Pith Number

pith:ATOPYTCQ

pith:2026:ATOPYTCQE7M42IQ37G2M4JYFEB

not attested not anchored not stored refs resolved

Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning

Iasonas Tsaklis, Marcus Binder Nilsen, Nikolay Dimitrov, Pierre-Elouan R\'ethor\'e, Teodor {\AA}strand, Tuhfe G\"o\c{c}men

Multi-agent reinforcement learning lets wind farm turbines steer wakes for higher total power while keeping load increases below set thresholds.

arxiv:2604.22795 v2 · 2026-04-13 · eess.SY · cs.LG · cs.SY

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The MARL agents successfully learn collaborative policies that prioritise power gain while actively retreating from high-DEL control strategies.

C2weakest assumption

The data-driven, local inflow sector-averaged surrogate model supplies sufficiently accurate real-time estimates of Damage Equivalent Loads that can be inserted directly into the shaped reward without introducing large policy errors.

C3one line summary

A multi-agent RL system using Independent Soft Actor-Critic and a local-inflow surrogate for damage-equivalent loads learns policies that raise wind-farm power while respecting explicit load-increase limits.

References

15 extracted · 15 resolved · 1 Pith anchors

[1] Thomsen K and Sørensen P 1999 Fatigue loads for wind turbines operating in wakesJournal of Wind Engineering and Industrial Aerodynamics80121–136 ISSN 0167-6105 1999

[2] Debusscher C M J, G¨ o¸ cmen T and Andersen S J 2022 Probabilistic surrogates for flow control using combined control strategiesJournal of Physics: Conference Series2265032110 2022

[3] Padullaparthi V R, Nagarathinam S, Vasan A, Menon V and Sudarsanam D 2022 Falcon- farm level control for wind turbines using multi-agent deep reinforcement learningRenewable Energy181445–456 ISSN 0960 2022

[4] Damiani R, Dana S, Annoni J, Fleming P, Roadman J, van Dam J and Dykes K 2018 Assessment of wind turbine component loads under yaw-offset conditionsWind Energy Science3173–189 ISSN 2366-7443 publisher 2018

[5] G¨ o¸ cmen T, Liew J, Kadoche E, Dimitrov N, Riva R, Andersen S J, Lio A W, Quick J, R´ ethor´ e P E and Dykes K 2025 Data-driven wind farm flow control and challenges towards field implementation: A 2025

Receipt and verification

First computed	2026-06-01T01:02:40.595997Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

04dcfc4c5027d9cd221bf9b4ce270520697a6537d37ad3f11e59c1da694017b1

Aliases

arxiv: 2604.22795 · arxiv_version: 2604.22795v2 · doi: 10.48550/arxiv.2604.22795 · pith_short_12: ATOPYTCQE7M4 · pith_short_16: ATOPYTCQE7M42IQ3 · pith_short_8: ATOPYTCQ

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/ATOPYTCQE7M42IQ37G2M4JYFEB \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 04dcfc4c5027d9cd221bf9b4ce270520697a6537d37ad3f11e59c1da694017b1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7ffa67741e517a12746c56affd9fd732d8e7984fcb2781b4b81fd5182c67c662",
    "cross_cats_sorted": [
      "cs.LG",
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-04-13T12:39:26Z",
    "title_canon_sha256": "063e60b2382a545a943ff86fb00992fb19953f74f1d4a3ed42f2bb296cc03abb"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.22795",
    "kind": "arxiv",
    "version": 2
  }
}