Hybrid Knowledge-data Driven Adaptive Voltage/Var Optimization Control of Photovoltaic Inverters in Distribution Networks

YANG Hao; WANG Jiayi; YI Wenfei; SUN Zhenglong; CAI Guowei; DING Zhaohao

doi:10.13334/j.0258-8013.pcsee.241838

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Hybrid Knowledge-data Driven Adaptive Voltage/Var Optimization Control of Photovoltaic Inverters in Distribution Networks

更新时间：2026-07-27

- Hybrid Knowledge-data Driven Adaptive Voltage/Var Optimization Control of Photovoltaic Inverters in Distribution Networks
- Issue 22, Pages: 8691-8705(2025)
- 作者机构：
  
  1. 现代电力系统仿真与绿色电能新技术教育部重点实验室(东北电力大学),吉林市,吉林省,132012
  2. 国网江苏省电力有限公司电力科学研究院,江苏省,南京市,211103
  3. 华北电力大学, 北京市昌平区,102206
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.241838
  CLC：
- Published：2025
- 稿件说明：
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YANG Hao, WANG Jiayi, YI Wenfei, et al. Hybrid Knowledge-data Driven Adaptive Voltage/Var Optimization Control of Photovoltaic Inverters in Distribution Networks[J]. 2025, (22): 8691-8705.
DOI：

YANG Hao, WANG Jiayi, YI Wenfei, et al. Hybrid Knowledge-data Driven Adaptive Voltage/Var Optimization Control of Photovoltaic Inverters in Distribution Networks[J]. 2025, (22): 8691-8705. DOI： 10.13334/j.0258-8013.pcsee.241838.

摘要

配电网中高渗透分布式光伏(photovoltaic，PV)出力的随机波动特性导致潮流状态复杂多变，频繁电压越限与运行网损增大的问题愈发突出。针对该问题，提出一种知识-数据融合驱动的配电网PV逆变器电压/无功优化自适应控制方法，实现领域知识融入与数据驱动学习相结合的高效电压优化控制。首先，结合PV与负荷历史/预测功率数据，利用潮流计算和最优潮流计算，构建面向PV电压/无功控制具备知识属性的PV调压数据集；然后，设计基于模糊控制理论的智能化PV电压控制器，并融入由PV节点电压和有功状态到PV无功决策的逻辑推理电压调控知识；接着，利用样本数据，采用数据驱动的梯度下降算法优化调整各PV电压控制器参数，实现由PV节点电压和有功状态到PV最优无功决策的准确映射，提升全局电压优化控制性能；最后，综合各PV电压控制器构成兼顾电压越限控制和系统网损优化的在线分散自治策略，能够响应运行状态进行自适应PV无功优化控制。仿真算例中对比多种控制方法，验证所提方法的有效性和优越性。

Abstract

The stochastic fluctuation of high-penetration distributed photovoltaic (PV) in distribution networks leads to complex power flow states

exacerbating the frequent voltage violation frequency and network losses. To overcome the issues

this paper proposes a hybrid knowledge-data driven adaptive voltage/var control strategy optimization of PV inverters in distribution networks to achieve efficient voltage control by combining domain knowledge and data-driven learning. First

through historical/forecasted power data of PV and loads

power flow and optimal power flow are both calculated to construct the knowledgeable dataset of PV voltage control. Then

an intelligent PV voltage controller based on fuzzy control theory is designed and the inference knowledge from PV voltage and PV active power to PV Var decision is embedded into the controller. Next

on the basis of the constructed dataset

a data-driven gradient descent algorithm is presented to optimize the parameters of PV voltage controller

so that the accurate map from PV voltage and active power states to the optimal Var decision can be realized to improve the global control performance. Finally

an online decentralized automatic strategy is developed with the optimized PV controllers

which considers both voltage violation control and network loss optimization

and performs adaptive PV Var control responding to operating states. Different control methods are compared by simulations

which verifies the effectiveness and superiority of the method proposed in this paper.

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