Abstract: A microgrid system includes a synchronous generator configured to convert mechanical power into electric power, an energy storage system configured to store and supply electric power, a controller configured to control operation of the energy storage system; and a point of common coupling bus connecting the synchronous generator and the battery energy storage system, wherein a controller parameter of the controller is determined based on a level of a disturbance using a trained artificial neural network in response to occurrence of the disturbance in the synchronous generator.

Abstract: A device and method for managing power transmission to a power grid from a Hybrid Grid Connected System (HGCS) is presented. The HGCS includes a plurality of energy resources, including of solar photovoltaic cells (PV), wind power generation, a battery energy storage system (BESS) and a fuel cell system paired with an electrolyzer. Energy sources are connected by a DC-link to a power electronic converter which supplies power to the power grid. A controller is configured to calculate the total power, maximize transmission of power to the power grid from the primary sources, supply the net power to the power grid from the fuel cell system and use the power generated by the PV system and the wind power system for charging the BESS and powering the electrolyzer system based on the relative state of charge of the BESS and the voltage of the DC-link.

Abstract: A reactive power support system for a radial distribution network includes multiple loads, a radial distribution transmission line (RDTL) connected at a first end to a grid transformer, a point of common coupling (PCC) connected to a second end of the RDTL, a solar power inverter connected in parallel with a photovoltaic module and an inverter controller connected to the inverter. An output terminal of the inverter is connected to the PCC. Each load is connected by a load bus to the RDTL. The inverter controller provides gate control signals which switch a set of transistor gates of the inverter to operate at less than or greater than a unity power factor to inject or to absorb the reactive power, respectively, at the PCC in order to regulate a voltage of the RDTL during a three-phase to ground fault on a load bus connected to one of the loads.

Abstract: A circuit and methods including super-twisting sliding mode controller (ST-SMC) control of an AC/DC quasi single-stage current-fed resonant converter for electric vehicle (EV) charging. The converter includes primary side switches and a secondary side bidirectional switch, achieving zero-voltage switching (ZVS) for efficient operation. The primary side includes an active clamp circuit, while the secondary side incorporates a resonant tank for smooth energy transfer. The ST-SMC generates pulse width modulation signals, ensuring ZVS at turn ON and turn OFF for primary switches, and ZVS at turn ON with low-voltage switching at turn OFF for the bidirectional switch. The circuit includes a feedback loop with an error calculation unit for precise control of grid current and output voltage, providing power factor correction and regulated output for 400V and 800V EV batteries.

Abstract: Systems and methods for allocating entries of a summarization table to ranges of packet field values in hardware table entries. A plurality of rules to be stored in at least one hardware table (e.g., a TCAM), specify value ranges which are used to match packets. For each value range, a corresponding cost reduction (reduced number of required hardware table entries) that would result from replacing the packet field value range with a first entry in a summarization table is determined. A value range having the greatest cost reduction is then determined and this value range is assigned to a first entry in the summarization table. This process is repeated for subsequent summarization table entries, with the corresponding cost reductions taking into account effects of value ranges having previously been assigned to the summarization table.

Abstract: Control system and method for integration and transition between standalone and grid-connected operations of a hybrid renewable microgrid system include a multiple-input multiple-output controller that manages a rotor-side and a grid-side converter, structured in a back-to-back configuration, alongside a DC-DC buck-boost converter. The system harnesses energy from a solar generation unit of interconnected photovoltaic panels and a wind generation unit linked to a permanent magnet synchronous generator. Both energy sources are efficiently coordinated through maximum power point tracking control. The solar unit is directly connected to a DC bus, which also interfaces with the grid-side converter. An energy storage unit is seamlessly integrated, providing power management and voltage regulation capabilities to maintain consistent power delivery and quality, even during fluctuations in renewable energy production.

Abstract: A passivity-based power distribution control system for a hybrid electric vehicle includes a proton-exchange membrane fuel cell module, a battery module, an ultra-capacitor module, an energy management controller, and a duty cycle controller. The proton-exchange membrane fuel cell module includes a proton-exchange membrane fuel cell (PEMFC) and a PEMFC boost converter. The PEMFC module generates a PEMFC current IFC. The battery module includes a battery and a battery buck/boost converter. The battery module generates a battery current Ib. The ultra-capacitor module includes an ultra-capacitor (UC) and a UC buck/boost converter. The UC module generates a UC current IUC. The duty cycle controller controls a PEMFC duty cycle D1 of the PEMFC boost converter, a battery duty cycle D23 of the battery buck/boost converter, and a UC duty cycle D45 of the UC buck/boost converter.

Abstract: A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ?i, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ?i comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)?ci*(t). The uicnt(t) satisfies ? i ( t ) ? d ? ? i ( t ) dt < 0 , when ?i(t)?0.

Abstract: An inverter topology circuit that includes a direct current source; a first capacitor; a second capacitor, and a number of switches (a first switch, a second switch, a third switch, a fourth switch, a fifth switch, a sixth switch, a seventh switch, an eighth switch, a ninth switch, a tenth switch, and an eleventh switch). A first side of the first switch and a first side of the third switch are connected to a first side of the direct current source. A first side of the second switch and a first side of the fourth switch are connected to a second side of the direct current source. The first capacitor and the second capacitor are connected in series and are connected in parallel with the direct current source.

Abstract: A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ?i, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ?i comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)?ci*(t). The uicnt(t) satisfies ? i ( t ) ? d ? ? i ( t ) dt < 0 , when ?i(t)?0.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ?i, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ?i comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)?ci*(t). The uicnt(t) satisfies ? i ( t ) ? d ? ? i ( t ) dt < 0 , when ?i(t)?0.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ?i, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ?i comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)?ci*(t). The uicnt(t) satisfies ? i ( t ) ? d ? ? i ( t ) dt < 0 , when ?i(t)?0.

Abstract: A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ?i, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ?i comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)?ci*(t).

Abstract: A system and a method for controlling a hybrid microgrid system (HMS) is disclosed. The HMS includes a WTG, an RSC, a GSC, a DC-link connecting the RSC and the GSC, a PV system that outputs a DC current to the DC-link, a rechargeable battery, a bidirectional BBC connected between the DC-link and the rechargeable battery, and a controller. The method for controlling the HMS includes: preparing a definition set including a characteristic element ci and equations defining desired value ci*, a fractional order sliding mode surface ?i, and a control law element uicnt; monitoring ci(t) and the HMS status; calculating the equations based on monitored information; and controlling the HMS based on the uicnt(t) calculated and in accordance with a global sliding mode control with fractional order terms. The ?i comprises a fractional time integral and fractional time derivative of ei(t), where ei(t)=ci(t)?ci*(t). The uicnt(t) satisfies ? i ( t ) ? d ? ? i ( t ) dt < 0 , when ?i(t)?0.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.

Abstract: A system, method, and solar photovoltaic (PV) network for solar PV variability reduction with reduced time delays and battery storage optimization are described. The system includes a Moving Regression (MR) filter; a State of Charge (SoC) feedback control; and a Battery Energy Storage System (BESS). The MR filter, SoC feedback control and BESS are configured to provide smoothing of solar PV variabilities. The MR filter is a non-parametric smoother that utilizes a machine learning concept of linear regression to smooth out solar PV variations at every time step.