Abstract
This chapter is about the intelligent techniques for the analysis of power quality problems in electrical power distribution system. The problems related with electrical power industry are becoming more widespread, complex, and diversified. The behaviour of power distribution systems can be monitored effectively using artificial intelligence techniques and methodologies. There is a need of understanding the power system operations from power utility perspectives and application of computational intelligence methods to solve the problems of the power industry. The real power quality (PQ) data is taken from a power utility in Victoria Australia. Principal Component Analysis Technique (PCAT) is used to reduce the large number of PQ data attributes of the power distribution system. After the pre-processing of PQ data using PCAT, intelligent computational techniques will be used for the analysis of power quality data. Neural network techniques will be employed to estimate the values of PQ parameters of the power distribution system. The Feed Forward Back Propagation (FFBP) neural network and Recurrent Neural Networks (RNN) are used for intelligent estimation of PQ data. The results obtained through these intelligent techniques are compared with the real data of power utility in Victoria, Australia for stability, reliability and enhanced power systems performance.
TopPower Distribution Network
Power distribution network holds a pivotal position in entire electrical power system. An electrical power system consists of mainly three components:
- 1.
Generating stations
- 2.
Transmission systems
- 3.
Distribution systems
These three components of electrical power systems are integrated together to supply electricity to consumers (Paracha & Doulai, 1998).
The typical power distribution system consists of power distribution networks which consist of high voltage distribution lines having a rating of 11kV, 22kV or 33kV. The traditional power distribution network will have these high voltage lines as overhead lines coming out of the substation. With the modern power distribution network the overhead high voltage distribution lines are being replaced by underground lines to ensure safety, reliability and considering the environmental impact of the power distribution network. In addition to high voltage distribution lines power distribution network consists of transformers and other auxiliary equipment in substations to ensure smooth availability of quality supply power to consumers. The most important requirement to run power system operation in advanced global world is to have sustained availability of quality and reliable supply of electric power. Power utilities around the world are focused on delivering a greater quality of power delivery due to increased customer competition in modern day challenging environment. There is an on-going demand for more reliable and inexpensive supply of power. PQ has become widely important and is a matter of concern to all of its stakeholders as it directly affects the running of their smooth operations.
TopImportance Of Power Quality Monitoring In Electrical Power System
Power Quality has become widely important and is a matter of concern to all of its stakeholders as it directly affects the running of their smooth operations. However, it is interesting to note that different stakeholders view power quality in different ways.
Utility Perspective
Power companies and utilities are interested on the provision of sustained power supply to their customers. So they focus on monitoring and troubleshooting of all those PQ issues which enable them to provide continuous power supply to their customers without any interruptions. With the liberalization of power industry and establishment of independent power plants (IPPs), their customers have the right to demand the higher quality of supply power at all times (Sakthivel et al., 2003).
Key Terms in this Chapter
Recurrent Neural Network (RNN): Artificial intelligence technique for accurate estimation of data: In our case it is power quality data
Principal Component Analysis Technique (PCAT): Technique to convert data with multiple attributes to two dimensional data
Power Quality (PQ): Quality of Electric Power Supply
Feed Forward Back Propagation (FFBP): Artificial intelligence technique for accurate estimation of data: In our case it is power quality data