A review on synchrophasor technology for power system monitoring

The Phasor Measurement Unit (PMU) is the heart of smart grid system as it provides the data such as voltage and phase angle measurements of all buses of the system and thereby maintaining the system observability. In this context, this paper summarizes the various research based on PMU for complete observability and monitoring of integrated power system. The survey indicates that most of the recent researches are focusing on optimal PMU placement (OPP) rather than design and modeling of PMU considering various cases. Moreover, the state estimation using synchrophasor technology are also presented as addition objective to obtain the optimal number of PMU that need to be installed in the system for power system analysis and economic benefits of the system. The trend of research based on synchrophasor technology are evolving for real-time power system monitoring application where it also covers for dynamic power system assessment

A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission

The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively