Classification of Electrical Network:
Electrical Networks and their classification– In this article we will be looking at classification of Electrical Network. An electrical network can be mainly divided into the following 5 different categories.
- Active and passive network
- Unilateral and Bilateral network
- Lumped and Distributed network
- Linear and Non-linear network
- Time invariant and Time variant network
The above classification mainly depends on two things, the very first one is the properties of the circuit elements which are used in the electrical network and the second most important thing is the response of the electrical network for the different excitations.
So, the first three types of the electrical networks are based on the properties of the circuit elements and the remaining two kinds of the electrical networks are based on the response of the electrical network for the different excitation. So, let’s see them one by one.
Active and passive network:
Let’s, first of all, understand the active and the passive elements. The active elements are the elements which are capable of delivering energy to the external devices.
The example of the active elements is the voltage and the current sources. So, now let’s see the passive elements. Thus, the energy elements are capable of accepting energy or power. The example of passive elements is the resistor, capacitor, and inductor. About the resistor you already know it dissipates energy in the form of heat. While the capacitor and the inductor store the limited amount of energy. Furthermore, later on, they can deliver the energy for the limited measure of time. So the capacitor and the inductor can deliver the energy for the limited measure of time. In contrast to the active elements of the electrical network, which are capable of delivering the energy for the boundless or infinite time. In this way, here we will add one term in the definition of active elements, that is they are capable of delivering the energy for the limitless time or for the infinite time. So if the electrical network contains active elements like voltage and current source can be called as the active network. Moreover, the network which contains the elements like the resistor, capacitor and inductor can be called as a passive network.
Unilateral and bilateral network:
Thus, how about we first understand the unilateral and bilateral elements used in the electrical network.
Bilateral elements are those elements in which the current can flow in both the directions. The most popular examples of the Bilateral elements is the Resistor, Capacitor, and the Inductor. If you take a look at the terminals of the resistors, capacitor, and inductor you will find that these elements are two terminal passive electronic devices, the current can enter through any of the two terminals, so the current can flow in any direction and there is no restriction on the current flow. So this is why the resistors are not provided with polarity signs, you can connect a resistor in any way, you don’t have to be worried about the correct orientation. So resistors got no polarity.
Unilateral Elements are those element in which the current can flow only in one direction. The most popular example is the Diode. The Diode is a passive electronic component and it allows the current to flow only in one direction, so we can say it restricts the flow of current only in one direction. Unlike the resistor and inductor a diode is also a two terminal electronic device. The two terminals are the Anode and Cathode. The Anode is the Positive while the cathode is the negative. Another example of the unilateral element is the transistor. A transistor also allow the flow of current only in one direction and this is because if you look the construction of a transistor you will find that the transistors are basically made up of the diodes “PNP or NPN”.
So, now after understanding the Bilateral and Unilateral elements it easy to define the Bilateral and Unilateral Network. If an electrical network consists of unilateral elements then the electrical network is called Unilateral Network and the most common example of the Unilateral network is the Rectifier circuit. If the electrical network consists of the Bilateral elements then the electrical network will be called as the Bilateral Network.
Lumped and Distributed Network:
Thus, in the lumped network, circuit elements like resistor, capacitor, and inductor can be separated physically.
In this way, in the lumped network we can undoubtedly eliminate such elements from the network, we can measure them, we can supplant them. Along these lines, on the off chance that the network contains such discrete elements, then such network can be called as lumped network. Now let’s understand what is a distributed network? in contrast to the lumped network, in the distributed network, we can’t separate the circuit elements like resistor, capacitor and the inductor, as they all go about as a solitary element. Furthermore, they are distributed along the length of the network. The example of the distributed network is the transmission line or coaxial cable. In this way, on the off chance that you see the specification of any co-axial cable, they used to characterize the resistance, capacitance and the inductance per unit length, as they are distributed along the length of the network.
Linear and non-linear network:
The fourth category in the electrical network classification is the Linear and non-linear network. So in a linear network the relation between the input and output is linear, it’s just that simple. Similarly, the verse is the non-linear network that is in the non-linear network the relation between the input and output is non linear. Additionally, the linear network follows the principle of superposition. That implies they have two properties, homogeneity, and the additivity. Thus, we should first observe the property of homogeneity. In this way, we should first accept that here we have one electrical network.
In this network, we are applying 10 V as input. Furthermore, in the response, we are getting a 2 V as an output, which is one-fifth of the input voltage. Thus, presently we should simply scale it up the input voltage by the factor of two. Presently the input voltage is 20 V. Presently, in the event that the network follows the property of homogeneity, then whenever we apply 20 V as input, then at the output we ought to get 4 V. That implies the response ought to likewise get scaled by a similar factor. Thus, in an overall way we can say that in the event that E1 is the excitation and R1 is the response, then whenever we scale it up the excitation by the factor of alpha, then the response ought to likewise get scaled by a similar factor. That is a property of homogeneity. Presently, how about we see the property of additivity. Presently, we should simply expect that we have one network. In this network whenever we are applying 10 V as input, we are getting a 4 V as output. In this network, whenever we are applying a 20 V as input, we are getting 8V as output. Thus, presently how about we simply add this two input voltages and apply it as an excitation to this network. In the event that the network follows the property of additivity, then the output response should be the summation of the individual responses. That implies whenever we apply 30 V as input, then at the output, we ought to get a response of 12 V. Or on the other hand in an overall manner, we can say that whenever we are applying E1 + E2 as input, then in the reaction we ought to get R1 + R2. Where R1 and R2 are the individual responses for excitation E1 and E2 respectively. In this way, that is a property of additivity. Presently we should simply combine the properties of additivity and the homogeneity. In this way, If we apply excitation, (Alpha* E1) + (beta*E2) to the network, then on the off chance that we are getting a response (alpha*R1) + (beta*R2) then we can say that the network is a linear. Or on the other hand in another manner, on the off chance that the network follows the principle of superposition, then we can say that the network is linear. Or on the other hand on the off chance that it doesn’t follow this superposition theorem, then we can say that the network is non-linear.
Time invariant and the time variant network:
Let’s assume that we have one network, and in this network, we are applying excitation E1 and at the response, we are getting Response R1. Presently the network can be called time invariant if the response R1 is independent of the time at which this excitation is applied. That implies on the off chance that we apply this excitation at time t or time t+T, and in both the cases on the off chance that we are getting same response R1 or in another manner if this response R1 is independent of the time at which this excitation is applied then we can say that the network is time invariant. Along these lines, on account of a time-variant network, the response R1 relies on the time at which the excitation is applied. We will get response R1 when we are applying excitation at time t. Presently, in the event that we apply a similar excitation at time t+T, then we will get a different response. Let’s say R2. In this way, in such case, we can say that the network is time variant network.