Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy...

Area of Science:

• Electrical Engineering
• Computer Science
• Data Science

Background:

• The Internet of Things (IoT) is crucial for modernizing electric power grids into smart grids (SGs).
• Harsh power grid environments introduce errors (measurement, quantization, transmission) into IoT data, compromising SG reliability.
• Traditional error mitigation techniques (redundancy, retransmission) are resource-intensive, increasing energy use and latency.

Purpose of the Study:

• To propose a novel statistical information fusion method for enhancing data accuracy in noisy wireless sensor networks within smart grid environments.
• To address data inaccuracies in both structured (chain, tree) and unstructured (graph) sensor network topologies.
• To evaluate the proposed method's performance against existing distributed estimation algorithms.

Main Methods:

• Development of a novel statistical information fusion algorithm.
• Simulation-based evaluation of the method's accuracy, energy savings, fusion complexity, and latency.
• Derivation of analytical upper bounds for aggregated value variance in structured networks.

Main Results:

• The proposed fusion method significantly outperforms existing distributed estimation algorithms across various network structures.
• Demonstrated superior performance in terms of fusion accuracy, complexity, and energy consumption in smart grid communication environments.
• Analytical bounds confirm the method's effectiveness in mitigating errors for aggregated data.

Conclusions:

• The novel statistical information fusion method offers a robust solution for improving data integrity in smart grids.
• The method provides significant advantages in accuracy, efficiency, and resource management compared to traditional approaches.
• This work contributes to the development of more reliable and efficient smart grid infrastructures through advanced data fusion techniques.