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Active islanding detection method using d-axis disturbance signal injection with intelligent control

IET GENERATION TRANSMISSION & DISTRIBUTION 2013年第5期7卷 537-550页

作者： Lin, Faa-Jeng Huang, Yi-Sheng Tan, Kuang-Hsiung Chiu, Jian-Hsing Chang, Yung-Ruei Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan Natl Ilan Univ Dept Elect Engn Ilan 260 Taiwan Natl Def Univ Chung Cheng Inst Technol Dept Elect & Elect Engn Tao Yuan 335 Taiwan Inst Nucl Energy Res Engn Technol & Facil Operat Div Tao Yuan 335 Taiwan

An active islanding detection method using d-axis disturbance signal injection with intelligent control is proposed in this study. The proposed active islanding detection method is based on injecting a new disturbance signal into the system through the d-axis current that leads to a frequency deviation at the terminal of the resistance inductance and capacitance (RLC) load when the grid is disconnected. The feasibility of the proposed method is evaluated under the UL1741 anti-islanding test configuration. The proposed d-axis disturbance signal-injection method is intended to achieve a reliable detection with quasi-zero non-detection zone, minimum effects on power quality and easy implementation without additional sensing devices or equipments. Moreover, in order to achieve accurate tracking control of active and reactive power and rapid response of the islanding detection of the distributed generator (DG) system, a wavelet fuzzy neural network (WFNN) intelligent controller is proposed to replace the conventional proportional-integral controller for the control of the DG system. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, the feasibility and effectiveness of the proposed d-axis disturbance signal-injection method are verified with experimental results.

关键词： distributed power generation intelligent control neurocontrollers power generation control power supply quality reactive power control active islanding detection method d-axis disturbance signal injection intelligent control d-axis current frequency deviation RLC load terminal UL1741 antiislanding test configuration detection reliability quasizero nondetection zone power quality active power tracking control reactive power tracking control distributed generator system DG system wavelet fuzzy neural network intelligent controller WFNN intelligent controller online learning algorithm

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online learning Approaches in Maximizing Weighted Throughput in an Unreliable Channel

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Tsinghua Science and Technology 2012年第5期17卷 567-574页

作者： Zhi Zhang Fei Li Department of Computer Science George Mason University Fairfax VA 22030 USA

We design online algorithms to schedule unit-length packets with values and deadlines through an unreliable communication channel. In this model, time is discrete. Packets arrive over time; each packet has a non-negative value and an integer deadline. In each time step, at most one packet can be sent. The ratio of successfully delivering a packet depends on the channel＇s quality of reliability. The objective is to maximize the total value gained by delivering packets no later than their respective deadlines. In this paper, we conduct theoretical and empirical studies of online learning approaches for this model and a few of its variants. These online learning algorithms are analyzed in terms of external regret. We conclude that no online learning algorithms have constant regrets. Our online learning algorithms outperform online competitive algorithms in terms of algorithmic simplicity and running complexity. In general, these online learning algorithms work no worse than the best known competitive online algorithm for maximizing weighted throughput in practice.

关键词： online learning algorithm unreliable communication channel competitive online algorithm

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online AUC learning for Biometric Scores Fusion

Online AUC Learning for Biometric Scores Fusion

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6th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Kim, Youngsung Toh, Kar-Ann Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea

ISBN: (纸本)9781424487554

In biometric fusion systems, it is common to find the number of available imposter scores being much larger than the number of genuine-user scores. In terms of training a stable fusion classifier, the area under the receiver operating characteristic curve (AUC) could be useful since it is less sensitive to class distributions [1], [2], [3]. A direct optimization of this AUC criterion thus becomes a natural choice for fusion classifier design. However, a direct formulation of search based on the AUC criterion would have the incoming data size growing almost exponentially. In this paper, we propose an online learning algorithm to circumvent this computational problem in multibiometric scores fusion. Since the proposed method involves pairing of data points of opposite classes, an online learning formulation becomes non-trivial. Our empirical results on two publicly available score-level fusion databases show promising potential in terms of verification AUC, Half Total Error Rate, Accuracy, and computational efficiency.

关键词： Accuracy Face Indexes Polynomials Protocols Speech Training area under the receiver operating characteristic curve biometrics (access control) fusion classifier design half total error rate learning (artificial intelligence) multibiometric scores fusion online learning algorithm pattern classification sensor fusion verification AUC

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learning Random Subspace Novelty Detection Filters

Learning Random Subspace Novelty Detection Filters

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International Joint Conference on Neural Networks (IJCNN)

作者： Hamdi, Fatma Bennani, Younes Univ Paris 13 CNRS LIPN UMR 7030 F-93430 Villetaneuse France

ISBN: (纸本)9781424496365

In this paper we propose a novelty detection framework based on the orthogonal projection operators and the bootstrap idea. Our approach called Random Subspace Novelty Detection Filter (RS - NDF) combines the sampling technique and the ensemble idea. RS - NDF is an ensemble of NDF, induced from bootstrap samples of the training data, using random feature selection in the NDF induction process. Prediction is made by aggregating the predictions of the ensemble. RS - NDF generally exhibits a substantial performance improvement over the single NDF. Thanks to an online learning algorithm, the RS - NDF approach is also able to track changes in data over time. The RS - NDF method is compared to single NDF and other novelty detection methods with tenfold cross-validation experiments on publicly available datasets, where the methods superiority is demonstrated. Performance metrics such as precision and recall, false positive rate and false negative rate, F-measure, AUC and G-mean are computed. The proposed approach is shown to improve the prediction accuracy of the novelty detection, and have favorable performance compared to the existing algorithms.

关键词： Data models F-measure metric G-mean metric Ionosphere NDF induction process Neural networks Noise measurement Testing Training Training data bootstrap idea cross-validation experiment ensemble idea false negative rate metric false positive rate metric learning (artificial intelligence) online learning algorithm orthogonal projection operator precision-and-recall metric random feature selection random subspace novelty detection filter sampling methods sampling technique

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Cognitive Interference Networks with Partial and Noisy Observations: a learning Framework

Cognitive Interference Networks with Partial and Noisy Obser...

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Levorato, Marco Firouzabadi, Sina Goldsmith, Andrea Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781424492688

An algorithm for the optimization of secondary user's transmission strategies in cognitive networks with imperfect network state observations is presented. The task of the secondary user is to maximize its performance while generating a bounded performance loss to the primary users' network. The state of the primary users' network, defined as a collection of variables describing features of the network (e. g., buffer state, ARQ state), evolves according to a Markov process whose statistics depend on the transmission strategy of the secondary user. The main contribution of this paper is an online learning algorithm that, without any a priori knowledge about the statistics of the network and state-observation map, iteratively optimizes the strategy of the secondary user based on a sample-path of noisy and partial state observations.

关键词： Cost function Markov process Markov processes Markov processes Protocols Sensors Signal to noise ratio Throughput cognitive interference networks cognitive radio imperfect network state observations learning (artificial intelligence) learning framework noisy observations online learning algorithm optimisation partial state observations primary user network radiofrequency interference secondary user transmission strategy state-observation map telecommunication computing

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A bio-inspired solution for a local autonomous, reflex, obstacle avoiding behavior

A bio-inspired solution for a local autonomous, reflex, obst...

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10th International Symposium on Signals, Circuits and Systems (ISSCS)

作者： Monica-Claudia, Dobrea Marius, Dobrea Dan Gh Asachi Tech Univ Fac Elect Telecommun & Informat Technol Iasi Romania

ISBN: (纸本)9781457702013

The main goal of our research consists in finding a simple, straightforward online solution for obstacle avoiding problem encountered in mobile robots. The solution allows the robot to develop a local autonomous obstacle avoiding behavior every time when a higher-level motor command that is driving the robot (e.g. go forward/backward) put it in imminent danger to collide. The solution we proposed for a robot with 36 evenly distributed infrared (IR) sensors is a very simple one, based only on a minimal artificial neural network (ANN) trained with a backpropagation-like algorithm. Computationally cheap, the online learning algorithm we implemented proved to be very successful in both, static and dynamic clustered environment. The results reported here were obtained in MobotSim 1.0.03 - a configurable 2D simulator of differential drive mobile robots.

关键词： Artificial neural networks Collision avoidance Mobile robots MobotSim 1.0.03 Neurons Robot sensing systems backpropagation-like algorithm bioinspired solution collision avoidance configurable 2D simulator differential drive mobile robots dynamic clustered environment infrared sensors learning systems local autonomous obstacle avoiding behavior minimal artificial neural network mobile robots neurocontrollers online learning algorithm reflex behavior sensors static clustered environment

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learning Interference Strategies in Cognitive ARQ Networks

Learning Interference Strategies in Cognitive ARQ Networks

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IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Firouzabadi, Sina Levorato, Marco O'Neill, Daniel Goldsmith, Andrea Stanford Univ Dept Elect Engn Stanford CA 94305 USA

ISBN: (纸本)9781424456383

Cognitive radios, which enable the coexistence on the same bandwidth of licensed primary and unlicensed secondary users, have the potential for dramatically increasing the efficiency of wireless networks. In this paper, we propose an on line learning algorithm to optimize the transmission strategy of secondary users in interference mitigation scenarios, where the secondary users are allowed to superimpose their transmission onto those of the primary users. Due to practical limitations, the secondary users have access to only a fraction of the current state of the primary users' network. Therefore, the strategy of the secondary users is defined on a reduced state space. Numerical results show that the proposed practical learning algorithm operates close to the performance of the system under full knowledge.

关键词： automatic repeat request automatic retransmission request cognitive ARQ networks cognitive radio cognitive radios interference mitigation interference suppression learning (artificial intelligence) learning interference strategy online learning algorithm

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An online learning Approach To In-Vivo Tracking Using Synergistic Features

An Online Learning Approach To In-Vivo Tracking Using Synerg...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Reiter, Austin Allen, Peter K. Columbia Univ Dept Comp Sci New York NY 10027 USA

ISBN: (纸本)9781424466757

In this paper we present an online algorithm for robustly tracking surgical tools in dynamic environments that can assist a surgeon during in-vivo robotic surgery procedures. The next generation of in-vivo robotic surgical devices includes integrated imaging and effector platforms that need to be controlled through real-time visual feedback. Our tracking algorithm learns the appearance of the tool online to account for appearance and perspective changes. In addition, the tracker uses multiple features working together to model the object and discover new areas of the tool as it moves quickly, exits and re-enters the scene, or becomes occluded and requires recovery. The algorithm can persist through changes in lighting and pose by using a memory database, which is built online, using a series of features working together to exploit different aspects of the object being tracked. We present results using real in-vivo imaging data from a human partial nephrectomy.

关键词： human partial nephrectomy integrated imaging invivo robotic surgical devices invivo tracking medical image processing medical robotics memory database object tracking online learning algorithm real invivo imaging real time visual feedback robot vision surgery surgical tools tracking synergistic features telerobotics

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An On-line learning algorithm for Complex Fuzzy Logic

An On-line Learning Algorithm for Complex Fuzzy Logic

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2010 IEEE World Congress on Computational Intelligence

作者： Aghakhani, Sara Dick, Scott Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada

ISBN: (纸本)9781424469208

We report on the development of an on-line learning algorithm for ANCFIS, a neuro-fuzzy architecture employing complex fuzzy sets. ANCFIS uses a hybrid learning rule, with rule consequent parameters determined by least-squares estimation in the forward pass, and premise parameters determined by a combination of gradient descent and chaotic simulated annealing in the backward pass. Our on-line learning algorithm replaces these with recursive least-squares in the forward pass, and the downhill-simplex algorithm in the backward pass. Experimental results on two time-series datasets show that this technique is comparable to existing results, although slightly inferior to the off-line ANCFIS results.

关键词： backward pass chaos chaotic simulated annealing complex fuzzy logic complex fuzzy set theory downhill-simplex algorithm forward pass fuzzy logic fuzzy neural nets fuzzy set theory gradient descent method gradient methods hybrid learning rule learning (artificial intelligence) least squares approximations neuro-fuzzy architecture off-line ANCFIS online learning algorithm recursive least-square estimation simulated annealing time-series datasets

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Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

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IET ELECTRIC POWER APPLICATIONS 2010年第5期4卷 314-332页

作者： Lin, F. -J. Kung, Y. -S. Chen, S. -Y. Liu, Y. -H. Natl Cent Univ Dept Elect Engn Chungli 320 Taiwan So Taiwan Univ Dept Elect Engn Tainan 710 Taiwan

A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of a multi-axis motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. First, the structure and operating principles of the LUSMs are introduced briefly. Since the dynamic characteristics and motor parameters of the LUSMs are non-linear and time varying, the RWENN is proposed to control the mover of the X-Y-theta motion control stage to track various contours precisely using a direct decentralised control strategy. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore compared with the conventional Elman neural network (ENN), both the precision and time of convergence are improved. Furthermore, the on-line learning algorithm based on the supervised gradient descent method and the convergence analysis of the tracking error using a discrete-type Lyapunov function of the RWENN are developed. Finally, some experimental results of various contours tracking show that the tracking performance of the RWENN is significantly improved compared with the ENN.

关键词： Spatial variables control linear motors wavelet transforms Control of electric power systems recurrent connective weights recurrent neural nets online learning algorithm ultrasonic motors neurocontrollers discrete systems Stability in control theory Linear machines gradient methods multi-axis motion control stage Integral transforms motion control discrete-type Lyapunov function Self-adjusting control systems Lyapunov methods linear ultrasonic motors learning systems recurrent wavelet-based Elman neural network control machine control Neurocontrol Discrete control systems Piezoelectric motors supervised gradient descent method convergence analysis direct decentralised control strategy

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