Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

An Efficient Hybrid TLBO-PSO Approach for Congestion Management Employing Real Power Generation Rescheduling

In the present deregulated electricity market, power system congestion is the main complication that an independent system operator (ISO) faces on a regular basis. Transmission line congestion trigger serious problems for smooth functioning in restructured power system causing an increase in the cost of transmission hence affecting market efficiency. Thus, it is of utmost importance for the investigation of various techniques in order to relieve congestion in the transmission network. Generation rescheduling is one of the most efficacious techniques to do away with the problem of congestion. For optimizing the congestion cost, this work suggests a hybrid optimization based on two effective algorithms viz Teaching learning-based optimization (TLBO) algorithm and Particle swarm optimization (PSO) algorithm. For binding the constraints, the traditional penalty function technique is incorporated. Modified IEEE 30-bus test system and modified IEEE 57-bus test system are used to inspect the usefulness of the suggested methodology.

A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Wind-solar Coupling

The transitional path towards a highly renewable power system based on wind and solar energy sources is investigated considering their intermittent and spatially distributed characteristics. Using an extensive weather-driven simulation of hourly power mismatches between generation and load, we explore the interplay between geographical resource complementarity and energy storage strategies. Solar and wind resources are considered at variable spatial scales across Europe and related to the Swiss load curve, which serve as a typical demand side reference. The optimal spatial distribution of renewable units is further assessed through a parameterized optimization method based on a genetic algorithm. It allows us to explore systematically the effective potential of combined integration strategies depending on the sizing of the system, with a focus on how overall performance is affected by the definition of network boundaries. Upper bounds on integration schemes are provided considering both renewable penetration and needed reserve power capacity. The quantitative trade-off between grid extension, storage and optimal wind-solar mix is highlighted.This paper also brings insights on how optimal geographical distribution of renewable units evolves as a function of renewable penetration and grid extent.

Secure Market Operation in Presence of Critical Model Parameters in State Estimation

This paper is concerned about the impact of network parameter errors on the reliable operation and management of electricity markets.Specifically,the paper investigates the so-called critical parameters in a network model whose errors cannot be detected or estimated due to the lack of local measurement redundancy.Due to this property of critical parameters,it will be impossible to detect,identify and correct errors in these parameters.Given the fact that electricity market applications are heavily model-dependent,the locational marginal prices(LMPs)can be shown to be seriously distorted in the presence of critical parameter errors.Furthermore,if such errors are maliciously injected by adversaries,they will go undetected.Meanwhile,prices and revenues associated with power transactions may be strategically manipulated.An approach for quantifying the impact of critical parameters on the management of electricity markets is proposed.Conditions related to network topology and measurement configuration leading to the appearance of critical parameters are classified,and meter placement strategies for avoiding critical parameters are presented as well.Simulation results obtained by using IEEE test systems are given to verify the proposed analysis and design methods.