WO2018200025A1 - Syntonisation d'un dispositif de commande afin d'atténuer des oscillations de puissance après la perte d'un composant d'un système d'alimentation - Google Patents

Syntonisation d'un dispositif de commande afin d'atténuer des oscillations de puissance après la perte d'un composant d'un système d'alimentation Download PDF

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Publication number
WO2018200025A1
WO2018200025A1 PCT/US2017/053181 US2017053181W WO2018200025A1 WO 2018200025 A1 WO2018200025 A1 WO 2018200025A1 US 2017053181 W US2017053181 W US 2017053181W WO 2018200025 A1 WO2018200025 A1 WO 2018200025A1
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WIPO (PCT)
Prior art keywords
component
power
processing device
power system
controller
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PCT/US2017/053181
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English (en)
Inventor
Amer Mesanovic
Ulrich Münz
Joachim Bamberger
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Siemens Aktiengesellschaft
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Publication of WO2018200025A1 publication Critical patent/WO2018200025A1/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0254—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Definitions

  • the present disclosure generally relates to energy control systems and more particularly to tuning a controller to damp power oscillations after dropout of a component from a power system.
  • Power systems provide a network of electronic components to generate, transfer, and supply electric power from a power generator to a power consumer.
  • Power systems can include a generator(s) to generate power, transmission systems to transfer the power across large distances, and distribution systems that distribute the power to power consumers.
  • An electrical grid is an example of a power system.
  • Energy control systems can be implemented to manage and control the various electronic components of power systems.
  • a computer- implemented method for tuning a controller to damp power oscillations after dropout of a component from a power system includes deriving, by a processing device, a dynamic nominal model of the power system. The method further includes determining, by the processing device, a dynamic fault model that contains an equivalent model of the component that describes a behavior of the component prior to drop out of the component. The method further includes determining, by the processing device, optimal parameters for the controller based at least in part on the dynamic fault model. The method further includes implementing, by the processing device, the optimal parameters in the controller in the power system to damp power oscillations after the drop out of the component.
  • a system for tuning a controller to damp power oscillations after dropout of a component from a power system includes a memory having computer readable instructions and a processing device for executing the computer readable instructions for performing a method.
  • the method includes deriving, by a processing device, a dynamic nominal model of the power system.
  • the method further includes determining, by the processing device, a dynamic fault model that contains an equivalent model of the component that describes a behavior of the component prior to drop out of the component.
  • the method further includes determining, by the processing device, optimal parameters for the controller based at least in part on the dynamic fault model.
  • the method further includes implementing, by the processing device, the optimal parameters in the controller in the power system to damp power oscillations after the drop out of the component.
  • a computer program product for tuning a controller to damp power oscillations after dropout of a component from a power system includes a computer readable storage medium having program instructions embodied therewith, the program instructions being executable by a virtual reality processing system to cause a processing device to perform a method.
  • the method includes deriving, by a processing device, a dynamic nominal model of the power system.
  • the method further includes determining, by the processing device, a dynamic fault model that contains an equivalent model of the component that describes a behavior of the component prior to drop out of the component.
  • the method further includes determining, by the processing device, optimal parameters for the controller based at least in part on the dynamic fault model.
  • the method further includes implementing, by the processing device, the optimal parameters in the controller in the power system to damp power oscillations after the drop out of the component.
  • FIG. 1 depicts a block diagram of an energy control system, according to aspects of the present disclosure
  • FIG. 2 depicts a flow diagram of a method for tuning a controller to damp power oscillations after dropout of a component from a power system, according to aspects of the present disclosure
  • FIG. 3 depicts a block diagram of a synchronous generator with modeled controllers, according to aspects of the present disclosure
  • FIG. 4 depicts a representation of an equivalent model of a generator dropout, according to aspects of the present disclosure
  • FIG. 5 depicts a representation of an equivalent model of a power line dropout, according to aspects of the present disclosure
  • FIG. 6 depicts a flow diagram of another method for tuning a controller to damp power oscillations after dropout of a component from a power system, according to aspects of the present disclosure
  • FIG. 7 depicts a processing system for implementing the techniques described herein, according to aspects of the present disclosure.
  • Power systems can utilize renewable power generation, which use renewable resources such as wind, solar, etc. to generate power. It may be desirable for power systems to be N-l secure such that a dropout (i.e., failure) of any component (e.g., a power line failure, a generator failure, a transformer failure, etc.) does not lead to a blackout. In order to avoid the black-out after a fault, the power oscillations caused by the dropout must be sufficiently damped by controllers within the power system.
  • a dropout i.e., failure
  • any component e.g., a power line failure, a generator failure, a transformer failure, etc.
  • One traditional approach used to damp oscillations in a power system includes tuning controllers like power system stabilizers within the power system. This tuning is traditionally performed manually and conservatively, which is time and cost intensive. This is possible today because the dominant oscillatory modes in the power system are time-invariant. With the increase of renewable generation in the future, these dominant oscillatory modes become time-variant (e.g. they may change from one day to another). In this environment, manual tuning of the controllers is time and cost prohibitive.
  • Another traditional approach used to damp oscillations in a power system includes gradient-free optimization techniques that vary the controller parameters and then simulate and evaluate each selected controller parameter set individually. The model is simulated iteratively until an "optimal" set of controller parameters is determined. However, this approach is slow and inefficient.
  • the present techniques provide for tuning of a controller to damp power oscillations after dropout of a component.
  • the present techniques derive a dynamic nominal model of the power system, determine a dynamic fault model that contains an equivalent model of the component (i.e., the fault component) that describes the behavior of the component prior to dropout, determine optimal parameters for the controller based on the dynamic fault model, and implement the optimal parameters for the controller in the power system to damp power oscillations after dropout.
  • the power system can operate closer to its stability limits without the addition of new controllers in the power system and at a minimal cost.
  • Example embodiments of the disclosure include or yield various technical features, technical effects, and/or improvements to technology.
  • Example embodiments of the disclosure provide an energy control system that damps power oscillations after dropout of a component from a power system to enable the power system to recover after a dropout and thereby enables operation of the power system close to its stability limits.
  • an energy control system in accordance with example embodiments of the disclosure represents an improvement to existing power system control techniques. It should be appreciated that the above examples of technical features, technical effects, and improvements to technology of example embodiments of the disclosure are merely illustrative and not exhaustive. These and other benefits will be apparent as described herein.
  • FIG. 1 depicts a block diagram of an energy control system 100, according to aspects of the present disclosure.
  • the energy control system 100 enables the control of components 110a, 110b, 110c, HOd, HOe (collectively "components 110") of a power system (e.g., a grid 150 and various loads 152a, 152b, 152c, 152d, 152e (collectively "loads 152")).
  • the components 110 produce electrical power and transmit the electrical power over the grid 150 to end users (represented by the loads 152).
  • the components 110 include electrical hardware such as a diesel generator
  • the energy control system 100 includes four control levels: low-level control, primary level control, secondary level control, and tertiary level control.
  • the generation hardware 114 can be controlled from various controllers, including: low-level controllers 112a, 112b, 112c, 112d, 112e (collectively "low-level controllers 112"); primary controllers 120a, 120b, 120c, 120d, 120e (collectively “primary controllers 120"); a secondary controller 130, and a tertiary controller 140. While FIG. 1 depicts centralized secondary and tertiary control levels as well as decentralized primary and low-level control levels, other configurations are also possible (e.g., control levels can be combined, a control level can be divided into multiple control levels, etc.). Accordingly, the exemplary embodiments shown in the accompanying drawings aid in the understanding of the present disclosure but are not limiting as other configurations with additional, fewer, or alternative components can be contemplated within the scope of this disclosure. The individual control levels are briefly described as follows.
  • the low-level controllers 112 provide decentralized, local control to the respective individual generation hardware 114 such that the output voltage of the generation hardware 114 satisfies certain conditions (e.g. 110V at 60 Hz, etc.).
  • the low-level controller 112a controls the diesel generator 114a
  • the low-level controller 112b controls the photovoltaic generator 114b, and so on. Since this requires very fast reaction (e.g., in the millisecond range to changes in the grid 150, short circuits, etc.), this control level is usually located within controller hardware of each component 110.
  • the primary controllers 120 also provide decentralized, local control to the components 110 and are used to achieve a fast power balancing between the individual components 110.
  • AC alternating current
  • f-P-droop controllers frequency-active power droop controllers
  • Q-U-droop controllers voltage-reactive power droop controllers
  • DC direct current
  • Primary controllers 120 typically run with a sampling rate between 100ms and I s and provide voltage and/or power set-points to the low-level controllers 112. Like the low- level controllers 1 12, the primary controllers 120 are usually implemented within controller hardware of each component 110.
  • the secondary controller 130 is a centralized controller in the sense that it controls multiple components 110.
  • the secondary controller 130 coordinates the individual primary controllers 120 for each component 1 10.
  • the secondary controller 130 can be an integral controller to achieve zero steady-state frequency offset for the stability of the grid 150.
  • the secondary controller 130 provides set-points to the primary controllers 120 with a sampling rate of multiple seconds to minutes, for example. Different centralized and decentralized secondary controllers can also be implemented.
  • the tertiary controller 140 provides another form of centralized control.
  • the tertiary controller 140 can be used for the economically optimal dispatch of the generators at sampling rates of, for example, 15 minutes.
  • different centralized and decentralized approaches for tertiary control can be implemented.
  • the tertiary controller 140 can be implemented in a centralized energy management system. In large energy systems, the tertiary controller is replaced, for example, by an energy market.
  • FIG. 2 depicts a flow diagram of a method for tuning a controller (e.g., one or more of the low-level controllers 112 and/or the primary controllers 120) to damp power oscillations after dropout of a component from a power system, according to aspects of the present disclosure.
  • the method can be performed by suitable processing systems, such as the energy control system 100, the processing system 700 of FIG. 7, suitable combinations thereof, and/or another suitable processing system.
  • the method 200 can be implemented, for example, by the secondary controller 130 and/or the tertiary controller 140 to tune one or more of the low-level controllers 112 and/or the primary controllers 120.
  • a processing device derives a dynamic model of the power system.
  • the dynamic model of the power system includes dynamic models of various components (e.g., components 110), such as generators and their associated controllers (e.g., one or more of the low-level controllers 112 and primary controllers 120).
  • the components 110 are interconnected with algebraic power flow equations presented subsequently.
  • the dynamic model of the power system is derived prior to a dropout of a component.
  • FIG. 3 depicts a block diagram of a synchronous generator 300 with modeled controllers, according to aspects of the present disclosure.
  • the modeled controllers include a power system stabilizer (PSS) 302, an exciter 304, and a turbine + governor (“turbine”) 306, which together are an example for low-level controllers 112.
  • PSS power system stabilizer
  • turbine turbine + governor
  • Other components such as high- voltage direct current (HVDC) converters, flexible AC transmission system (FACTS) elements, renewables etc. can be modeled as well.
  • HVDC high- voltage direct current
  • FACTS flexible AC transmission system
  • renewables etc.
  • the controllers e.g., the low-level controllers 112
  • the controllers e.g., the low-level controllers 112
  • the vector of all tunable parameters in the power system is denoted K.
  • the PSS 302 receives changes in the rotational frequency of the generator rotor ⁇ and is tuned to damp power oscillation, such as after a dropout of a component.
  • the PSS 302 is traditionally tuned manually and conservatively, the present techniques enable the PSS 302 to be tuned automatically and less conservatively so that the power system can operate with greater stability reserve.
  • the exciter 304 controls the terminal voltage of the synchronous generator
  • the present techniques can use a one-state excitation system; however, more complex exciters can be used as well.
  • the inputs to the exciter 304 are the reference voltage Vref, the generator terminal voltage VT, and the output of the PSS 302 VPSS.
  • the output of the exciter is the field winding voltage ⁇ .
  • the synchronous generator 300 is controlled via mechanical power P m from the turbine 306, which is controlled by the governor based on changes in the rotational frequency of the generator rotor ⁇ .
  • the synchronous generator 300 is also controlled via the field voltage ⁇ from the exciter 304.
  • p t and q t are the injected active and reactive power into the i-th bus in the grid (e.g., the grid 150) respectively
  • v t and 6>j are the magnitude and angle of the voltage phasor at the i-th bus respectively
  • B ⁇ are elements of the conductance and susceptance matrix G and B of the grid respectively.
  • the dropout of the component can be modeled.
  • the processing device uses the dynamic nominal model to determine a dynamic fault model of the power system that contains an equivalent model for the component that drops out that describes the behavior of this component prior to the dropout.
  • the component 110 can be a generator, a power line, a transformer, and the like.
  • FIG. 4 depicts a representation of an equivalent model 400 of a generator 402 dropout, according to aspects of the present disclosure. Many different events can cause generator 402 (e.g., the generator 114a) to drop out of a power system.
  • a switch 404 is opened that separates the generator 402 from the power system almost instantly.
  • a generator dropout is modeled as a step-wise change 406 of the generator infeed to zero as shown in FIG. 4. Since the generator 402 is disconnected from the grid, the dynamics of the generator 402 do not influence other elements (e.g., components, controllers, etc.) in the power system, and the dynamics of the generator 402 do not have to be modeled. Accordingly, when the dropout of a generator is considered, it is modeled as a constant power load whose apparent power infeed step-wise changes to zero.
  • an equivalent model for the generator dropout is a stepwise load change, which can then be optimized as described herein. With this method, the dropout of any other component coupled in parallel to the power system, such as parallel flexible alternating current transmission systems (FACTS) devices etc., can be modeled as well.
  • FACTS parallel flexible alternating current transmission systems
  • FIG. 5 depicts a representation of an equivalent model of a power line 500 dropout, according to aspects of the present disclosure.
  • the dropout of the power line 500 presents a structural change in the power system. In a state-space representation of the system, this would mean that the power system matrices A and B change, while the system inputs remain the same. This is not suitable for, e.g., H ⁇ or Hi optimization, since each considers input-output behavior.
  • the dropout of the power line 500 is also converted to an equivalent load step 508 in the system as shown in FIG. 5, and accordingly the power line can be omitted from the power system during optimization as described herein.
  • the dropout of any component connected in series to the power system such as a transformer etc., can be optimized.
  • the model describing power line dropout has four inputs: Pi, Pj, Qi, and Qj.
  • the dynamic fault model containing the equivalent model of the component that drops out can be linearized.
  • the processing device linearizes the dynamic fault model.
  • x is the vector of all generator states in the model
  • w is the vector of external inputs into the system, including the inputs herein with respect to modeling the dropout of a generator or powerline
  • K is the vector of tunable parameters of all generators
  • / represents the generator dynamics
  • h represents the power flow equations (la) and (lb).
  • power system constraints can include one or more of the following: frequency limits in the power system buses in the steady state and during transients; voltage limits of power system buses in the steady state and during transients; power line capacities in the steady state and during transients; apparent power limits of the individual generator(s) in the steady state and during transients; and box constraints for the parameter vector K.
  • K is defined as follows:
  • m ⁇ ;min and m ⁇ max are the respective vectors of minimal and maximal allowed steady state values for the defined power system constraints after the power system input changes to w ⁇ , and the notation ⁇ and > is defined element-wise for vectors.
  • nit is the vector of maximal allowed transient values for the defined power system constraints.
  • constraints can be defined, for example, by constraining the "energy- to-peak" norm for the power system as follows:
  • optimal parameters for the controller can be determined using gradient descent techniques. For example, with continued reference to FIG. 2, at block 210, the processing device determines optimal parameters of the controller in the power system to damp power oscillations.
  • the optimal parameters can be determined based at least in part on one or more of the dynamic fault model and the linearized dynamic fault model. This minimizes various transient performance objectives after the dropout of the component.
  • the following optimization problem determines the optimal parameters for the controller, which minimize the oscillations in the system after the dropout of a component, as follows: min II G(K)
  • Equation (18a) is an abbreviation for all power system constraints described above. Equation (18a) can be solved, for example, using non-smooth optimization techniques, with linear matrix inequalities (LMI) optimization techniques, etc. For example, compared to current gradient-free optimizations based on simulations, the present techniques provide for gradient descent optimization (e.g., using an analytical gradient), which is much faster than gradient-free optimization approaches.
  • gradient descent optimization e.g., using an analytical gradient
  • the optimal parameters of the controller can be implemented in the controller to damp power oscillations.
  • the processing device implements the optimal parameters in the controller to damp power oscillations in the power system after the dropout of the component. That is, the optimal parameters can be transmitted, such as by a communication network, to the controller (e.g., one or more of the low-level controllers 112 and/or the primary controllers 120).
  • the controller can use the optimal parameters to damp power oscillations when a component drops out of the power system.
  • FIG. 6 depicts a flow diagram of another method 600 for tuning a controller
  • a processing device derives a dynamic nominal model of the power system.
  • the processing device determines a dynamic fault model that contains an equivalent model of the component that describes a behavior of the component prior to drop out of the component.
  • the processing device determines optimal parameters for the controller based on the dynamic fault model.
  • the processing device implements the optimal parameters in the controller in the power system to damp power oscillations after the drop out of the component.
  • FIG. 7 illustrates a block diagram of a processing system 700 for implementing the techniques described herein, according to aspects of the present disclosure.
  • processing system 700 has one or more central processing units (processors) 721a, 721b, 721c, etc. (collectively or generically referred to as processor(s) 721 and/or as processing device(s)).
  • processors 721 may include a reduced instruction set computer (RISC) microprocessor.
  • RISC reduced instruction set computer
  • processors 721 are coupled to system memory (e.g., random access memory (RAM) 724) and various other components via a system bus 733.
  • RAM random access memory
  • ROM Read only memory
  • BIOS basic input/output system
  • I/O adapter 727 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 723 and/or a tape storage drive 725 or any other similar component.
  • I/O adapter 727, hard disk 723, and tape storage device 725 are collectively referred to herein as mass storage 734.
  • Operating system 740 for execution on processing system 700 may be stored in mass storage 734.
  • a network adapter 726 interconnects system bus 733 with an outside network 736 enabling processing system 700 to communicate with other such systems.
  • a display (e.g., a display monitor) 735 is connected to system bus 733 by display adaptor 732, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller.
  • adapters 726, 727, and/or 732 may be connected to one or more I/O busses that are connected to system bus 733 via an intermediate bus bridge (not shown).
  • Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI).
  • PCI Peripheral Component Interconnect
  • Additional input/output devices are shown as connected to system bus 733 via user interface adapter 728 and display adapter 732.
  • a keyboard 729, mouse 730, and speaker 731 may be interconnected to system bus 733 via user interface adapter 728, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.
  • processing system 700 includes a graphics processing unit 737.
  • Graphics processing unit 737 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display.
  • Graphics processing unit 737 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.
  • processing system 700 includes processing capability in the form of processors 721, storage capability including system memory (e.g., RAM 724), and mass storage 734, input means such as keyboard 729 and mouse 730, and output capability including speaker 731 and display 735.
  • system memory e.g., RAM 724
  • mass storage 734 collectively store at least portions of the operating system 740 to coordinate the functions of the various components shown in processing system 700.

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Abstract

L'invention concerne des exemples de techniques de syntonisation d'un dispositif de commande afin d'atténuer des oscillations de puissance après la perte d'un composant d'un système d'alimentation. Selon un mode de réalisation donné à titre d'exemple, un procédé informatique consiste : à déduire, par un dispositif de traitement, un modèle nominal dynamique du système d'alimentation ; à déterminer, par le dispositif de traitement, un modèle de défaillance dynamique qui contient un modèle équivalent du composant décrivant un comportement du composant avant la perte du composant ; à déterminer, par le dispositif de traitement, des paramètres optimaux du dispositif de commande sur la base, au moins en partie, du modèle de défaillance ; et à mettre en œuvre, par le dispositif de traitement, des paramètres optimaux dans le dispositif de commande dans le système d'alimentation afin d'atténuer les oscillations de puissance après la perte du composant.
PCT/US2017/053181 2017-04-26 2017-09-25 Syntonisation d'un dispositif de commande afin d'atténuer des oscillations de puissance après la perte d'un composant d'un système d'alimentation WO2018200025A1 (fr)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2688191A1 (fr) * 2012-07-17 2014-01-22 ABB Research Ltd. Commande HVDC de terminal multiple

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2688191A1 (fr) * 2012-07-17 2014-01-22 ABB Research Ltd. Commande HVDC de terminal multiple

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CATALIN GAVRILUTA ET AL: "Design considerations for primary control in multi-terminal VSC-HVDC grids", ELECTRIC POWER SYSTEMS RESEARCH, 1 May 2015 (2015-05-01), pages 33 - 41, XP055430753, Retrieved from the Internet <URL:http://www.nstg-project.nl/uploads/media/7_TUD-130426_-NSTG_WP_6.2_part_I.pdf> [retrieved on 20171130], DOI: 10.1016/j.epsr.2014.12.020 *

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