Which statement best describes the relationship between Thevenin and Norton equivalents?

Which statement best describes the relationship between Thevenin and Norton equivalents?

• A. They are alternate representations that yield the same terminal behavior for linear networks.
• B. They are mutually exclusive; you must choose one.
• C. Thevenin uses a current source in parallel with a resistor.
• D. They apply only to AC circuits.

Answer: (A)

The important idea is that Thevenin and Norton are two interchangeable ways to model a two-terminal linear network so that the terminals behave the same for any load. A Thevenin representation uses a voltage source in series with a resistor, while a Norton representation uses a current source in parallel with a resistor. They are equivalent because they produce the same I–V relationship at the terminals, so any load sees the same voltage and current regardless of which form you use. You can convert between them with Vth = In × Rth (and In = Vth / Rth). For example, a Thevenin model with 12 V and 5 Ω corresponds to a Norton model of 2.4 A in parallel with 5 Ω, and the load current for any RL will be the same in either representation. This equivalence holds for linear networks and lets you choose the form that makes analysis easiest.

The important idea is that Thevenin and Norton are two interchangeable ways to model a two-terminal linear network so that the terminals behave the same for any load. A Thevenin representation uses a voltage source in series with a resistor, while a Norton representation uses a current source in parallel with a resistor. They are equivalent because they produce the same I–V relationship at the terminals, so any load sees the same voltage and current regardless of which form you use. You can convert between them with Vth = In × Rth (and In = Vth / Rth). For example, a Thevenin model with 12 V and 5 Ω corresponds to a Norton model of 2.4 A in parallel with 5 Ω, and the load current for any RL will be the same in either representation. This equivalence holds for linear networks and lets you choose the form that makes analysis easiest.