基于时空特征的光伏电站草木阴影遮挡故障诊断

doi:10.19595/j.cnki.1000-6753.tces.240181

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Abstract

Vegetation shading often occurs in mountain power stations and some centralized power stations. In severe summer and autumn, it will cause a loss of about 30% of power generation, seriously affecting the power generation efficiency and safety of photovoltaic(PV) power stations. It is the top priority for power station failure operation and maintenance in summer and autumn. Regarding shadings in stations, current research has not fully considered the performance deviation between different PV strings and the output change process of the same string attenuated with life on a long-term scale. Based on the PV string output characteristics, it's noted that different PV string currents exhibit strong similarities, yet vary across different days. Therefore, PV string time series current data are chosen as the defining characteristics. Compared with the normal PV string current, the PV string current blocked by vegetation has a different decline rate. The pattern changes obviously with seasonality. The common characteristic of vegetation is that they all have growth periodicity. After analyzing time series current curves of power stations under different conditions like vegetation or mountain shading across various seasons, it was observed that once fixed shadings are established by objects such as mountains or telephone poles, the current curve remains consistent without any seasonal variations or errors. Clouds and other objects causing random occlusion only happen sporadically and show no seasonal variability. Compared with other shadings, the characteristics of vegetation shadings show growth and seasonal changing trends. Based on this, vegetation shadings can be distinguished from other shading faults.
In summer, once maintainable vegetation shading is established, the PV string current remains lower than normal throughout the day, worsening gradually over time. With unmaintained vegetation shading, the current curve during the day displays inefficiencies at certain periods. By analyzing the current data characteristics of both healthy PV strings and those shaded by vegetation horizontally across time slices, and longitudinally examining the evolution of current data characteristics over time, the temporal-spatial mapping relationship between vegetation shading and temporal current can be effectively establish. Then a temporal-spatial characteristic matrix function is constructed to accurately diagnose the fault type of vegetation shadings. The characteristic matrix function quantifies the loss of current in a faulty string due to vegetation shading compared to normal strings in space. Over time, the characteristic matrix function reveals differences in when different string groups appear and disappear under vegetation shading, along with how current degrades in faulted string groups blocked by vegetation shadings.
To accurately identify fault characteristics of vegetation shading, this paper primarily extracts and analyzes the temporal-spatial characteristics of maintainable and unmaintainable vegetation. The threshold is established using the interquartile range of historical data from power stations. When shaded by maintainable vegetation (like vines) and unmaintainable vegetation (such as trees), the PV string time series current data exhibit a deteriorating trend in temporal-spatial characteristics, albeit with distinct differences in their change trends. Temporal-spatial characteristic values of PV string current under unmaintainable vegetation shading remain consistently high year-round, whereas those under maintainable vegetation shading exhibit a continuous upward trend only during certain parts of the season. Hence, this characteristic can effectively differentiate between the two types of vegetation shading faults.
Ultimately, effectiveness of the proposed method was validated using real field data from PV power stations. Due to the high similarity between the PV string current under maintainable vegetation and the current of normal strings, maintainable vegetation has a lower likelihood of being incorrectly classified as normal. 3.8% of unmaintainable vegetation samples was misjudged as maintainable vegetation. On-site observation revealed that this occurred because the trees are short and utilize the slope to fully obscure the PV strings. This situation rarely occurs. The fault diagnosis method boasts an impressive accuracy of 98.3%, with a false alarm rate for different fault types staying under 1.7%. There are a small number of other types of faulty strings in the sample, such as hot spots and component fragmentation, which affects the judgment results.
This paper constructs a temporal-spatial characteristic matrix function of the PV string current under vegetation shadings to enhance diagnostic accuracy in identifying such shading. Its practical applicability makes it suitable for widespread implementation.

Key words： PV power stations vegetation shadings time series current temporal-spatial characteristics fault diagnosis

Received: 26 January 2024

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TM615

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	Ma Mingyao
	Wang Zeao
	Ma Wenting
	Fang Zhenyu
	Zhang Rui

Cite this article:

Ma Mingyao,Wang Zeao,Ma Wenting等. Vegetation Shading Fault Diagnosis in Photovoltaic Power Stations Based on Temporal-Spatial Characteristics[J]. Transactions of China Electrotechnical Society, 0, (): 2492910-2492910.

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