Actuator Design of a New AC PM Contactor with Energy-Saving and Noise-Free Characteristics
Developments in power electronic switches and lowcost actuator based on an electronic control module (ECM) have made it possible to design and implement modern control strategies for a new AC permanent magnet (PM) contactor. Such an attempt has made in this work for a colenoid actuator controlling the AC PM contactor. Certain important issues in design including the making signal generator and its amplifier, breaking signal generator and its amplifier, exciting coils, and a voltage regulator have been resolved. The proposed scheme is validated through extensive numerical simulations and a laboratory scale implementation.
Design and optimization of energy-saving wind power grid-connected contactor based on nano two-phase composite magnetic materials
Based on nano two-phase composite magnetic material, a design and optimization scheme of energy-saving wind power grid contactor is proposed, which solves the problems of traditional contactor's high iron loss, low speed and large AC noise. In this paper, the electromagnetic system of the contactor is redesigned by the characteristics of high remanence, high coercivity and wide hysteresis loop of nano two-phase composite magnetic material. A new finite element simulation model is established and compared with the traditional static and dynamic characteristics of the contactor, so as to optimize the overall structural parameters. Finally, a prototype is made and the effectiveness of the design is verified by experiments.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.