面向微电网的智能电能质量监测系统
摘 要
本文介绍了微电网电能质量监测所面临的挑战以及现有监测技术存在的问题，提出了优化和解决方案。针对监测算法方面，本文基于深度学习技术提出了一种电能质量监测算法的优化方案，通过深度学习模型对监测数据进行建模、分类、识别和预测，减少了人工干预对监测数据进行分析的需求，提高了监测数据的精度和实时性。针对数据采集和传输方案，本文提出采用无线传感网络进行数据采集和传输，通过无线通信技术实现对微电网中各个节点的实时监测数据采集和传输。针对数据管理和分析方案，本文提出采用云平台进行数据管理和分析，通过云平台对监测数据进行存储、管理、分析和共享，为用户提供便捷的数据访问和处理方式。本文提出的方案具有高实时性、高精度性、低成本和高可扩展性特点，为微电网电能质量监测提供了一个有效的解决方案。
关键词：微电网、电能质量监测、深度学习
Abstract
This paper introduces the challenges of power quality monitoring in microgrid and the problems existing in the existing monitoring technology, and puts forward the optimization and solutions. In terms of monitoring algorithms, this paper proposes an optimization scheme of power quality monitoring algorithm based on deep learning technology. The deep learning model is used to model, classify, identify and predict monitoring data, which reduces the need for manual intervention to analyze monitoring data and improves the accuracy and real-time performance of monitoring data. Aiming at the data acquisition and transmission scheme, this paper proposes to adopt wireless sensor network for data acquisition and transmission, and realize real-time monitoring data acquisition and transmission of each node in microgrid through wireless communication technology. Aiming at the data management and analysis scheme, this paper proposes to use the cloud platform for data management and analysis, to store, manage, analyze and share monitoring data through the cloud platform, and to provide users with convenient data access and processing methods. The proposed scheme has the characteristics of high real-time performance, high precision, low cost and high scalability, which provides an effective solution for power quality monitoring of microgrid.
Keyword:Microgrid, power quality monitoring, deep learning
目 录
1. 绪论 1
1.1 研究背景 1
1.2 研究目的和意义 1
1.3 研究内容和方法 2
2. 相关概念及理论基础 3
2.1 微电网的概念和特点 3
2.2 电能质量的相关概念 3
2.3 监测系统的基本原理与结构 4
3. 电能质量监测问题与解决方案 5
3.1 微电网电能质量监测面临的挑战 5
3.2 现有电能质量监测技术存在的问题 5
3.3 针对以上问题的需求和期望 6
4. 电能质量监测的技术方案优化 7
4.1 基于深度学习的电能质量监测算法优化 7
4.2 基于无线传感网络的数据采集和传输方案 7
4.3 基于云平台的数据管理和分析方案 8
5. 结论 9
参考文献 10
致 谢 11
摘 要
本文介绍了微电网电能质量监测所面临的挑战以及现有监测技术存在的问题，提出了优化和解决方案。针对监测算法方面，本文基于深度学习技术提出了一种电能质量监测算法的优化方案，通过深度学习模型对监测数据进行建模、分类、识别和预测，减少了人工干预对监测数据进行分析的需求，提高了监测数据的精度和实时性。针对数据采集和传输方案，本文提出采用无线传感网络进行数据采集和传输，通过无线通信技术实现对微电网中各个节点的实时监测数据采集和传输。针对数据管理和分析方案，本文提出采用云平台进行数据管理和分析，通过云平台对监测数据进行存储、管理、分析和共享，为用户提供便捷的数据访问和处理方式。本文提出的方案具有高实时性、高精度性、低成本和高可扩展性特点，为微电网电能质量监测提供了一个有效的解决方案。
关键词：微电网、电能质量监测、深度学习
Abstract
This paper introduces the challenges of power quality monitoring in microgrid and the problems existing in the existing monitoring technology, and puts forward the optimization and solutions. In terms of monitoring algorithms, this paper proposes an optimization scheme of power quality monitoring algorithm based on deep learning technology. The deep learning model is used to model, classify, identify and predict monitoring data, which reduces the need for manual intervention to analyze monitoring data and improves the accuracy and real-time performance of monitoring data. Aiming at the data acquisition and transmission scheme, this paper proposes to adopt wireless sensor network for data acquisition and transmission, and realize real-time monitoring data acquisition and transmission of each node in microgrid through wireless communication technology. Aiming at the data management and analysis scheme, this paper proposes to use the cloud platform for data management and analysis, to store, manage, analyze and share monitoring data through the cloud platform, and to provide users with convenient data access and processing methods. The proposed scheme has the characteristics of high real-time performance, high precision, low cost and high scalability, which provides an effective solution for power quality monitoring of microgrid.
Keyword:Microgrid, power quality monitoring, deep learning
目 录
1. 绪论 1
1.1 研究背景 1
1.2 研究目的和意义 1
1.3 研究内容和方法 2
2. 相关概念及理论基础 3
2.1 微电网的概念和特点 3
2.2 电能质量的相关概念 3
2.3 监测系统的基本原理与结构 4
3. 电能质量监测问题与解决方案 5
3.1 微电网电能质量监测面临的挑战 5
3.2 现有电能质量监测技术存在的问题 5
3.3 针对以上问题的需求和期望 6
4. 电能质量监测的技术方案优化 7
4.1 基于深度学习的电能质量监测算法优化 7
4.2 基于无线传感网络的数据采集和传输方案 7
4.3 基于云平台的数据管理和分析方案 8
5. 结论 9
参考文献 10
致 谢 11
