The adequacy of the present practice in dynamic aggregated modelling of wind farm systems

Large offshore wind farms are usually composed of several hundred individual wind turbines, each turbine having its own complex set of dynamics. The analysis of the dynamic interaction between wind turbine generators (WTG), interconnecting ac cables, and voltage source converter (VSC) based High Voltage DC (HVDC) system is difficult because of the complexity and the scale of the entire system. The detailed modelling and modal analysis of a representative wind farm system reveal the presence of several critical resonant modes within the system. Several of these modes have frequencies close to harmonics of the power system frequency with poor damping. From a computational perspective the aggregation of the physical model is necessary in order to reduce the degree of complexity to a practical level. This paper focuses on the present practices of the aggregation of the WTGs and the collection system, and their influence on the damping and frequency characteristics of the critical oscillatory modes. The effect of aggregation on the critical modes are discussed using modal analysis and dynamic simulation. The adequacy of aggregation method is discussed

Two generalizations of Kohonen clustering

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ

Lag synchronization and scaling of chaotic attractor in coupled system

We report a design of delay coupling for lag synchronization in two unidirectionally coupled chaotic oscillators. A delay term is introduced in the definition of the coupling to target any desired lag between the driver and the response. The stability of the lag synchronization is ensured by using the Hurwitz matrix stability. We are able to scale up or down the size of a driver attractor at a response system in presence of a lag. This allows compensating the attenuation of the amplitude of a signal during transmission through a delay line. The delay coupling is illustrated with numerical examples of 3D systems, the Hindmarsh-Rose neuron model, the R\"ossler system and a Sprott system and, a 4D system. We implemented the coupling in electronic circuit to realize any desired lag synchronization in chaotic oscillators and scaling of attractors.Comment: 10 pages, 7 figure

Case-based reasoning: concepts, features and soft computing

Here we first describe the concepts, components and features of CBR. The feasibility and merits of using CBR for problem solving is then explained. This is followed by a description of the relevance of soft computing tools to CBR. In particular, some of the tasks in the four REs, namely Retrieve, Reuse, Revise and Retain, of the CBR cycle that have relevance as prospective candidates for soft computing applications are explained