Inter-trip links incorporated optimal protection coordination

Due to advances in smart grid, different communication links as delay, inter-trip and activation are used between relays to enhance the protection system performance. In this paper, the effect of inter-trip links on optimal coordination of directional overcurrent relays (DOCRs) is analytically investigated and modelled. Moreover, an index is proposed to find the optimum locations for inter-trip link installation to reach the minimal fault clearance times under the selectivity constraint. Then a method is proposed to determine the candidate locations of inter-trip links and the associated reduced operating times. An Exhaustive search approach is also used to validate the efficiency of the proposed method. The method is simulated and tested on distribution network of IEEE 33 bus using the Power Factory software and MATLAB optimization toolbox. Genetic algorithm is used as an optimization tool to find optimal settings of relays. The results indicate the capability of proposed method in optimal protection coordination with optimum inter-trips

Multi‐agent protection scheme for microgrid using deep learning

Abstract Producing clean energy and feeding critical loads in islanding mode are the main reasons for interest in microgrids. Different operation topologies of microgrids make traditional protection schemes inefficient. This paper proposes a multi‐agent protection scheme in which each protection agent can detect different fault events and isolate faulty phases at a fast rate. A unique algorithm is utilized for determining fault location in microgrids and system operators are informed accordingly. Microgrids have various operation modes due to the stochastic behavior of distributed generators and different topologies. Here, a significant number of operating conditions of the studied microgrid are considered. These operation conditions are simulated in the DIgSILENT Power Factory, and different parameters are stored. Raw measured parameters need to be pre‐processed by a signal processing method in MATLAB. Discrete wavelet transform is chosen for this purpose. Deep learning is used as a machine learning technique due to the various operation modes of the microgrid. Deep neural networks are constructed using Python programming language. The proposed scheme ensures high accuracy in fault detection and fault location in the microgrid, as well as fault isolation in different operation conditions