Application of ANN and Genetic Algorithm for Evaluation the Optimum Location of Arresters on Power Networks due to the Switching Overvoltages

Switching surges are of primary importance in insulation co-ordination of EHV lines, as well as in designing insulation of apparatuses. The magnitude and shape of the switching overvoltages vary with the system parameters, network configuration and the point-on-wave where the switching operation takes place. This paper presents an artificial neural network (ANN) based approach to estimate the peak value of overvoltages and the global risk of failure generated by switching transients during line energizing or re-energizing in different nodes of a power network. Then a genetic algorithm (GA) based method is developed to find the best position of surge arresters on power networks so as to minimize the global risk of the network

Resiliency-oriented operation of distribution networks under unexpected wildfires using multi-horizon information-gap decision theory

Extreme events may trigger cascading outages of different components in power systems and cause a substantial loss of load. Forest wildfires, as a common type of extreme events, may damage transmission/distribution lines across the forest and disconnect a large number of consumers from the electric network. Hence, this paper presents a robust scheduling model based on the notion of information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. Since the thermal rating of a transmission/distribution line is a function of its temperature and current, it is assumed that the tie-line connecting the distribution network to the main grid is equipped with a dynamic thermal rating (DTR) system aiming at accurately evaluating the impact of a wildfire on the ampacity of the tie-line. The proposed approach as a multi-horizon IGDT-based optimization problem finds a robust operation plan protected against the uncertainty of wind power, solar power, load, and ampacity of tie-lines under a specific uncertainty budget (UB). Since all uncertain parameters compete to maximize their robust regions under a specific uncertainty budget, the proposed multi-horizon IGDT-based model is solved by the augmented normalized normal constraint (ANNC) method as an effective multi-objective optimization approach. Moreover, a posteriori out-of-sample analysis is used to find (i) the best solution among the set of Pareto optimal solutions obtained from the ANNC method given a specific uncertainty budget, and (ii) the best resiliency level by varying the uncertainty budget and finding the optimal uncertainty budget. The proposed approach is tested on a 33-bus distribution network under different circumstances. The case study under different conditions verifies the effectiveness of the proposed operation planning model to enhance the resilience of a distribution network under a close wildfire. © 2022 The Author(s

A Novel Approach To Utilize Plc To Detect Corroded And Eroded Segments Of Power Transmission Lines

A great number of existing power transmission lines have aged throughout the world. A considerable proportion of them are located in the environments that exacerbate their corrosion and erosion process. In-time detection of defects necessitates the use of nondestructive testing (NDT) techniques. Scanning the lines with robots and helicopters are the methods in use. In this paper, we propose a new NDT technique that uses the elements of power-line carriers, generally for information communication purposes, to detect the possibly corroded and eroded segments of each phase along a transmission line. The proposed NDT technique is based on the backward-wave propagation produced by a change in the characteristic impedance of a power transmission line, resulting from corrosion and erosion. To demonstrate the viability of the proposed approach, EMTPWorks is used to perform simulations. It is shown that the proposed technique can locate the surface defects and estimate the severity of them in transmission line conductors

A Model Predictive Control Based Virtual Active Power Filter Using V2G Technology

This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability