Integration Monitoring of On-grid Photovoltaic System: Case Study

Jasiński, Michał; Rezmer, J.; Sikorski, Tomasz; Szymańda, Jarosław M.

doi:10.3311/ppee.13423

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…The addition to the current electrical network of photovoltaic and wind systems entails an intermittent energy generation and the appearance of new loads that affect the electrical network's stability [5,22]. In fact, some authors research all the necessary components for renewable sources to specify the origin of the disturbances [9,[23][24][25][26][27], although a few researchers go further, providing more unstable but fundamental data, such as the weather [28,29]. Table 3 compares the number of analyzed papers with PV systems, wind systems, or both, which provides a little overview on the application of PQ monitoring in renewable energies.…”

Section: Resultsmentioning

confidence: 99%

Current Status and Future Trends of Power Quality Analysis

et al. 2022

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In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network.

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Section: Resultsmentioning

confidence: 99%

Current Status and Future Trends of Power Quality Analysis

et al. 2022

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“…The noticeable change was the use of short-term flicker severity in place of long-term flicker severity. This change is connected with the time aggregations of the parameters; the long-term severity has 2 h, and the short-term one has 10 min [82,83]. Thus, the application of short term flicker severity enables a database consisting of parameters that are aggregated with 10 min intervals to be built, as is demanded in the standard of International Electrotechnical Commission (IEC) 61000-4-30 [80].…”

Section: Parameters Included To the Input Databasementioning

confidence: 99%

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

et al. 2020

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This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.

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“…Additionally, the mean value of THDu measured data was defined. It indicates that when the THDu level is high (higher than 5%) for a long period of time it may lead to a negative assessment [15]…”

Section: Comparative Study Of Recent Developments In Power Quality Rementioning

confidence: 99%

Influence of Measurement Aggregation Algorithms on Power Quality Assessment and Correlation Analysis in Electrical Power Network with PV Power Plant

et al. 2019

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Recently a number of changes were introduced in amendment to standard EN 50160 related to power quality (PQ) including 1 min aggregation intervals and the obligation to consider 100% of measured data taken for the assessment of voltage variation in a low voltage (LV) supply terminal. Classical power quality assessment can be extended using a correlation analysis so that relations between power quality parameters and external indices such as weather conditions or power demand can be revealed. This paper presents the results of a comparative investigation of the application of 1 and 10 min aggregation times in power quality assessment as well as in the correlation analysis of power quality parameters and weather conditions and the energy production of a 100 kW photovoltaic (PV) power plant connected to a LV network. The influence of the 1 min aggregation time on the result of the PQ assessment as well as the correlation matrix in comparison with the 10 min aggregation algorithm is presented and discussed. Energies 2019, 12, 3547 2 of 18 measurement time intervals are aggregated over a 150/180-cycle interval (150 cycles for 50 Hz nominal or 180 cycles for 60 Hz nominal), 10 min interval and 2 h interval.The review of present literature indicates that there is some discussion related to the assessment of power quality in terms of the influence of the aggregation time interval on the effect of the assessment. This issue has a significant meaning in terms of the assessment of power quality at the point of the common coupling (PCC) of the distributed generation (DG), especially when the observed DG is characterized by high variations of the energy production. Common examples are PV installations with their inherent relation of generated energy with cloud effect. The discussed aspect has already been reflected in the amendment to standard EN 50160:2015 where a 1 min aggregation interval is suggested for the assessment of voltage variation in low voltage (LV) power systems. Selected issues related to aggregation interval influence can be found in the below works:

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Integration Monitoring of On-grid Photovoltaic System: Case Study

Cited by 13 publications

References 11 publications

Current Status and Future Trends of Power Quality Analysis

Current Status and Future Trends of Power Quality Analysis

The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

Influence of Measurement Aggregation Algorithms on Power Quality Assessment and Correlation Analysis in Electrical Power Network with PV Power Plant

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