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Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies to effectively detect anomalies in PV sequence data, as this helps operators and experts understand and interpret anomalies within PV arrays when making response decisions. However, traditional methods that rely on manual labor and regular data collection are difficult to monitor in real time, resulting in delays in fault detection and localization. Traditional machine learning algorithms are slow and cumbersome in processing data, which affects the operational safety of PV plants. In this paper, we propose a visual analytic approach for detecting and exploring anomalous sequences in a PV sequence dataset via sequence learning. We first compare the sequences with their reconstructions through an unsupervised anomaly detection algorithm (Long Short-Term Memory) based on AutoEncoders to identify anomalies. To further enhance the accuracy of anomaly detection, we integrate the artificial intelligence of things (AIoT) technology with a strict time synchronization data collection and real-time processing algorithm. This integration ensures that data from multiple sensors are synchronized and processed in real time. Then, we analyze the characteristics of the anomalies based on the visual comparison of different PV sequences and explore the potential correlation factors to analyze the possible causes of the anomalies. Case studies based on authentic enterprise datasets demonstrate the effectiveness of our method in the anomaly detection and exploration of PV sequence data. [ABSTRACT FROM AUTHOR]

To understand the effect of internal leakage on the torque field and characteristics of a torque converter (TC), a transient analysis was performed on the internal flow of a TC and the pressure pulsation characteristics of monitoring points in the convection channel. It was found that dividing the leakage area of the TC into a separate watershed improved simulation accuracy by 4%. When there was a leakage area, there were distinct collision, mixing, and assimilation stages between the leakage flow and the main flow. These phenomena caused energy loss that was highest at low speed ratios. However, the leakage flow always accounted for 12% of the main flow regardless of the speed ratio. At the same time, the leakage flow had a larger influence on pressure pulsation inside the TC and especially the low frequency band was more substantial. This shows that the leakage area has a large influence on the TC performance, energy loss, and flow state. Analysis of the leakage area showed that reducing the leakage area helps to improve powertrain performance and fuel economy.