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The metering accuracy and stability of the power grid transformer devices are of great concern to all parties of the power grid, power plants and large users, as well as the relevant government departments, with the further deepening of the electric power system reform and the gradual establishment of the electric power spot trading market. The outcomes demonstrate the viability and efficacy of the employed diagnosis method. The completed research can serve as a reference point for China's development of a capacitive voltage transformer line monitoring system. [ABSTRACT FROM AUTHOR]

Ensuring the optimal performance of power transformers is a laborious task in which the insulation system plays a vital role in decreasing their deterioration. The insulation system uses insulating oil to control temperature, as high temperatures can reduce the lifetime of the transformers and lead to expensive maintenance. Deep learning architectures have been demonstrated remarkable results in various fields. However, this improvement often comes at the cost of increased computing resources, which, in turn, increases the carbon footprint and hinders the optimization of architectures. In this study, we introduce a novel deep learning architecture that achieves a comparable efficacy to the best existing architectures in transformer oil temperature forecasting while improving efficiency. Effective forecasting can help prevent high temperatures and monitor the future condition of power transformers, thereby reducing unnecessary waste. To balance the inductive bias in our architecture, we propose the Smooth Residual Block, which divides the original problem into multiple subproblems to obtain different representations of the time series, collaboratively achieving the final forecasting. We applied our architecture to the Electricity Transformer datasets, which obtain transformer insulating oil temperature measures from two transformers in China. The results showed a 13% improvement in MSE and a 57% improvement in performance compared to the best current architectures, to the best of our knowledge. Moreover, we analyzed the architecture behavior to gain an intuitive understanding of the achieved solution. [ABSTRACT FROM AUTHOR]

Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment. [ABSTRACT FROM AUTHOR]

Natural ester insulating oil not only has a biodegradation rate of almost 100% but also meets the carbon emission requirements of China’s “Carbon Peak and Carbon Neutrality” and the “European Green Deal” proposed by the European Union. It is considered to be a good substitute for mineral insulating oil. However, due to its higher kinematic viscosity than traditional mineral oil in low-temperature environments, it limits the safety promotion and application of transformers in cold regions. First, we design an experiment to test the kinematic viscosity and density of natural ester insulating oil. Under extremely harsh experimental conditions, we measure key experimental data such as kinematic viscosity. Second, the structure of the four main triglyceride molecules is optimized, and the molecular dynamics (MD) simulation technology is used to establish an MD model that can predict the kinematic viscosity of natural ester insulating oil (−20 °C to 20 °C). Finally, the model is further simplified by the free volume theory to better predict the kinematic viscosity of natural ester insulating oil. This study makes up for the lack of laboratory low-temperature testing of the kinematic viscosity of natural ester insulating oil and provides a convenient and reliable tool for predicting the kinematic viscosity of natural ester insulating oil. It can not only predict the natural ester insulating oil mixed in various proportions but also predict the kinematic viscosity of a certain natural ester molecule. Furthermore, it also provides a guiding direction for the improvement of low-temperature kinematic viscosity of natural ester insulating oil, as well as a strong reference for predicting other properties of other substances. [ABSTRACT FROM AUTHOR]

Consumers’ transformers play an important role in power systems, and they are essential for operation reliability and commercial benefits. In the past, maintenance personnel had to spend plenty of time on examining consumers’ transformers one by one. Nowadays, with the wide deployment of power user electric energy data acquire system (PUEEDAS), informative metering data are becoming available, which can be utilized for further condition monitoring. Thus, this paper proposes a data-driven abnormal condition monitoring algorithm of data acquisition for consumers’ transformers, which could timely send abnormal condition alerts to operators and maintenance personnel. In the proposed algorithm, Spearman's rank correlation coefficient is utilized to show the degree of correlation among phase currents, and its t-Statistics is used to determine whether abnormal condition of data acquisition exists based on the hypothesis testing. Finally, actual acquisition data from Zhejiang power system in China are employed to validate the effectiveness of the proposed algorithm, and to analyze the characteristics of normal and abnormal conditions, respectively. Sensitive analyses on different significant levels and sampling rates are performed for considering its impact on monitoring results; the application in real power systems is also given to demonstrate the practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, the urban rail transit has a great development in many cities of China. It is important to keep the DC transit grid strong and the power transmitted/power transmission high quality. During the process of rail operation, braking energy leads to a wide range of load oscillation for the traction and city grid, which cannot be absorbed inside the rail transit system. In this study, a novel architecture of urban rail transit power supply based on power electronics transformer braking energy feedback is put forward. Based on the circle grid, the topology adds an inverter to LLC series resonant converter to carry the power from 1500 V DC traction grid to 1180 V AC. Analysis together with simulation on the topology and control strategy is made on the basis of MATLAB/Simulink. Process of braking energy feedback and transmission of energy are illustrated with simulation. By paralleling the three-phase H bridge and braking energy feedback device, double direction of the power flow is ensured. As a consequence, the power quality increases and harmonics reduce. The energy can be used more efficiently to achieve better effects on energy conservation. [ABSTRACT FROM AUTHOR]

• Excessive noise emission problems of an urban 110kV indoor substation are discussed and tackled comprehensively in this paper. • Dynamic vibration absorber with the target noise reduction frequency at 100Hz is firstly designed and applied in actual 110kV transformer, as a positive attempt to control the noise from the source. • A M-typed dissipative muffler and a hybrid muffler are designed and implemented to control the additional noise radiation by the ventilation devices. • After the integrated noise abatement project, the overall acoustic environment are improved and the noise level at the boundary of substation could meet the acoustic environmental quality standard of China. As an important node in urban city, power substation provides the clean energy to thousands of households, ensuring a stable and high quality of urban life. However, the influence of noise emission from substation is always underestimated. With the improvement of environmental consciousness, making the substations quietly hidden in urban cities becomes an urgent issue which should be thoroughly considered. A real case is presented in this paper to vividly reflect how such a comprehensive noise issue is faced and tackled through proper techniques and novel applications. Based on the low-frequency noise characteristics of 110 kV power transformers and the limited space features of indoor substation, integrated noise control schemes are proposed with consideration of low-noise design of ventilation and heat dissipation systems. Dynamic vibration absorber is firstly investigated and designed to minimize the vibration of transformer tank. After the practical implementation, the acoustic environment is reevaluated and the measurement results verified the noise control effect. Consequently, the acoustic environment quality within and out of substation is improved and the negative impact of urban substation is controlled. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification accuracy and robustness of PNN. The standard GWO easily falls into the local optimum because the update mechanism is too single. The update strategy proposed in this paper enhances the convergence ability and exploration ability of the algorithm, which greatly alleviates the dilemma that GWO is prone to fall into local optimum when dealing with complex data. In this paper, a reliability analysis of thirteen diagnostic methods is conducted using 555 transformer fault samples collected from Jiangxi Power Supply Company, China. The results show that the diagnostic accuracy of the IGWO-PNN model reaches 99.71%, which is much higher than that of the traditional IEC (International Electrotechnical Commission) three-ratio method. Compared with other neural network models, IGWO-PNN also has higher diagnostic accuracy and stability, and is more applicable to the field of transformer fault diagnosis. [ABSTRACT FROM AUTHOR]