Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

Abstract

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events; however the false alarm rate is high. To address this problem, this paper uses a well-known 'time lag shift' method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto-and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T 2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

“…In our early work, some data-driven methods based on linear principal component analysis (PCA) [87] were applied in power system data analysis [88], setting up a distributed adaptive learning framework for wide-area monitoring, capable of integrating machine learning and intelligent algorithms in [89]. In order to handle power system dynamic data and nonlinear variables, dynamic PCA [90] and recursive PCA [91] were also developed to improve the model accuracy. It is worth mentioning that linear PCA is unable to handle all process variables due to the normal Gaussian distribution assumption imposed on them, and many extensions using neural networks have been developed [92,93].…”

Implementation of Controlled Islanding Scheme for Self-healing Smart Grid

“…Further an application of slow coherency theory of an islanding scheme was presented in paper [9]. Principal Component Analysis (PCA) based fault identification techniques have been brought into islanding detection in power grid [10][11][12]. Synchrophasor measurements are real time data and can be utilized in better controlled separation of the large network into smaller islands with minimum power imbalance with generation/load shedding [13][14].…”

Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches

“…Inspired by the statistical way of analysing industrial process data, principal component analysis (PCA) based fault detection methods have been introduced into islanding detection in power systems [10]- [12]. PCA is able to provide an effective means to choose multiple reference sites across the network, which can process the data collected from PMUs at different locations simultaneously, thus the situation can be avoided that some reference sites are themselves nonsynchronous.…”