Flow distribution at the core inlet and the fraction of bypass flow are important aspects of thermal hydraulic design for reactor. Three-dimensional fluid dynamics analysis was used to numerically simulate the overall flow phenomenon in the pool type low temperature heating reactor. Model of porous media was used to modify fuel assemblies. Thermal hydraulic parameters such as flow distribution of inlet and bypass flows were obtained. The results show that the deviation between the maximum flow rate and the average flow rate at the core inlet is 4.36%, and the deviation between the minimum flow rate and the average flow rate is -3.08%. The fraction of bypass flow of reflectors is 2.3%, and the fraction of bypass flow between baffles and core shroud is 0.34%. The total pressure drop from inlet to outlet is 56.8 kPa. It can be concluded that the distribution of inlet is relatively uniform, and the fraction of bypass flows is within the allowable range. The calculation results can provide a basis for subsequent engineering design.

To address the issues of high human interference and low efficiency in traditional manual methods for classifying and rating steel scrap, we propose the development of CSBFNet, a deep learning-based model for multi-category steel scrap classification and rating. Firstly, we built a 1:3 physical model of steel scrap quality inspection to simulate the unloading of a truck. We used a high-resolution vision sensor to capture the morphological characteristics of various steel scraps. Next, we trained the CSBFNet model using this data to obtain characteristic information for classifying and judging various types of scrap steel. Finally, we tested and improved the CSBFNet model at a Chinese steel mill. The results demonstrate that the model can effectively determine the automatic rating for different grades of scrap. The average accuracy rate of all types of steel scrap reaches 92.4% for the full category, with an mAP of 90.7%. Compared to traditional artificial quality detection methods, it has clear advantages in accuracy and fairness. This model solves the problem of evaluating the quality of steel scrap in the recycling process.

针对单片机与嵌入式系统课程综合实践,设计了以单片机为核心的可燃气体报警系统.该系统具有声光报警、自动排风、拨打预置电话及发送短信功能.系统工作时,气敏传感器检测到可燃气体浓度超过设定值时,控制发光二极管闪烁、蜂鸣器发声报警,同时打开排风扇排风疏散可燃气体.当超过设定时间无人进行处理时,通过通信模块自动拨打预置手机号码并发送短信给予提示,避免发生火灾、爆炸等严重事故.该项目通过与实际应用紧密结合,融合多个知识点,培养学生学习的主动性与创造性,实践教学效果良好.

变速抽水蓄能可成为电网负荷频率控制、平衡波动、消纳新能源出力的有效手段.该文对不同工作模式下影响变速抽蓄机组功率调节能力的因素进行分析,研究变速抽蓄机组电气、性能与控制参数与运行调节特性的关系.进一步基于改进模糊层次分析法对变速抽水蓄能机组的功率调节能力进行量化评估.建立功率调节能力评估体系并计算各影响因素权重大小,得出各指标参数权重排序及最终评估得分.此量化评估过程及结果可为变速抽水蓄能机组的建设和实际运行提供参考.

针对风电场中风速随机性大,难以准确和高效预测的问题,提出一种基于密度峰值聚类的风电场风速预测方法.该方法首先对风电机组采用密度峰值算法进行聚类,随后采用长短期记忆网络模型,对同类风电机组的风速进行预测.考虑到实际聚类时各指标存在不等重要性的情况,基于加权理论对数据进行了预处理,同时通过用主成分分析对数据进行降维,避免了密度峰值聚类面对高维数据时聚类效果差的现象.最后根据风电场实测数据对该方法的有效性进行了验证,实验结果表明,该方法具有较高的预测精度.

为了对大型加压重水反应堆(PHWR)安全防御系统研发提供帮助,研究了 PHWR网络控制系统中潜在的攻击方式,并提出了一种基于樽海鞘群优化高斯过程回归算法的隐蔽攻击方法.该方法在对PHWR网络控制系统实施虚假数据注入时,通过樽海鞘群优化高斯过程回归算法进行系统辨识,获得PHWR受攻击区域高精度的估计模型,并利用该估计模型实现隐蔽攻击.仿真结果表明,该攻击方法对PHWR造成一定破坏性的同时具有高度的隐蔽性能.

为研究薄板自动焊接过程中的焊缝识别与跟踪,提出了基于线阵CCD实现薄板对焊焊缝识别与跟踪参数的测量方法.采用小波滤波方法对线阵CCD采集的图像信息进行去噪处理,利用最大类间方差法对图像信息进行动态阈值计算,对图像进行二值化处理后分割出焊缝信息,进而计算得到焊缝左右边界位置、中心位置以及焊缝宽度等参数信息.通过焊接实验对所设计的测量系统进行了验证,实验结果表明,所提方法在薄板对焊自动焊接过程中焊缝识别与测量精度较高,运行可靠.

Cognitive computing involves discovering hidden rules and patterns in massive volumes of data. Density peaks clustering (DPC) is a powerful data mining tool that can identify density peaks in decision graphs and assign labels to them without requiring iterations. It can efficiently and simply detect clusters of arbitrary shapes. However, on the one hand, density measurement using the ϵ neighbor or Gaussian kernel only reflects the global structure of the data, so that correct density peaks cannot be found, and performance on manifold datasets is weakened. On the other hand, the one-step allocation strategy results in chain reaction. Once a point with high density is misallocated, a series of points will be incorrectly assigned. To solve this problem, this paper proposes the Jaccard coefficient to measure the similarity between points. The proposed density measurement based on Jaccard coefficient is only related to the k points that share the max similarity with the given point, which can reflect the local structure of manifold datasets, and the density peaks can be identified accurately. Aiming at the chain reaction caused by the assignment strategy of DPC, we develop a two-step allocation strategy based on label propagation and the proposed measurement of similarity. The first step is to assign labels to points close to the clustering centers, where these are equal to labeled points in the label propagation algorithm. The second step is to complete the assignment of labels to the remaining points according to labeled data which is the nearest to each unassigned sample. We compared the proposed algorithm with four algorithms on synthetic datasets and real-world datasets. The three metrics among these algorithms show that the proposed algorithm outperforms other algorithms. The results of clustering on synthetic datasets verified the effectiveness of the proposed method for manifold datasets, and three metrics on the UCI datasets and the Olivetti Faces dataset show that it can reveal the patterns and associations of real-world datasets.

As a promising renewable solution for sustainable power generation worldwide, wind energy is receiving continuing attention from both industry and the academic community. However, the randomness and intermittency of wind energy will affect the stable operation and stability of the power system and further affect the economic benefits of the power grid. What makes the matter worse is the inevitable coupling between each pair of wind turbines in the large-scale wind farm. Besides, the resolution of prediction is severely limited by the spatial scale of wind farms. These problems bring great difficulties for the control and scheduling of wind farms. To this end, this paper proposes a novel wind speed prediction method for wind farms by borrowing some wisdom from machine learning methods. First, density peak clustering (DPC) is employed to separate the tremendous number of scattered wind turbines into a much significantly reduced number of groups, the wind turbines in each of which are treated as a unity. Based on the priority setting of each indicator in clustering, the data are preprocessed with different weightings. Principal component analysis is utilized to avoid DPC's poor clustering effects in case the dataset is high-dimensional. Finally, by considering simultaneous effects from historical and present data, long short-term memory based deep learning neural networks are trained and used to iteratively predict the potential of the wind energy in each unit for each time slot. The effectiveness of the proposed algorithm is verified by taking an in-service wind farm in China as an example.

LI Cai,XI Wen-kang,ZHANG De-long,ZHANG Yan,ZHANG Hao,LI Mei-cheng (North China Electric Power University,Beijing 102206,China) Abstract:Flexible DC transmission has gradually become one of the most advantageous transmission methods for delivering new energy,and it also puts forward higher requirements for the stable operation of the power system. In order to solve the problem of grid-connected impact caused by the inherent volatility of new energy in the flexible system,consider shifting the variable-speed pumping unit into the system to stabilize the wind power output fluctuation and ensure the stability of the flexible DC transmission power. Based on the above problems,this paper first introduces the basic principle of the variable speed pumped storage unit;Secondly,this paper constructs a model of wind turbine,variable speed pumping unit and flexible DC combined system,and proposes a coordinated control strategy to stabilize wind power fluctuations by using variable speed pumped storage units;Finally,the responsiveness of the variable speed pumping unit and its coordinated control with the wind power system are simulated and analyzed. Research shows that the variable speed pumping unit has high flexibility and reliability,and is an effective tool to ensure safe and stable operation of the power grid. The results show that the variable speed pumping unit has high flexibility and reliability,and is a reliable tool to ensure the safe and stable operation of the power grid. It also verifies the effectiveness of the coordinated control strategy proposed in this paper.

Steam supply scheduling (SSS) plays an important role in providing uninterrupted reliable energy to meet the heat and electricity demand in both the industrial and residential sectors. However, the system complexity makes it challenging to operate efficiently. Besides, the operational objectives in terms of economic cost and thermodynamic efficiency are usually contradictory, making the online scheduling even more intractable. To this end, the thermodynamic efficiency is evaluated based on exergetic analysis in this paper, and an economic-exergetic optimal scheduling model is formulated into a mixed-integer linear programming (MILP) problem. Moreover, the ε-constraint method is used to obtain the Pareto front of the multi-objective optimization model, and fuzzy satisfying approach is introduced to decide the unique operation strategy of the SSS. In the single-period case results, compared with the optimal scheduling which only takes the economic index as the objective function, the operation cost of the multi-objective optimization is increased by 4.59%, and the exergy efficiency is increased by 9.3%. Compared with the optimal scheduling which only takes the exergetic index as the objective function, the operation cost of the multi-objective optimization is decreased by 19.83%, and the exergy efficiency is decreased by 2.39%. Furthermore, results of single-period and multi-period multi-objective optimal scheduling verify the effectiveness of the model and the solution proposed in this study.

Data uncertainty in practical person reID is ubiquitous, hence it requires not only learning the discriminative features, but also modeling the uncertainty based on the input. This paper proposes to learn the sample posterior and the class prior distribution in the latent space, so that not only representative features but also the uncertainty can be built by the model. The prior reflects the distribution of all data in the same class, and it is the trainable model parameters. While the posterior is the probability density of a single sample, so it is actually the feature defined on the input. We assume that both of them are in Gaussian form. To simultaneously model them, we put forward a distribution loss, which measures the KL divergence from the posterior to the priors in the manner of supervised learning. In addition, we assume that the posterior variance, which is essentially the uncertainty, is supposed to have the second-order characteristic. Therefore, a Σ-net is proposed to compute it by the high order representation from its input. Extensive experiments have been carried out on Market1501, DukeMTMC, MARS and noisy dataset as well.

China is attaching great importance to the development of the energy industry and focuses on promoting green and low-carbon energy. In this background, the concept of multi-station integration and its combination modes are introduced. In this paper, a system evaluation theory (SET) based method is proposed to determine the optimal combination modes. By employing the proposed method, eight combination modes are selected and systematically evaluated. Four kinds of power systems are defined, and the adaptability of different combination modes to the power systems with different characteristic is investigated based on the analytic hierarchy process (AHP) method. The simulation results show that our study can help to choose a combination mode that will be suitable for the real applications in power systems.

In order to realize the goal of constructing a "three-type, two-network" world-class energy internet enterprise and fully utilize the value of the surplus resources in substations, the integration of the substation, data center station, energy storage station, charging and battery-replacing station, photovoltaic (PV) station and 5G station has become an inevitable trend. However the multi-station integration is not suitable for all substations. In this background, the planning strategy of multi-station integration for substations in power systems is proposed. Firstly, the multi-station integration requirements and available resources in the substation are firstly analyzed. Then the linear programming based multi-station integration planning model is formulated and efficiently solved by a commercial solver. Finally, the validity of the proposed method is verified with the IEEE 9-bus system.

An MECS (multiple energy carrier system) could meet diverse energy needs owing to the integration of different energy carriers, while the distinction of quality of different energy resources should be taken into account during the operation stage, in addition the economic principle. Hence, in this paper, the concept of exergy is adopted to evaluate each energy carrier, and an economic–exergetic optimal scheduling model is formulated into a mixed integer linear programming (MILP) problem with the implementation of a real-time pricing (RTP)-based demand response (DR) program. Moreover, a multi-objective (MO) operation strategy is applied to this scheduling model, which is divided into two parts. First, the ε-constraint method is employed to cope with the MILP problem to obtain the Pareto front by using the state-of-the-art CPLEX solver under the General Algebraic Modeling System (GAMS) environment. Then, a preferred solution selection strategy is introduced to make a trade-off between the economic and exergetic objectives. A test system is investigated on a typical summer day, and the optimal dispatch results are compared to validate the effectiveness of the proposed model and MO operation strategy with and without DR. It is concluded that the MECS operator could more rationally allocate different energy carriers and decrease energy cost and exergy input simultaneously with the consideration of the DR scheme.

Abstract Regional energy is an energy system solution designed to solve such issues as regional warm, heat, cooling and power supply and satisfy intra-regional energy demands. With the advancement of power marketization reform, newly added transaction subjects will gradually participate in such links as investment, construction and operation of integrated energy system. Considering the exchange relationship of energy, capital and service flows between various interests subjects of integrated energy system, the paper classifies the operation modes of regional integrated energy system according to the combination relationship between power generation, distribution and sales businesses, and also supplies case analysis according to actual demands.

This paper constructs an evaluation index system of smart grid from the perspectives of development quality and development benefit. Subsequently, a combination of subjective and objective evaluation method is used to comprehensively assess the development level of smart grid. The evaluation result can reflect both the intrinsic development degree and external benefit of smart grid, to better understand its overall development level. Finally, a case application is carried out to analyze the development level of smart grid in a certain area. And a development strategy is proposed through the evaluation results combined with the actual situation in this area.

Regional integrated energy system is a combination designed to connect various energy supply and demand systems based on the in-depth integration of urban regional energy and information communication. With the integrated energy system in a certain industrial park as research object, the paper aims to determine energy constitutions within the integrated energy system and various load curves, calculate operating economic interests and users' energy consumption cost of various subjects under an independent node, analyze cooperation modes of these subjects in cooperation situations, discuss specific economic returns of such subjects under different combination modes and compare theses combination modes, and rationally distribute the total returns of the integrated energy system by Shapley value method, based on which calculate the incremental economic benefits of various subjects.

Under the background of the deepening reformation of the power market, the introduction of power spot market can increase the activity of the reform. The systematic "orderly liberalization" of the market-trading power is also the key guarantee for the power electricity market reforming. On this basis, a power management model of generating units considering spot market trading is constructed. By means of marketization, the allocation of generating capacity is more reasonable and fairer, and the enthusiasm of coal-fired power plants is improved, and a reasonable and transparent spot trading mechanism on generation side is established. Keywords—electric power reform; spot market; power generation plan

Megaprojects carry high risks and many uncertainties, are difficult to coordinate, and involve many stakeholders. Traditional project management concepts and strategies have been proven to be less-than-efficient for modern megaprojects. To a certain extent, project governance could effectively resolve some limitations that traditional project management shows, including the clarification of responsibilities, privileges, and benefits between participating stakeholders. This study contributes to the development of a comprehensive conceptual governance model, specifically for megaprojects, that quantifies governance status over the period of megaproject development and delivery. A megaproject case study in China was used to demonstrate the way this model could facilitate a dynamic governance research approach. We also found project governance status and project performance to be positively correlated. Adjusting governance strategies properly and in a timely manner could help improve the overall project development, and as a result, lead to successful project delivery.