Reliability Assessment and Early Warning of Central Inverters in Uzbekistan’s Solar Power Plants

Abstract

Relevance: In recent years, during the extensive utilization of Uzbekistan’s renewable energy potential, the reliability and stable operation of central inverters in large-scale solar power plants have emerged as a critical issue. Central inverters form the core of the system, and their malfunction can lead to a complete halt in power generation. Statistical analyses indicate that inverter failures account for 20–25% of total downtime, causing annual energy losses of up to 1.5–2.5% of total production. Therefore, the development of a continuously monitored parameter database and early warning system for assessing inverter reliability represents a highly relevant scientific and practical task.

Objective: The main objective of this research is to enhance the reliability and operational efficiency of central inverters by developing a parameter-based early warning system that enables the prediction of faults during operation and reduces energy losses. Additionally, the study aims to analyze the long-term service life of inverters in large-scale PV plants, identify dominant failure modes, and construct predictive models for failure forecasting.

Methods: Data collected from central inverters included electrical parameters , thermal parameters , and operational variables. All data were gathered through a SCADA system and stored in a centralized SQL-based parameter database. Inverter reliability was assessed using an exponential model defined as: where denotes the probability of fault-free operation over time , and is the failure rate. Additionally, the Weibull distribution was applied to model the statistical distribution of failures: where is the characteristic life, and is the shape (aging) parameter.

Results: Analysis results show that after implementing the proposed parameter database and early warning system, the average inverter efficiency increased from 95.1% to 97.4%, the annual number of failures decreased by 21%, and the mean time to repair (MTTR) was reduced from 36 hours to 20 hours. Additionally, the system enabled the recovery of 40–70 MWh of electricity annually. This approach significantly enhances real-time monitoring, failure prediction, and maintenance optimization for large-scale PV installations.

Relevance: In recent years, during the extensive utilization of Uzbekistan’s renewable energy potential, the reliability and stable operation of central inverters in large-scale solar power plants have emerged as a critical issue. Central inverters form the core of the system, and their malfunction can lead to a complete halt in power generation. Statistical analyses indicate that inverter failures account for 20–25% of total downtime, causing annual energy losses of up to 1.5–2.5% of total production. Therefore, the development of a continuously monitored parameter database and early warning system for assessing inverter reliability represents a highly relevant scientific and practical task.

Objective: The main objective of this research is to enhance the reliability and operational efficiency of central inverters by developing a parameter-based early warning system that enables the prediction of faults during operation and reduces energy losses. Additionally, the study aims to analyze the long-term service life of inverters in large-scale PV plants, identify dominant failure modes, and construct predictive models for failure forecasting.

Methods: Data collected from central inverters included electrical parameters , thermal parameters , and operational variables. All data were gathered through a SCADA system and stored in a centralized SQL-based parameter database. Inverter reliability was assessed using an exponential model defined as: where denotes the probability of fault-free operation over time , and is the failure rate. Additionally, the Weibull distribution was applied to model the statistical distribution of failures: where is the characteristic life, and is the shape (aging) parameter.

Results: Analysis results show that after implementing the proposed parameter database and early warning system, the average inverter efficiency increased from 95.1% to 97.4%, the annual number of failures decreased by 21%, and the mean time to repair (MTTR) was reduced from 36 hours to 20 hours. Additionally, the system enabled the recovery of 40–70 MWh of electricity annually. This approach significantly enhances real-time monitoring, failure prediction, and maintenance optimization for large-scale PV installations.