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The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer. [ABSTRACT FROM AUTHOR]

Changes in soil electrical parameters can affect the distribution of earth potential in high-voltage direct-current (HVDC) ground electrodes when climatic conditions changes. This paper proposes a model to describe the relationship between surface-layer soil moisture and surface-layer soil resistivity under short-term climatic influence, by using image recognition technology. Based on the relatively stable resistivity of lower soil layers, a soil model more reflective of the actual operating conditions is established for soils near the ground electrode, and a finite element method is adopted to calculate the earth-surface potential (ESP). The experimental results indicate the following: (1) Compared with other measurement methods, image recognition of surface soil resistivity is a low-cost, real-time, online, and accurate method; and (2) changes in surface-layer soil moisture affect both ESP and step voltage. These effects are large in the case of high resistivity for the soil layer where the ground electrode is buried. This large fluctuation in step voltage particularly results in a potential safety hazards during ground electrode operation. Therefore, in order to ensure personal safety and obtain more accurate electrical parameters, it is necessary to consider the effect of natural climate on the soil surface resistivity. Finally, the value of the step voltage can be observed using image recognition, this also provides a new method for the safety monitoring of the DC ground electrode.