A novel method for locating the source of sustained oscillation in power system using synchrophasors data

Abstract

Large interconnected power systems are usually subjected to natural oscillation (NO) and forced oscillation (FO). NO occurs due to system transient response and is characterized by several oscillation modes, while FO occurs due to external perturbations driving generation sources. Compared to NO, FO is considered a more severe threat to the safe and reliable operation of power systems. Therefore, it is important to locate the source of FO so corrective actions can be taken to ensure stable power system operation. In this paper, a novel approach based on two-step signal processing is proposed to characterize FO in terms of its frequency components, duration, nature, and the location of the source. Data recorded by the Phasor Measurement Units (PMUs) in a Wide Area Monitoring System (WAMS) is utilized for analysis. As PMU data usually contains white noise and appears as multi-frequency oscillatory signal, the first step is to de-noise the raw PMU data by decomposing it into a series of intrinsic mode functions (IMF) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) technique. The most appropriate IMF containing the vital information is selected using the correlation technique. The second step involves various signal processing and statistical analysis tools such as segmented Power Spectrum Density (PSD), excess kurtosis, cross PSD etc. to achieve the desired objectives. The analysis performed on the simulated two-area four-machine system, reduced WECC-179 bus 29 machine system, and the real-time power system PMU data set from ISO New England, demonstrates the accuracy of the proposed method. The proposed approach is independent of complex network topologies and their characteristics, and is also robust against measurement noise usually contained in PMU data.

A Review of Machine Learning Approaches in Synchrophasor Technology

“…With the evolution of PMUs and synchrophasor technology, the monitoring of FOs becomes easier due to the high sampling rates of PMUs when compared with traditional SCADA systems. The common methods adopted to monitor the PMU data having FOs and to localise and mitigate the causes are well explained in [159]- [160]. As a result, the methods are classified into the analysis of travelling waves, estimation of mode shape, damping torque analysis, energy-based analysis, machine learning as well as deep learning analysis.…”

Multisource Heterogenous Data Fusion for Fault Identification of Buried Substations

“…EMD and LMD are improved based on WT, which both belong to recursion mode decomposition. It is seen that the error will gradually be accumulated in the decomposition process, and there are problems with modal mixing and endpoint effect, which may affect the subsequent fault identification results (Zuhaib et al, 2020). In order to overcome the shortcomings of the analysis method mentioned earlier, a novel variable-scale processing method was proposed, which was called variational mode decomposition (VMD).…”

An investigation on the application and challenges for wide area monitoring and control in smart grid

“…For example, the July 31, 2012, power outage of the northern and eastern parts of India [12]. The goal of OSM is to discover poorly damped oscillations as they can affect system power flows and reduces power system reliability [22,23] Studied how the OMS is extracting the data of electromechanical oscillations from PMU in real-time. PMU information is useful in recognizing the undamped electromechanical oscillations because of its high sampling rate.…”