冬季太阳能光伏系统故障检测——一种深度学习方法

Venkata Siva Prasad Machina, Koduru Sriranga Suprabhath, S. Madichetty
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引用次数: 1

摘要

近年来,电力系统的场景正经历着快速的变化。微电网的发展为可再生能源发电并网创造了广阔的空间,最终实现了清洁能源的目标。直流微电网(DCMG)是一种较好的电力系统设置,因为我们家庭中使用的大多数日常电器都是直流电。太阳能光伏(SPV)系统在直流发电系统中占比最大。通过分析电流-电压(I-V)特性,可以更好地理解SPV系统的运行。从太阳能电池板获得的电流和电压值是高度可变的,取决于天气条件。SPV系统的电气故障会降低效率。在夏季,正常晴天和正常阴天的分类是正确的。在冬季太阳能发电的一些异常天气条件下,如刮风、下雪、阴天,电流值被归类为故障运行。使用机器学习(ML)和深度学习(DL)算法可以避免这种错误分类。数据集包括电气故障和正常运行;ML和DL模型使用不同的激活函数和优化器在该数据集上进行训练。计算了评价指标的精度。Python3.8.6已被用作检测故障的编程语言。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault Detection in Solar Photovoltaic Systems During Winter Season- A Deep Learning Approach
In recent times, the scenario of power systems is undergoing a rapid change. The evolution of microgrids has created a great scope for integrating renewable power generation into the grid, which ultimately achieves the goal for clean energy. DC microgrid (DCMG) is a preferable power system setup, as most of the daily appliances used in our households work on direct current (DC). Solar photovoltaic (SPV) systems contribute the most to DC power generating systems. The operation of SPV systems is better understood by analyzing the current-voltage (I-V) characteristics. Current and voltage values obtained from the solar panel are highly variable and depend on the weather conditions. The electric faults in the SPV systems will reduce the efficiency. During the summer season, normal sunny day and normal cloudy day are classified correctly. In winter season during some aberrant weather conditions for solar power generation like wind, snowy and cloudy, the current values are classified as faulty operation. This misclassification is avoided using machine learning (ML) and deep learning (DL) algorithms. Dataset includes electrical faults and normal operations; the ML and DL models are trained on this dataset with different activation functions and optimizers. Evaluation metric accuracy is calculated. Python3.8.6 has been used as a programming language to detect faults.
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