Search

The safe and reliable operation of the power system is an important guarantee for the steady and rapid development of social economy. The safe operation of transformer equipment is the basis for the stability and reliability of the power system, and it is of great significance to the whole system to take effective measures to make accurate predictions of abnormal conditions or faults inside the transformer. Therefore, this paper proposes a data-driven fault prediction method for oil-filled transformers, combining association rules, gray prediction model and random forest algorithm to achieve fault mode and location prediction through four stages: feature parameter sets selection, fine-grained feature parameters selection, feature parameters trend prediction and fault classification. Based on the actual data collected, the accuracy and effectiveness of the method are verified by two groups of experiments, which is beneficial to the practical application of engineering, and to a certain extent improves the automation level of equipment maintenance.

Refined measurement of electricity consumption information on the residential side is an important foundation for achieving demand-side management and promoting smart grid construction. In order to obtain highly accurate appliancelevel energy consumption data, this paper proposes a load identification method based on feature extraction of change-point and XGBoost classifier. Firstly, the event detection is performed by using secondary window determination to determine whether an event has occurred based on changes in power statistics; then, according to the detection results, comprehensively extract the transient-state and steady-state features of the electrical signal in the window; lastly, it is input into the XGBoost model for load energy decomposition. In the case validation phase, the PLAID dataset is compared with five benchmark algorithms, and the results show the effectiveness and superiority of the method proposed in this paper for resident-side load identification.

This paper proposes a three-phase three-wire photovoltaic generation inverter grid-connected operating and harmonic elimination hybrid system. The hybrid system mainly consists of photovoltaic array battery, photovoltaic output filter, three-phase voltage-type inverter, inverter output filter and passive filters. Based on working principle and working characteristics of the proposed hybrid system, the composite control strategy about active power, reactive power and harmonic suppression is proposed. The composite control strategy mainly consists of a single closed-loop control slip of active power and reactive power, double closed-loop control slip of harmonics. Simulation results show the correctly of this paper's contents, the hybrid system have an effective to improve power factor, supply active power for loads and suppress harmonics of micro-grid.

Considering a large number of negative sequence and harmonic currents caused by V/V traction structure and locomotive load, a reasonable structure of RPC (Railway Static Power Conditioner) was discussed to improve the power quality. The electrical characteristics of V/V traction structure and the negative sequence compensation principle were analyzed. And a method was proposed to obtain the reference instruction signals in accordance with vector analysis of negative sequence compensation. Then the mathematical model and close-loop control diagram were established for comprehensive compensation system. Finally, the correctness of the analysis has been confirmed by the simulation results.