1,721,047 research outputs found
Neuro-fuzzy networks for voltage security monitoring based onsynchronized phasor measurements
The ability to rapidly acquire synchronized phasor measurements from around a power network opens up new possibilities for power system operation and control. A novel neuro-fuzzy network, the fuzzy hyperrectangular composite neural network, is proposed for voltage security monitoring (VSM) using synchronized phasor measurements as input patterns. This paper demonstrates how neuro-fuzzy networks can be constructed offline and then utilized online for monitoring voltage security. The neuro-fuzzy network is tested on 3000 simulated data from randomly generated operating conditions on the IEEE 30-bus system to indicate its high classification rate for voltage security monitoring電子
Neural-Network-Based Fuzzy Model and its Application to Transient Stability Prediction in Power Systems
We present a general approach to deriving a new type of neural network-based fuzzy model for a complex system from numerical and/or linguistic information. To efficiently identify the structure and the parameters of the new fuzzy model, we first partition the output space instead of the input space. As a result, the input space itself induces corresponding partitions within each of which inputs would have similar outputs. Then we use a set of hyperrectangles to fit the partitions of the input space. Consequently, the premise of an implication in the new type of fuzzy rule is represented by a hyperrectangle and the consequence is represented by a fuzzy singleton. A novel two-layer fuzzy hyperrectangular composite neural network (FHRCNN) can be shown to be computationally equivalent to such a special fuzzy model. The process of presenting input data to each hidden node in a FHRCNN is equivalent to firing a fuzzy rule. An efficient learning algorithm was developed to adjust the weights of an FHRCNN. Finally, we apply FHRCNNs to provide real-time transient stability prediction for use with high-speed control in power systems. From simulation tests on the IEEE 39-bus system, it reveals that the proposed novel FHRCNN can yield a much better performance than that of conventional multilayer perceptrons (MLP's) in terms of computational burden and classification rat
Rule Extraction for Voltage Security Margin Estimation
Research efforts have been devoted to estimating voltage security margins which show how close the current operating point of a power system is to a voltage collapse point as assessment of voltage security. One main disadvantage of these techniques is that they require large computations, therefore, they are not efficient for on-line use in power control centers. In this paper, we propose a technique based on hyperrectangular composite neural networks (HRCNNs) and fuzzy hyperrectangular composite neural networks (FHRCNNs) for voltage security margin estimation. The technique provides us with much faster assessments of voltage security than conventional techniques. The two classes of HRCNNs and FHRCNNs integrate the paradigm of neural networks with the rule-based approach, rendering them more useful than either. The values of the network parameters, after sufficient training, can be utilized to generate crisp or fuzzy rules on the basis of preselected meaningful features. Extracted rules are helpful to explain the whole assessment procedure so the assessments are more capable of being trusted. In addition, the power system operators or corresponding experts can delete unimportant features or add some additional features to improve the performance and computational efficiency based on the evaluation of the extracted rules. The proposed technique was tested on 3000 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficienc
Neuro-fuzzy approach to real-time transient stability prediction based on synchronized phasor measurements
With new systems capable of making synchronized phasor measurements there are possibilities for real-time assessment of the stability of a transient swing in power systems. In the future, on-line control will be necessary as operating points are pushed closer toward the margin and fast reaction time becomes critical to the survival of the system. In this paper we develop a novel class of fuzzy hyperrectangular composite neural networks which utilize real-time phasor angle measurements to provide fast transient stability prediction for use with high-speed control. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.補正完
Application of a Novel Fuzzy-Neural Network to Real Time Transient Stability Swings Prediction Based on Synchronized Phasor Measurements
The ability to rapidly acquire synchronized phasor measurements from around the system opens up new possibilies for power system protection and control. In this paper we develop a novel class of fuzzy hyperrectangular composite neural networks which utilize synchronized phasor measurements to provide fast transient stability swings prediction for use with high-speed control. Primary features of the method include constructing a fuzzy neural network for all fault locations, using a short window of realistic-recision post-fault phasor measurements for the prediction, and testing robustness to variations in the operating point. From simulation tests on a sample power system, it reveals that the proposed tool can yield a highly successful prediction rate in real-time.電子
Going Beyond Counting First Authors in Author Co-citation Analysis
The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Wearable Bidirectional Human–Machine Interface: Merging Motion Capture and Vibrotactile Feedback in a Wireless Bracelet
Humans interact with the environment through a variety of senses. Touch in particular contributes to a sense of presence, enhancing perceptual experiences, and establishing causal relations between events. Many human–machine interfaces only allow for one-way communication, which does not do justice to the complexity of the interaction. To address this, we developed a bidirectional human–machine interface featuring a bracelet equipped with linear resonant actuators, controlled via a Robot Operating System (ROS) program, to simulate haptic feedback. Further, the wireless interface includes a motion sensor and a sensor to quantify the tightness of the bracelet. Our functional experiments, which compared stimulation with three and five intensity levels, respectively, were performed by four healthy participants in their twenties and thirties. The participants achieved an average accuracy of 88% estimating three vibration intensity levels. While the estimation accuracy for five intensity levels was only 67%, the results indicated a good performance in perceiving relative vibration changes with an accuracy of 82%. The proposed haptic feedback bracelet will facilitate research investigating the benefits of bidirectional human–machine interfaces and the perception of vibrotactile feedback in general by closing the gap for a versatile device that can provide high-density user feedback in combination with sensors for intent detection
Variations on the Author
“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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