Power transformers are crucial components of power transmission and transformation networks. Their operational status has a direct impact on the reliability of power supply systems. As such, the security and stability of power systems depend heavily on the state of transformers within them. The oil temperature of a transformer is a critical indicator of its working condition. Accurately and rapidly predicting transformer oil temperature is therefore of significant practical importance for ensuring the safe and effective operation of power systems. To address this prediction problem, this article proposes a transformer oil temperature prediction method based on empirical mode decomposition-bidirectional long short-term memory (EMD-BiLSTM). The time series of oil temperature is first cleaned before being processed. Next, the EMD algorithm is used to decompose the time series into relatively stable components. The BiLSTM neural network is then utilized to predict the complex nonlinear long-term series. The proposed method is evaluated using the open data set Electricity Transformer Temperature (ETT)-small. Experimental results show that the EMD-BiLSTM model outperforms traditional LSTM, BiLSTM, EMD-BP, and Wavelet Transform-Bidirectional Long Short-Term Memory (WT-BiLSTM) methods, demonstrating that it is an effective and accurate prediction method for transformer oil temperature.
With the continuous progress of the society, the demand for electrical power is urgent. The transformer plays an important role in the power energy transmission. The oil temperature inside transformer effectively could reflect working condition of the transformer, which makes it necessary to monitor and forecast the oil temperature to monitor the operating status of the power transformer. However, the oil temperature time series data generated by the power transformer has the characteristics of being complex and nonlinear. In recent years, long and short time memory networks (LSTM) are often used to predict transformer oil temperature. Gated recurrent unit (GRU) is a new version for LSTM. In the structure of GRU, there exist two gates, which are updating gate and resetting gate, respectively. Compared with LSTM network, The structure of GRU is simpler and its effect is better. A novel predicting method for transformer oil temperature is proposed based on time series theory and GRU in this paper, which is verified on the dataset of the oil temperature of the transformers in the two regions. The experimental results are compared with traditional time series prediction models to demonstrate that the proposed method is effective and feasible.
Abstract. This paper presents a multi-sensor information fusion technology based on electrical system fault diagnosis model, analysis the multi-sensor information fusion technology of several structure layer and realization method, and discusses its application in fault diagnosis of electrical system. Select and implement appropriate methods for the information fusion, the different error characteristic parameters in electrical system can be fused and calculated by this model, and a more valuable conclusion for the fault diagnosis of electrical system can be provided.
Current is the most important parameter in power system, and its accurate measurement is the basis of system operation. With the research of new current measurement technology based on magnetic field, such as Hall effect and giant magnetoresistance, the current can be measured without contact with the conduct. However, due to the different wire diameters and the deviation of wire position, it is impossible to measure the multi-range current through the sensor with a single structure in practical use. In this paper, the error analysis of current technology based on magnetic field is studied. First, the measurement model is analyzed, and the measured wire current and magnetic field measuring point model is established. Through simulation, the influence of wire diameter and position deviation on the measurement accuracy is tested, which supports the design of current sensor based on magnetic field.
Secondary system is an important link that affects the reliable operation of power system. However, the current improvement measures for accurate data acquisition and reliable operation in secondary systems are mainly concentrated at the equipment level. The solution at the equipment level not only increases the complexity of the system, but also can only optimize a single link or problem, which is difficult to improve the overall system level. In order to enhance the information accuracy, operation and maintenance precision and operation reliability of smart substation secondary system, this paper proposes bad data identification and fault diagnosis methods based on secondary system information redundancy. Firstly, according to the analysis of secondary information redundancy, this paper constructs the data information redundancy model of the smart substation secondary system. Then the data information state estimation method based on the least square method and the bad data identification method based on the information redundancy are proposed. Finally, case analysis is carried out to verify that the proposed method can effectively increase the information accuracy of smart substation, which also provides new research route and foundations for secondary system fault diagnosis.
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