基于模型驱动的电力系统安全稳定分析与控制策略

doi:10.11930/j.issn.1004-9649.202410010

中国电力 ›› 2025, Vol. 58 ›› Issue (8): 156-163.DOI: 10.11930/j.issn.1004-9649.202410010

基于模型驱动的电力系统安全稳定分析与控制策略

张鼎衢¹(), 钱斌²^,³(), 杨路¹(), 陈峰¹(), 罗奕²^,³()

1. 广东电网有限责任公司计量中心，广东清远　511545
2. 南方电网科学研究院有限责任公司，广东广州　510663
3. 广东省电网智能量测与先进计量重点实验室，广东广州　510663

收稿日期:2024-10-08 发布日期:2025-08-26 出版日期:2025-08-28
作者简介:
张鼎衢（1987），男，硕士，高级工程师，从事电能计量装置运行管理与技术应用研究，E-mail：13822289520@139.com
钱斌（1989），男，硕士，高级工程师，从事电能计量技术研究，E-mail：qianbin@csg.cn
杨路（1991），男，硕士，工程师，从事电能计量新技术研究，E-mail：yanglu910715@126.com
陈峰（1994），男，硕士，工程师，从事电能计量装置运行管理与技术应用研究，E-mail：15815538083@163.com
罗奕（1991），男，博士，高级工程师，从事电能计量、互感器、高级量测技术等研究工作，E-mail：luoyi_csg@outlook.com
基金资助:
中国南方电网有限责任公司科技项目（GDKJXM20220280）。

Data Driven Analysis and Control of Power System Security and Stability

ZHANG Dingqu¹(), QIAN Bin²^,³(), YANG Lu¹(), CHEN Feng¹(), LUO Yi²^,³()

1. Metrology Center of Guangdong Power Grid Co., Ltd., Qingyuan 510080, China
2. Electric Power Research Institute, CSG, Guangzhou 510663, China
3. Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510663, China

Received:2024-10-08 Online:2025-08-26 Published:2025-08-28
Supported by:
This work is supported by Science and Technology Project of China Southern Power Grid Co., Ltd. (No.GDKJXM20220280).

摘要/Abstract

摘要：

为实现对暂态过电压的精确控制，及时且有效地应对电压的急剧变化，提出了一种高比例光伏发电并网情况下的基于模型驱动的电力系统安全稳定分析与控制策略。该策略针对高比例光伏并网系统的电压波动等关键电气指标，构建了基于径向基函数-粒子群优化（radial basis function-particle swarm optimization，RBF-PSO）的神经网络模型。基于实时采集的数据，所提模型能够预测电网的暂态行为，一旦预测结果显示可能存在暂态过电压风险，系统将根据预设的优先级设定待控制节点，并结合光伏接入点的灵敏度分析调节光伏逆变器的无功补偿与有功输出，自动激活预设的暂态过电压控制机制，以迅速恢复系统电压稳定，保障设备安全。实验验证显示，该方法不仅响应速度快，还能显著减少光伏逆变器所需的无功功率。

关键词: 光伏电站, 峰值电压, 暂态过电压控制

Abstract:

In order to control the transient over-voltage accurately and deal with the sharp change of voltage in time and effectively, this paper proposes a data-driven power system security and stability analysis and control strategy under the condition of high proportion of photovoltaic power generation connected to the grid. This strategy involves continuous monitoring of key electrical indicators such as voltage fluctuations in the high-penetration PV grid-connected system, upon which a neural network model based on Radial Basis Function-Particle Swarm Optimization (RBF-PSO) is constructed. Using real-time data, the model can predict the transient behavior of the power grid. Once the prediction indicates a risk of transient over-voltage, the system immediately sets the nodes to be controlled according to preset priorities and, in combination with sensitivity analysis at PV connection points, automatically activates the preset transient over-voltage control mechanism by adjusting the reactive power compensation and active power reduction functions of the PV inverters. This aims to swiftly restore system voltage stability and ensure equipment safety. Experimental verification shows that this method not only responds rapidly but also significantly reduces the reactive power required by PV inverters.

Key words: photovoltaic power station, peak voltage, transient overvoltage control

张鼎衢, 钱斌, 杨路, 陈峰, 罗奕. 基于模型驱动的电力系统安全稳定分析与控制策略[J]. 中国电力, 2025, 58(8): 156-163.

ZHANG Dingqu, QIAN Bin, YANG Lu, CHEN Feng, LUO Yi. Data Driven Analysis and Control of Power System Security and Stability[J]. Electric Power, 2025, 58(8): 156-163.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202410010

https://www.electricpower.com.cn/CN/Y2025/V58/I8/156

图/表 11

图 1 RBF-PSO神经网络构建流程

Fig.1 RBF-PSO neural network construction flowchart

图 2 实验用电网

Fig.2 Experimental power grid

表 1 电网参数

Table 1 Grid parameters

参数名称		参数值
电网总装机容量		100 MW
光伏装机容量		40 MW (40%光伏渗透率)
单台逆变器最大输出功率		500 kW
电压监测点数量		10个（位于电网关键节点，如变电站、重要负荷点）
电气参数采样频率		1 kHz
额定电压（示例）		230 kV
电压监测范围（相对于额定电压）		±10%（即207 kV至253 kV）

图 3 电网运行数据分类

Fig.3 Classification of power grid operation data

表 2 RBF-PSO神经网络的参数设置

Table 2 Parameter settings of RBF-PSO neural network

参数名称		参数值
种群规模		60
迭代轮次		200
加速系数		1.8
位置范围		–1~1
速度范围		–1~1
维度		50
惯性权重极值		0.34、0.88
学习因子		1.402

表 3 RBF-PSO神经网络参数优化验证

Table 3 Parameter optimization validation of RBF-PSO neural network

实验编号	网络层数	节点数量（每层）	验证集准确率/%	均方误差
1	初始猜测	5, 10, 1	85.00	0.050
2	PSO优化	5, 11, 1	92.00	0.030
3	PSO优化	4, 12, 1	90.00	0.040
4	PSO优化	5, 9, 1	88.00	0.045

图 4 基准测试函数逼近示意

Fig.4 Schematic diagram of benchmark function approximation

图 5 光伏逆变器母线电压

Fig.5 Bus voltage of photovoltaic inverter

图 6 光伏逆变器无功功率

Fig.6 Reactive power of photovoltaic inverter

图 7 暂态过电压控制响应时间

Fig.7 Transient overvoltage control response time

图 8 光伏逆变器精准度

Fig.8 Precision of photovoltaic inverter

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基于模型驱动的电力系统安全稳定分析与控制策略

Data Driven Analysis and Control of Power System Security and Stability

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基于模型驱动的电力系统安全稳定分析与控制策略

Data Driven Analysis and Control of Power System Security and Stability

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