This document discusses the use of artificial intelligence techniques in power systems. It first provides background on power systems and defines artificial intelligence. It then discusses the need for AI in power systems due to complex data and computations. The main AI techniques discussed are expert systems, artificial neural networks, and fuzzy logic. Expert systems encode human expertise to solve problems while artificial neural networks and fuzzy logic can handle uncertain or incomplete data. The document outlines various applications of AI in power system operations, planning, control, automation, and forecasting and concludes that AI can increase power system efficiency and reliability.

1. Ganesh Bhandari
Himal Chaulagain
Paras Subedi
Pankaj Sah

2. Introduction
Power System
 Network of electrical components used to generate,
transmit and use electric power
 Main components:
generating station
transmission lines
distribution system

3. Artificial Intelligence
 Founded at conference on
the campus of Dartmouth
College in 1956
 AI is the ability of a computer
to act like human beings.
 Intelligence exhibited by
machines and software such
as robots and computer
programs
 Used to the project of
developing systems equipped
with the intellectual
processes features and
characteristics of human

4. Need for A.I. in power system
 Power system analysis by conventional technique
becomes more difficult because of
`(i) Complex, versatile and large amount of information
which is used in calculation, diagnosis and learning.
(ii) Increase in the computational time period
(iii) Extensive and vast system data handling

5. Artificial intelligence techniques
 Three major families of AI techniques
(i) Expert System Technique
(ii) Artificial Neural Network
(iii) Fuzzy Logic

6. Expert system techniques
 Set of computer
programs that
manipulates
knowledge to solve
problem in specified
area
 Obtain the knowledge
of human expert in a
narrow specified
domain into a
machine
implementable form

7.  Use interface mechanism and knowledge to solve the
problem
 Writing code is better and simpler than actually
calculating and estimating value of power system
network parameter
 Tested by being placed in the same real world problem
solving situation

8. Advantages
 Permanent and consistent
 Easily documented
 Easily transferred or reproduced
Disadvantages
 Unable to learn or adapt to new problems or situations

9. Artificial Neural Network
 Derived from biological neuron
 Covert a set of input into a set of output by a network
of neurons
 Each neuron produces one output as a function of
inputs
 Neurons are treat like a processor which make simple
non-linear operation of its input producing a single
output
 Working of neurons and pattern of their
interconnection can be used to construct computers
for solving real world problems

10.  Architecture: Three layers
(i) Input layer
(ii) Hidden layer
(iii) Output layer
 Problems in generation, transmission and distribution
of electric energy is fed to the ANNs so that a suitable
solution can be obtained
 Line parameter can be numerically calculated by
ANNs taking various factor like environmental factors,
unbalancing conditions

11. Architecture of ANN of
Architecture of a ANN
Typical structure of an ANN

12. Advantages:
 Speed of processing
 Ability to handle situations of incomplete data and
information, corrupt data
 Fault tolerant
 Capability to generalize
Disadvantages:
 Large dimensionality.
 Results are always generated even if the input data are
unreasonable.
 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

13. Fuzzy logic
 First proposed in early 1990’s by
Zadeh
 Form of knowledge
representation suitable for
notations that cannot be defined
precisely, distinctly or clearly
 Similar to human decision
making with ability to produce
exact and accurate solution from
certain approximate information
and data

14.  Works like human brain and can be implement this
technology in machines
 Use for desigining the physical components of power
system
 As most of the data used in power system analysis are
approximate values and assumption, Fuzzy logic can
be of greatly use to derive a stable, exact and
ambiguity-free output

15. Benefits of using fuzzy logic

16. Application of AI in power system
 Operation of power system like hydro-thermal
coordination, maintenance scheduling, load and
power flow.
 Planning of power system like generation
expansion planning, power system reliability,
transmission expansion planning, reactive power
planning.
 Control of power system like voltage control,
stability control, power flow control, load
frequency control.

18.  Automation of power system like restoration,
management, fault diagnosis, network security
 Applications of distribution system like planning and
operation of distribution system, demand side
response and demand side management, operation
and control of smart grids, network reconfiguration
 Applications of distributed generation like distributed
generation planning, solar photovoltaic power plant
control, wind turbine plant control and renewable
energy resources
 Forecasting application like short term and long term
load forecasting, electricity market forecasting

19. Result
 Replacing human workers for dangerous and
highly specialized operations
 Operation in hazardous environments, such
as radioactive locations in nuclear plants
 Fuzzification provides superior expressive
power, higher generality and an improved
capability to model complex problems at low
or moderate solution cost.
 Stability analysis and enhancement.

20.  Power system control, fault diagnosis and load
forecasting
 Reactive power planning and its control
 Automation of power system like restoration
management, fault diagnosis, network security
 Can be used in anything from small circuits to large
mainframes
 Can be used to increase the efficiency of the
components used in power systems

21. Conclusion
 The main feature of power system design and planning is
reliability. Conventional techniques don't fulfill the
probabilistic essence of power systems.This leads to
increase in operating and maintenance costs.Plenty of
research is performed to utilize the current interest on
Artificial Intelligence for power system applications.
 A lot of research is yet to be performed to perceive full
advantages of this upcoming technology for improving
the efficiency of electricity market investment,
distributed control and monitoring, efficient system
analysis, particularly power systems which use
renewable energy resources for operation

22. Any queries???

23. THANK YOU!!!