Power electronics imp MCQs for SSC JE RRB JE 2025

Power Electronics – Previous Year Questions (ECE)

1. In a single-phase full converter feeding an R–L load, the average output voltage is controlled by varying:
   A) Supply frequency
   B) Firing angle ✅
   C) Load resistance
   D) Load inductance
2. For a single-phase half-wave controlled rectifier with a resistive load, the average output voltage is:
   $$V_{avg} = \frac{V_m}{2\pi}(1 + \cos\alpha)$$
3. In a full-bridge inverter using 180° conduction mode, how many switches conduct simultaneously?
   A) 1
   B) 2 ✅
   C) 3
   D) 4
4. A single-phase full converter feeds a 10 Ω load. The input voltage is 230 V, 50 Hz. If firing angle is 60°, the average output voltage is:
   $$V_{avg} = \frac{2V_m}{\pi}\cos\alpha = \frac{2 \times 325}{\pi} \times \cos60° \approx 103.6V$$
5. A freewheeling diode in a converter circuit:
   A) Increases output voltage
   B) Decreases output current
   C) Provides continuity of current in inductive loads ✅
   D) Blocks reverse current
6. A step-down chopper operates from 220 V DC supply with duty ratio 0.4. Average output voltage:
   $$V_o = 0.4 \times 220 = 88V$$
7. In a step-up chopper, output voltage is:
   $$V_o = \frac{V_s}{1 - \alpha}$$
8. In a class-C chopper, power flows:
   A) From source to load
   B) From load to source
   C) In both directions ✅
   D) None
9. The control characteristic of an SCR shows that latching current is:
   A) > Holding current ✅
   B) < Holding current
   C) = Holding current
   D) Not related
10. The turn-off time of a thyristor depends on:
    A) Gate current
    B) Forward voltage
    C) Device parameters and circuit conditions ✅
    D) Frequency only
11. In a single-phase bridge inverter using square wave, the output contains:
    A) Only fundamental
    B) Odd harmonics only ✅
    C) Even harmonics only
    D) Both even and odd
12. In a cycloconverter, the frequency of output is:
    A) Equal to supply
    B) Lower than supply ✅
    C) Higher than supply
    D) Independent of supply
13. In a single-phase AC voltage controller with resistive load, RMS output voltage:
    $$V_{o,rms} = V_m \sqrt{\frac{1}{2\pi}(\pi - \alpha + \frac{\sin2\alpha}{2})}$$
14. A TRIAC can conduct in:
    A) One quadrant
    B) Two quadrants ✅
    C) Three quadrants
    D) Four quadrants
15. In a DC-DC buck converter, increasing duty cycle will:
    A) Increase output voltage ✅
    B) Decrease output voltage
    C) No effect
    D) Reverse current
16. Snubber circuits are used for:
    A) Reducing dv/dt ✅
    B) Increasing gate current
    C) Reducing conduction losses
    D) Reducing firing angle
17. In a three-phase full converter, the output DC voltage:
    $$V_{dc} = \frac{3V_m}{\pi}\cos\alpha$$
18. In PWM inverters, harmonics are reduced by:
    A) Increasing carrier frequency ✅
    B) Reducing modulation index
    C) Reducing switching frequency
    D) Using square-wave control
19. Commutation in an SCR means:
    A) Turning ON the device
    B) Turning OFF the device ✅
    C) Increasing current
    D) Limiting voltage
20. In a single-phase full converter feeding RLE load, for continuous current, the average output voltage:
    $$V_{dc} = \frac{2V_m}{\pi}\cos\alpha$$

Read also : transient analysis RL and RC 

Huffman Coding in Communication Systems

Huffman Coding in Communication Systems

Introduction

Data compression plays a crucial role in modern communication systems. Whether it’s reducing the size of files, transmitting multimedia content, or improving storage efficiency, compression techniques help achieve faster and more reliable communication. One of the most widely used lossless compression techniques is Huffman Coding.

Huffman coding is a method to assign variable-length codes to input characters, with shorter codes assigned to more frequent characters. This ensures that the total number of bits required to represent the data is minimized, leading to efficient data compression.

Basic Concept of Huffman Coding

The main idea behind Huffman coding is:

1. Frequency-based coding: Characters with higher frequency get shorter codes, while characters with lower frequency get longer codes.

2. Prefix-free codes: No code is the prefix of another, which avoids ambiguity during decoding.

3. Binary tree representation: A Huffman tree is constructed to generate the variable-length codes.

Steps in Huffman Coding

1. Count the frequency of each symbol in the data.

2. Arrange the symbols in ascending order of frequency.

3. Build a binary tree:

Take two nodes with the smallest frequency.

Combine them into a new node whose frequency is the sum of the two.

Repeat until only one node (the root) remains.

4. Assign binary codes:

Traverse the tree, assigning 0 to the left branch and 1 to the right branch.

The resulting codes form the Huffman code for each character 

Example of Huffman Coding

Let’s consider the message:

DATA COMPRESSION

Step 1: Frequency of characters

D → 1

A → 2

T → 1

C → 1

O → 1

M → 1

P → 1

R → 1

E → 1

S → 2

I → 1

N → 1

Step 2: Construct Huffman Tree

Combine lowest-frequency nodes step by step.

Continue until one root node remains.

(Due to space, we won’t draw the full tree here, but in a blog you can insert an image showing the Huffman tree for clarity.)

Step 3: Assign Huffman Codes

An example set of Huffman codes could be:

A → 010

S → 111

D → 0000

T → 0001

O → 0010

M → 0011

P → 0110

R → 0111

E → 1000

I → 1001

N → 1010

C → 1011

(Note: Exact codes may differ depending on how ties are handled, but the total compressed length will remain the same.)

Step 4: Compressed Output

Original text = "DATA COMPRESSION"

Original size = 15 characters × 8 bits = 120 bits

Huffman encoded size ≈ 50–60 bits (depends on frequency distribution).

Thus, Huffman coding achieves nearly 50% compression in this case.

Applications of Huffman Coding

File compression tools (e.g., ZIP, GZIP)

Image compression (JPEG uses Huffman coding in its entropy coding stage)

Multimedia communication (audio, video codecs)

Data transmission in communication systems

Conclusion

Huffman coding is a fundamental and efficient lossless data compression algorithm widely used in communication and storage. By assigning shorter codes to frequently occurring symbols and longer codes to rare symbols, Huffman coding minimizes redundancy and optimizes data transfer.

It remains one of the simplest yet most powerful algorithms in the field of data compression.