Micro grids decentralized hybrid data-driven cuckoo search based adaptive protection model

Abstract

Micro-grid protection presents great technical challenges to utility company engineers. Several protection schemes have been developed towards a reliable operation on these type of networks, however, the methods are strongly dependent on robust communication systems. This paper presents a decentralized adaptive protection scheme and introduces a data-driven and communication-less approach. The presented solution uses an Artificial Neural Network to train Intelligent Electronic Devices as fault classifiers, also, it uses a cuckoo search metaheuristic to its quasi-optimal adjustment. The Artificial Neural Network enables each classifier to detect faults with only local voltage and current measurements. Presented solution does not assume communication between devices, however, each device brings support to their neighboring devices as back-up protection. Further, system dynamics on the electrical network, such as, changes of topologies, micro-grid status or Distributed Energy Resources outage are considered. The time coordination is simple and easily adjustable due to each fault label of the data-driven model has its own time operation. The presented method is validated on the modified IEEE 34-nodes test feeder. The results of the adaptive protection scheme show accuracy values above of 96% and dependability of 99%. Also, the solution reveals a correlation between the location and the combination of features and hyper-parameters for each Intelligent Electronic Device. The method is development to be easy-to-implement, without hard-to-design parameters, and with highlights potential aspects for real-life applications.