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This study focuses on the day-ahead forecasting of the power generation of the grid-connected photovoltaic (PV) system. In the literature, most PV power forecasting models depends on the information from the numerical weather prediction (NWP). The errors of the weather prediction might cause the performance of the PV power forecasting model to decrease. Therefore, in this paper, the proposed day-ahead PV power forecasting model only utilizes the historical PV power generations as the input data. The proposed forecasting model is composed of two parts, pattern analysis and the state transition. The pattern analysis evaluates the similarity of the time series of the daily PV power generations based on Euclidean distance and group the time series with similar patterns by k-means time series clustering algorithm. Each cluster is regarded as one state. Then, the probabilities of the state transitions between two consecutive days are calculated. Finally, the expected PV power pattern of tomorrow is obtained as the forecasted PV power outputs. In this study, the proposed model is compared with the traditional feed-forward neural network (FFNN). The results of the cast study demonstrate the proposed model leads to smaller errors in 10 sets of the testing data.

Side-channel attacks (SCAs) are to obtain physical information (e.g., time information, power consumption, electromagnetic leakage) from the physical implementation of a cryptosystem for further cracking the cryptosystem. In this paper, we study the protection to SCAs for the Advanced Encryption Standard (AES) cryptosystem. We add extra circuits (components) to the AES circuit to produce almost equal power consumption (for different key values) without affecting the encryption results. Experimental results show that the approach works well in practice.

As an important component in antiradiation direct-head, the uniformity and reliability of transmitter paly the key role in the electrical performance. The characteristic of missile transmitter with high integration is plenty sidewall connectors, larger size, and the 4047 aluminum alloy plate is easy to cause weld airtight failure in helium leak detection process. To keep an excellent microwave properties and high reliability, it is of great importance to insure the gas tightness of transmitter. For this reason, higher requirements are proposed for the weld penetration and sealing quality, while the temperature rise should be under the melting point of low temperature brazing weld. In this paper, the laser welding technology in different energies was employed, weld penetration and sealing quality of the components were investigated after the laser welding process, meanwhile the temperature around the RF connector was also tested. On the basis of this, the appropriate weld penetration and corresponding laser energy for large-size transmitter are obtained, so that the gas tightness performance of the components is satisfied and superior to the requirements of GJB548B.

The propagation of SPPs in solid state systems is usually reciprocal, which means that a forward-traveling SPP wave can be easily backscattered by disorders. Since such backscattering is detrimental for SPP-based information processing applications, the development of nonreciprocal plasmonic platforms that enforce unidirectional SPP propagations is of great importance.

Renewable wind energy has become an advantageous alternative. By the end of 2020, the total global offshore wind energy capacity has exceeded 35GW, growing more than 106% in the 5 past years, and the capacity has exceeded 6GW. It is estimated that by 2025, the global installed capacity of offshore wind energy will reach 100GW. In 2020, China's new offshore wind energy installed capacity exceeded 3GW, accounting for 50.45% of the world's new installed capacity and became the second-largest offshore wind energy market in the world. Offshore wind energy projects will produce noise pollution with different characteristics during the construction and operation stages, which will cause temporary or permanent damage to marine life to some extent. In this paper, the on-site survey of the underwater noise during the construction period of the second section of the Shapa offshore wind farm in Yangjiang, Guangdong. The survey was conducted on two days, and the measurements were carried out at different distances outside the wind farm. Time-domain waveform analysis and frequency spectrum analysis were performed on the data. The research in this paper can provide technical reference for environmental impact assessment of offshore wind energy projects, and has great application value. Through the analysis of the underwater noise characteristics of the offshore wind farm at different distances, the following results are obtained: Firstly, as the distance increases, the measured sound pressure values far away from the wind farm site are slightly smaller than those at the edge of the wind farm. Due to the multi-path effect, the duration of the acoustic pulse signal measured at the edge of the wind farm is shorter than that outside the wind farm. The noise power of piling at the edge of the wind farm tends to be stable, which has little impact on the apogee. Secondly, the sound pulse of piling causes the underwater noise sound pressure level at the edge of the wind farm to be generally higher than that at the site far away from the wind farm. With the distance increasing, the frequency of the sound pressure peak will be shifted. The noise intensity has the largest change in the frequency range of 0~1000Hz, and it has little impact when the frequency range is over 1000Hz. The underwater noise at the edge of the wind farm is greater than the underwater noise far away from the wind farm by a relatively constant value. Thirdly, as the distance from the wind farm increases, the frequency spectrum within the frequency range of 150Hz to 250Hz changes significantly. Fourthly, the wind farm has little impact on the nearby underwater acoustic environment during the construction period, and the impact range is small, which is not enough to damage the hearing of creatures near the wind farm. However, whether the noise has a long-term impact on the physiology and behavior of nearby marine organisms remains to be verified by further experimental studies.

When the millimeter-wave radar is tracking the target, it can only track the movement state of the target, and cannot detect the category of the tracking target. In order to obtain the category of the tracking target, a vision camera is usually used to detect the category of the tracking target when detecting the tracking target. So as to solve the problems of high time-consuming and low-precision caused by traditional machine vision vehicle detection methods used by vision cameras, this paper uses the YOLOv3 deep learning algorithm based on convolutional neural networks to detect tracking targets. In order to speed up the detection speed of the algorithm, the YOLOv3 algorithm model is simplified by the method of pruning quantization, and the training of the YOLOv3-tiny algorithm model is completed, so that the real-time and accurate tracking target detection requirements in terms of detection time and detection accuracy can be achieved.

The use of power electronics technology to transform the distribution network, realize multi-terminal flexible closed-loop operation and greatly improve the power supply capacity is an important form of future development of the distribution network. At the same time, along with the large-scale promotion of electric vehicles (EVs), the disorderly access of charging load is bound to bring challenges to the safe and stable operation of the distribution network. Therefore, it is necessary to evaluate and analyse the impact of EV charging load access in the flexible distribution network. Firstly, this paper constructs operational constraints based on the operating characteristics of the flexible distribution network; secondly, based on the disorderly access characteristics of EVs, the EV charging load model is constructed through a multi-factor combination; finally, tests are carried out in the IEEE 33 interconnection system. The results show that the flexible interconnection can effectively optimize the distribution of tides over time and suppress the voltage deviation.

This paper studies the selection process and information visualization method of local cultural gene evaluation indicators based on the analysis of data by analytic hierarchy process (AHP). First, the local cultural gene map is constructed by information visualization tools. Then the qualitative analysis of the cultural gene map is carried out through user perception. Finally, the multi-dimensional data and analytic hierarchy process are used to evaluate the visualization of local cultural genes. The example of South China University of technology is used to verify the effectiveness and feasibility of the new scheme.

Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.

In order to solve the mismatch between the construction of distribution network and the popularization of EV, an evaluation method of the limit number of EV accepted by distribution network is proposed based on the operational risk, so as to evaluate the acceptance capacity of distribution network. Secondly, a method of distribution network reconstruction and closed-loop operation to improve the acceptance capacity of distribution network is proposed. Then the comprehensive goal of orderly charging is established. Based on which the model of EV charging period transfer probability is established to guide users to stagger charging and improve the acceptance capacity of distribution network. Finally, the simulation verification is given through the analysis of an example.

As the wind power penetration increases, the frequency stability of the power system is deteriorating. Thus, it is expected that the wind power plants (WPPs) will be required to produce the inertial response during the short-term large-scale frequency disturbances. To study the effects of the inertial response of the WPP, the dynamic equivalent model (DEM) of the WPP should be able to emulate the inertial response of the actual WPP. Otherwise, the simulated system frequency would be different from the actual one. Therefore, the inertial response identification algorithm (IRIA) is proposed in this paper. The IRIA is able to identify the values of the droop gain and the inertia constants of the WPP according to the actual system frequency and the active power output of the actual WPP. The simulation results demonstrate that the DEM of the WPP and the actual WPP have similar responses of the active power output and the rotor speed under two frequency disturbances. In addition, the FNs of the simulated and actual system frequencies are very close. As a results, the effectiveness of the IRIA is successfully proved.

Based on RRT* algorithm, an improved sampling-based motion planning algorithm is proposed in this paper. Firstly, the kinematics analysis of the Schunk lwa3 manipulator is performed, and the D-H model is built. Then the forward kinematics solution of the manipulator is obtained, and the working space of the manipulator is solved based on the Monte Carlo method. Finally, based on the classic RRT motion planning algorithm and the rewiring method in RRT*, the probabilistic-root rapidly-exploring random tree motion planning algorithm is designed and verified by experiments.