| 光伏发电系统自动化调度数据异常检测方法 |
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| 引用本文：陈飞1,苏适2,徐佳宁1,卢欣辰1,林莉1.光伏发电系统自动化调度数据异常检测方法[J].电网与清洁能源,2025,41(4):128~133 |
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| 基金项目:中国南方电网有限责任公司科技项目（YNKJXM20220207） |
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| 中文摘要:为提升分布式光伏发电系统的发电性能和调度性能，提出一种光伏发电系统自动化调度数据异常检测方法。采用静态路由绑定的动态传感瞬时检测跟踪识别方法，实现对光伏发电系统耗损电量情况动态特征分析，计算光伏辐射量和光伏发电的负载参数信息；通过光伏发电系统并网逆变器的输出电流异常特性对调度数据进行异常特征监测，提取光伏发电系统自动化调度数据的异常频谱特征量；根据静态路由绑定后传感信息的时域波形检测结果，实现对光伏发电系统调度数据的异常检测。测试结果表明，采用该方法进行光伏发电系统自动化调度数据异常检测的即时性较好、抗干扰能力较强，降低了系统负载开销，提高了光伏发电系统的输出稳定性。 |
| 中文关键词:静态路由绑定 光伏发电系统 自动化调度 异常检测 |
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| An Anomaly Detection Method for Automated Scheduling Data of Photovoltaic Power Generation Systems |
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| Abstract:To enhance the power generation and scheduling performance of distributed photovoltaic power generation systems， this paper proposes an anomaly detection method for automated scheduling data of photovoltaic power generation systems. The method employs a dynamic sensing instantaneous detection and tracking recognition technique， utilizing static routing binding to achieve dynamic feature analysis of the electricity consumption in the photovoltaic power generation system， and calculates the photovoltaic radiation and load parameter information of photovoltaic power generation. Subsequently， the abnormal characteristics of the scheduling data are monitored by analyzing the abnormal output current characteristics of the grid-connected inverter of the photovoltaic power generation system， extracting the abnormal spectral characteristics of the automated scheduling data of the photovoltaic power generation system. Finally， based on the time-domain waveform detection results of the sensing information after static routing binding， anomaly detection of the scheduling data of the photovoltaic power generation system is accomplished. Test results demonstrate that this method for anomaly detection in automated scheduling data of photovoltaic power generation systems exhibits good real-time performance， strong anti-interference capabilities， reduces system load overhead， and improves the output stability of photovoltaic power generation systems. |
| keywords:static route binding photovoltaic power generation system automatic scheduling anomaly detection |
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