Abstract:
The present invention is concerned with improving spectral and dynamic performance and scalability of controlling a Modular Multilevel Converter (MMC). According to the invention, a converter flux control is proposed that includes virtual converter flux tracking for a grid-connected MMC based on Optimized Pulse Patterns (OPP). The proposed flux control enables all required STATCOM tasks, including i) injection of harmonic currents and ii) unbalanced operation of the STATCOM for negative sequence current injection and operation under grid imbalances. A per-device switching frequency and switching losses are significantly decreased compared to a PWM-based control for a similar spectrum shape during nominal operation, while all spectral requirements on the output current are met. The proposed converter flux control may preferably include a Model Predictive Pulse Pattern Controller (MP3C).
Abstract:
A method for controlling a three-phase electrical converter (12) comprises: selecting a three-phase optimized pulse pattern (20) from a table (22) of precomputed optimized pulse patterns based on a reference flux (ψ αβ,ref ); determining a two-component optimal flux {ψ* αβ ) from the optimized pulse pattern (20) and determine a one-component optimal third variable (ζ*); determining a two-component flux error from a difference of the optimal flux ( ψ* αβ ) and an estimated flux (ψ αβ ) estimated based on measurements in the electrical converter; determining a one-component third variable error from a difference of the optimal third variable (ζ*) and an estimated third variable (ζ); modifying the optimized pulse pattern (20) by time-shifting switching instants (28) of the optimized pulse pattern (20) such that a cost function depending on the time-shifts is minimized, wherein the cost function comprises a flux error term and a third variable error term, wherein the flux error term is based on a difference of the flux error and a flux correction function providing a flux correction based on the time-shifts and the third variable error term is based on a difference of the third variable error and a third variable correction function providing a third variable correction based on the time-shifts; and applying the modified optimized pulse pattern (26) to the electrical converter (12).
Abstract:
A system includes a wet extrusion process machine configured to receive, mix, and convey a plurality of ingredients to an extrusion die, the plurality of ingredients include a protein powder, an oil, and water. The system includes an electronic process control system (EPCS) configured to control the wet extrusion machine using a plurality of process settings effective to produce an extrusion die mixture which is forced into, passes through, and is output from the extrusion die. The system further comprises a supervisory machine intelligence control system (SMICS) operatively coupled with at least one of a direct fibrosity measurement (DFM) subsystem configured to directly measure one or more physical fibrosity parameters of the extrusion die mixture, and an indirect fibrosity measurement (IFM) subsystem configured to measure one or more extrusion process parameters associated with the extrusion die mixture. The SMICS is configured to modify one or more of the plurality process settings in response to at least one of the one or more physical fibrosity parameters, and the one or more extrusion process parameters, effective to modify the extrusion die mixture.
Abstract:
A method for controlling an electrical converter (12), the electrical converter (12) being adapted for converting a DC voltage (v dc ) into a multi-phase voltage with at least two voltage levels, comprises: determining a modulating signal vector (u* abc ) from a stator flux reference vector (ψ* s, αβ ); determining a switching pattern (54) from the modulating signal vector (u* abc ) via pulse width modulation, the switching pattern (54) comprising a sequence of switching transitions, wherein a switching transitions defines a switch position, at which a phase of the converter is switched from one voltage level to another voltage level, and a transition time instant at which the phase of the converter is switched; determining a stator flux error ( ψ* s,err,αβ ) by subtracting the stator flux reference vector (ψ* s,αβ ) from an estimated stator flux vector (ψ s,αβ ), which is estimated from measurements in the electrical converter (12); modifying the switching pattern (54) by moving transition time instants of switching transitions of the switching pattern (54), such that the stator flux error (ψ s,err,αβ ) is minimized; and applying at least a part of the modified switching pattern to the electrical converter (12).
Abstract:
A method for controlling a three-phase electrical converter comprises: selecting a three-phase optimized pulse pattern from a table of pre-computed optimized pulse patterns based on a reference flux; determining a two-component optimal flux from the optimized pulse pattern and determine a one-component optimal third variable; determining a two-component flux error from a difference of the optimal flux and an estimated flux estimated based on measurements in the electrical converter; determining a one-component third variable error from a difference of the optimal third variable and an estimated third variable; modifying the optimized pulse pattern by time-shifting switching instants of the optimized pulse pattern such that a cost function depending on the time-shifts is minimized, wherein the cost function comprises a flux error term and a third variable error term, wherein the flux error term is based on a difference of the flux error and a flux correction function providing a flux correction based on the time-shifts and the third variable error term is based on a difference of the third variable error and a third variable correction function providing a third variable correction based on the time-shifts; and applying the modified optimized pulse pattern to the electrical converter.
Abstract:
A system includes a wet extrusion process machine configured to receive, mix, and convey a plurality of ingredients to an extrusion die, the plurality of ingredients include a protein powder, an oil, and water. The system includes an electronic process control system (EPCS) configured to control the wet extrusion machine using a plurality of process settings effective to produce an extrusion die mixture which is forced into, passes through, and is output from the extrusion die. The system further comprises a supervisory machine intelligence control system (SMICS) operatively coupled with at least one of a direct fibrosity measurement (DFM) subsystem configured to directly measure one or more physical fibrosity parameters of the extrusion die mixture, and an indirect fibrosity measurement (IFM) subsystem configured to measure one or more extrusion process parameters associated with the extrusion die mixture. The SMICS is configured to modify one or more of the plurality process settings in response to at least one of the one or more physical fibrosity parameters, and the one or more extrusion process parameters, effective to modify the extrusion die mixture.
Abstract:
A method for controlling a modular converter (16) connected to an electrical grid (12) for active power filtering the electrical grid (12) to compensate for a load (14) connected to the electrical grid (12), comprises: receiving an actual load current (i i ) and an actual converter state (x) of the modular converter (16); determining, from the actual load current and a history of previous load currents, a sequence of future load currents over a prediction horizon (40); predicting a sequence of future converter states of the modular converter (16) and a sequence of manipulated variables (U) for the modular converter (16) over the prediction horizon (40) by solving an optimization problem based on the actual converter state (x) and the future load currents by minimizing an objective function mapping control objectives to a scalar performance index subject to the dynamical evolution of a prediction model of the modular converter (16) and subject to constraints (42); and applying a next switching state, which is determined from a first element of the sequence of manipulated variables (U), to the modular converter (16).