This tutorial is superposition theorem. The superposition theorem states:
‘In any network which is made up of linear resistances & containing more than 1 source of e.m.f., the resultantcurrent flowing in any branch is the algebraic sum of the currents that would flow in that branch if eachsource was considered separately, all other sourcesbeing replaced at that time by their respective internal resistances.’
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)
‘In any network which is made up of linear resistances & containing more than 1 source of e.m.f., the resultantcurrent flowing in any branch is the algebraic sum of the currents that would flow in that branch if eachsource was considered separately, all other sourcesbeing replaced at that time by their respective internal resistances.’
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