Infosys Solar Inverter Predictive Maintenance Application

Overview

The Infosys Solar Inverter Predictive Maintenance Application is a solution for precisely predicting the failures of Solar Inverters and enabling predictive maintenance. It is built on advanced patented artificial intelligence (AI) and machine learning (ML) techniques. This application helps reduce the downtime of solar inverters, thereby enabling Infosys to meet its commitment to 100% renewable energy. This digital application promotes energy security, mitigates global warming, and upholds social responsibility.

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Key Market Drivers

Most solar inverter predictive maintenance solutions in the market fall short of customer expectations. Many of these software solutions cannot distinguish between alerts and outliers in real-time, without offering diagnostic features to help operational teams rectify issues and enhance performance. With the rapid growth in renewable energy usage, many organizations lack subject matter experts who have knowledge of solar energy and AI & ML technologies. Therefore, the goal is to make information available to users for their appropriate action.

Key Features

To address these challenges, the Solar Inverter Predictive Maintenance Application offers the following features:

Solar Inverter Predictive Maintenance Application helps predict issues 3 days in advance, thereby alerting the maintenance staff to cross-verify the status of the inverters and take corrective measures related to inverter equipment. This saves time and reduces the maintenance cost of the solar power plant.
It also helps the ground support staff directly check specific solar inverters in the field rather than going through each inverter individually and checking their performance.
Monitors the solar inverter-wise/field station-wise/site-wise continuously with user-friendly dashboards.
Provides comprehensive exploratory analysis of data, which helps improve inverter performance, along with alerts and outliers displayed in the charts.
Provides field station-wise information on alerts, outliers, and trends of various parameters.
Provides alerts and outliers 3 days prior to the actual incident, thereby reducing the downtime of the inverter.
Captures energy loss for each inverter-wise, field-station-wise, and for the entire plant-wise on a daily/weekly/monthly/quarterly/yearly basis.
Predicts solar energy generation using AI, utilizing real-time and historical data. It utilizes patented solutions (IN560398 & US2023015468A1 - Method and system for real-time cross-verification of alarms & IN552627 - Method and System for Predictive Maintenance of a Machinery Asset) for the inference engine to predict outliers and alerts.

The application utilizes user-friendly visualizations on inverter-wise, field station-wise, and site-wise information on alerts and outliers, as well as on energy losses.

Challenges & Solutions

You need a smart solution to manage and improve the performance of multiple solar installations in a unified monitoring system. An enterprise-level application, which is available and ready to be deployed on cloud or on-premises, can be customized for various types of OEM inverters to provide detailed analytics, predict different types of failures, and help the ground team take proactive measures, which results in optimal solar plant performance.

You have a solar inverter predictive maintenance system but need advanced analytics to improve solar plant performance. The user-friendly, cloud-agnostic design includes features such as alerts, outliers, and energy loss for each individual inverter, each field station, and each site.

You want a system to effectively manage the maintenance program to reduce unexpected failures. The Infosys Solar Inverter Predictive Maintenance Application provides alerts and outliers 3 days prior to the actual incident, thereby reducing the downtime of the inverter and preventing unexpected plant shutdowns. This saves time and maintenance cost of solar inverters for organizations with proven capability. It also helps the ground support staff directly check specific solar inverters in the field rather than going through each inverter individually and checking their performance. It reduces the downtime of solar inverters, thereby reducing energy losses in a solar power plant.