Noninvasive Load Identification Based on Combined Weighting-TOPSIS of Multifeature Fusion

Abstract

The traditional nonintrusive load identification (NILD) algorithms result in high computational costs based on classification models. And they cannot accurately identify loads with similar voltage-current trajectories that are frequently encountered in practical applications. Aiming at these deficiencies, an NILD algorithm is proposed based on combined weighting-the technique for order preference by similarity to ideal solution (TOPSIS) of feature fusion. The feature is fused by one image feature and eight numerical features with the combination weighting method. The weights are calculated by combining the principal component analysis, entropy, and the criteria importance through intercriteria correlation weighting methods to improve the utilization of features. The similarity between these nine features of a load and the corresponding features of other loads is calculated by the TOPSIS algorithm. Similarity analysis is used to determine whether or not a load is a "known load," and to obtain an identification result. If it is an "unknown load", the result can be obtained by dynamically updating the database and identifying it again. The results obtained indicate that this proposed algorithm can significantly improve the accuracy of load identification while reducing the computational costs.

Keywords:

• NILD
• PCA
• Entropy Weighting
• CRITIC
• TOPSIS

AUTHORS’ CONTRIBUTIONS

Chunning wrote and revised the manuscript. Na Luo performed the experiments, Feng Li and Huan Pan validated the experiment results.

COMPETING INTERESTS

No competing interests were disclosed.

ACKNOWLEDGMENTS

The authors thank the anonymous reviewers for their helpful suggestions and comments that retrofitted this work.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation of China under Grant No. 52167006, the Key Research and Development Program of Ningxia Province under Grant No. 2020BDE03003 and the Key Research and Development Project-Special Project for East-West Cooperation in Ningxia Province under Grant No. 2021BEE03016.

Notes on contributors

Chunning Na

Chunning Na received the Ph.D. degree from North China Electric Power University in 2017. She works as an associate professor at the School of Electronic and Electrical Engineering in Ningxia University. Her research interest covers new energy grid-connected consumption, power management, and NILM.

Na Luo

Na Luo received a B.S. degree from Yangtze University in 2017. She is currently studying for a master’s degree at Ningxia University. Her main research interest is NILM.

Feng Li

Feng Li received a Ph.D. degree from Tianjin University in 2017. He works as an associate professor at the School of Electronic and Electrical Engineering, Ningxia University. His research interest covers photovoltaic power generation technology, drive control of AC motors, and NILM.

Huan Pan

Huan Pan received a Ph.D. degree from Central South University in 2012. He works as a professor at the School of Electronic and Electrical Engineering, Ningxia University. His research interest covers modeling and analysis of NILM, cooperative control of microgrids, and grid-connection control of distributed generations.