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Related Concept Videos

Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
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Three-Winding Transformers01:19

Three-Winding Transformers

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Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Control of Power Flow

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There are several methods to control power flow in power systems:
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Power Factor01:11

Power Factor

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The power factor is defined as the ratio of average (or active) power to apparent power, as illustrated by the relation
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Generation of Three-Phase Voltage01:21

Generation of Three-Phase Voltage

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A three-phase AC generator has a rotor with a rotating magnet placed within the stator mounted with the stationary three-phase winding to generate three-phase voltages via mutual induction. These windings are evenly distributed around the inner circumference of the stator and are arranged 120 electrical degrees apart. Three-phase stator windings consist of three separate coils or groups of coils, known as phases, each connected in Y (star) configuration or Delta configuration.
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Analysis of Inverter Efficiency Using Photovoltaic Power Generation Element Parameters.

Su-Chang Lim1, Byung-Gyu Kim2, Jong-Chan Kim1

  • 1Department of Computer Engineering, Sunchon National University, Suncheon 57992, Republic of Korea.

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|October 16, 2024
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Summary
This summary is machine-generated.

This study introduces a Long Short-Term Memory (LSTM) model to predict photovoltaic power generation and assess inverter efficiency degradation. The model effectively identifies performance decline in solar power equipment, aiding proactive maintenance.

Keywords:
AILSTMPV power forecastingPV systemdata analysisdeep learning

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Energy
  • Photovoltaic Performance Monitoring

Background:

  • Photovoltaic (PV) power generation is susceptible to environmental variables and equipment condition.
  • Maintaining optimal equipment performance, particularly inverters, is crucial for sustained energy production.
  • Predictive assessment of equipment health is essential for efficient PV plant operation.

Purpose of the Study:

  • To propose and validate a method for determining inverter efficiency degradation in PV systems.
  • To utilize Long Short-Term Memory (LSTM) networks for predictive maintenance of PV equipment.
  • To quantify the impact of operational duration on inverter efficiency.

Main Methods:

  • Correlation and linear analysis were performed on PV power generation and environmental sensor data.
  • A predictive model was trained using solar radiation and power data highly correlated with generation.
  • The trained LSTM model was applied to analyze inverter performance data from 2020-2022.

Main Results:

  • The predictive model achieved a Mean Absolute Percentage Error (MAPE) of 7.36, Root Mean Square Error (RMSE) of 27.91, Mean Absolute Error (MAE) of 18.43, and R-squared (R2) of 0.97.
  • Statistical analysis revealed an average increase in error rate of 159.55W in 2022 compared to 2020.
  • This indicates a 0.75% decrease in inverter efficiency over the three-year operational period.

Conclusions:

  • The developed LSTM-based method is effective for analyzing inverter efficiency in operational PV plants.
  • The findings demonstrate a quantifiable decrease in inverter efficiency with prolonged use.
  • This predictive approach supports proactive maintenance strategies for photovoltaic systems.