Deriving Value from Data as Utilities Embark on Digital Transformation

By May 26, 2022Use Cases

Executive Summary 

As the energy transition perpetuates the ideas of decentralization, decarbonization, and digitization, many utility stakeholders require an additional level of understanding of the environment on their substations. Parasanti and Chata AI provide an end-to-end data ingestion, processing, and visualization tool that enables granular visibility. Additional government allocations toward grid modernization will enable utility companies to expand their sensor network on generation and transmission systems. Data generated by these new sensors is often stored away in the cloud never to be used again, if it is even collected at all. 

Data is the New Oil 

Networks that manage grid equipment infrastructure are regularly controlled and monitored by edge devices. These devices often are capable of rudimentary analytics, but mostly focused on reactive management strategies. These strategies mitigate damage but fail to prevent failures and issues before they occur. The lack of sufficient upgrades of hardware and software on the substation has resulted in a capability shortfall for condition monitoring. A sufficiently capable solution would allow owners and operators to codify processes governing both automated and manual processes. 

As the Energy transition continues, tools must be developed to transition from reactive and responsive analytical controls to predictive, prescriptive, and autonomous systems. This effort will require the fusion of data streams that have historically been analyzed in silos. Cross-referencing data from sensors that produce information on the load, chemical, thermal, and mechanical status of the system is required to fully understand the health and status of the system. 

Figure 1: Data ingestion and visualization at the substation power inverter 

Parasanti’s Nightfall software is capable of ingesting data from edge sensors, executing ETL processes, conducting in-flight analytics on data, and loading data into a 

database for further query. These data streams can be used to establish autonomous command and control pathways, enabling continuous monitoring while the asset is in use, and reactive measure implementation while keeping the O&M team in the loop. Once loaded into a database, Chata’s UI tool and automated query processes can enable manual interaction with this data, allowing for scheduled monitoring and manual analysis on the collected data. 

Parasanti and Chata’s combined solution enables the ingestion, processing, transformation, storage, and visualization for all types of data. NightFall currently supports several SCADA communications protocols such as MODBUS TCP, OPC, KNX, and Siemens S7. Additionally, due to NightFall’s capability to use a variety of programming languages including python, java, ruby, and groovy, our system can rapidly create new processors to support other SCADA communication protocols. 

Figure 2: NightFall low code, no code, drag and drop user interface 

Nightfall has a low code GUI to build out, copy, and generate new data flows, as shown in Figure 2 above. This capability enables operators to rapidly manage data from a variety of different SCADA sources, without the need to program. The software supports in-flight AI/ML for data flows. 

In the world of IoT, especially when it comes to the Utilities industry, situational awareness of key assets is critical to operational efficiency. Being proactive vs reactive in cases such as transformer failures is one such example. More sophisticated IoT implementations are targeted towards programmatic event monitoring that is set up to alert key personnel that an adverse event is likely to occur. To determine if the adverse event is a true positive vs a false positive, the human decision maker needs access to additional monitoring information to make a better data-driven decision on what to do. 

Enabling non-technical decision makers ad-hoc access to key monitoring data can produce positive outcomes and additional ROI on IoT investments. 

In the case of our transformer example in the Utilities domain, imagine an alert is triggered around a potential fault event. The human who has received the alert can now use a natural learning interface to easily access the critical electrical, chemical, and thermal data at specific timestamps to better track and eliminate incipient fault events. This NL interface could further be used by the non-technical user to set up new alerts or to access the detailed information behind asset health reports generated by the Utilities organization. 

In the energy industry, security is an absolute necessity. Parasanti and Chata provide a solution with full encryption of data both in-flight and at rest, up to AES-256 standard. This tool can also ingest raw PCAP data, allowing for advanced analytics of the network environment. NightFall identifies failed authentication attempts and generates a notification of attempted intrusion while providing unified identity management across the entire platform. These tools also provide full data lineage and provenance, so analysts can retroactively identify issues with data flows and find points of intrusion. 

The data is only useful if it is presented in a digestible format to the stakeholders that need access to it. As utilities replace their aging infrastructure, these stakeholders who historically have not needed access to this data will require the ability to understand the status of the equipment on the substation. Infrastructure replacement and upgrade is not an overnight process and requires the prioritization of capital to replace systems where the largest return will be achieved. This prioritization requires an extremely intimate understanding of the status of the systems already in place, such as robust risk assessment of the hardware population already in service, the relative criticality of these systems, condition assessments of individual hardware, and proactive risk mitigation. 

With Parasanti and Chata’s combined solution, the utility company of the future will have the insights that they need into their operating environment and hardware. This ability to truly understand the landscape of the grid will provide a more robust and optimized system for their customers, ultimately reducing downtime and costs.