基于蛾焰优化的模型-预测-控制超导磁储能-电池混合储能系统参数估计

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2023-04-28 DOI:10.1049/stg2.12111
Lu Liu, Jie Sheng, Hanyu Liang, Jinshan Yang, Haosheng Ye, Junjie Jiang
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引用次数: 0

摘要

超导磁储能-电池混合储能系统(HESS)在低频/高频干扰下具有快速动态响应能力,在平衡直流(DC)电网电压方面具有广阔的应用前景。具有高采样率和广泛适用性等特点的模型预测控制(MPC)可应用于 HESS。然而,考虑到相关电路参数会随着环境温度、干扰和老化而变化,MPC 的效果不可避免地会变差。本文针对 SMES 电池 HESS 提出了一种改进的 MPC 策略,采用蛾焰优化(MFO)算法实时计算电路参数。实际参数由 MFO 更新,然后发送到模型预测控制器,以尽量减少模型失配。通过与灰狼优化和粒子群优化的比较,验证了该算法具有精度高、收敛速度快的优点。仿真结果表明,采用所提方案后,直流母线电压更加稳定,在交流电网故障和新能源输出波动的情况下,即使模型参数与实际参数不一致,超导磁储能也能保持 95% 以上的容量利用率,并避免过放电。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Moth-flame-optimisation based parameter estimation for model-predictive-controlled superconducting magnetic energy storage-battery hybrid energy storage system

Moth-flame-optimisation based parameter estimation for model-predictive-controlled superconducting magnetic energy storage-battery hybrid energy storage system

Superconducting magnetic energy storage-battery hybrid energy storage system (HESS) has a broad application prospect in balancing direct current (DC) power grid voltage due to its fast dynamic response ability under low-frequency/high-frequency disturbances. Model-predictive-control (MPC) with characteristics such as high sampling rate and wide applicability could be applied to HESS. However, considering that the relevant circuit parameters would change with ambient temperature, interference and ageing, the effect of MPC may deteriorate inevitably. This article proposes an improved MPC strategy for SMES-Battery HESS, taking moth-flame-optimisation (MFO) algorithm to calculate the circuit parameters in real time. The actual parameters are updated by MFO and then sent to model predictive controller to minimise the model mismatches. The advantages of high accuracy and fast convergence speed is verified by comparison with grey wolf optimisation and particle swarm optimisation. The simulation shows that by taking the proposed scheme, DC bus voltage are more stable and the superconducting magnetic energy storage can maintain more than 95% capacity utilisation and avoid over-discharge even if the model parameters are inconsistent with the actual ones under circumstances of alternating current grid fault and fluctuation of new energy output.

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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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