Real-time Load Information Research Articles

Stability Analysis of Equilibrium of DC Microgrid Under Distributed Control

Constant power loads (CPLs) often lead to system instability and voltage collapse. This paper analyzes the existence and stability of equilibrium of meshed DC microgrids with multiple CPLs under distributed control, which aims to realize current sharing and voltage regulation. Firstly, the power-flow equation of the system is derived. To analyze the solvability of power-flow equation, we transform the problem of the multidimensional quadratic equation solvability into the existence of a fixed-point for a contraction mapping. Then, the sufficient solvability condition is derived based on Banach's fixed-point theorem. Secondly, we build the small-signal model to determine the system qualitative behavior around equilibrium. By analyzing the eigenvalue of system Jacobian matrix based on inertia theorem, the analytical stability conditions are obtained. Further, under the load uncertainty, a robust stability condition which only depends on the maximum load information instead of the real-time load information is derived. Simulation results verify the feasibility of the proposed theorems.

An N-1 security analysis method of distribution network based on minimum isolation area information matrix

To solve the problem of fast online analysis of distribution network operation safety, an N-1 security analysis method of distribution network based on minimum isolation area information matrix is proposed. Firstly, the minimum isolation area information matrix model is established to describe the switch and distribution transformer information in the minimum isolation area of all feeders in the distribution network. Then the real-time load information matrix of the minimum isolation area is used to determine the total load to be transferred downstream of the fault. Secondly, the active power margin of the switch in the load transfer path is described by the load transfer path switch information matrix and the switch capacity information matrix. Finally, the recoverable power supply downstream of the fault area is analyzed by comparing the minimum margin information matrix and the total transfer load. This method can quickly realize the N-1 safety analysis of the distribution network and identify the minimum isolation areas and weak links that do not meet N-1. It can meet the needs of on-site, real-time online calculation and analysis.

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers’ privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx.13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx.96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.

Compressive Traffic Monitoring in Hybrid SDN

Hybrid software-defined network (SDN) is a network where legacy routers and SDN routers coexist during the incremental deployment of SDNs. The existing SDN router placement methods mainly focus on maximizing the traffic engineering performance under a limited budget. Traffic engineering requires real-time link-load information. However, the latency for collecting the global link-load information can be prohibitively long in a wide area network due to the long interior gateway protocol convergence time. Inspired by the sparsity of the link load, we propose a novel compressive traffic monitoring method for collecting real-time load information of all links. In this method, the controller only needs to collect the load of a small subset of important links and then estimates the link load of the rest. The minimal number of SDN routers is placed to cover these important links. We use real-world topologies and traffic matrices to evaluate our method. Experiment results show that our method can quickly estimate the global link load at an error rate of 5% within sub-seconds. Compared with the state-of-the-art methods, our method has better adaptability to the dynamic traffic changes and can reduce the maximal link usage by 39%.

Development and application of maintenance decision-making support system for aircraft fleet

A new reliability evaluation method based on the real-time load information for aircraft structures.Minimize the maintenance cost and maximize the availability of a fleet.A multi-objective decision...

Development and application of maintenance decision-making support system for aircraft fleet

With the arrival of on-board monitoring systems for aircraft structures, maintainers have access to a near real-time assessment of component health. Due to the traditional reliability assessment methods for aircraft structures lack of the ability to make full use of real-time status information collected by the monitoring systems, a new reliability evaluation method based on the real-time load information is proposed for aircraft structural reliability. Condition-based-maintenance is a maintenance scheme which recommends maintenance decisions according to equipment status collected by monitoring systems over a period of time. As most researches on condition-based maintenance for aircraft focused on solely minimizing maintenance cost or maximizing the availability of single aircraft, a multi-objective decision-making model based on condition-based-maintenance concentrated on both minimizing the maintenance cost and maximizing the availability of a fleet (total number of available aircraft in fleet) is established for an aircraft fleet. Finally, a maintenance decision-making support system (MDMSS) is developed based on the proposed model. The system integrates the process of data acquisition (real-time status information of aircraft), data processing (reliability assessment of aircraft structures) as well as maintenance decision-making. Therefore, it simplifies the complex process of equipment management and takes a step further in engineering application.

Fatigue crack sizing in rail steel using crack closure-induced acoustic emission waves

The acoustic emission (AE) technique is a promising approach for detecting and locating fatigue cracks in metallic structures such as rail tracks. However, it is still a challenge to quantify the crack size accurately using this technique. AE waves can be generated by either crack propagation (CP) or crack closure (CC) processes and classification of these two types of AE waves is necessary to obtain more reliable crack sizing results. As the pre-processing step, an index based on wavelet power (WP) of AE signal is initially established in this paper in order to distinguish between the CC-induced AE waves and their CP-induced counterparts. Here, information embedded within the AE signal was used to perform the AE wave classification, which is preferred to the use of real-time load information, typically adopted in other studies. With the proposed approach, it renders the AE technique more amenable to practical implementation.Following the AE wave classification, a novel method to quantify the fatigue crack length was developed by taking advantage of the CC-induced AE waves, the count rate of which was observed to be positively correlated with the crack length. The crack length was subsequently determined using an empirical model derived from the AE data acquired during the fatigue tests of the rail steel specimens. The performance of the proposed method was validated by experimental data and compared with that of the traditional crack sizing method, which is based on CP-induced AE waves. As a significant advantage over other AE crack sizing methods, the proposed novel method is able to estimate the crack length without prior knowledge of the initial crack length, integration of AE data or real-time load amplitude. It is thus applicable to the health monitoring of both new and existing structures.

Probabilistic-based workload forecasting and service redeployment for multi-tenant services

This paper presents a two-stage service migration decision method which combines business workload forecasting with real-time load sensing, and thus adds business forecasting to previous load balancing approaches that rely solely upon real-time load sensing. The migration decision procedure and the detailed causal analysis algorithms based on Bayesian networks are also given. After the critical business indicators have been obtained from causal analysis, business fluctuation related with the critical indicators can be forecasted by using Markov chain method. And then, the migration decision can be made based on the forecasting results and the real-time load information together. We evaluate the migration decision method through three sets of experiments. We found that by migrating service on a shared multi-tenant service environment, the QoS requirement can be assured dynamically and the capability of workloads increases under same resource cost, which is helpful in optimised deploying for multi-tenant applications.

The Research and Design of Two-Level Load Balancer Based on Web Server Cluster

At present, the extendible Web server clusters are the important framers for the high performance network servers. The architecture based on load balancer is often used on web server clusters. The load balancer is the main bottleneck in improving performance of cluster. For it is hard to complete the task of assigning the requests from the client while collecting the real load information of the web servers efficiently. In this paper, we propose a model of two levels load balancer for web cluster to resolve the contradiction. When web requests are arrive, the first-level balancer continuously assigned them by to the second-level which consist of some load balancers connect to real web servers and receive real-time load information of them. Round robin algorithm is used at the first-level load balancer to schedule the client’s requests in order to shorten the response time. The proposed algorithm in our model is run at the second-level load balancers to schedule the requests to real web servers according to the real load information. At last, a simple test is carried out to make sure the performance of the model. The result of the test shows that the model has made better load balancing.