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<p>The steady-state characteristic parameters of the loads are used to identify new energy grid-connected loads using an event-based network analysis method in this paper. The analysis of interdependence among elements is done by studying the element layer and special structure of the network analysis method. Using the principle of limit relative ranking vector calculation, the supermatrix and weighting matrix of the ANP element layer are constructed, and the basic steps of ANP model weights are determined. The ANP-based load monitoring algorithm is evaluated by combining the load identification accuracy with the F-value. The results show that event detection algorithms are generally more than 70% accurate. 17 out of 18 times of identifying load events of computers can be correctly identified with an accuracy rate of 89.37%, 9 out of 10 times of identifying fluorescent lamps with an accuracy rate of 89.98%, and 14 out of 15 times of identifying microwave ovens with an accuracy rate of 92.75%. The new energy grid-connected load can also be detected by combining the harmonic content rate. The harmonic content rates when the desktop computer was turned on were 93.04%, 86.67%, 60.16%, 61.76%, and 23.46%, respectively. This study helps to improve the accuracy of new energy grid-connected load monitoring.</p>
</abstract>

The steady-state characteristic parameters of the loads are used to identify new energy grid-connected loads using an event-based network analysis method in this paper. The analysis of interdependence among elements is done by studying the element layer and special structure of the network analysis method. Using the principle of limit relative ranking vector calculation, the supermatrix and weighting matrix of the ANP element layer are constructed, and the basic steps of ANP model weights are determined. The ANP-based load monitoring algorithm is evaluated by combining the load identification accuracy with the F-value. The results show that event detection algorithms are generally more than 70% accurate. 17 out of 18 times of identifying load events of computers can be correctly identified with an accuracy rate of 89.37%, 9 out of 10 times of identifying fluorescent lamps with an accuracy rate of 89.98%, and 14 out of 15 times of identifying microwave ovens with an accuracy rate of 92.75%. The new energy grid-connected load can also be detected by combining the harmonic content rate. The harmonic content rates when the desktop computer was turned on were 93.04%, 86.67%, 60.16%, 61.76%, and 23.46%, respectively. This study helps to improve the accuracy of new energy grid-connected load monitoring.

Research on new energy grid-connected load monitoring method based on the network analysis method

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The steady-state characteristic parameters of the loads are used to identify new energy grid-connected loads using an event-based network analysis method in...

Research on new energy grid-connected load monitoring method based on the network analysis method

Pubblicato online: 25 nov 2023

Pagine: -

Ricevuto: 12 gen 2023

Accettato: 05 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01240

Parole chiave
Network analysis method, Load monitoring algorithm, Matrix construction, Recognition accuracy, F-value

© 2023 Qing Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on new energy grid-connected load monitoring method based on the network analysis method

Pubblicato online: 25 nov 2023

Pagine: -

Ricevuto: 12 gen 2023

Accettato: 05 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01240

Parole chiaveNetwork analysis method, Load monitoring algorithm, Matrix construction, Recognition accuracy, F-value

© 2023 Qing Wang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Network analysis method, Load monitoring algorithm, Matrix construction, Recognition accuracy, F-value