1. Assessment of harmonic distortion in school buildings equipped with grid-connected PV systemsFranjo Pranjić, 2025, strokovni članek Opis: The integration of photovoltaic systems into power grids can impact power quality, particularly concerning voltage and current harmonics. This study investigates the power quality of a photovoltaic system integrated into the electrical system of an educational facility, focusing on harmonic distortion in both voltage and current. Comprehensive measurements were conducted across three phases and analysed according to the EN 50160 and IEEE 519 Standards. The results demonstrated that, while the voltage quality meets EN 50160 requirements consistently, indicating stable voltage levels, the current measurements revealed significant harmonic distortion. Notably, Phase 2 exhibited Total Harmonic Distortion values substantially above the acceptable limits, with Phase 1 and Phase 3 also showing elevated Total Harmonic Distortion. To address these issues, the study recommends the implementation of advanced harmonic filters and optimisation of inverter technologies. These measures are crucial for enhancing power quality, and ensuring compliance with the industry Standards in high photovoltaic penetration scenarios.
Ključne besede: power quality analysis, grid-connected PV systems, EN 50160 standard, IEEE 519 Standard, IEC 61000 standard, voltage harmonics, current harmonics, harmonic distortion, renewable energy integration, compliance assessment
Objavljeno v DKUM: 01.10.2025; Ogledov: 0; Prenosov: 2
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2. Quantifying power system frequency quality and extracting typical patterns within short time scales below one hourYounes Mohammadi, Boštjan Polajžer, Roberto Chouhy Leborgne, Davood Khodadad, 2024, izvirni znanstveni članek Opis: This paper addresses the lack of consideration of short time scales, below one hour, such as sub-15-min and sub1-hr, in grid codes for frequency quality analysis. These time scales are becoming increasingly important due to
the flexible market-based operation of power systems as well as the rising penetration of renewable energy
sources and battery energy storage systems. For this, firstly, a set of frequency-quality indices is considered,
complementing established statistical indices commonly used in power-quality standards. These indices provide
valuable insights for quantifying variations, events, fluctuations, and outliers specific to the discussed time
scales. Among all the implemented indices, the proposed indices are based on over/under frequency events (6
indices), fast frequency rise/drop events (6 indices), and summation of positive and negative peaks (1 index), of
which the 5 with the lowest thresholds are identified as the most dominant. Secondly, k-means and k-medoids
clustering methods in a learning scheme are employed to identify typical patterns within the discussed time
windows, in which the number of clusters is determined based on prior knowledge linked to reality. In order to
clarify the frequency variations and patterns, three frequency case studies are analyzed: case 1 (sub-15-min scale,
10-s values, 6 months), case 2 (sub-1-hr scale, 10-s values, 6 months), and case 3 (sub-1-hr, 3-min values, the
year 2021). Results obtained from the indices and learning methods demonstrate a full picture of the information
within the windows. The maximum value of the highest frequency value minus the lowest one over the windows
is about 0.35 Hz for cases 1 and 2 and 0.25 Hz for case 3. Over-frequency values (with a typical 0.1% threshold)
slightly dominates under-frequency values in cases 1 and 2, while the opposite is observed in case 3. Medium
fluctuations occur in 35% of windows for cases 1 and 2 and 41% for case 3. Outlier values are detected using the
quartile method in 70% of windows for case 2, surpassing the other two cases. About six or seven typical patterns
are also extracted using the presented learning scheme, revealing the frequency trends within the short time
windows. The proposed approaches offer a simpler alternative than tracking frequency single values and also
capture more comprehensive information than existing approaches that analyze the aggregated frequency values
at the end of the specific time windows without considering the frequency trends. In this way, the network
operators have the possibility to monitor the frequency quality and trends within short time scales using the most
dominant indices and typical patterns.
Ključne besede: quantifying power system frequency quality, statistical indices, pattern extracting, machine learning, short time scales, renewable energy sources
Objavljeno v DKUM: 23.08.2024; Ogledov: 50; Prenosov: 42
 Celotno besedilo (12,67 MB)
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4. The impact of plug-in hybrid vehicles in low-voltage distribution systems using a Monte Carlo simulationEvica Smilkoska, Vasko Zdraveski, Jovica Vuletić, Jordančo Angelov, Mirko Todorovski, 2023, izvirni znanstveni članek Opis: The growing presence and randomness of renewable-based Distributed Generation, such as solar, photovoltaic, and wind power, and heavy Plug-in Hybrid Electric Vehicle loads in residential distribution grids result in both a higher degree of imbalance and a wide range of voltage fluctuations. When increasing the number of Plug-in Hybrid Electric Vehicles that are simultaneously charged, the additional unpredicted load may cause several problems to the current grid in terms of voltage deviations, thermal overloads, power losses, increased aging of transformers and lines, decreased quality of supply, and power outages. This paper proposes an approach that models Plug-in Hybrid Electric Vehicles’ behaviour and performs power flow analysis on CIGRE low voltage benchmark grid to investigate the impact on the current distribution grid.
Ključne besede: plug-in hybrid electric vehicle, power quality, non-deterministic approach, voltage deviations, power losses, distribution systems
Objavljeno v DKUM: 11.10.2023; Ogledov: 464; Prenosov: 119
Celotno besedilo (6,44 MB)
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