The requirement of increasing the deployment of a distributed monitoring system as the network expanding is becoming a new research focus. The problem is where to put those monitors and by how much to put. This paper ...
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The paper presents the performance evaluation of the DELFI (Deep Learning for False voltage dip Identification) classifier for evaluating voltage dip validity, now available in the QuEEN monitoringsystem. In addition...
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The paper presents the performance evaluation of the DELFI (Deep Learning for False voltage dip Identification) classifier for evaluating voltage dip validity, now available in the QuEEN monitoringsystem. In addition to the usual event characteristics, QuEEN now automatically classifies events in terms of validity based on criteria that make use of either a signal processing technique (current criterion) or an artificial intelligence algorithm (new criterion called DELFI). Some preliminary results obtained from the new criterion had suggested its full integration into the monitoringsystem. This paper deals with the comparison of the effectiveness of the DELFI criterion compared to the current one in evaluating the events validity, starting from a large set of events. To prove the enhancement achieved with the DELFI classifier, an in-depth analysis has been carried out by cross-comparing the results both with the neutral system configuration and with the events characteristics (duration/residual voltage). The results clearly show a better match of DELFI classifications with network and events characteristics. Moreover, the DELFI classifier has allowed us to highlight specific situations concerning power quality at regional level, resolving the uncertainties due to the current validity criterion. In details, three groups of regions can be highlighted with respect to the frequency of the occurrence of false events.
The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quali...
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The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification;otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods.
This paper presents a new approach of an integrated distributed monitoring system for an autonomous unmanned helicopter. The navigation mission consists of reaching areas of interest on a path composed of specific way...
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ISBN:
(纸本)9781467322768
This paper presents a new approach of an integrated distributed monitoring system for an autonomous unmanned helicopter. The navigation mission consists of reaching areas of interest on a path composed of specific waypoints. The task of such a monitoringsystem is to determine whether the helicopter behaves as intended in order to detect faults in the mission execution early and to enable a re-planning of the mission or a failure recovery in due time. This is a very important issue for autonomous systems as they have to cope not only with ordinary subsystems failures, but also with unexpected situations which make the mission goals unattainable. The approach uses a special combination of Petri Nets and Monte Carlo Methods (particle filter) and integrates different layers of the system architecture such as control actions (path-planning, low-level control), environment perception (vision-based recognition of landmarks), state observation (estimation of speed, position, attitude) and decision making (mission planning and re-planning) in a unified model.
Website response time is one of the most important performance parameter of website. It can be used to assess website performance to forecast the status of website. Large amounts of data are applied by a distributed m...
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ISBN:
(纸本)9781479958368
Website response time is one of the most important performance parameter of website. It can be used to assess website performance to forecast the status of website. Large amounts of data are applied by a distributed monitoring system that monitoring a university website response time. Support vector machine with information granulation is studied to predict the response time. It can predict accurately the range of ultimate response time, the relative accuracy of the forecast average response time can reach 96.2%.
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