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Enzymes play vital roles in cellular functions by catalyzing biochemical reactions in high activity and selectivity, which is largely attributed to the cooperation between the exquisitely distribut...

Photovoltaic output is affected by solar irradiance, ambient temperature, instantaneous cloud cluster, etc., and the output sequence shows obvious intermittent and random features, which creates great difficulty for photovoltaic output prediction. Aiming at the problem of low predictability of photovoltaic power generation, a combined photovoltaic output prediction method based on variational mode decomposition (VMD), maximum relevance minimum redundancy (mRMR) and deep belief network (DBN) is proposed. The method uses VMD to decompose the photovoltaic output sequence into modal components of different characteristics, and determines the main characteristic factors of each modal component by mRMR, and the DBN model is used to fit the modal components and the corresponding characteristic factors, then the predicted results of each modal component is superimposed to obtain the predicted value of the photovoltaic output. By using the data of a certain photovoltaic power station in Yunnan for comparative experiments, it is found that the model proposed in this paper improves the prediction accuracy of photovoltaic output.

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies, this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient, the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

Automatic seizure detection technology can automatically mark the EEG by using the epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic diseases. This paper presents an EEG classification framework based on the denoising sparse autoencoder. The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data. The sparsity constraint applied in the hidden layer of the network makes the expression of data as sparse as possible so as to obtain a more efficient representation of EEG signals. In addition, corrupting operation used in input data help to enhance the robustness of the system and make it suitable for the analysis of non-stationary epileptic EEG signals. In this paper, we first imported the pre-processed training data to the DSAE network and trained the network. A logistic regression classifier was connected to the top of the DSAE. Then, put the test data into the system for classification. Finally, the output results of the overall network were post-processed to obtain the final epilepsy detection results. In the two-class (nonseizure and seizure EEGs) problem, the system has achieved effective results with the average sensitivity of 100%, specificity of 100%, and recognition of 100%, showing that the proposed framework can be efficient for the classification of epileptic EEGs.

In today's energy shortage and environmental pollution, governments are actively developing new and renewable clean energy, wind energy as an outstanding representative of clean energy has been rapid development in recent years. However, with the increasing proportion of wind power, the problem of abandoned wind is becoming more and more serious in our country, especially in the northern region during the winter heating season. In order to solve the problem of abandoned wind, this paper puts forward a heating system with wind power and heat storage electric boiler by combining with the characteristics of power system in our country, and the optimization model of wind power supply heating dispatching is established considering the uncertainty of wind power. Finally, the simulation is carried out, it proves that the heating system composed of wind power and heat storage type electric boiler can realize the flexible participation of heating load in power system dispatching, helps to increase the absorptive capacity of wind power in system, reduces the abandoned quantity of wind power, and has obvious economic benefits.

The electric vehicle (EV) replacing the traditional fuel vehicle has become an inevitable trend of the development of low carbon economy because of its characteristics of green environmental protection, energy saving and high eliiciency. With the popularization of electric vehicles, random charging of scale electric vehicles in time will increase the influence of uncertain factors in power grid operation, which brings new challenges to the economic operation of the power grid scheduling and control. Therefore, it is necessary to study the electric vehicle charging method, reduce the impact of scale electric vehicle charging on the power grid and improve the operation efficiency of charging facilities and convenience of electric vehicle charging, according to the power demand and energy demand. This paper focus on EV battery swapping station coordinated charging dispatch method based on CS algorithm. The main works are as follows: The out-of-order random charging behavior of electric vehicles in the time may cause the peak load of the grid load. This paper aims at improving the load curve of power network and reducing the power supply cost of power plant by using the schedulability of charging load in time and on the premise of meeting the demand of vehicle power exchange, this paper puts forward the method of time scheduling for the electric vehicle power station from the time scale. Considering the randomness of constraints, this method solves the mathematical model of ordered time scheduling using CS algorithm and achieve the optimization of daily charging plan of battery swapping station(BSS). Simulation example based on the actual operation data of the Olympic power station shows that this method can make the BSS meets the energy demand of the vehicle and avoid the peak load of the power grid, so as to avoid peak load and reduce the operation cost.

Infrastructure constructions in highly mountainous areas (especially in Qinghai-Tibet Plateau with an average altitude close to 5000 m) are developed rapidly in recent years. Meanwhile, the lower atmospheric pressure effects caused by higher altitude on building material have to be carefully evaluated. In this study, the microstructure characterization during hydration behavior of Portland cement under applied low atmospheric pressures, i.e., 0.2, 0.5, 0.8, 0.9 times of 1.0 atmospheric pressure (P0), was experimentally investigated using X-Ray diffraction (XRD), thermogravimetric analysis (TGA), and low temperature nitrogen adsorption techniques, in which the degree of hydration (DoH), the evolution of hydrates, and pore size distribution were assessed. Experimental results revealed that lower curing pressures had diminishing effects on the DoH, especially less than seven days. Although XRD cannot quantitatively identify the hydrate phases under lower curing pressures, TGA analysis provided evidences that both portlandite, ettringite, and calcium silica hydrate (CSH) phases decreased with reduced curing pressures from P0 to 0.2P0. Lower curing pressures caused increased cumulative volumes in micropores and mesopores, but have marginal effects on macropores based on nitrogen absorption evaluation on pore size distribution at 28 days of hydration. Moisture evaporation due to the lower curing pressure was confirmed by TGA measurement. The experimental results under lower curing pressures were served as inputs for thermodynamic simulation to predict hydrated products which confirmed the experiment data and indicated that the lower curing pressure caused more capillary pores in cement pastes. There were three concerns including DoH effect, w/c effect, and moisture evaporation effect that were considered as main reasons influencing the microstructures of cement paste cured under lower pressures.

The renewable distributed generation such as solar energy and wind energy, which is connected to the distribution network, can enhance the reliability of the power system and produce more economic benefits. This paper analyzes the impact of distributed power access network under active management (AM) at first. And mainly establishes the voltage distribution index and compares the voltage distribution in different ways of reactive power control strategies. Meanwhile, a reasonable economic evaluation model is proposed according to the general model of line loss and pollutant discharge standards. Finally, the evaluation index and model of three kinds of DGs under different accesses are verified by computing and analyzing simulation case with IEEE30 node model. Results of numerical analysis show that reasonable planning for the access of distributed power can improve voltage profiles reduce line losses and produce greater economic benefits under active management.

A novel control strategy for capturing maximum wind energy under multi-operating condition based on T-S fuzzy model method is proposed in this paper. Firstly, the T-S fuzzy model of the permanent magnet synchronous wind power generator (PMSG) is constructed under multi-operating condition. The state observer is designed for estimating the states accurately with considering the random wind disturbances existence. Meanwhile, the Lyapunov stability theory and the parallel distributed compensation(PDC) strategy are used to track maximum wind power steadily in real time. The gain of the proposed controller and state observer are solved through linear matrix inequality(LMI) method. The simulation results demonstrated that the performance of the proposed control strategy is significantly better than PI method, and the maximum wind power tracking can be reached effectively.

Large-scale accident which wind turbines are off the grid has happened in the base of Jiuquan wind farm. The majority of accidents are closely related to unreasonable operation technology and unreasonable run manner of 35kV system of wind farm, unreasonable selection of equipment.Study the wiring structure of the 35kV system of the wind power, the actual operation of the wind power, the restrict of technical means, reference current proven advanced technology, propose optimization from system design, equipment selection and run manner etc. Operation reliability of 35kV wind power system has been great increased.

With the ocean having become the hot of new energy development,there is a sharp increase in the investment of ocean energy development. Dozens of unites have carried out researches of ocean energy technology under the National Marine Renewable Energy special funds to support. With the depth of the projects, there are many difficulties. Especially there are many universal technical problems in the study of wave and tidal energy. We could not complete the above topics successfully until the key technologies have been grasped. Based on floating anchor and fixed piling equipment for tidal energy and wave energy. In this article, we present the way of ocean energy capture, power transmission(excluding onshore section)and structural optimization. We also describe the design, insulation seal and manufacture of the underwater generator. The universal key technologies will be discussed and analyzed for the reference of the relevant R & D personnel.

Fault diagnosis expert system obtains the operation of power grid by monitoring the change of dynamic database. While all kinds of measurement, switching value and a part of status records and documents about the status of power grid operation transferred by fault information system and SCADA system are stored in the dynamic database. As the change of power grid operation state and the passage of time, we update the contents in the database. Once the failure and abnormality occur, the fault diagnosis expert system is started, so we can judge whether start core diagnostic program by starting detection. If the program starts, the whole diagnosis process should be under the unified dispatching. We can obtain the related information about the fault diagnosis continuously from dynamic database so as to form our own information chain and begin to carry out the reasoning. Finally, we make the diagnosis results be displayed to the dispatcher in a specified format so as to make the diagnosis results be as the reference of fault recovery processing.

The paper analyzes the influence of user lateral grid-connected PV generation on relay protection of distribution network, and proposes the opinions for the economic technique and selectivity of protection. The paper applies DIgSILENT PowerFactory software to make simulation calculation and analysis on the actual projects, and proposes relay protection allocation scheme and protection setting rules, which provides engineering references for the grid-connected PV power projects.

The alternating load of aerodynamics, inertial force and elastic force of blade of wind turbine generator system can make elastic vibrator and tower generate coupling vibration to eliminate resonance. The paper takes the blade waving and bend of tower as an example. Firstly, the paper researches irrational tower and rotator wind wheel, and uses modal method to establish the operation equation of rotor hub and engine room tower. Then, the paper uses the deformation consistency condition of rotor hub tower system to couple the operation equation of rotor hub and engine room tower, for establishing complete operation formation of turbine rotor/tower system. Lastly, the paper introduces model coordinate to decouple coupled equations. The response curve the system is achieved. The stability of the system is analyzed, and the factors influencing the inherent frequency of the system is found out.

Defects such as cracks and voids in the insulation medium of a power transformer may affect the dielectric response of transformer insulation system. This paper uses the finite element method (FEM) to analysis the influence of electric field distribution on the dielectric response of transformer oil-paper insulation system. Firstly a simple transformer winding insulation model consisting of several layers of pressboard and oil ducts is established in FEM. Here, two circumstances are taken into consideration to represent normal insulation and insulation with cracks penetrating all the layers. The complex capacitance (real part and imaginary part) is calculated to obtain general tendency of dielectric response of winding insulation in a transformer. Secondly, this paper proposes FEM model of a three phase oil-immersed power transformer insulation system. Based on the developed model, the dependency between electric field displacement, current density at the selected point with the applied electric field is discussed in detail. Moreover, the complex capacitance of transformer insulation is also studied. This will help to understand the correlations among insulation defects, electric field variations, and dielectric response of insulation.

This paper put forward a compound method to predict and judge the faults of transformer from the gas that dissolved in oil. First, the database system was developed to store the data of gases that was timing collected from senior machine. Then, the improved grey model would read the data after it filtered, calculate and forecast an equal interval one. At the same time, the residual error and the corrected parameters was formed. The new data and the corrected parameter were read and computed by the self-learning neural networks, and the final predicted value was formed. And then another neural network judgment system would read the value, and locate the type of faults.