Low-temperature and humid drying experiments [temperatures of 20–30 °C; relative humidity (RH) of 40–60%] were conducted to investigate the drying shrinkage of lignite at low temperatures. The moisture content and volume variations of lignite during low-temperature drying were measured to analyze the change in the water content and volume drying shrinkage rate under low-temperature drying conditions. The results show that in the first 48 h of drying, the water evaporated rapidly. The amount of external water evaporated and lost accounted for 70–90% of the total water lost during the entire low-temperature drying period, and the average water content is reduced to about 12.8%. When the rapid loss of external water decreased to less than 12.8%, the water adsorbed on the external surfaces, the movable water between large particles was completely lost, and saturated lignite underwent heterogeneous volume shrinkage. The drying shrinkage was slow during the first 48 h, accounting for 20.8% of the total drying shrinkage in the entire low-temperature drying process. The volume shrinkage occurred in four stages as the water content decreased with time. With increasing drying time, the decrease in the water content occurred in four stages: the thermal expansion stage, rapid shrinkage stage, slow shrinkage stage, and stable shrinkage stage. The dry shrinkage rate has a significant positive correlation with the water evaporation quality and significant negative correlations with the water content and evaporation rate. The lower the evaporation rate, the greater the dry shrinkage rate when the saturated lignite is dried under low-temperature and humid conditions (temperature of <30 °C; RH of <60%). There is a time lag between volume shrinkage and water loss, and there is also a difference in their quantities. The volume shrinkage is lower than the water loss, and the difference is largest about 48 h into the initial stage of low-temperature drying. As the low-temperature drying time increases, the shrinkage due to drying becomes stable, and the moisture content remains unchanged. The larger the ratio of RH to temperature, the larger the stable shrinkage.
Feature extraction of electrical load plays a vital role in providing a reliable basis and guidance for power companies. In this paper, we propose a novel clustering algorithm named the Density-based Matrix Transformation (DBMT) Clustering method to extract features (peaks, valleys and trends) of electrical load curves. The main objective of the algorithm is to reorder the data items until the data items belonging to the same cluster are organized together; that is, the adjacent matrix is rearranged to the type of block diagonal. This method adaptively determines the number of clusters and filters out noise without input global parameters. Moreover, for the specific characteristics of raw electrical load data, we propose a variant of Dynamic Time Warp (DTW) distance, dsDTW, which aligns the peaks, valleys and trends of load curves meanwhile dealing with missing values in different situations. After feeding the dsDTW adjacent matrix to DBMT, the results indicate that our proposal can accurately extract the feature of the load curves compared to different clustering methods.
The popularity of smart metres has brought a huge amount of demand‐side data, which provides important information for the demand response of the power sector, to guide practitioners to understand the customers' electricity usage behaviours and patterns. Clustering analysis of customers' daily load data is an important tool for mining users' consumption habits and achieve non‐fixed market segmentation. Since the load data is time series, it is inappropriate to perform clustering directly without extracting targeted features. Therefore, according to the shape features of the daily load curve, a shape‐based clustering algorithm called BDKM is proposed. The algorithm first uses the B‐splines regression to fit the time series data to extract morphological features, and then the objects are segmented based on the dynamic time warping distance by clustering. Finally, the real world daily customers' load data is used to prove the effectiveness of the proposed algorithm based on B‐splines regression.
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