Detection Of Topological Changes Research Articles

A Sample Covariance Model-Based Method for Topology Change Detection and Location of Power Grids With High-Level Renewables

A Sample Covariance Model-Based Method for Topology Change Detection and Location of Power Grids With High-Level Renewables

Topology change detection in networks due to false data injections: a priori assessment based on graph matching techniques

Topology change detection in networks due to false data injections: a priori assessment based on graph matching techniques

Optimal Placement of μPMUs in Distribution Networks with Adaptive Topology Changes

With the increasing integration of energy sources and the growing complexity of distribution networks, it is crucial to monitor and early detection of topological changes to ensure grid stability and resilience. Current methods, for optimizing the placement of micro Phasor Measurement Units (μPMUs) focus on achieving observability and efficient monitoring. These algorithms aim to minimize the number of μPMUs needed while maintaining system observability or meeting criteria for observability. However, they may not consider all real-world constraints and uncertainties. In this study, we introduce a strategy for placing μPMUs with the objective of enhancing observability and monitoring capabilities. Our proposed algorithm employs a technique that makes optimal decisions at each step to approximate the global optimum. To determine the locations for μPMUs our algorithm takes into account parameters such as network structure, key nodes, and system stability. One distinguishing feature is its adaptability to distribution networks, including changes, in topology or potential device failures. Unlike classical approaches, our algorithm can continuously provide optimal placement solutions even in evolving network conditions. We have demonstrated that our suggested method achieves better results in terms of observability value and the required number of μPMUs compared to the state-of-the-art. By strategically placing μPMUs, operators can improve system observability, quickly detect and locate faults, and make informed decisions for effective network operations. This research helps improve optimal placement strategies for μPMUs by providing practical and effective solutions to improve distribution network reliability, resilience, and performance in the face of changing dynamics.

Power grid surveillance: Topology change detection system using power line communications

This paper proposes an efficient channel impulse response (CIR)-based technique to detect topology changes in the power grid. The features of the proposed approach include the following aspects: (i) it is a software-only solution, not requiring any intervention on the current smart grid architecture; (ii) topology changes can be detected via a simple distributed algorithm that requires only local communications; and, (iii) both memory and computational footprints of the proposed solution are minimal. The above-mentioned features make this contribution notably appealing for the resource-constrained smart grid domain. Furthermore, the paper provides a detailed discussion of the introduced technique, accompanied by an implementation reflecting a realistic use case, and presents an extensive simulation campaign to show the quality and viability of the proposed approach. A unique feature of our solution is that it performs well even when the communication channel is affected by a high noise level. For instance, with noise levels below 100 dBμV (the typical PLC noise power range), the proposed solution showed a ≈100% detection rate.To the best of our knowledge, our solution is the first one proposing a fully CIR-based, distributed, deterministic algorithm for intrusion detection and localization. We believe that the performance and advantages of the proposed technique pave the way for further smart grid applications and solutions.

Cross-variance analysis to online estimate power flow Jacobian matrix using limited PMU data

This paper proposes a data-driven approach to estimate the power flow Jacobian matrix online with only small-scale data set collected by phasor measurement unit (PMU). For data in limited amount, one of the greatest challenges to estimate the Jacobian matrix is how to quantify and treat the interference of multiple uncertainties, which are analytically intractable by traditional approaches. To tackle these uncertainties, in this work, through analyzing the cross-variance matrix between voltage measurements and load data, the joint density probability of uncertainties is modeled in matrix-level. Furthermore, a random matrix theory (RMT) based approach is proposed to shrink the singular values of the cross-variance matrix to alleviate the unfavorable influence of uncertainties. This approach is based on the property that the singular values of a random matrix are asymptotically converged to a deterministic distribution. Numerous cases prove that the proposed method is capable of obtaining more precise estimation results even with limited PMU data. Besides, this estimation would supply extra information assistant for power system monitoring, such as voltage stability assessment (VSA) and topology change detection (TCD).

Reliability analysis of complex water distribution systems: the role of the network connectivity and tanks

Abstract A reliable water distribution network (WDN) can provide an adequate supply service to customers under both normal and abnormal working conditions. The WDN reliability analysis, therefore, is a keystone to improve the supply service efficiency. Strategies for reliability analysis are usually proved on small WDNs, which do not compare with large real complex systems in terms of number of water tanks, pressure reduction valves, variable speed pumps, controlled devices and possible alternative water supply schemes. The topological changes due to pipeline interruptions impact on emptying–filling of water tanks and network pressure status. This work proposes a two-level procedure for mechanical reliability assessment, suited for large real WDNs. It leverages a path/connectivity-based approach to set up reliability indicators for global-level analysis and local screening of the most critical scenarios. The employed advanced hydraulic model includes the automatic detection of topological changes and the robust modelling of water level in tanks using the generalized global gradient algorithm. The extended period simulation enables the reliability assessment of alternative water supply schemes and the sensitivity of tanks and controlled devices to single failure events. The procedure is demonstrated on a real complex network, being consistent with the ongoing digital transition in the WDN management sector.

Sequential Voxel-Based Leaflet Segmentation of Complex Lipid Morphologies.

As molecular dynamics simulations increase in complexity, new analysis tools are necessary to facilitate interpreting the results. Lipids, for instance, are known to form many complicated morphologies, because of their amphipathic nature, becoming more intricate as the particle count increases. A few lipids might form a micelle, where aggregation of tens of thousands could lead to vesicle formation. Millions of lipids comprise a cell and its organelle membranes, and are involved in processes such as neurotransmission and transfection. To study such phenomena, it is useful to have analysis tools that understand what is meant by emerging entities such as micelles and vesicles. Studying such systems at the particle level only becomes extremely tedious, counterintuitive, and computationally expensive. To address this issue, we developed a method to track all the individual lipid leaflets, allowing for easy and quick detection of topological changes at the mesoscale. By using a voxel-based approach and focusing on locality, we forego costly geometrical operations without losing important details and chronologically identify the lipid segments using the Jaccard index. Thus, we achieve a consistent sequential segmentation on a wide variety of (lipid) systems, including monolayers, bilayers, vesicles, inverted hexagonal phases, up to the membranes of a full mitochondrion. It also discriminates between adhesion and fusion of leaflets. We show that our method produces consistent results without the need for prefitting parameters, and segmentation of millions of particles can be achieved on a desktop machine.

Analysis of normal human retinal vascular network architecture using multifractal geometry.

To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina. Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images, corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms, applying the standard box-counting method. Statistical analyses were performed using the GraphPad InStat software. The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions (Dq ) for q=0, 1, 2, the width of the multifractal spectrum (Δα=αmax - αmin ) and the spectrum arms' heights difference (|Δf|) of the normal images were expressed as mean±standard deviation (SD): for segmented versions, D0 =1.7014±0.0057; D1 =1.6507±0.0058; D2 =1.5772±0.0059; Δα=0.92441±0.0085; |Δf|= 0.1453±0.0051; for skeletonised versions, D0 =1.6303±0.0051; D1 =1.6012±0.0059; D2 =1.5531±0.0058; Δα=0.65032±0.0162; |Δf|= 0.0238±0.0161. The average of generalized dimensions (Dq ) for q=0, 1, 2, the width of the multifractal spectrum (Δα) and the spectrum arms' heights difference (|Δf|) of the segmented versions was slightly greater than the skeletonised versions. The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases.

GPU based detection of topological changes in Voronoi diagrams

The Voronoi diagrams are an important tool having theoretical and practical applications in a large number of fields. We present a new procedure, implemented as a set of CUDA kernels, which detects, in a general and efficient way, topological changes in case of dynamic Voronoi diagrams whose generating points move in time. The solution that we provide has been originally developed to identify plastic events during simulations of soft-glassy materials based on a lattice Boltzmann model with frustrated-short range attractive and mid/long-range repulsive-interactions. Along with the description of our approach, we present also some preliminary physics results.

Distributed detection of topological changes in communication networks

Changes in the topology of communication networks, such as sudden appearance or disappearance of links or nodes, may signal malicious attacks or malfunctions. A topology change detector may thus be useful to trigger alarms or self-reconfiguration procedures. Here we present a novel approach that enjoys several desirable qualities such as fast convergence, intrinsically distributed computations, and scalability w.r.t. communication and computational requirements. We characterize the performance of this technique from analytical and practical points of view, providing theoretical results on its performance. We thus show how it is possible to tune and trade-off the accuracy of the change detection results with the communication requirements of the procedure.

Automatic Detection of Topological Changes for Digital Road Map Updating

This paper highlights a method to detect topological changes of digital road maps automatically. Here, the road network updating can be done in real time. It relies on collected GPS data from vehicles. These data are fused with existing road map data by an unscented Kalman filter in a centralized scheme. We modeled the map data as a sensor that allows accounting for the errors and uncertainties of land surveying. The core of our method is a probabilistic map-matching approach that is used to manage the road network database through the computation of the Mahalanobis distance. We show experimental results from an urban transport network scenario in which we deal with the case of opened roads and the recent conversion of a crossroad into a roundabout. Then, the obsolete map database is updated to ensure a more reliable route planning.

Intelligent routing information protocol using full triggered update mechanism

The heartbeat of network is the routing services which are available upon the execution of protocols. Many routing protocols emerge over one another to strive for betterment in performing the main function of routing the packets to destination efficiently. Despite the superiority of the new protocols in certain feature, the old routing information protocol (RIP) is still widely used due to its simplicity. RIP updates its routing table on a constant interval period by checking the existence of neighbor nodes and changes on the path availability. This mechanism, however, has some drawbacks of bandwidth consumption and higher routing overhead generation. This paper studies the affect of periodic update mechanism on the consumption of bandwidth and proposes a new routing update mechanism. Our technique eliminates the periodic update mechanism in RIP and replaces it with full triggered update by topology change detection mechanism (TRIP). The proposed mechanism triggers the update when topology change is detected. We tested our proposed mechanism in Network Simulator 2 (Ns 2) simulations. The results show that TRIP consumes less bandwidth and sends less routing packets by 70% than RIP. This work contributes significantly to small network as bandwidth is obviously limited. Key words: Distance vector routing, routing information protocol (RIP), triggered update, periodic update, link failure detection.

３次元映像情報メディア技術 Ｔｉｍｅ‐Ｖａｒｙｉｎｇ Ｍｅｓｈの位相変化検出とインタラクティブなメッシュ編集

We present an automatic topology change detection and mesh editing interface for Time-Varying Meshes (TVMs). A TVM is a sequence of 3D mesh models of real-world objects generated from multiple-view images. Changes in their topology are detected by extracting skeletons from them and tracking the motion of the skeletons. An interactive tool for mesh editing has also been developed. As a result, the time consistency of the mesh topology can be maintained thus enabling us to apply several mesh processing algorithms to TVMs. The validity of our system was confirmed by applying an edited TVM sequence to motion tracking. Experimental results demonstrate that the tracking accuracy was improved by 859.4 mm in the best case.

Algorithm for Automatic Detection of Topological Changes in Water Distribution Networks

Topological and pressure-driven analyses are an integral part of reliability/risk considerations for a water distribution system. For example, it is often necessary to identify which parts of the distribution network are isolated from water sources after the valves have been closed in response to a mechanical pipe failure. Pressure-driven analysis is then necessary to ascertain the consequences of pipe failures in terms of the performance of the functioning subsystem while pipe breaks are being fixed in the isolated area. Therefore, it is extremely useful to have an algorithm for the automatic identification of nodes/pipes disconnected from the water sources. However, this is a complex problem because valves sometimes significantly modify the network topology. Furthermore, the use of isolation valves can cause a demand shortage to some customers due to pressure reduction during the abnormal operating conditions in the system. Thus, pressure-driven simulation of the network behavior is required. For these reasons, a novel algorithm capable of automatic detection of topological network changes is coupled with a robust pressure-driven simulation model. This algorithm is tested on two case studies involving a small artificial water distribution system and a larger, real-life network. The results obtained clearly demonstrate the robust- ness of the algorithm developed.