As the penetration of electric vehicles (EVs) accelerates according to eco-friendly policies, the impact of electric vehicle charging demand on a power distribution network is becoming significant for reliable power system operation. In this regard, accurate power demand or load forecasting is of great help not only for unit commitment problem considering demand response but also for long-term power system operation and planning. In this paper, we present a forecasting model of EV charging station load based on long short-term memory (LSTM). Besides, to improve the forecasting accuracy, we devise an imputation method for handling missing values in EV charging data. For the verification of the forecasting model and our imputation approach, performance comparison with several imputation techniques is conducted. The experimental results show that our imputation approach achieves significant improvements in forecasting accuracy on data with a high missing rate. In particular, compared to a strategy without applying imputation, the proposed imputation method results in reduced forecasting errors of up to 9.8%.

Because of environmental friendly and higher fire point, Natural ester fluids are widely used for transformer cooling materials. Natural ester has higher viscosity than that mineral oil. Top oil temperature and hottest temperature will be warmer when using natural ester fluid in transformers designed for conventional transformer oil. Although natural ester has less influence on deterioration of cellulose paper than mineral oil, an increased hottest temperature might have an effect on the aging of insulating paper in coil winding. When comparing natural ester with mineral oil at the same designed transformers up to 500 kVA. The deviation of hottest temperature between the two is increased with load. For preventing additional aging of insulation paper, design changes are required when substituting natural ester for conventional transformer oil.

Polypropylene (PP) has excellent heat resistant and is an environmentally friendly insulation material that can be recycled comparing to XLPE. In Europe, PP insulated HV class cables were commercialized as following the MV class. Therefore, we developed the PP insulated cables and performed thermal, physical and electrical test. The most important thing will be to verify the design life of developed cables. We developed the long-term reliability evaluation method using frequency acceleration and established a draft standard for PP insulated MV cable.

Billions of electric equipment are connected to Internet of Things (IoT)-based sensor networks, where they continuously generate a large volume of status information of assets. So, there is a need for state-aware information retrieval technology that can automatically recognize the status of each electric asset and provide the user with timely information suitable for the asset management of electric equipment. In this paper, we investigate state-aware information modeling that specializes in the asset management of electric equipment. With this state-aware information model, we invent a new asset state-aware ranking technique for effective information retrieval for electric power and energy systems. We also derive an information retrieval scenario for IoT in power and energy systems and develop a mobile application prototype. A comparative performance evaluation proves that the proposed technique outperforms the existing information retrieval technique.

Data-based decisions have been being made in various fields due to the development of sensors throughout the industries. Likewise, in the power system field, data-based decisions are being made in various tasks, including establishing distribution investment plans. However, in order for it to have validity, it is necessary to get rid of abnormal data or data with low representativeness of a temporary nature. Although in general, such a series of processes are done by preprocessing, the those of power system data should be handled not only noise but also data fluctuations caused by temporary change in operations such as load transfers, as mentioned above. In addition, the characteristics of load data of distribution lines (DLs) can be different depending on the characteristics of the load itself, the characteristics of the connected DLs, and regional characteristics of each DLs, so it is essential to propose and apply the optimized preprocessing method for each DL. In this study, therefore, an optimal preprocessing algorithm for each DL was proposed by mixing standard pattern calculations and polynomials based statistical method, and its appropriateness was verified by comparing the results with actual load transfer records. As a result of the verification, it was confirmed that the load transfer detection accuracy of the proposed method was 88.89%, and the maximum load of the target DL can be reduced up to 11.59% by removing the load transfer data.