Pulsewidth modulation (PWM) voltage source converters are becoming a popular interface to the power grid for many applications. Hence, issues related to the reduction of PWM harmonics injection in the power grid are becoming more relevant. The use of high-order filters like LCL filters is a standard solution to provide the proper attenuation of PWM carrier and sideband voltage harmonics. However, those grid filters introduce potentially unstable dynamics that should be properly damped either passively or actively. The second solution suffers from control and system complexity (a high number of sensors and a high-order controller), even if it is more attractive due to the absence of losses in the damping resistors and due to its flexibility. An interesting and straightforward active damping solution consists in plugging in, in cascade to the main controller, a filter that should damp the unstable dynamics. No more sensors are needed, but there are open issues such as preserving the bandwidth, robustness, and limited complexity. This paper provides a systematic approach to the design of filter-based active damping methods. The tuning procedures, performance, robustness, and limitations of the different solutions are discussed with theoretical analysis, selected simulation, and experimental results.

Filter-Based Active Damping of Voltage Source Converters With $LCL$ Filter

This paper describes the design procedure and performance of an LCL grid filter for a medium-voltage neutral-point clamped converter to be adopted for a multimegawatt (multi-MW) wind turbine. The unique filter design challenges in this application are driven by a combination of the medium-voltage converter, a limited allowable switching frequency, component physical size and weight concerns, and the stringent limits for allowable injected current harmonics. Traditional design procedures of grid filters for lower power and higher switching frequency converters are not valid for a multi-MW filter connecting a medium-voltage converter switching at low frequency to the electric grid. This paper demonstrates a frequency-domain-model-based approach to determine the optimum filter parameters that provide the necessary performance under all operating conditions given the necessary design constraints. To achieve this goal, new concepts, such as virtual-harmonic content and virtual filter losses are introduced. Moreover, a new passive-damping technique that provides the necessary damping with low losses and very little degradation of the high-frequency attenuation is proposed.

/pdf/grid-filter-design-for-a-multimegawatt-medium-voltage-55gvmheqkp.pdf

Grid-Filter Design for a Multimegawatt Medium-Voltage Voltage-Source Inverter

The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

This study addresses the challenge of identifying the features of the Centre of pressure (COP) trajectory that are most sensitive to postural performance, with the aim of avoiding redundancy and allowing a straightforward interpretation of the results. Postural sway in 50 young, healthy subjects was measured by a force platform. Thirty-seven stabilometric parameters were computed from the one-dimensional and two-dimensional COP time series. After normalisation to the relevant biomechanical factors, by means of multiple regression models, a feature selection process was performed based on principal component analysis. Results suggest that COP two-dimensional time series can be primarily characterised by four parameters, describing the size of the COP path over the support surface; the principal sway direction; and the shape and bandwidth of the power spectral density plot. COP one-dimensional time series (antero-posterior (AP) and medio-lateral (ML)) can be characterised by six parameters describing COP dispersion along the AP direction; mean velocity along the ML and AP directions; the contrast between ML and AP regulatory activity; and two parameters describing the spectral characteristics of the COP along the AP direction. On the basis of the results obtained, some guidelines are suggested for the choice of stabilometric parameters to use, with the aim of promoting standardisation in quantitative posturography.

Feature selection of stabilometric parameters based on principal component analysis.

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

A feature extraction unsupervised neural network for an environmental data set

Three-phase active rectifiers based on the voltage source converter topology can successfully replace traditional thyristor based rectifiers or diode bridge plus chopper in interfacing dc-systems to the grid. However, if the application in which they are employed has a high safety issue or if there are other loads connected to the same point of common coupling (PCC), and sensitive to the harmonics produced by the switching of the converter, an LCL-filter has to be used. This kind of solution has been studied in theory but its sensitivity to the values of the LCL-filter passive elements employed, to the grid side stiffness and to the parameters of the controller has never been detailed. In this paper the experimental results of an LCL-filter-based three-phase active rectifier are analyzed with the circuit theory approach. A "virtual circuit" is synthesized in role of the digital controller and of the feedback filters to have a homogenous model that allows a sensitivity analysis, which is rigorous and straightforward for the industry.

Analysis of the grid side behavior of a LCL-filter based three-phase active rectifier

In this paper a new unsupervised neural network, characterized by the absence of competition between its elements, is introduced. The kernel of this network is a new neural unit able to perform clustering even acting alone. It is shown how this network overcomes some of the major drawbacks of classical unsupervised competitive architectures. >

A new non competitive unsupervised neural network for clustering

Video database management systems require efficient methods to abstract video information. Identification of shots in a video sequence is an important task for summarizing the content of a video. We describe a neural network based technique for automatic clustering of video frames in video sequences. From each frame the features that describe the image content are extracted to form a signature. These signatures are clustered using a rival penalized competitive learning (RPCL) neural network owing its capability to being able to automatically detect the number of classes in the data set. Results presented in the paper show that for images clustering in video sequences, the RPCL network is able to automatically extract the correct number of classes, hence the correct number of scenes, and to produce a class partition which agrees with a human model of sequences.

https://ieeexplore.ieee.org/iel4/5607/15056/00687144.pdf

Scene segmentation in video sequences by an RPCL neural network

In this paper we describe a method to recognize missing components on manufactured products. The proposed approach exploits the wavelet transform to extract features from the acquired data, while the diagnosis is performed by means of a neural network. The results show that this method achieves an high recognition rate. At the same time the method allows to use a very cheap diagnostic system.

An automatic method to detect missing components in manufactured products

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