基于人工智能动态优化的配电网网络安全风险评估方法研究

Research on Cybersecurity Risk Assessment Method for Distribution Networks Based on Artificial Intelligence Dynamic Optimization

摘要: 随着智能配电网建设不断推进，配电网运行过程逐渐依赖环境传感器、相量测量单元、智能电表、电力线路传感器、SCADA监测系统以及通信接口等多源数据进行状态感知、运行分析和控制决策。与此同时，虚假数据注入、异常网络流量、恶意数据模式以及针对智能控制过程的攻击行为，可能影响配电网基础设施的安全运行和决策结果的可靠性。针对现有网络安全风险评估方法实时性不足、多源数据利用不充分以及风险结果难以支撑运行控制等问题，本文提出一种基于人工智能动态优化的配电网网络安全风险评估方法。该方法首先采集原始电力负荷、环境数据、设备工作状态、发电侧数据、供电侧信息、组件温度、网络状态以及网络安全风险因素等数据；其次对多源数据进行汇聚、清洗、标准化和特征提取，形成标准化安全评估输入；然后结合入侵检测、威胁建模、风险因素不确定性度量和安全阈值判断，实现对网络安全风险的动态评估；最后将风险评估结果输入决策支持系统，形成安全警报、风险缓解策略或控制信号。仿真分析表明，该方法能够将网络安全风险识别结果与实时网格优化、运行状态反馈和决策控制过程相结合，为智能配电网的安全运行和韧性提升提供支撑。

Abstract: With the development of smart distribution networks, operational monitoring and control increasingly rely on multi-source data collected from environmental sensors, phasor measurement units, smart meters, power line sensors, SCADA systems and communication interfaces. Meanwhile, false data injection, abnormal network traffic, malicious data patterns and attacks against intelligent control processes may affect secure operation and decision reliability. To address insufficient real-time performance, inadequate utilization of multi-source data and weak support of risk assessment results for operation control, this paper proposes a cybersecurity risk assessment method for distribution networks based on artificial intelligence dynamic optimization. Multi-source operational and security data are collected, aggregated, cleaned, normalized and extracted to form standardized security assessment inputs. Intrusion detection, threat modeling, uncertainty measurement of risk factors and security threshold judgment are then combined to achieve dynamic cybersecurity risk assessment. Finally, the assessment results are sent to the decision support system to generate security alerts, risk mitigation strategies or control signals. The analysis shows that the proposed method can integrate cybersecurity risk identification with real-time grid optimization, operational feedback and decision control, thereby supporting secure operation and resilience improvement of smart distribution networks.