Embedded photovoltaic systems device data management and green energy

Large-scale photovoltaic embedded systems need constant monitoring, real-time data capture, and management for optimal operation, often with limited or no connectivity. Device data technology is essential for processing the vast amounts of data produced by these embedded devices and coupled sensors. These embedded systems consist of a variety of electronics control boxes, power inverters, and related monitoring components such as reference sensors, weather stations, and energy measuring devices. Each device must collect numerous measurements, analyze available data, and, in many cases, send results to a higher-level data management system.

Photovoltaic systems are designed and built by embedded developers with expertise relating to the green energy market. Device manufacturers assign those experts to build systems for deployment in locations with limited or no connectivity. Those devices must be able to process, store, and transmit timestamped data captured from optoelectronic sensors, temperature sensors, pressure sensors, and more. Devices often keep data for months or years at a time, query data for local display, and export data periodically.

Whether a device manufacturer is building a new system for green energy or upgrading an existing application to locally manage and process data, they are confronted with challenges that will pose a risk to the longevity of the data captured by those devices.

Manufacturing a system that integrates various hardware and software components – such as a real-time operating system, file system, database, and connectivity tools – can be a significant design and development challenge. All software components need to integrate smoothly in a constrained hardware environment, with data management responsibilities at every level.

In the era of the Internet of Things (IoT), these embedded systems need to not only collect and retrieve live data samples but also understand and react to changes in the data. ITTIA, a software company, recognised that Edge devices need a flexible modern database solution to dynamically add and remove groups of sensors, recover from unexpected power loss, automatically remove, and replace the oldest values, and occasionally correct stored values. Accordingly, ITTIA software designers architected a solution, ITTIA DB, for the demands of such a green energy market.

When each data point has a name, timestamp, and value, device applications need the capability to query data in diverse ways. For example, a built-in display may need to graph the data for specific sensors, chosen by name, over any period. A field engineer may also export that same data to removable media in various formats that combine data from a dynamic list of sensors in timestamp order. While these queries are executing, devices must continue storing new data as it arrives, in the background, without interruption.

In a typical use case, a device will accumulate millions of items from the quantity of data that each sensor produces. A method to convert this storm of rapidly expanding information into valuable data will improve decisions, enhance end-user consumer experiences, and generate more action with less memory and other resources overhead. Grid-connected systems, home power inverters, and commercial electrical systems are examples of such use cases. To fulfill the energy needs, these can be used in alternation or in conjunction with the electrical grid when necessary. To guarantee service continuation, certain stand-alone systems are also built with a battery system.

Applications for photovoltaic systems that incorporate ITTIA DB come with a strong, contemporary embedded database that manages the tasks of gathering, capturing, enhancing, and archiving time series data. ITTIA DB identifies desired valuable information, as defined by the owner of the application, and only captures and manages valuable data. ITTIA DB continuously detects unwanted data, protects data when there is unexpected power failure, and tremendously reduces data challenges, offering manufactures the chance to go to market on time.