Search arXivSearch

arXiv · 2008.12499

Synthesizing Averaged Virtual Oscillator Dynamics to Control Inverters with an Output LCL Filter

Abstract

In commercial inverters, an LCL filter is considered an integral part to filter out the switching harmonics and generate a sinusoidal output voltage. The existing literature on the averaged virtual oscillator controller (VOC) dynamics is for current feedback before the output LCL filter that contains the switching harmonics or for inductive filters ignoring the effect of filter capacitance. In this work, a new version of averaged VOC dynamics is presented for islanded inverters with current feedback after the LCL filter thus avoiding the switching harmonics going into the VOC. The embedded droop-characteristics within the averaged VOC dynamics are identified and a parameter design procedure is presented to regulate the output voltage magnitude and frequency according to the desired ac-performance specifications. Further, a power dispatch technique based on this newer version of averaged VOC dynamics is presented to simultaneously regulate both the active and reactive output power of two parallel-connected islanded inverters. The control laws are derived and a power security constraint is presented to determine the achievable power set-point. Simulation results for load transients and power dispatch validate the proposed version of averaged VOC dynamics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Muhammad Ali, Hendra I. Nurdin, John E. Fletcher. 2020-08-28. Synthesizing Averaged Virtual Oscillator Dynamics to Control Inverters with an Output LCL Filter. https://arxiv.org/abs/2008.12499

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Co-Investment with Payoff-Sharing Mechanism for Cooperative Decision-Making in Network Design Games

Network-based systems are inherently interconnected, with the design and performance of subnetworks being interdependent. However, the decisions of self-interested operators may lead to suboptimal outcomes for users and the overall system. This paper explores cooperative mechanisms that can simultaneously benefit both operators and users. We address this challenge using a game-theoretical framework that integrates both non-cooperative and cooperative game theory. In the non-cooperative stage, we propose a network design game in which subnetwork decision-makers strategically design local infrastructures. In the cooperative stage, co-investment with payoff-sharing mechanism is developed to enlarge collective benefits and fairly distribute them. To demonstrate the effectiveness of our framework, we conduct case studies on the Sioux Falls network and real-world public transport networks in Zurich and Winterthur, Switzerland. Our evaluation considers impacts on environmental sustainability, social welfare, and economic efficiency. The proposed framework provides a foundation for improving interdependent networked systems by enabling strategic cooperation among self-interested operators.

eess.SY

Generalizable Optimal Control with Transformers: One Policy Across Diverse Systems

Classical optimal control designs a separate controller for each plant. Even for the Linear Quadratic Regulator (LQR), every new model must be identified and its Riccati equation re-solved. We ask whether a single learned policy can instead serve an entire family of systems, and we show that one transformer can. We train the policy to imitate optimal LQR state feedback across a collection of heterogeneous Multiple-Input, Multiple-Output (MIMO) Linear Time-Invariant (LTI) systems that differ in their state and input dimensions and in their cost objectives. A shared representation lets the same parameters control every member of the family. It combines system-wise standardization, zero-padding and masking across dimensions, and an explicit encoding of the cost matrices. At run time, the policy maps a short window of recent states and the specified cost to a control action. It uses no plant matrices and identifies the dynamics implicitly from the state history. We evaluate on $28$ simulated systems over $9{,}675$ closed-loop rollouts, and no unstable rollout was observed in any of them. On the systems seen during training, it attains a median relative sub-optimality of $0.022\%$, even under parameter perturbations of up to $\pm10\%$. It transfers to unseen systems with lightweight fine-tuning, reaching a median sub-optimality of $0.19\%$. These results support transformers as generalizable near-optimal controllers for structured families of linear systems.

eess.SY

Two-Timescale Asymptotic Simulations of Hybrid Inclusions with Applications to Stochastic Hybrid Optimization

Convergence properties of model-free two-timescale asymptotic simulations of singularly perturbed hybrid inclusions are developed. A hybrid inclusion combines constrained differential and difference inclusions to capture continuous (flow) and discrete (jump) dynamics, respectively. Sufficient conditions are established under which sequences of iterates and step sizes constitute a two-timescale asymptotic simulation of such a system, with limiting behavior characterized via weakly invariant and internally chain-transitive sets of an associated boundary layer and reduced system. To illustrate the applicability of these results, conditions are given under which a two-timescale stochastic approximation of a hybrid optimization algorithm asymptotically recovers the behavior of its deterministic counterpart.

eess.SY