Utama Ida Bagus Krishna Yoga, Pamungkas Radityo Fajar, Faridh Muhammad Miftah, Jang Yeong Min
Department of Electronics Engineering, Kookmin University, Seoul 02707, Republic of Korea.
Sensors (Basel). 2023 Jul 25;23(15):6674. doi: 10.3390/s23156674.
Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day's power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.
由于光伏电站行业的加速发展，多个光伏电站正在各地建设。在不同地点运营和维护多个光伏电站很困难。因此，需要一种在单个平台上监测多个光伏电站的方法，以满足当前行业对在单个平台上监测多个光伏电站的需求。这项工作提出了一种使用物联网平台进行多个光伏电站监测的方法。还提出了次日发电量预测和实时异常检测，以增强所开发的物联网平台。结果表明，实现了一个用于监测多个光伏电站的物联网平台，其中使用五种人工智能模型进行次日发电量预测，并利用自适应阈值隔离森林对每个光伏电站进行传感器异常检测。在为发电量预测开发的五个人工智能模型中，双向长短期记忆网络（BiLSTM）成为最佳模型，其均方误差（MSE）、平均绝对百分比误差（MAPE）、平均绝对误差（MAE）和决定系数（R2）的最佳值分别为0.0072、0.1982、0.0542和0.�664。同时，所提出的自适应阈值隔离森林在检测光伏电站传感器异常时表现最佳，最高精度为0.9517。
