Volt/VAR Inverter Control for Reverse Power Flow Loss Reduction
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Solution Overview
Problem
Legacy electrical grids face challenges in managing reverse power flows and line losses due to increased penetration of local power generation from photovoltaic systems, as existing methods for reactive power compensation are either costly, complex, or inefficient, particularly in determining optimal inverter parameters for voltage regulation.
Innovation Solution
A method involving a node in a power distribution system with a local or remote controller, sensors, an emulator, and a supervised learning model to optimize inverter control parameters for Volt/VAr control, allowing for short-term adjustments without prior knowledge of the entire grid, using a training phase to determine optimal control parameters for minimizing power transmission losses and voltage rises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inverter control parameters are optimized using traditional methods (e.g., maximum measured voltage), then voltage regulation is achieved, but the system requires long measurement periods (months) and may result in over-injection of reactive power
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing voltage and reactive power measurements over a short period (e.g., one week) to pre-determine optimal control parameters before actual operation. This preliminary optimization phase allows the system to have control parameters ready immediately rather than requiring months of measurement, thus resolving the contradiction between measurement precision and time loss.
2Reliability
If reactive power compensation is increased to manage reverse power flows, then voltage regulation improves, but reactive power losses increase
Solution Approach 1:
The system implements feedback by continuously monitoring voltage and reactive power measurements and using this information to dynamically adjust control parameters. The optimization process analyzes the relationship between reactive power injection and voltage changes, allowing the system to determine the optimal amount of reactive power compensation needed to maintain voltage stability while minimizing reactive power losses. This feedback mechanism ensures that reactive power is only injected when necessary and at the optimal level.
3Strength
If physical transmission lines are upgraded to handle increased power flows, then robustness improves, but cost increases significantly
Solution Approach 1:
Instead of physically upgrading transmission lines, the system changes operational parameters by optimizing inverter control settings. By adjusting control parameters such as reactive power injection levels and voltage setpoints, the system enables existing infrastructure to handle increased power flows from distributed generation without requiring expensive physical upgrades. This parameter-based solution achieves line robustness through intelligent control rather than increased physical capacity.
4Manufacturing precision
If Volt/VAr control is implemented to regulate voltage, then power quality improves, but determining optimal control parameters becomes complex
Solution Approach 1:
The system implements self-service by automatically determining optimal control parameters through an optimization process that analyzes measured voltage and reactive power data. Rather than requiring complex manual calculations or external expert intervention, the system uses its own measurements and a predetermined optimization algorithm to self-determine the control parameters needed for Volt/VAr control. This automated approach improves power quality while reducing the complexity of parameter determination.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient optimization of inverter control parameters, reducing reactive power losses and voltage deviations, and adapts quickly to changes in the network, improving the overall efficiency and reliability of power distribution.
Implementation Method 1
Modern inverters are capable of injecting power at a given power factor between −1 and 1 rather than simply as active power at unity power factor. By modifying the power factor, an inverter can inject or absorb reactive power (Q)
Implementation Method 2
an emulator adapted to emulate the behaviour of said inverter and said controller (insofar as it controls the transfer function of the inverter) so as output a value of emulated reactive power on the basis of an input value of said voltage
Implementation Method 3
a supervised learning model; and an optimiser arranged to output said control parameters for said controller and to determine their optimum values
Data Source
AI summary
The invention relates to a method of operating a node of a power distribution system in an optimised manner so as to minimise transmission losses on an external power bus. A power generator feeds in electrical energy via an inverter capable of carrying out Volt/VAR control under the command of an optimiser associated with a supervised learning model such as a support vector machine.This optimisation takes place in two distinct training phases, one in which the supervised learning model is trained, another in which optimal control parameters for the inverter are obtained.