Power Quality Issues Concerning Photovoltaic Generation in Distribution Grids

Niitsoo, Jaan; Jarkovoi, Marek; Taklaja, Paul; Klüss, Joni; Palu, Ivo

doi:10.4236/sgre.2015.66014

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…As shown in Figure 19, the THDV reached values up to 2.81% (THDV PVS O f f (1) ) and 3.92% (THDV PVS On(1,2) ) for without and with PV injection for Phase A, both with lowest load power interval (0-2.5%). That means that low-load and low-PV injection conditions had a significant influence on the THDV, which indicates that THDV decreased as the PV injection increased and agreed with the results of Niitso et al [13], Ahmed et al [17], and Grycan et al [18]. However, THDV values were always lower than the 8% limit ( Table 2).…”

Section: Probability Of Occurrence Of Each Scenariosupporting

confidence: 88%

“…Thus, a scenario matrix is a tool to analyse data obtained for different instants with similar PV power and power demand conditions. In a manner slightly similar to this proposal, some studies evaluate the indicators, taking into account the level of PV injection [13,19]; other studies compare of parameters during PV injection with standards [10,11,14,18] or compared average, maximum, or minimum values of indices for two conditions: with and without PV injection [9,[15][16][17]20]. The scenario matrix was created as follows:…”

Section: Data Classification Into Scenario Matrixmentioning

confidence: 99%

“…Moreover, some research has shown that the THDI and IHDI are significant for a low PV power injection but decrease with a higher PV power injection [10][11][12][13][14][15][17][18][19][20]. Nemes et al [14] studied the effect of a 3 kWp PV system in an LV network from the comparison of PQ parameters of a grid using the limits of the IEEE 519, IEC 61000, and EN 50160 standards.…”

Section: Introductionmentioning

confidence: 99%

“…These studies can be tackled by different approaches. Some authors compared specific PQ parameters with limits or ranges established by standards [10,11,14,18]; other authors classified the data and compared them according to PV injection levels [13,19]; and some other authors compared the parameters for scenarios with and without injection to find the average, maximum, or minimum values [9,[15][16][17]20].…”

Section: Introductionmentioning

confidence: 99%

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Impact Evaluation of Grid-Connected PV Systems on PQ Parameters by Comparative Analysis based on Inferential Statistics

et al. 2020

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The intermittent injection of power and the nature of power electronic devices used for photovoltaic (PV) systems can affect the power quality (PQ) of the grid to which they are connected. This study proposes to quantify and evaluate the impact of PV injection on the PQ of a low-voltage (LV) network by applying a statistical analysis through hypothesis testing for the mean comparison of populations of parameters with and without a PV system. The effects of PV power injection and load demand at the point of common coupling on PQ are monitored. The methodology includes the selection and monitoring of PQ, the use of a matrix for classification of data with similar load and PV power injection conditions, and the application of the Wilcoxon rank sum test. This methodology was applied to evaluate the impact of a 9.8 kWp PV system on the PQ of an LV network.

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Section: Probability Of Occurrence Of Each Scenariosupporting

confidence: 88%

Section: Data Classification Into Scenario Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact Evaluation of Grid-Connected PV Systems on PQ Parameters by Comparative Analysis based on Inferential Statistics

et al. 2020

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“…The single‐ and multi‐events of PQDs are the great cause of controlling sensitivity and damaging of equipment in smart‐grid, distribution, and transmission system …”

Section: Introductionmentioning

confidence: 99%

Classification of multiple power quality events via compressed deep learning

Liu

Hussain

Shen

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper presents a recently established compressed sensing (CS) and sparse autoencoder (SAE) based on deep learning (DL) method for classification of single and multiple power quality disturbances (PQDs). The CS technique is paying considerable attention in recent years due to below sampling rate comparatively Nyquist sampling. Initially, the CS technique is applied to extract the features of PQD waveforms. The extracted features are applied as inputs to the sparse autoencoder based on DL for classification of nine single and 22 combined classes of PQDs. The DL helps to remove a redundant feature and improves classification performance. Finally, backpropagation is applied to fine‐tune the entire network. The effectiveness of the proposed algorithm has been tested with more than 6580 numbers of real and synthetic single and multiple PQD data, and the results are recorded. High correct classification rate is obtained with noise and without noise level. Noise level was considered from 20 to 50 dB. The performance of the proposed technique has been assessed by comparing the results against recently reported methods. Results show that the proposed CS‐ and SAE‐based DL algorithms can be efficiently used for single and multiple PQDs classifications.

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