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Reactive power flow optimization in AC drive systems

Sanjay Chandrasekaran, Catalin Arghir, Pieder Joerg, Florian Doerfler, Silvia Mastellone

TL;DR

The paper tackles reactive-power management in medium-voltage AC drive systems under current- and modulation-limit constraints. It proposes two outer-loop strategies—an activation-function approach and Online Feedback Optimization (OFO)—to adjust the reactive-power set-point $Q_g^*$ while preserving DC-link regulation and avoiding inner-loop saturation. The authors provide formal convergence guarantees for the OFO method and demonstrate, via high-fidelity simulations of a pumped-hydro drive, that both methods improve robustness and availability compared to conventional current limiting. The results show effective mitigation of constraint violations during transients such as voltage dips and grid overvoltage, with practical implications for industrial drives and grid-support capabilities.

Abstract

This paper explores a limit avoidance approach in the case of input (modulation) and output (current) constraints with the aim of enhancing system availability of AC drives. Drawing on the observation that, in a certain range of reactive power, there exists a trade-off between current and modulation magnitude, we exploit this freedom and define a constrained optimization problem. We propose two approaches, one in the form of an activation-function which drives the reactive power set-point towards safety, and an approach which uses online feedback optimization to set the reactive power dynamically. Both methods compromise reactive power tracking accuracy for increased system robustness. Through a high fidelity simulation, we compare the benefits of the two methods, highlighting their effectiveness in industrial applications.

Reactive power flow optimization in AC drive systems

TL;DR

The paper tackles reactive-power management in medium-voltage AC drive systems under current- and modulation-limit constraints. It proposes two outer-loop strategies—an activation-function approach and Online Feedback Optimization (OFO)—to adjust the reactive-power set-point while preserving DC-link regulation and avoiding inner-loop saturation. The authors provide formal convergence guarantees for the OFO method and demonstrate, via high-fidelity simulations of a pumped-hydro drive, that both methods improve robustness and availability compared to conventional current limiting. The results show effective mitigation of constraint violations during transients such as voltage dips and grid overvoltage, with practical implications for industrial drives and grid-support capabilities.

Abstract

This paper explores a limit avoidance approach in the case of input (modulation) and output (current) constraints with the aim of enhancing system availability of AC drives. Drawing on the observation that, in a certain range of reactive power, there exists a trade-off between current and modulation magnitude, we exploit this freedom and define a constrained optimization problem. We propose two approaches, one in the form of an activation-function which drives the reactive power set-point towards safety, and an approach which uses online feedback optimization to set the reactive power dynamically. Both methods compromise reactive power tracking accuracy for increased system robustness. Through a high fidelity simulation, we compare the benefits of the two methods, highlighting their effectiveness in industrial applications.

Paper Structure

This paper contains 17 sections, 2 theorems, 26 equations, 9 figures.

Table of Contents

  1. INTRODUCTION
  2. Problem statement
  3. Drive system model
  4. Drive system objectives
  5. Inner loop definition
  6. Control problem
  7. Activation-function approach
  8. OFO: Problem Formulation
  9. OFO: Problem Solution
  10. Convergence Guarantees
  11. Experimental results
  12. Voltage dip
  13. Reference step
  14. Over-voltage
  15. Conclusion
  16. ...and 2 more sections

Key Result

Theorem 1

Let Assumptions Modellingassumptions,PI_assumption, disturbance_assumption hold. For any learning rate $\mu(k) \in (0,\frac{2\lVert v_g(k)\rVert^2}{1 + \gamma\lVert v_g(k)\rVert^2})$, the sequence of $Q_g^\star(k)$ generated using PGD satisfies the following recursive relation: where, $\Delta \bar{Q}_g^\star(k)= \bar{Q}_g^\star(k)-\bar{Q}_g^\star(k+1)$, $\epsilon(k)= 1-\mu(k)(\gamma + \lVert v_g(

Figures (9)

  • Figure 1: The PQ diagram for the considered drive system at nominal conditions, showing the admissible $Q_g$ set-point as constrained by the active power demand.
  • Figure 2: Block diagram of the circular current reference limiter with $\Gamma_1$.
  • Figure 3: Block diagram of the circular modulation limiter with $\Gamma_2$.
  • Figure 4: Block diagram of the simulated setup. From left to right: speed control, torque control, voltage control and current control loops typically implemented in a drive system, together with a Phase-locked-loop (PLL) that extracts the grid voltage and the angle for the $dq$-transformation (as seen on the converter side of the transformer). The anti-windup tracking controllers are denoted by satPI, while further feed-forward (FF) and active-damping (AD) terms are appropriately added to improve the fidelity of the simulation.
  • Figure 5: Block diagram of the Forward Euler implementation of the activation function controller \ref{['AF_eqn']}.
  • ...and 4 more figures

Theorems & Definitions (10)

  • Remark 1
  • Remark 2
  • Remark 3
  • Theorem 1
  • proof
  • proof
  • Corollary 1
  • proof
  • proof
  • Remark 4