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With the development of data science, there are more and more ways to dig for patterns hidden in the data. If we can apply the advanced model of data science to the power data, we can mine the potential value of power data. In this paper, the monitoring and inspection data of 110kV main transformers and related basic environmental data are fused to establish conditional inference tree model and AdaBoost algorithm to evaluate whether the 110kV main transformers have defects and faults. The accuracy of the two algorithms is compared. Finally, we select the AdaBoost algorithm with higher accuracy for building the 110kV main transformer fault evaluating model. This paper provides a reference for the automatic detection of power grid faults, and provides new ideas for the application of power grid big data.

Distributed condenser will be used on wind farm in the future to supply reactive power support to the power system. How would the power system stabilizer (PSS) on the condenser effect the stability of the power system? How to configure PSS parameters to make PSS have better effec? These questions are researched in this paper.

The proper work of main transformers plays an important role in the stability of the power grid. The development of power data allows us to evaluate the instant status of the main transformer. If we use appropriate data classification algorithm, the laws and values hidden behind the mass power data can be discovered. This paper took basic power data, main transformers’ status data and oil chromatography data into consideration, and used Logistic regression and Naive Bayes algorithm to evaluate 110kV main transformers’ defects and faults from regression analysis and probability statistics seperately. The results of these two algorithms were obtained and compared. Finally, a more accurate algorithm of evaluating main transformer fault was selected, which provided a new reference for the application of power big data.

Nearly 3 billion of people, across the globe are using biomass cookstove to meet their energy requirement for cooking, water heating and space heating. These cookstoves work at a low efficiency and emits harmful pollutants which affect the health of women and children. Clean combustion and high efficiency are two focus areas that need to be improved in biomass cookstoves. . In this context, there are several improved cookstoves (based on top lit up-draft (TLUD) gasification principle) coming up as a solution to the above mentioned issues. However, the appropriate solution for clean cooking is yet to come. Considering the local need and biomass availability, performance of a TLUD stove (designed to meet the local need in China) was analyzed in detail. A selected set of biomass fuel, such as corn cob, wood chips, coal, wood char, biomass pellets and briquettes were considered for testing the performance of the cookstove. The TLUD stove was analyzed with respect to a maximum and minimum firepower. In addition, the biomass fuels burning rate, rate of power output and efficiency were tested for comparison and evaluation of the cookstove performance and fuel suitability. Biomass characteristics are analyzed with its proximate analysis and energy content. The results showed that the wood char (CO-45.8%, H2-23.7%, CO2-4.1%, CH4-0.6% and N2 -25.7%) was found as a good fuel from the compositions of the gases as compared to other fuels. The thermal efficiency of the woochar was achieved (17.8%), and tar production (7g).