Contributions to Power Grid System Analysis Based on Clustering Techniques.

Area of Science:

• Electrical Engineering
• Computer Science
• Artificial Intelligence

Background:

• Modernizing power systems is essential for integrating smart technologies.
• Smart grids, smart metering, smart homes, and electric cars are interconnected concepts.
• Advancements in AI, telecommunications, and computing infrastructure are key enablers.

Purpose of the Study:

• To model consumer classification and load profiling in electrical power networks.
• To evaluate the efficiency of clustering techniques for consumer profiling.
• To simulate the load on distribution transformers and electricity meters.

Main Methods:

• Development of models for consumer classification and load profiling.
• Application and simulation of clustering techniques for data analysis.
• Load flow simulation from medium-voltage/low-voltage transformers to meters.

Main Results:

• Demonstrated effectiveness of clustering techniques in classifying consumers.
• Provided insights into load profiling accuracy for smart grid applications.
• Quantified load simulations for distribution network components.

Conclusions:

• Accurate load profiling and consumer classification are vital for smart grid stability.
• Clustering techniques offer an efficient approach to understanding consumer behavior.
• The study provides a foundation for optimizing power distribution and management.