Oil‐immersed transformers play an important role in the stable operation of power systems. Aiming at the low accuracy of traditional transformer fault diagnosis methods, a transformer fault diagnosis method using an improved sparrow search algorithm (ISSA) optimised support vector machine (SVM) is proposed. First, use Sin chaotic to initialise the population, then introduce the fusion of Cauchy mutation and opposition‐based learning to optimise the selection of the population to improve the SSA global optimisation capability. Secondly, use the improved Sparrow algorithm to optimise the SVM kernel function parameters and penalty coefficients and establish a fault diagnosis model‐‐ISSA‐SVM for dissolved gas analysis. Enter the data into ISSA‐SVM for fault diagnosis and combine it with K nearest neighbour (KNN) algorithm, gradient boosting decision tree (GBDT), sparrow search optimised deep extreme learning machine (SSA‐DELM), SVM, sparrow search algorithm optimised Support vector machine (SSA‐SVM), and other diagnostic results are compared. The results show that the fault diagnosis rate of ISSA‐SVM is 91.43%, which is 12.86%, 7.14%, 5.71%, 2.86%, and 1.43% higher than that of KNN, GBDT, SSA‐DELM, SVM, and SSA‐SVM, respectively. It can more accurately judge the current operating state of the transformer.
The optimal utilization of wind power and the application of carbon capture power plants are important measures to achieve a low-carbon power system, but the high-energy consumption of carbon capture power plants and the uncertainty of wind power lead to low-carbon coordination problems during load peaks. To address these problems, firstly, the EEMD-LSTM-SVR algorithm is proposed to forecast wind power in the Belgian grid in order to tackle the uncertainty and strong volatility of wind power. Furthermore, the conventional thermal power plant is transformed into an integrated carbon capture power plant containing split-flow and liquid storage type, and the low-carbon mechanism of the two approaches is adequately discussed to give the low-carbon realization mechanism of the power system. Secondly, the mathematical model of EEMD-LSTM-SVR algorithm and the integrated low-carbon economic dispatch model are constructed. Finally, the simulation is verified in a modified IEEE-39 node system with carbon capture power plant. Compared with conventional thermal power plants, the carbon emissions of integrated carbon capture plants will be reduced by 78.248%; the abandoned wind of split carbon capture plants is reduced by 53.525%; the total cost of wind power for dispatch predicted using the EEMD-LSTM-SVR algorithm will be closer to the actual situation, with a difference of only USD 60. The results demonstrate that the dispatching strategy proposed in this paper can effectively improve the accuracy of wind power prediction and combine with the integrated carbon capture power plant to improve the system wind power absorption capacity and operational efficiency while achieving the goal of low carbon emission.
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