DEVELOP A FAULT LOCATION MONITOR BY USING ZIGBEE WITH FLISR
Aim: Design a system where as soon as fault occurs it would be communicated to the main power substation. A FLISR system is integrated in a smart grid. The IEDs located at the consumers end communicate the fault to the local manager using Zigbee wireless communication and the local manager then communicates the fault to the main power substation through Wimax- a 3G technology. The main goals of our project were: Reliable fault detection Fast and efficient wireless communication Local manager and consumer devices communicate to each other via Zigbee since the range is not very large. The local manager and the main power station are quite far away so they communicate through Wimax. As soon as the users experience any fault, the FLISR sensors located at their end communicate the problem to local manager (via Zigbee) and the local manager will then forward it to the main power station (via Wimax).
Having more than 6 years of experience in machine learning, image processing and computer vision I believe that I can do any task related to ML. I have used almost all algorithms for ML like pca, lbp, lpq, hog, surf, sift etc for features extraction, KNN, decision trees, naïve bayes, random forests, svm, Neural Networks, K-means for classification, clustering and predictive models. I have worked on different Python libraries and frameworks like Tensorflow, pandas, scikit learn,nltk, numpy matplotlib,scrapy, beautifulsoup etc I have used AWS for deploying different algorithms over web and used Flask for making rest Apis. I have also worked on many internet of things related projects in which I integrated machine learning models with iot. I have used node.js and Mqqt for iot related projects. I can make web, desktop and mobile applications related to these fields. Do you need to discuss my previous projects? Just ping me! Just inbox your requirements and consider your work done!!