System and method for intelligent electronic safety response interface

The invention and subject of this disclosure is an Electronic Safety Response Interface (EsRi) system comprised of multiple processors and software that manages electric energy flow in real time to the electric energy grid under both routine and abnormal conditions using risk informed pre-programmed controllers to operate existing controls that manage a reliable electric flow between the electric energy grid and attached energy storage systems. The EsRi interface includes EsRi Intelligence and EsRi Control at the interconnection between the electric energy grid and the generation and energy storge facilities as described above and summarized below.

• An EsRi Intelligence processor using multiple data sources (for, example, weather existing electric energy grid equipment configurations, electric energy use (load) contracts, and other data) processed through the EsRi artificial intelligence (AI) to develop risk profiles of the electric energy grid and attached generation and/or storage systems then informing the networked controller processors to operate the safety (protective relaying) controls through pre-programmed (hard-wired) sequences informed (directed) by the intelligence processor outputs using signal(s) from SaRa, the EsRi predictive risk assessment module in real time.
• Using SaRa to provide the risk profile signal to the EsRi Control processor in real time allows the selection of the appropriate control sequence. The AI predictive risk profile provided by the EsRi Intelligence server node through the SaRa risk system to the EsRi controller governs which control sequence the controller will use to respond to electric energy grid and/or attached electric generation and/or storage system failures. The sequence selection will change in real time for each state of the electric energy grid provided by the EsRi Intelligence processer through the SaRa risk assessment system.
• An EsRi controller processor is preloaded with control sequences that correspond to the state of the electric energy grid and attached electric generation and/or storage systems as represented by the SaRa risk profile. This allows the safety systems in real time to manage a reliable electric energy flow to the electric energy grid during different types of system failures. The EsRi Control sequence both overrides and uses the existing safety controls in a pre-established manner.
• Additionally, the EsRi system provides, data storage, data logging, audit capability, alarm and other operational functions that will enhance the machine learning AI accuracy, human operator awareness, and system reliability of an EsRi Intelligence server node connected to at least one EsRi controller over a network;
• a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to:
• a. receive sensory data from a sensor array attached to the electric energy grid coupled to the at least one EsRi controller;
• b. parse the sensory data to derive a plurality of features;
• c. query a local electric energy grid database to retrieve local historical electric energy grid-related data collected from the electric energy grid in real time;
• d. generate at least one feature vector based on the plurality of features and the historical electric energy grid-related data;
• and
• e. provide at least one feature vector to the ML module configured to generate a predictive model indicating at least one programming parameter for re-sequencing of the at least one EsRi controller (actuating electric energy grid protective relaying).