Artificial intelligence techniques like artificial neural networks, fuzzy logic, and expert systems can help address complex problems in power systems that were previously difficult to solve. They have applications in areas like economic load dispatch, load forecasting, transmission capacity and optimal power flow, generator limits, and system protections. By using environmental sensors and AI techniques together, the performance of transmission lines can be improved by changing line parameters based on conditions and diagnosing and addressing any faults. AI provides benefits like increased reliability, efficiency, and cost savings within power systems.

1. Artificial Intelligence: An Advanced
Approach in Power Systems

2.  Early to mid-1980s, providing a solution to complex problems in many areas of
power system engineering was tough and tedious.
 Presently with Artificial Intelligence (AI), many constraints can be handled
easily such as
 economic load dispatch,
 load forecasting,
 optimisation of generation and scheduling,
 transmission capacity and optimal power flow,
 real and reactive power limits of generators,
 bus voltages and transformer taps,
 load demand in interconnected large power system and their
protections etc.
 Now, most of the efforts in power system analysis have been successfully
reduced by AI techniques.

3. Power Systems:
Power system engineering is an important branch of electrical engineering
that deals with the generation, transmission, distribution, and utilisation of electric
power.

4. Artificial Intelligence:
AI is the science of automating intelligent behaviours presently
accomplishable by a computer interfaced with machines like robots.
Artificial General Intelligence (AGI) is the intelligence of a hypothetical
machine or computer which can accomplish any intellectual assignment
successfully which a human being can accomplish.

5. Necessity of AI in Power Systems:
For industrial development with power system expansion; stability,
strengthening, reliability, technical advancements, selection and dynamic response of
the power system are essential.
With the growth of the power system, complexity in the networks is
increased tremendously.
As a consequence of this power system analysis by conventional techniques
and conclusions from the acquired data, the process for the information, management
of remote devices and utility became more complicated and time-consuming.
As necessity is the mother of invention, AI is developed with the help of
sophisticated computer tools and applied to resolve all aforesaid problems for large
power systems.

6. AI Techniques:
Modern AI technologies include the following techniques:
 Artificial Neural Networks (ANNs)
 Expert System Techniques (XPS)
 Fuzzy Logic systems (FL)
 Genetic algorithm (GA)
These are the major families of AI techniques which are considered in the
field of modern power system.

7. AI – Exposure in Power System:
 Several problems in power systems cannot be solved by conventional techniques.
 Therefore, AI techniques in power system applications are being focused widely.
 Particular emphasis has been put on Artificial Neural Networks (ANN), Fuzzy
Logic (FL) and Expert system (XPS).
 Some of the areas of the power system applications are highlighted here.
1) Economic load dispatch, generation and operational planning based on
load forecasting, optimisation of hydrothermal generation scheduling.
2) Power transmission capacity and optimal power flow, real and reactive
power limits of generators, and system reliability.
3) Control of voltage and frequency for system stability, sizing and control
of FACTS devices.

8. 4) Analysis of electricity markets and strategies for bidding.
5) Automation for power restoration and management, fault diagnosis, and
security margins.
6) Planning and operation of distribution, network reconfiguration,
demand-side response and management, operation and control of smart grids.
 Some typical application of power system protection, ANN application, have
been introduced to CT and VCT transient correction.
 For digital relays, fuzzy criteria signals, fuzzy settings, and multi-criteria decision
making have been applied.
 FL and ANN application is applied for Differential protection for power
transformers.

9. AI – Techniques for Transmission Line Performance
Improvement:
 A practical application to improve the performance of transmission line is
described with the help of a combination of AI techniques.
 To improve the performance of a transmission line, the following functions are
allotted to various AI techniques as:
1) Fuzzy systems: To diagnose the fault.
2) ANNs: Trained to change the values of line parameters based on
environmental conditions.
3) Expert systems: To deploy outputs as a value of line parameters.
4) Environmental sensors: To sense the environmental and atmospheric
conditions and provide input to the expert systems.

10.  If any fault occurs in the transmission line, the angular difference between
phasors of fault and pre-fault current is detected and fed to the fuzzy system for
diagnosis.
 The environmental sensors sense the environmental and atmospheric conditions
as inputs to the expert systems.
 The expert systems provide the value of line parameters to be deployed as the
output.
 ANNs improves and check the performance corresponding to the parameters
provided by environmental sensors, if needed it changes the line parameters within
the specified range to achieve the desired performance of the line.
 Since the processing speed is directly proportional to the number of neurons,
therefore, to improve the performance up to the desired level, a number of hidden
layers and a number of neurons in each layer can be varied.
 To acquire desired output, networks take different activation functions between
input and hidden layer and hidden and output layer. Similarly, different neurons can
also be taken for different layers.

11. AI – New Applications in Power Systems:
 Many problems in power systems are based on several non-feasible
requirements.
 Therefore, AI techniques are the only option to solve them.
 Current approach of AI in power system applications are:
1) Planning for Generation expansion, power system reliability,
transmission expansion, and reactive power.
2) Control of voltage, frequency and stability, and power flow.
3) Control of a Fuel Cell and thermal power plant.
4) Automation for restoration management, fault analysis and network
security.
5) Planning and operation of the distribution system, demand-side
response and management, smart grids operation and control, and network
reconfiguration.
6) Forecasting for electricity market, solar power, and wind power.

12. Saving Potential in AI:
 To avoid an impact on the environment, reliable and efficient power supply has
become an important need of the world.
 This is being achieved by close monitoring of the power system equipment and
consumption.
 It needs AI-based techniques which are highly reliable, accurate and automated
systems such as EMS, Intelligent sub-station ornamented by high-speed protection,
monitoring, and communication systems.
 With the promotion of these developments by AI techniques, savings can be
achieved in the field of remote monitoring of equipment, operation, maintenance,
and production.
 Plenty of research has been performed, and a lot of research is yet to be
performed to derive full advantages of AI technology for cost reduction by
improving the efficiency of the power system, distributed control and monitoring
system, renewable energy resources system, and electricity market and investment
system also.