Dynamic load modelling based on measurements in medium voltage distribution network

Stojanovic, D.; Korunović, Lidija M.; Milanović, Jovica V.

doi:10.1016/j.epsr.2007.02.003

Cited by 78 publications

(52 citation statements)

References 22 publications

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“…It has been concluded that power system voltage stability behaviour and choice of compensation techniques significantly depends on selection of the load model and its parameters [21] [22]. In order to effectively analyze the dynamics of decentralized hybrid power system, loads need to be regarded along with the generators in a transient stability analysis.…”

Section: Probabilistic Load Disturbancesmentioning

confidence: 99%

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

Saxena¹,

Kumar²

2015

WJET

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This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.

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Section: Probabilistic Load Disturbancesmentioning

confidence: 99%

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

Saxena¹,

Kumar²

2015

WJET

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“…An equation that can describe the load power dependency as a function of system voltage and frequency under the static exponential load model is as follows [30,31]:…”

Section: The Load Modelmentioning

confidence: 99%

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method

et al. 2016

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Abstract:In this paper a realistic medium voltage (MV) network with four different distributed generation technologies (diesel, gas, hydro and wind) along with their excitation and governor control systems is modelled and simulated. Moreover, an exponential model was used to represent the loads in the network. The dynamic and steady state behavior of the four distributed generation technologies was investigated during grid-connected operation and two transition modes to the islanding situation, planned and unplanned. This study aims to address the feasibility of planned islanding operation and to investigate the effect of unplanned islanding. The load sharing islanding method has been used for controlling the distributed generation units during grid-connected and islanding operation. The simulation results were validated through various case studies and have shown that properly planned islanding transition could provide support to critical loads at the event of utility outages. However, a reliable protection scheme would be required to mitigate the adverse effect of unplanned islanding as all unplanned sub-cases returned severe negative results.

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“…Static load models have been developed in [11] considering the polynomial, linear and exponential representation. 1 st order dynamic model along with model parameters has been presented in [12]. Considering the contribution of both static and dynamic characteristics in aggregate (substation level) load, composite model has been discussed in [7].…”

Section: Introductionmentioning

confidence: 99%

“…A time-dependent load model based on 2 nd order transfer function has been developed in [9] by utilizing genetic algorithm as the optimization process. Some of the works highlight the load model selection aspects [7,11,12]. Static load models have been developed in [11] considering the polynomial, linear and exponential representation.…”

Section: Introductionmentioning

confidence: 99%

A step-by-step data processing guideline for load model development based on field measurements

Hasan

Milanović

Turner

et al. 2015

2015 IEEE Eindhoven PowerTech

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Abstract-Load modelling attracts renewed interests these days in maintaining peak load conditions, supplying new types of loads and accommodating more renewable generation into electricity networks. This work describes real measurement data acquisition and step-by-step signal processing for developing aggregate load models at 11kV and 6.6kV level. Challenges in analyzing real measurement data are highlighted and issues to improve measurement-based load modelling are discussed. Load models at 15 substations from a UK distribution network are presented with subsequent model parameters. These load models will provide an insight to the operational flexibility, network resilience and management requirements of the measurement sites and related up-and-downstream substations.Index Terms--Distribution system, load-voltage relationship, measurement-based load model, operational flexibility.

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Dynamic load modelling based on measurements in medium voltage distribution network

Cited by 78 publications

References 22 publications

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

Distributed Generation Islanding Effect on Distribution Networks and End User Loads Using the Load Sharing Islanding Method

A step-by-step data processing guideline for load model development based on field measurements

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