Multi-Terminal HVDC Master Control System Optimization

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Solution Overview

Problem

Conventional control systems for Multi-Terminal High Voltage Direct Current (MTDC) systems lack stability and load balancing capabilities, particularly after severe disturbances such as converter station failures, and fail to prevent overload and adverse limiter interactions, making them unreliable and prone to saturation.

Innovation Solution

A master control system layer is introduced that employs mathematical optimization and dynamic modeling to generate optimized controller settings, considering system constraints and operational limits, and uses Model Predictive Control (MPC) to minimize errors and manage thermal overload, ensuring optimal operation and reducing adverse limiter interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional system control is used in MTDC systems, then the control structure is simple, but the system lacks stability and load balancing capabilities after severe disturbances

Engineering Contradiction:
Improvesystem stability after disturbancesVSAvoidcontrol structure complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The control system is segmented into three distinct layers: system control layer (centralized optimization), converter unit control layer (distributed control), and firing control layer (execution). This segmentation allows each layer to perform specialized functions, improving overall system reliability while managing complexity through modular architecture.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The converter unit control layer acts as an intermediary between the system control layer and firing control layer. It receives optimized reference values from the system control layer and translates them into converter-specific control commands, enabling stable operation after disturbances while maintaining a manageable control structure.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Reliability

If droop control schemes are used to achieve stability, then the local station controller stability is ensured, but perfect reference tracking is lost

Engineering Contradiction:
Improvelocal station controller stabilityVSAvoidreference tracking accuracy
Core Design Contradiction:
ReliabilityVSMeasurement precision

Solution Approach 1:

The system control layer implements feedback mechanisms that continuously monitor actual DC power and voltage, comparing them against reference values. This feedback enables the system to maintain both stability (through droop characteristics) and reference tracking accuracy (through continuous correction), resolving the contradiction between the two requirements.

Inventive Principle:
Principle #23Feedback

Solution Approach 2:

The control system dynamically adjusts droop constants and reference values based on system conditions. During normal operation, the system maintains accurate reference tracking, while during disturbances, it dynamically switches to droop-based stability control, thereby achieving both objectives at different operational phases.

Inventive Principle:
Principle #15Dynamics

3Adaptability or versatility

If controllers operate near their limits to handle severe disturbances, then the system adaptability is improved, but the controllers become less reliable and prone to saturation

Engineering Contradiction:
Improvesystem adaptability to disturbancesVSAvoidcontroller reliability
Core Design Contradiction:
Adaptability or versatilityVSReliability

Solution Approach 1:

The system control layer performs preliminary optimization calculations to determine safe and optimal reference values before disturbances occur. By pre-calculating reference values that respect controller limits, the system maintains adaptability to disturbances while preventing controllers from operating in unreliable saturation regions.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The optimized control approach incorporates safety margins and limit checks in advance, cushioning the system against controller saturation. By predicting potential disturbances and pre-adjusting reference values within safe operating ranges, the system maintains both adaptability and controller reliability.

Inventive Principle:
Principle #11Beforehand cushioning (Prior cushioning)

4Reliability

If limiters are introduced to constrain controller output, then the system safety is improved, but harmful interactions between limiters occur

Engineering Contradiction:
Improvesystem safetyVSAvoidlimiter interactions
Core Design Contradiction:
ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The system control layer acts as an intermediary that coordinates limiter actions across different controllers. By centralizing the optimization and reference value generation, it harmonizes the operation of multiple limiters, preventing harmful interactions while maintaining system safety through coordinated constraint enforcement.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentEP2875575B1Multi terminal HVDC control
Publication Date: 2021.02.17 HITACHI ENERGY SWITZERLAND AG
  • EP2875575B1 patent drawingFigure 1~2
  • EP2875575B1 patent drawingFigure 3

AI summary

The present invention is concerned with a master control system layer for a Voltage Source Converter (VSC) based Multi-Terminal High Voltage Direct Current (MTDC) system. The invention is applicable to general topologies of the MTDC grid, including meshed topologies and isolated islands, and its benefits become specifically apparent in MTDC systems with five or more terminals where management of all possible different operating condition will require unacceptable engineering efforts if only simple feedback control loops are used. The invention includes mathematical optimization procedures to determine, in real-time and based on actual operating conditions, controller settings that minimize a cost criterion or optimize any other objective function. Controller settings include set-points or reference values as well as controller parameters such as droop constants or gains. Furthermore, it introduces model predictive control to include predictions of the effect of control actions on the system state evolution and predictions of future operating conditions in the optimization procedure.