Smart meter technology has been developed rapidly in modern industrial environment in the context of smart grid connected residential load system. For the smart meter’s application, knowledge about instantaneous load pattern is crucial. Non-intrusive load monitoring (NILM) is a well-known method to assess the power consumption of individual load as well as its operating behavior.​ Since the modern household appliances can inject unwanted harmonics to the system, identification of harmonic polluting loads has also become an issue for such load monitoring schemes. In this article, an improved technique to identify the harmonic polluting loads has been presented using only input aggregated voltage–current data of a residential system. A search-based optimization, i.e., Artificial Bee Colony (ABC) algorithm is used in the proposed load monitoring technique. Unlike the existing methods, this technique does not require any heavy training mechanism for the system. The algorithm is verified using the PLAID datasets and results are compared to some state-of-the-art techniques. Suitable simulations and experimentally created dataset analysis have been carried out on a residential system to demonstrate the ability of the proposed methodology.

在智能电网连接的住宅负载系统的背景下，智能电表技术在现代工业环境中得到了快速发展。对于智能电表的应用，有关瞬时负载模式的知识至关重要。非侵入式负载监控（NILM）是一种用于评估单个负载的功耗及其运行行为的众所周知的方法。由于现代家用电器可以向系统注入有害的谐波，因此识别谐波污染负载也已成为现实。成为此类负载监控方案的一个问题。在本文中，仅使用住宅系统的输入汇总电压-电流数据，提出了一种改进的技术来识别谐波污染负荷。基于搜索的优化，即 人工蜂群算法（ABC）被用于所提出的负荷监测技术中。与现有方法不同，此技术不需要为系统增加任何繁重的训练机制。使用PLAID数据集验证了该算法，并将结果与​​某些最新技术进行了比较。在住宅系统上进行了合适的模拟和实验创建的数据集分析，以证明所提出方法的能力。