An unsupervised approach for fault diagnosis of power transformers

Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.     

Simulation and analysis of dual-reflux pressure swing adsorption using silica gel for blue coal gas initial separation

A dual-reflux pressure swing adsorption (DR-PSA) process was proposed and simulated to initially separate the blue coal gas, aiming to capture carbon dioxide (CO₂) and enrich hydrogen (H₂), simultaneously. With a feed flow rate of 7.290 slpm, a light product reflux flow rate of 0.505 slpm and the heavy product reflux flow rate of 3.68 slpm, the developed DR-PSA process could capture CO₂ up to 64.01% with a recovery of 99.60% and enrich H₂ up to 34.66% with a recovery of 97.63% from the blue coal gas (36.2% N₂/28.5% H₂/13.9% CO/12.7% CO₂/8.7% CH₄). In addition, in order to optimize the process, the effects of various operating parameters on the DR-PSA process performance in terms of product purity and recovery were discussed in detail, including the feed position, the light product reflux ratio and the heavy product reflux ratio. Moreover, the dynamic distribution behaviors of pressure, temperature and gas-solid concentration were presented to explain and evaluate the process separation performance in depth under different operating conditions.     

Natural Gas Production

Current data on natural gas production, as well as a breakdown of production by country. Updated on a monthly basis.     

AP对HATO热分解影响的机制

为详细了解高氯酸铵(AP)对5,5'-联四唑-1,1'-二氧二羟铵(HATO)热分解影响的机制,采用热重-质谱-傅里叶红外光谱(TG-MS-FTIR)联用技术、差示扫描量热法(DSC)、傅里叶红外光谱(FTIR)方法,对HATO和HATO/AP共混物的热分解特性、气体产物以及凝聚相变化进行了研究。结果表明,HATO具有两个连续热分解阶段,HATO/AP共混物则有3个热分解阶段;HATO、AP共混后,HATO使得AP熔融峰消失,AP可使HATO的热分解初始温度提前,热分解时间延长且不影响分解完全性;HATO热分解气体产物有CO_2、N_2O、HCN、NH_3、NO、N_2、H_2O,而HATO/AP共混物热分解产生气体主要有N_2、CO_2、N_2O、HCN、NH_3、H_2O、HCN、NO、HCl、NOCl;另外,采用等转化率法计算HATO和HATO/AP共混物四唑环基团的活化能分别为53.38 kJ·mol~(-1)和60.69 kJ·mol~(-1);通过对比HATO和HATO/AP共混物热分解特性以及凝聚相特征基团的变化,阐释了AP使HATO热分解温度提前的机理很可能是:AP的铵根离子与HATO之间发生了质子转移;推测AP导致HATO热分解时间延长的原因为:HATO/AP共混物产生的NH_3与热分解中间体1,1'-二羟基-5,5-联四唑(BTO)反应生成5,5'-联四唑-1,1'-二氧铵盐(ABTOX)。     

Sources and dynamics of semivolatile organic compounds in a single‐family residence in northern California

Semivolatile organic compounds (SVOCs) emitted from building materials, consumer products, and occupant activities alter the composition of air in residences where people spend most of their time. Exposures to specific SVOCs potentially pose risks to human health. However, little is known about the chemical complexity, total burden, and dynamic behavior of SVOCs in residential environments. Furthermore, little is known about the influence of human occupancy on the emissions and fates of SVOCs in residential air. Here, we present the first‐ever hourly measurements of airborne SVOCs in a residence during normal occupancy. We employ state‐of‐the‐art semivolatile thermal‐desorption aerosol gas chromatography (SV‐TAG). Indoor air is shown consistently to contain much higher levels of SVOCs than outdoors, in terms of both abundance and chemical complexity. Time‐series data are characterized by temperature‐dependent elevated background levels for a broad suite of chemicals, underlining the importance of continuous emissions from static indoor sources. Substantial increases in SVOC concentrations were associated with episodic occupant activities, especially cooking and cleaning. The number of occupants within the residence showed little influence on the total airborne SVOC concentration. Enhanced ventilation was effective in reducing SVOCs in indoor air, but only temporarily; SVOCs recovered to previous levels within hours.     

Crude Oil Production

Current data on crude oil, field/lease condensate, and natural gas liquids produced from NGL plants. Updated on a monthly basis.     

物质平衡-拟压力近似条件法确定气藏储量

物质平衡法作为一种有效的手段被广泛应用于气藏储量或井控储量的确定。为简便准确地确定气藏储量，笔者从物质平衡原理出发导出了"物质平衡条件"，求解气体渗流数学模型，得到了"拟压力近似条件"。将二者结合，提出了"物质平衡-拟压力近似条件法"（MB-QAC方法）。利用该方法，通过边界控制流阶段（产量波动相较于边界作用可忽略）的生产数据便可准确地计算气藏储量。定产量、定流压以及复杂生产计划条件下的数值模拟数据以及气藏实例验证了其有效性。在生产数据记录可靠的条件下，储量计算误差一般小于5%。同时，数值模拟结果表明，动态物质平衡方程是一种近似关系，拟压力近似条件对于模拟的各种情形均成立。由于考虑了岩石和束缚水的压缩性、气井产量的波动和气体性质的变化，MB-QAC方法具有一定的理论基础，对于异常高压和正常压力系统的气藏均适用，但不适用于水体活跃的气藏。     

Natural Gas Production

Current data on natural gas production, as well as a breakdown of production by country. Updated on a monthly basis.