About the Customer
The customer is one of India’s leading Independent Power Producers (IPP) with a diverse portfolio of power plants that includes 1000 MW+ of wind and 250 MW+ of solar energy projects across multiple states. They are committed to building a low-carbon nation and contributing to India’s ambition of providing clean and sustainable energy.
Challenges Faced
The customer, a leading power company, faced a challenge with one of its plants underperforming. Despite having access to a large amount of data from various sources, it was difficult to determine the root cause of the underperformance. The reasons for the poor performance could have been varied, such as inverter breakdowns, weather conditions, temperature changes, radiation loss, or low-performing strings. Despite attempting various remedial actions, the performance of the plant only saw marginal improvements.
How Prescinto Helped
Prescinto’s methodology involves enabling the client’s team in monitoring and analyzes the on-site devices to help site engineers operate and maintain the plant at its peak performance. The detection was conducted in two phases.
Data Analysis and String-Level Underperformance Detection
The customer team used the Prescinto platform to analyze data from the power plant and detect underperforming strings within the system. By using the key performance indicator (KPI) of Relative Specific Energy (RSE) or Relative Specific Yield, the team was able to leverage the data gathered from the plant and identify that there were some underperforming strings within the plant that were affecting the overall performance. The string analytics feature within the Prescinto platform also made it easy for the team to identify and isolate any outliers in the data that were contributing to the plant’s underperformance.
String Analytics-based Inspection and Correction
The team used the Prescinto platform to conduct string analytics-based inspection and correction, inverter efficiency correction, and cleaning efficacy assessment. By using the KPI of Temperature corrected PR (tPR), which takes into account the factors of Irradiation and Temperature, the team was able to increase the tPR by 0.7% from the previous financial year, resulting in a 5.4 GWh increase in energy generation within a year.
Result
Through the use of Prescinto’s data-driven platform, The customer was able to gain valuable insights into the performance of its underperforming solar plant. By utilizing advanced data science techniques and AI algorithms, the Prescinto team was able to pinpoint the specific issues causing the underperformance and provide solutions for improvement.
As a result of the data-driven interventions, The customer was able to see an increase in energy generation of 5.4 GWh within a year. Additionally, by identifying and addressing underperforming assets, The customer was able to improve the overall efficiency and reliability of the plant. This not only helped to minimize energy losses but also ensured that the plant was operating at its peak performance at all times.
Conclusion
Prescinto’s data-driven platform proved to be a valuable asset for The customer to improve the performance of its underperforming solar plants. By utilizing advanced data science techniques and AI algorithms, the Prescinto team was able to provide valuable insights and solutions for improvement, ultimately leading to a significant increase in energy generation and financial gain for the company.
