Artificial intelligence in power system
Power system is one of the large interconnected network that may be mesh connected or radial. With the expansion of our network due to the increasing demand of electrical load inclusion of several generating stations and sub station has become necessary. Each substation acts as a bus (busbar). Thus depending upon the geography of a country the number of these buses vary. Artificial intelligence in power system
A system of almost 28000 buses do exists. In such a big system apart from generator and loads there are several other components like shunt capacitor, series capacitor or reactor or renewable energy sources also called as DGs which are to be placed at certain location in the system which increases the system reliability and reduces losses and many more. So for such a large system we need to go for optimization. Artificial intelligence in power system Optimization is the major application in the power system for placement of these components or optimal scheduling of the generators. For these application AI TECHNIQUES are used these days. A lot of research is going on in this field using lot of optimization techniques available like genetic algorithm, PSO , Artificial bee colony etc. Artificial intelligence in power system
Artificial intelligence in power system Artificial Neural Networks are biologically inspired systems which convert a set of inputs into a set of outputs by a network of neurons, where each neuron produces one output as a function of inputs. A fundamental neuron can be considered as a processor which makes a simple non linear operation of its inputs producing a single output. The understanding of the working of neurons and the pattern of their interconnection can be used to construct computers for solving real world problems of classification of patterns and pattern recognition. Artificial intelligence in power system
Advance technology and time
Artificial intelligence in power system They are classified by their architecture: number of layers and topology: connectivity pattern, feed forward or recurrent. Input Layer: The nodes are input units which do not process the data and information but distribute this data and information to other units. Hidden Layers: The nodes are hidden units that are not directly evident and visible. They provide the networks the ability to map or classify the nonlinear problems. Output Layer: The nodes are output units, which encode possible values to be allocated to the case under consideration.
Features of advance AI | Advantages / DisAdvantages
1.1. Advantages: Artificial intelligence in power system
(i) Speed of processing.
(ii) They do not need any appropriate knowledge of the system model.
(iii) They have the ability to handle situations of incomplete data and information, corrupt data.
(iv) They are fault tolerant.
(v) ANNs are fast and robust. They possess learning ability and adapt to the data.
(vi) They have the capability to generalize.
1.2. Disadvantages: Artificial intelligence in power system
(i) Large dimensionality.
(ii) Results are always generated even if the input data are unreasonable.
(iii) They are not scalable i.e. once an ANN is trained to do certain task, it is difficult to extend for other tasks
without retraining the neural network.
1.3. Applications: Artificial intelligence in power system
Power system problems concerning encoding of an unspecified non-linear function are appropriate for ANNs. ANNs can be particularly useful for problems which require quick results, like those in real time operation. This is because of their ability to quickly generate results after obtaining a set of inputs.
1.4. How ANNs can be used in power systems:
As ANNs operate on biological instincts and perform biological evaluation of real world problems, the problems in generation, transmission and distribution of electricity can be fed to the ANNs so that a suitable solution can be obtained. Given the constraints of a practical transmission and distribution system, Artificial intelligence in power system the exact values of parameters can be determined. For example, the value of inductance, capacitance and resistance in a transmission line can be numerically calculated by ANNs taking in various factors like environmental factors, Artificial intelligence in power system
unbalancing conditions, and other possible problems. Also the values of resistance, capacitance and inductance of a transmission line can be given as inputs and a combined, normalized value of the parameters can be obtained. In this way skin effect and proximity effect can be reduced to a certain extent. Artificial intelligence in power system