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Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper proposes a two-way Genetic Algorithm Back Propagation Neural Networks (GABPNN) missing data completion model to handle data loss, which is common power load data mining. A particle swarm optimization - supporting vector regression (PSO-SVR) algorithm is further used to integrate the two-way completion results with better accuracy. In addition, the paper introduces a combined ultra-short-term load forecasting model for industrial power users. The proposed model combines the Cubature Kalman filter (CKF) prediction model with good performance in nonlinear dynamic systems and the least square support vector machine (LS-SVM) prediction model with good performance in small-scale data prediction. The grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting. Lastly, we test the proposed strategy in case study with real industrial data and demonstrate that the proposed model has a high degree of precision in load forecasting.

Space-borne radar altimeters can provide global data of significant wave height (SWH) of total waves, including wind-sea and swells. But wind-seas and swells are often separately considered in the fields of marine science and marine engineering. In this study, a wind-sea-swell SWH separation model was developed using the simultaneous 10-m wind speed and SWH measurement, which are available from altimeter data. The model was based on a look up table extrapolated by an artificial neural network trained by the insitu data from National Data Buoy Center. After applying the model to altimeter data, the results showed that the accuracy of the model is fairly good with a root-mean-square error of wind-sea/swell SWH of only ~0.5 m. This method provides a new method to investigate global wind-sea and swell using remote sensing data.

Irreversible electroporation therapy (IRE) is a new tumor ablation therapy. The IRE treatment method is to place a minimally invasive needle electrode in or around the target area to generate a series of short electrical pulses. This therapy uses electrical pulses to generate an external electric field to change the transmembrane potential of the cell membrane through a non-thermal mechanism, which can irreversibly destroy tumor cells. The effect of IRE treatment on the tissue is related to the distribution of the electric field in the tissue, which depends on the pulse parameters used and the conductivity of the tissue. We built a conductivity measurement system for porcine liver tissue based on a pulse signal generator, and tested the conductivity change of porcine liver tissue due to the electroporation effect of cells. We fitted the experimentally measured conductivity to the applied pulse intensity and got good results. The measurement system built in the experiment is simple and easy to use. Our accurate measurement of conductivity under different pulse width and electric field intensity can effectively improve the accuracy of the numerical model in the treatment planning.

Smart Grid is the deep interactive fusion of power information system and physical system under the background of modern energy interconnection. Compared with the traditional power grid, its performance has been greatly improved. The essence of smart grid is the application of cyber physical system (CPS) in power industry, which is called power CPS. In view of the cyber security risk caused by the integration of the two systems in power CPS, which may cause the system to suffer complex and severe cascading failures and blackouts, it is of great significance to study the power CPS modeling and security defense based on the integration of the two networks. Different things have different classification methods from different angles. This paper summarizes the main research and analysis methods of power CPS modeling and security defense protection at home and abroad, and puts forward the future development direction, hoping to provide reference for the relevant researchers.

With the development of the national economy and the change of policies, the development of the electric power industry continues to expand. The New Electricity Reform proposes that electricity price is the core of electricity reform, and the power generation side is the main member of the market. Its quotation strategy affects the listing price in the electricity market. Therefore, this paper summarizes the electricity market trading models and bidding strategies on the power generation side, and discusses the bidding strategy based on electricity price prediction. As the core of electricity price forecasting, load forecasting is studied and discussed in detail in this paper. Finally, the future trend of bidding strategy in electricity market is forecasted.

Metallized polypropylene film (MPPF) capacitors are widely used as DC-link capacitors which work under the DC superimposed harmonic condition. A DC superimposed harmonic breakdown experimental platform has been set up to study the key parameters in the self-healing process of MPPF. The 6μm MPPF is used as the specimen with an active area of 8×4cm2. Key parameters during the self-healing process such as the electric breakdown strength (E b ), electric breakdown phase angle (α b ), self-healing energy (W sh ) and de-metallized area (S d ) are studied under the pure DC as well as the DC superimposed 100Vrms harmonic conditions and the order of the superimposed harmonic is from 1st to 5th. Experiment results show that E b changes little under the various superimposed harmonics order compared with the pure DC condition. Breakdown tends to occur at or around the positive peak. With the superimposed harmonics order increasing, the probability of the single self-healing decreases whereas the successive one increases. The superimposed harmonics have no influence on W sh , but S d of the successive self-healing is larger than that of the single one. Considering the effect of larger occur probability, the higher superimposed harmonic order might lead to the faster capacitance loss.

Power data cleaning technology of micro grid is introduced in this paper. Firstly, an IOT data acquisition framework is concluded from practice and literature. Data anomalies are analyzed in the framework according to the data sources and business logics, and then these anomalies are divided into environmental exceptions that need to be cleaned and business exceptions that do not need to be cleaned. Secondly, the ADM data cleaning framework is given and under this framework the Singledimensional and information fusion methods which can extract additional information from time series data are summarized. Thirdly, qualitative and quantitative evaluation criterion of the effectiveness in data cleaning are stated at last.

Moisture accelerates the accelerated of metallized film capacitors. The moisture ingreessed to capacitor leads to corrosion of electrode, and increasing of capacitor loss. In this paper, the electrode corrosion and moisture ingress process of metallized film capacitors(MFC) were studied under high temperature and different humidity conditions. Damp-heat ageing test platform for capacitor was established for the study. Three groups of metallized film capacitors have been stressed under an applied voltage of 305 Vac at 85 °C and 85 %R.H., 60 %R.H., 35 %R.H., respectively. After 716.5 h of experiment, at 85 %R.H., the average AC % dropped by 70.35 %, tanδ risen to 0.01685, at 35 %R.H., the average AC % dropped by 3.81 %, tanδ risen to 0.00082 The moisture ingress process was studied in this paper. Experment results indicated that Corrosion occurs at multiple locations on the electrode at the same time, if there is a lot of moisture inside the capacitor. And the IR calculations match well with the experimental results.

With the rapid development of the distributed renewable energy technologies, there is more significant increase in the amount of data generated by the smart grids, and there are a higher delay problem in the task processing, with the traditional centralized control methods, which brings in an adverse effect on the stable operation of the smart grid. Aiming at the problem, this paper proposes a distributed computing scheme that combines edge computing and cloud computing to improve power system data processing capabilities and system response capabilities. Besides, the paper analyzes the finiteness of edge node computing ability. According to the task load balancing theory, a task performance minimization as an objective function is proposed, and a high-performance load balancing strategy for immune chaotic particle swarms algorithm (ICPSO) is proposed based on this distributed computing architecture. Finally, the feasibility and effectiveness of the system architecture and load balancing strategy are verified through the simulation results.

Crossing swell, is a complicated sea state characterized by the co-existence of swell systems generating from different sources. Although many investigations have focused on the global swell climatology, our understanding of global statistical distribution for the crossing swell is still limited so far. In this paper, we present a global view of crossing swell using 10-years Synthetic Aperture Radar (SAR) derived directional swell spectra from Envisat ASAR in Wave Mode from 2003 to 2011. In contrast to analyze the directly but occasionally SAR captured sea state of crossing swell, we employ an approach of propagating observed swell taking advantage of the internal consistency of swells. Results indicate three dominated crossing swell areas termed “crossing swell pools”, in Pacific, Atlantic and Indian Oceans. The pool in Atlantic Ocean shows a relative stable behavior for all seasons, in contrast to the one in Indian Ocean with seasonal occurrence and the one in Pacific Ocean shrinking during boreal summer. Forming and seasonal variation of the crossing swells are interpreted using the swell origins derived from ASAR Wave Mode.