HelioCloud

Predict and prevent failure in solar plants

7 followers

Predict and prevent failure in solar plants

7 followers

SmartHelio helps solar utilities automatically predict and prevent downtime disruptions, increasing their annual revenue by 10% from their solar plants. We are working with 15 large solar utilities to help them generate extra $25M per MW per year.

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Launch tags:Analytics•Artificial Intelligence•Climate Tech

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HelioCloud

Maker

Hi All. I am VP of Marketing at SmartHelio. I am very excited to see solar companies start using these convenient tools. Over the past 2 years, we worked with 50+ leading solar companies in Europe, Asia and USA. As we dug deeper into their problems in managing their solar assets (performance of their solar plants), we realized that almost all of these companies manage plants in a reactive way. Commercial and industrial plants produce millions of data points every day and we have now put that data to use. These tools will enable the digitization and automation which the industry required. Let us know what do you think of these tools? If you have any questions which can help you understand our tools better, feel free to inquire. Any comment which can help us improve our tools, let us know.

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4yr ago

HelioCloud

Maker

Tool [3/4]: Dynamic Cleaning Scheduler Purpose of the Tool: The wide-area spread of the solar plants creates operational challenges for the solar asset managers. In most cases, the soil disposition across the plant is non-uniform and dynamic in nature. Moreover, meteorological events (like rainfall, snow, dust storm, etc.) play a vital role in the overall deposition of soil; hence, these factors should also be considered in the cleaning schedule. A fixed cleaning schedule that normally does not cover these factors, leads to inefficient cleaning and eventually leads to significant loss of energy production and resources (time, water, and human). Creators: @kritz03 & @dorianguzman The Methodology: The Dynamic Cleaning Schedule helps solar asset managers explore the most optimal timing for cleaning. This helps to maximize the energy production, reduce the cleaning cost and minimize the usage of water used for cleaning the solar panels. Dynamic Cleaning Scheduler considers the historical & current soiling trends, meteorological & cleaning events, weather forecast, and the cost of cleaning and follows an AI-based optimization process to generate a dynamic cleaning schedule for different sections of the plant.

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4yr ago

HelioCloud

Maker

Hi all, I am Govinda CEO of SmartHelio. We are really excited to launch 4 powerful analytics tools that can help solar utilities automatically predict and prevent solar plant failures and increase their overall performance. We have taken the 4 most pressing challenges faced by the solar PV industry today (bad and missing data, inaccurate weather forecast, uneven & unknown panel degradation, and ineffective cleaning schedule) and designed easy-to-use tools with one click to solve these challenges. In the making of these tools, we have used our subject expertise - understanding of PV System Engineering, weather modeling, and climate change modeling, and coupled it with advanced computational methods and machine learning. Please give your comments. Happy to answer your queries.

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4yr ago

HelioCloud

Maker

Tool [1/4]: AI-based Data Sanitization Service Purpose of the Tool: The data acquisition system deployed in the solar industry frequently suffers from sudden communication failure and monitoring equipment malfunction. This leads to significant missing data sets and a lot of bad values in the system database. Therefore, any analysis based on these data sets leads to inaccurate results, higher assumptions, and bad conclusions. Creators: @bhavya_dureja & @dorianguzman The Methodology: The algorithm is formulated in a way that it first removes all the bad/corrupt data which are called as outliers using the PV system’s configuration and expected performance behaviour. Next, it identifies the data gaps which could be due to network/communication errors, bad weather data, or fault measurement devices. Finally, using AI & Machine Learning algorithms, the model first learns trends and seasonality of the data and later predicts these data gaps with high accuracy to reduce data loss.

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4yr ago

HelioCloud

Maker

Tool [4/4]: PV Panel Analytics Purpose of the Tool: Early degradation and permanent damage of the solar modules because of delayed interventions are becoming the major problems in the solar industry. As per one estimate, Solar PV waste will be one of the largest types of e-waste globally by 2050. With the module analytics service, we can remotely identify the degraded panels in the system and execute many more panel-level analytics in real-time with a click of a button on our computers. It also helps to optimize drone usage. Creators: @sourabh470, divyansh & @humaid_mohammed_niyaz The Methodology: Provides users with comprehensive monitoring and advanced diagnosis of PV modules. It analyzes the module level current, voltage, and temperature data collected by the Device. Post-processing of collected data informs the user about instantaneous power generation, energy generation on an hourly, daily, weekly and monthly basis. As PV module’s performance is sensitive to temperature change, the tool calculates the temperature coefficient of voltage as per IEC 60891 standard to assess the temperature sensitivity of different PV modules or different installations.

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4yr ago

Congratulations on the launch Neeraj... These analytics tools would be revolutionary in the Solar power industry. Good luck...

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4yr ago

HelioCloud

Maker

@aditya_009 Hey thanks Aditya.

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4yr ago

Congrats Govi! Following you and your company since 2020, I am positively sure that you will bring a real impact to our world. Your approach so close to the market need will definitively be the new standard in the field 👍

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4yr ago

HelioCloud

Maker

@christophe_barraud thank you

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4yr ago

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