The future's smart grid consists of an increasing number of dispersed energy generation and consumption devices. Due to its complexity, the energy sector yields the development of decentralized monitoring and control framework, where each node in the grid will be a potential location for power quality devices. In order to continually improve the stability of the energy system, those devices need a tool for accurate and real-time detection and classification of the power quality disturbances. However, despite the continuous progress in the field, the development of that kind of tool is still a challenge. This article presents a real-time implementation of an optimized power quality events classifier for the detection and classification of 21 classes of single and combined disturbances. The focal point of the presented classifier is the real-time implementation of optimal feature extraction, optimized classification, accurate zero-crossing detection, and efficient handling of different noise levels present in the voltage signal. The implementation is performed on myRIO-1900 using the LabVIEW interface. Testing and validation results show that this implementation exhibits high classification accuracy, even in cases when the classification relies on classes obtained as the combination of four power quality disturbances.

中文翻译：

---

优化电能质量事件分类器的实时实现

未来的智能电网由越来越多的分散的能源生产和消费设备组成。由于其复杂性，能源部门产生了分散式监测和控制框架的发展，其中电网中的每个节点都将成为电能质量设备的潜在位置。为了不断提高能源系统的稳定性，这些设备需要一种工具来准确、实时地检测和分类电能质量扰动。然而，尽管该领域不断取得进展，但这种工具的开发仍然是一个挑战。本文介绍了一种优化的电能质量事件分类器的实时实现，用于检测和分类 21 类单一和组合干扰。所提出的分类器的重点是实时实现最佳特征提取、优化分类、准确过零检测以及有效处理电压信号中存在的不同噪声级别。使用 LabVIEW 界面在 myRIO-1900 上执行该实现。测试和验证结果表明，即使在分类依赖于作为四种电能质量干扰的组合获得的类别的情况下，该实现也表现出很高的分类精度。