Instantaneous active and reactive load signature applied in non‐intrusive load monitoring systems

Abstract

Abstract The performance of non‐intrusive load monitoring (NILM) systems heavily depends on the uniqueness of the load signature extracted from the electrical appliances. Different load signatures have been proposed. Recently, in particular, v–i trajectory feature extraction is attracting more and more attention due to its unique characteristics. Herein, instantaneous p–q load signature (IpqLS) feature extraction is first proposed and applied in NILM, which shows that conventional methods cannot distinguish load signatures under some situations. Applying IpqLS with several machine learning algorithms is not only extracting unique features compared to the overlapping problems of P–Q and v–i trajectory but also improving load classification accuracy. Simulations and experimental results verified the effectiveness of the proposed method.

Keywords

• domestic appliances
• feature extraction
• learning (artificial intelligence)
• load (electric)
• load management
• power engineering computing