Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience, efficiency, and security of urban/building energy systems while safeguarding occupant comfort. However, data r...
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Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience, efficiency, and security of urban/building energy systems while safeguarding occupant comfort. However, data reflecting such behavior are often considered as outliers, and removed or smoothed during preprocessing, limiting insights into their potential impacts. This paper proposes an abnormal behavior analysis method that identifies outliers (considering data distribution) and anomalies (considering the physical context) based on the statistical principle and domain knowledge, assessing their effects on energy supply security. A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems. The method is illustrated by data from 1,451 buildings in a city. Results show that the proposed method can identify abnormal data effectively. Buildings in the primary industry have more outliers, while those in the tertiary industry have more anomalies. Seven buildings affecting both the security and economy of urban energy systems are identified. The outliers rise more frequently from 8:00 to 18:00, on weekdays and in the summer and winter months. However, the anomaly distribution has a weak connection with time. Moreover, the abnormal electricity usage behavior positively correlates with outdoor air temperatures. This method provides a new perspective for identifying potential risks, managing energy usage behavior, and enhancing flexibility of the urban energy systems.
In order to effectively detect the abnormal data of the liquid drop in the droplet analysis system and to improve the accuracy of the liquid drop fingerprint, a new method based on boxplot is put forward. After optimi...
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ISBN:
(纸本)9781510806450
In order to effectively detect the abnormal data of the liquid drop in the droplet analysis system and to improve the accuracy of the liquid drop fingerprint, a new method based on boxplot is put forward. After optimizing the 12 dimensional feature vectors of the liquid drop fingerprint, visualization of statistics is applied on the optimized 6 dimensional feature vectors by using boxplot method. With the median(±5%) as the threshold values, abnormal droplets are screened. Experimental results show that the detection recognition ratio of the abnormal liquid drop can be ensured after feature optimization, together with the greatly reduced computational complexity. boxplot method is effective in detection of abnormal liquid drop fingerprint, with its accuracy up to 100% among selected samples.
This paper investigates the adaptive event-triggered control problem for a class of nonlinear multiagent systems with non-continuous nonlinear communication faults and dynamic uncertainties. First, the non-continuous ...
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This paper investigates the adaptive event-triggered control problem for a class of nonlinear multiagent systems with non-continuous nonlinear communication faults and dynamic uncertainties. First, the non-continuous nonlinear communication fault mathematical model is proposed, and we design a fault detection method based on the boxplot method and a piecewise adaptive law to detect and compensate the impact of faults, respectively. Second, an event-triggered mechanism which does not need to design additional intermediate control signals is proposed to save communication resources. Then, the disturbance observer is used to solve the problem of dynamic uncertainties. Moreover, an event-triggered cooperative strategy is constructed, which can guarantee all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the tracking control performance can also be guaranteed. Finally, some simulation results confirm the effectiveness of the proposed control scheme.
In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and r...
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In recent years, the problem of data drifted of the smart grid due to manual operation has been widely studied by researchers in the related domain areas. It has become an important research topic to effectively and reliably find the reasonable data needed in the Supervisory Control and Data Acquisition (SCADA) system has become an important research topic. This paper analyzes the data composition of the smart grid, and explains the power model in two smart grid applications, followed by an analysis on the application of each parameter in density-based spatial clustering of applications with noise (DBSCAN) algorithm. Then a comparison is carried out for the processing effects of the boxplot method, probability weight analysis method and DBSCAN clustering algorithm on the big data driven power grid. According to the comparison results, the performance of the DBSCAN algorithm outperforming other methods in processing effect. The experimental verification shows that the DBSCAN clustering algorithm can effectively screen the power grid data, thereby significantly improving the accuracy and reliability of the calculation result of the main grid's theoretical line loss.
The wind-induced galloping of the overhead transmission line threatens safety and serviceability of the power systems. In this study, a novel distributed optical fibre sensor system based on the polarisation-sensitive...
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The wind-induced galloping of the overhead transmission line threatens safety and serviceability of the power systems. In this study, a novel distributed optical fibre sensor system based on the polarisation-sensitive optical time-domain reflectometry technology is proposed, which can monitor the long-distance transmission line of over dozens of kilometres online in full scale by measuring its accompanying overhead ground wire grounding wire. Also it has good ability for surviving in harsh environments and easy maintenance. To meet the needs of long-distance monitoring, an equalisation processing method is used to reduce the influence of signal attenuation with the fibre length extension. In the continuous time–space statistics, adaptive galloping signal detection and the galloping level division of wind-induced disturbances are realised through the boxplot method; and in the continuous frequency–space statistics, the galloping frequency distribution along the cable is analysed simultaneously. Through the time–space and the frequency–space statistics upon the big data, the abnormal galloping moments and locations can be determined automatically in real time.
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