰāĻ™ā§āϗ⧇āϰ āĻ—ā§œ āĻŽāĻžāύ \(0 \text{ V}\)āĨ¤

⧍. āĻ•āĻžāĻ°ā§āϝāĻ•āϰ āĻŽāĻžāύ (Effective Value) āĻŦāĻž RMS āĻŽāĻžāύ (Root Mean Square Value) āύāĻŋāĻ°ā§āϪ⧟:

āϕ⧋āύ⧋ āĻ­ā§‹āĻ˛ā§āĻŸā§‡āϜ āϤāϰāĻ™ā§āϗ⧇āϰ RMS āĻŽāĻžāύ āύāĻŋāĻ°ā§āĻŖā§Ÿā§‡āϰ āϏ⧂āĻ¤ā§āϰ āĻšāϞ⧋:

\[ V_{rms} = \sqrt{\frac{1}{T} \int_0^T v(t)^2 dt} \]

āϝ⧇āĻšā§‡āϤ⧁ āϤāϰāĻ™ā§āĻ—āϟāĻŋ \(0\) āĻĨ⧇āϕ⧇ \(1 \text{ ms}\) āĻĒāĻ°ā§āϝāĻ¨ā§āϤ \(+10 \text{ V}\) āĻāĻŦāĻ‚ \(1 \text{ ms}\) āĻĨ⧇āϕ⧇ \(2 \text{ ms}\) āĻĒāĻ°ā§āϝāĻ¨ā§āϤ \(-10 \text{ V}\) āĻĨāĻžāϕ⧇, āϤāĻžāχ \(v(t)^2\) āϏāĻŦāϏāĻŽā§Ÿ \(10^2 = 100 \text{ V}^2\) āĻšāĻŦ⧇:

\[ V_{rms} = \sqrt{\frac{1}{2 \times 10^{-3}} \left[ \int_0^{1 \times 10^{-3}} (10)^2 dt + \int_{1 \times 10^{-3}}^{2 \times 10^{-3}} (-10)^2 dt \right]} \] \[ V_{rms} = \sqrt{\frac{1}{2 \times 10^{-3}} \left[ \int_0^{1 \times 10^{-3}} 100 dt + \int_{1 \times 10^{-3}}^{2 \times 10^{-3}} 100 dt \right]} \] \[ V_{rms} = \sqrt{\frac{1}{2 \times 10^{-3}} \left[ [100t]_0^{1 \times 10^{-3}} + [100t]_{1 \times 10^{-3}}^{2 \times 10^{-3}} \right]} \] \[ V_{rms} = \sqrt{\frac{1}{2 \times 10^{-3}} \left[ (100 \times 1 \times 10^{-3} - 0) + (100 \times 2 \times 10^{-3} - 100 \times 1 \times 10^{-3}) \right]} \] \[ V_{rms} = \sqrt{\frac{1}{2 \times 10^{-3}} \left[ 100 \times 10^{-3} + 200 \times 10^{-3} - 100 \times 10^{-3} \right]} \] \[ V_{rms} = \sqrt{\frac{1}{2 \times 10^{-3}} \left[ 200 \times 10^{-3} \right]} \] \[ V_{rms} = \sqrt{100} \] \[ V_{rms} = 10 \text{ V} \]

āϏ⧁āϤāϰāĻžāĻ‚, āĻ­ā§‹āĻ˛ā§āĻŸā§‡āϜ āϤāϰāĻ™ā§āϗ⧇āϰ āĻ•āĻžāĻ°ā§āϝāĻ•āϰ āĻŽāĻžāύ (RMS) \(10 \text{ V}\)āĨ¤

Satt AI
Satt AI
10 hours ago
771
āĻļāĻŋāĻ•ā§āώāĻ•āĻĻ⧇āϰ āϜāĻ¨ā§āϝ āĻŦāĻŋāĻļ⧇āώāĻ­āĻžāĻŦ⧇ āϤ⧈āϰāĻŋ

ā§§ āĻ•ā§āϞāĻŋāϕ⧇ āĻĒā§āϰāĻļā§āύ, āĻļā§€āϟ, āϏāĻžāĻœā§‡āĻļāύ āĻ“
āĻ…āύāϞāĻžāχāύ āĻĒāϰ⧀āĻ•ā§āώāĻž āϤ⧈āϰāĻŋāϰ āϏāĻĢāϟāĻ“āϝāĻŧā§āϝāĻžāϰ!

āĻļ⧁āϧ⧁ āĻĒā§āϰāĻļā§āύ āϏāĻŋāϞ⧇āĻ•ā§āϟ āĻ•āϰ⧁āύ — āĻĒā§āϰāĻļā§āύāĻĒāĻ¤ā§āϰ āĻ…āĻŸā§‹āĻŽā§‡āϟāĻŋāĻ• āϤ⧈āϰāĻŋ!

āĻĒā§āϰāĻļā§āύ āĻāĻĄāĻŋāϟ āĻ•āϰāĻž āϝāĻžāĻŦ⧇
āϜāϞāĻ›āĻžāĻĒ āĻĻ⧇āϝāĻŧāĻž āϝāĻžāĻŦ⧇
āĻ āĻŋāĻ•āĻžāύāĻž āϝ⧁āĻ•ā§āϤ āĻ•āϰāĻž āϝāĻžāĻŦ⧇
Logo, Motto āϝ⧁āĻ•ā§āϤ āĻšāĻŦ⧇
āĻ…āĻŸā§‹ āĻĒā§āϰāϤāĻŋāĻˇā§āĻ āĻžāύ⧇āϰ āύāĻžāĻŽ
āĻ…āĻŸā§‹ āϏāĻŽāϝāĻŧ, āĻĒā§‚āĻ°ā§āĻŖāĻŽāĻžāύ
āĻĒā§āϰāĻļā§āύ āĻāĻĄāĻŋāϟ āĻ•āϰāĻž āϝāĻžāĻŦ⧇
āϜāϞāĻ›āĻžāĻĒ āĻĻ⧇āϝāĻŧāĻž āϝāĻžāĻŦ⧇
āĻ āĻŋāĻ•āĻžāύāĻž āϝ⧁āĻ•ā§āϤ āĻ•āϰāĻž āϝāĻžāĻŦ⧇
Logo, Motto āϝ⧁āĻ•ā§āϤ āĻšāĻŦ⧇
āĻ…āĻŸā§‹ āĻĒā§āϰāϤāĻŋāĻˇā§āĻ āĻžāύ⧇āϰ āύāĻžāĻŽ
āĻ…āĻŸā§‹ āϏāĻŽāϝāĻŧ, āĻĒā§‚āĻ°ā§āĻŖāĻŽāĻžāύ
āĻ…āĻŸā§‹ āύāĻŋāĻ°ā§āĻĻ⧇āĻļāύāĻž (āĻāĻĄāĻŋāϟāϝ⧋āĻ—ā§āϝ)
āĻ…āĻŸā§‹ āĻŦāĻŋāώāϝāĻŧ āĻ“ āĻ…āĻ§ā§āϝāĻžāϝāĻŧ
OMR āϏāĻ‚āϝ⧁āĻ•ā§āϤ āĻ•āϰāĻž āϝāĻžāĻŦ⧇
āĻĢāĻ¨ā§āϟ, āĻ•āϞāĻžāĻŽ, āĻĄāĻŋāĻ­āĻžāχāĻĄāĻžāϰ
āĻĒā§āϰāĻļā§āύ/āĻ…āĻĒāĻļāύ āĻ¸ā§āϟāĻžāχāϞ āĻĒāϰāĻŋāĻŦāĻ°ā§āϤāύ
āϏ⧇āϟ āϕ⧋āĻĄ, āĻŦāĻŋāώāϝāĻŧ āϕ⧋āĻĄ
āĻ…āĻŸā§‹ āύāĻŋāĻ°ā§āĻĻ⧇āĻļāύāĻž (āĻāĻĄāĻŋāϟāϝ⧋āĻ—ā§āϝ)
āĻ…āĻŸā§‹ āĻŦāĻŋāώāϝāĻŧ āĻ“ āĻ…āĻ§ā§āϝāĻžāϝāĻŧ
OMR āϏāĻ‚āϝ⧁āĻ•ā§āϤ āĻ•āϰāĻž āϝāĻžāĻŦ⧇
āĻĢāĻ¨ā§āϟ, āĻ•āϞāĻžāĻŽ, āĻĄāĻŋāĻ­āĻžāχāĻĄāĻžāϰ
āĻĒā§āϰāĻļā§āύ/āĻ…āĻĒāĻļāύ āĻ¸ā§āϟāĻžāχāϞ āĻĒāϰāĻŋāĻŦāĻ°ā§āϤāύ
āϏ⧇āϟ āϕ⧋āĻĄ, āĻŦāĻŋāώāϝāĻŧ āϕ⧋āĻĄ
āĻāĻ–āύāχ āĻļ⧁āϰ⧁ āĻ•āϰ⧁āύ āĻĄā§‡āĻŽā§‹ āĻĻ⧇āϖ⧁āύ
ā§Ģā§Ļ,ā§Ļā§Ļā§Ļ+
āĻļāĻŋāĻ•ā§āώāĻ•
ā§Šā§Ļ āϞāĻ•ā§āώ+
āĻĒā§āϰāĻļā§āύāĻĒāĻ¤ā§āϰ
āĻŽāĻžāĻ¤ā§āϰ ā§§ā§Ģ āĻĒ⧟āϏāĻžā§Ÿ āĻĒā§āϰāĻļā§āύāĻĒāĻ¤ā§āϰ
ā§§ āĻ•ā§āϞāĻŋāϕ⧇ āĻĒā§āϰāĻļā§āύ, āĻļā§€āϟ, āϏāĻžāĻœā§‡āĻļāύ āϤ⧈āϰāĻŋ āĻ•āϰ⧁āύ āφāϜāχ

Complete Exam
Preparation

Learn, practice, analyse and improve

1M+ downloads
4.6 ¡ 8k+ Reviews