Real-time Microgrid Energy Optimization Using Deep Reinforcement Learning
Real-time microgrid energy optimization using deep reinforcement learning and a deep recurrent neural network to handle stochastic renewables and AC power flow constraints.
Real-time microgrid energy optimization using deep reinforcement learning and a deep recurrent neural network to handle stochastic renewables and AC power flow constraints.
Overview of grid interoperability and transition strategies integrating legacy RS?485 wired systems with modern Ethernet and wireless technologies using TI reference designs.
Technical overview of switch selection and types for smart grid devices, covering reliability, sealing, contact materials, and KSC/E/7000 series options.
Technical overview of using ADE7816 energy-measurement ICs with current sensors (including Rogowski coils) for multichannel smart-metering, RMS, energy and fault diagnostics.
Technical comparison of current measurement: current transformers and Rogowski coils; discusses Hall-effect sensors, accuracy, installation, and signal conditioning.
Review of smart grid electronics and power modules: power-supply designs, wide-input regulators, and low-quiescent solutions for meters, relays, gateways, and inverters.
Technical overview of MCUs and integrated security for smart energy and smart metering: architectures, protocols, power optimizations, and standards.
Overview of IoT security practices including secure boot, digital signatures, PKI, data encryption, and use of embedded Trusted Platform Module (TPM) hardware for device identities.
Technical overview of smart grid components, detailing smart meter power-measurement ICs, LED driver/lighting control ICs, and sensor roles for grid efficiency.
Smart grid device design and integration guide: metering, connectivity, demand-response, and energy monitoring requirements for appliances and utilities.