Khan Wajid, Yousaf Muhammad Zain, Singh Arvind R, Khalid Saqib, Bajaj Mohit, Zaitsev Ievgen
School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang University, Zhuji, 311816, Zhejiang, China.
Sci Rep. 2024 Nov 16;14(1):28342. doi: 10.1038/s41598-024-80033-w.
The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.
本文提出了一种通过增强基于多项式逼近的改进伽辽金法（MGM），利用实时射频识别（RFID）数据采集来评估微电网中转子角稳定性的新方法。由于传统的转子角稳定性方法依赖于假设，在在线暂态稳定性测试中经常失败。MGM通过使用一系列多项式和系数逼近状态变量，成功地捕捉了微电网的动态行为。诸如振动或噪声信号等冗余数据可能会导致缺陷诊断延迟并降低诊断准确性。通过集成RFID技术解决了这个问题。RFID技术有可能与混合卷积神经网络-长短期记忆（CNN-LSTM）模型一起用于开发复杂的故障诊断系统。这需要通过使用信号处理技术和特征提取方法（如傅里叶变换和时域统计特征）来识别故障特征。此外，我们使用总谐波失真（THD）来减少多余数据。所提出的技术显著提高了故障检测效率和精度，以0.94的分类准确率优于现有技术。通过对IEEE 3机9节点系统进行广泛的案例研究来说明其有效性，结果表明在故障检测速度和准确性方面有明显提高，使微电网运行更安全、更可靠。
