<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>High-permeability photovoltaic (PV) systems are a key technical way to bring about new energy changes in electric power networks. Risk assessment and electrical regulation during extreme weather can make PV systems with high permeability work much more efficiently during severe weather. We establish an operational risk model for a high penetration rate PV system using the PV probability model, load parameter model, and probabilistic trend calculation model. Based on the results of the tidal current calculation, the risk assessment indexes of PV systems are constructed, including the voltage overrun risk index and branch circuit tidal current overrun risk index. The Monte Carlo simulation method using the Latin hypercubic sampling method is used to simulate the operational loads of PV systems under extreme weather conditions such as windy, rainy, and snowy conditions and to form a fault simulation set. Finally, we implement the QMC method to assess the risk of PV systems with high permeability and use the RBF neural network design to achieve automatic control of PV electrical equipment. When the weather is the same extreme, the risk of overrun at each node goes up with the number of nodes. When the weather is different, the risk of voltage overrun at each node under ice-covered load goes up by about half compared to rain load, and the risk of active overrun at each branch goes up by almost six times compared to rain load.</p>
</abstract>

High-permeability photovoltaic (PV) systems are a key technical way to bring about new energy changes in electric power networks. Risk assessment and electrical regulation during extreme weather can make PV systems with high permeability work much more efficiently during severe weather. We establish an operational risk model for a high penetration rate PV system using the PV probability model, load parameter model, and probabilistic trend calculation model. Based on the results of the tidal current calculation, the risk assessment indexes of PV systems are constructed, including the voltage overrun risk index and branch circuit tidal current overrun risk index. The Monte Carlo simulation method using the Latin hypercubic sampling method is used to simulate the operational loads of PV systems under extreme weather conditions such as windy, rainy, and snowy conditions and to form a fault simulation set. Finally, we implement the QMC method to assess the risk of PV systems with high permeability and use the RBF neural network design to achieve automatic control of PV electrical equipment. When the weather is the same extreme, the risk of overrun at each node goes up with the number of nodes. When the weather is different, the risk of voltage overrun at each node under ice-covered load goes up by about half compared to rain load, and the risk of active overrun at each branch goes up by almost six times compared to rain load.

Risk assessment and electrical control of high-permeability renewable energy photovoltaic system under extreme weather conditions

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

High-permeability photovoltaic (PV) systems are a key technical way to bring about new energy changes in electric power networks. Risk assessment and...

Risk assessment and electrical control of high-permeability renewable energy photovoltaic system under extreme weather conditions

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 07. Apr. 2024

Akzeptiert: 21. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2194

Schlüsselwörter
Probabilistic tidal current calculation, Voltage crossing risk, QMC, Photovoltaic system, Risk assessment

© 2024 Qi Dong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Risk assessment and electrical control of high-permeability renewable energy photovoltaic system under extreme weather conditions

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 07. Apr. 2024

Akzeptiert: 21. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-2194

SchlüsselwörterProbabilistic tidal current calculation, Voltage crossing risk, QMC, Photovoltaic system, Risk assessment

© 2024 Qi Dong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Probabilistic tidal current calculation, Voltage crossing risk, QMC, Photovoltaic system, Risk assessment