Comprehensive Review of Radial Distribution Test Systems for Power System Distribution Education and Research

Bouchekara, Houssem R. E. H.; Latreche, Y.; Naidu, Kanendra; Mokhlis, Hazlie; Dahalan, Wardiah Mohd; Javaid, Muhammad Sharjeel

doi:10.18799/24056537/2019/3/196

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…The impact of false data on voltage regulation is tested in a case study, namely, a 118-node distribution network, as described in [36]. Its topology is reported in Figure 5; the figure first highlights the radial structure with four main feeders and a PS, identified by a red square marker; each node corresponds to a SS with some connected customers.…”

Section: Case Studymentioning

confidence: 99%

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False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

Bragatto

Bucarelli

et al. 2023

Sensors

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The increasing penetration of distributed generation (DG) across power distribution networks (DNs) is forcing distribution system operators (DSOs) to improve the voltage regulation capabilities of the system. The increase in power flows due to the installation of renewable plants in unexpected zones of the distribution grid can affect the voltage profile, even causing interruptions at the secondary substations (SSs) with the voltage limit violation. At the same time, widespread cyberattacks across critical infrastructure raise new challenges in security and reliability for DSOs. This paper analyzes the impact of false data injection related to residential and non-residential customers on a centralized voltage regulation system, in which the DG is required to adapt the reactive power exchange with the grid according to the voltage profile. The centralized system estimates the distribution grid state according to the field data and provides the DG plants with a reactive power request to avoid voltage violations. A preliminary false data analysis in the context of the energy sector is carried out to build up a false data generator algorithm. Afterward, a configurable false data generator is developed and exploited. The false data injection is tested in the IEEE 118-bus system with an increasing DG penetration. The false data injection impact analysis highlights the need to increase the security framework of DSOs to avoid facing a relevant number of electricity interruptions.

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Section: Case Studymentioning

confidence: 99%

“…This is a profile in p.u. ; therefore, it was scaled up according to the loads assigned to the buses in the original network in [36]. Moreover, a coefficient that randomly varies from 0.85 to 1.15 was applied.…”

Section: Case Studymentioning

confidence: 99%

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

Bragatto

Bucarelli

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…And thus, most of the electrical distribution networks (EDNs) draw more reactive power from the main grid, resulting in inadequate voltage magnitudes and voltage instability/blackouts [1]. In addition, the radial structure and high r/x ratio branches of EDNs cause the network performance to worsen significantly [2]. Thus, many researchers have been focused on reactive power compensation in power systems after experiencing blackouts.…”

Section: Introductionmentioning

confidence: 99%

Optimal Integration of Multiple D-SVCs for Voltage Stability Enhancement in Radial Electrical Distribution System Using Adaptive Firefly Algorithm

2023

IJIES

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Most electrical distribution systems (EDS) are radially structured so that primary and secondary protection control devices can work together well. Because they have a high R/X ratio in their design, they also have a bad voltage profile, high distribution losses, and less stability margins. As a result, integrating reactive power compensation devices like the distribution-static VAr compensator (D-SVC) can solve these issues. Yet, the situation can get worse if D-SVCs are placed and rated incorrectly in radial distribution networks (RDNs). Determining the optimum locations and their ratings simultaneously requires an updated version of the Firefly algorithm (FA) with adaptive parameters, which is introduced in this paper as the adaptive firefly algorithm (AFA). The multi-objective function that has been presented relates to improving loadability, voltage stability, and reducing active power loss. On the IEEE 69-bus, simulations are run for three different VAr compensation levels. In comparison to the base case, the losses are reduced by 34.04% and 33.14% with 50% and 75% VAr compensation, respectively. But for the optimal VAr compensation of 73.14 percent by AFA, the losses are reduced by 35.29 percent, which is higher than both under and over compensation cases. Similarly, the loadability margin is increased to 3.099 p.u. with optimal VAr compensation, but it is observed as only 2.833 p.u. and 2.939 p.u. with 50% and 75% VAr compensation, respectively. On the other hand, the findings produced with APF demonstrate its efficiency for resolving complex optimization issues and outperform those obtained with previous research. Also, the proposed D-SVCs allocation has improved RDN's overall performance, demonstrating how well it adapts to real-time applications.

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State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

Sachit,

Al-Anbarri

2024

IJEER

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The conventional weighted least square (WLS) method is the most effective technique used in the state estimation of high voltage transmission system. Unfortunately, the application of WLS in radial distribution network encounter difficulties due to the inherent characteristics of these systems, such as the low measurement redundancy and high r/x ratio of the distribution systems. Given the structure of bulky systems that require a bulky number of measurements, the use of artificial neural networks is considered an effective alternative to estimate these values using a lesser number of measurements than conventional techniques. Due to state estimation based on ANN technique, the time-consuming gain matrix manipulation and pseudo measurements required in the conventional WLS method are no longer necessary. The efficiency of deep learning neural networks such as multi-layer perceptron (MLP) and Legendre neural network (LeNN) depends on the position of the measurements and the number of neural networks applied. Determining the applicable number of neural networks to ensure high estimation accuracy plays an important role in the estimation process. This aspect is addressed in this study, where multiple neural networks are used to improve performance compared to a single neural network. The results obtained indicate that determining the applicable number of neural networks depends on several factors such as the position of the measurements and the diversity of the data. The application of LeNN on state estimation of a 69-bus radial distribution network is used as an illustrative example to explain the distinctive feature of the proposed technique.

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Comprehensive Review of Radial Distribution Test Systems for Power System Distribution Education and Research

Cited by 3 publications

References 34 publications

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

Optimal Integration of Multiple D-SVCs for Voltage Stability Enhancement in Radial Electrical Distribution System Using Adaptive Firefly Algorithm

State Estimation of Radial Distribution Systems Based on Multiple Legendre Neural Networks

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