Space-Time Approach for Disturbance Detection and Classification

Abstract

A major challenge facing future grid systems is to identify the source of abnormal behaviors caused by faults or voltage instability. In this paper Phasor Measurement Units (PMUs) have been considered for detecting disturbances and degradation in the grid. Considering that the source of voltage instability mainly impacts neighboring areas, we present a simple and yet efficient algorithm that can identify affected areas. The algorithm is based on K-Mean optimization that classifies PMUs into different classes of power quality. We introduce the concept of a space-time solution to identify multiple faults. Since this is a multi-objective problem, we extend the K-mean algorithm to achieve space-time optimization in two successive stages (i.e., time and space). As an alternative approach to the space-time solution, we also present a hierarchical network that is based on a distributed mapping technique. This approach may only require time-based clustering as it limits the PMU population search to a small local region. We used our real-time Virtual PMU (VPMU) embedded into our software in the loop testbed to verify the performance of the proposed schemes. For the IEEE 39-bus transmission system, it is shown that the proposed space-time synchrophasor data classification scheme is capable of detecting and isolating areas in the grid that suffer from multiple disturbances, such as faults.

A Three Decades of Marvellous Significant Review of Power Quality Events Regarding Detection & Classification

“…This article is based on MMG (multi-resolution morphology gradients) [120], but its process uses half the MMG strategy. In [121] paper k-mean optimization algorithm is presented which classify PMU (Phasor measurement units) into different power quality classes. In [122] the author tried to use different models to get results with the help of Multi agent system the results are shown in Figure 2 and Figure 3.…”

Synchrophasor Sensor Networks for Grid Communication and Protection

“…In order to detect regions that have been impacted by voltage instability caused by faults or sudden overloads, we have considered K -means optimization [48]. It was shown that by selecting suitable observation vectors, the K -means approach would be able to classify the power quality into different clusters according to the degree of voltage instability.…”

“…By replacing P i = [ V 1 , V 2 , ……, V m ] with (18) for time and (19) for space, we can then form the inputs to the K -means clustering process, which are carried out separately based on each observation vector and in consecutive stages of merging and splitting clusters [48]. …”

“…In fact, the main reason behind using the 390-bus system was to generate more synchrophasor data sets. The proposed synchrophasor classifications method, which is based on the Newton–Raphson method, has also been compared with our earlier space–time approach [48]. In the latter, we mainly focused on selecting a suitable set of initial centroid values to perform a faster convergence, as well as reducing the possibility of locking into a local minimum.…”

“…It consists of three distinct parts: grid networking that is based on synchrophasor technology for monitoring and protection, communication networking, and signal processing that is required for power quality monitoring and fault detection. For power quality classifications, we expand on the concept of the space–time partitioning introduced in our recent work [48]. The expansion is based on the formation of a joint space–time observation vector to perform a stable K -means clustering.…”