A novel recurrent neural network forecasting model for power intelligence center

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

Application of Distributed Intelligent Power Supply Technology in Expressway Tunnels

With the coordinated development of today's social economy with science and technology,various advanced technologies are being used in highway engineering,especially the distributed intelligent power supply technology in expressway tunnels,which has a very significant advantage.In order to realize the effective application of this technology and promote the power supply effect in expressway tunnel,this study analyzes the advantages of this technology and its application in expressway tunnel,hoping to provide scientific reference for the application of distributed intelligent power supply technology and the engineering development of expressway tunnels.

Social Energy:Mining Energy From the Society

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social systems.From this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system interactions.The recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and management.In this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy concept.In the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

Design of BLDCM Driving and Control System for Motorized Treadmill

To satisfy the requirement of developing a new generation of motorized treadmill for a famous domestic manufacturer, a brushless DC motor （BLDCM） driving and control system for motorized treadmill is developed. High integration and reliability of this system are ensured under the condition that intelligent power module （IPM） is used and the protection module is included. Periodic current control method is applied to reduce the average current flowing through the armature winding of the motor when the treadmill is required to start with low speed while large load is added. Piecewise proportion-integration-differentiation （PID） control algorithm is applied to solve the problem of speed fluctuation when impulse load is added. The motorized treadmill of a new generation with the driving and control system has the advantages of high reliability, good speed stability, wide timing scope, low cost, and long life-span. And it is very promising for practical applications.

A review on the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment

The economy of distribution networks largely depends on the utilization rate of distribution network equipment.Most of the emerging intelligent power consumption technologies have a positive effect on equipment utilization and their use can save investment of distribution networks.In this paper,the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment is reviewed.The evaluation methods and indexes are assessed first and then intelligent power consumption equipment with energy storage function,vehicle-to-grid(V2G)technology and time-of-use(TOU)tariff are reviewed respectively.It is concluded that these intelligent power consumption technologies and measures have great potential to improve utilization rate of distribution network equipment because of their effective improvement to power load.Meanwhile,recommendations on how to utilize these intelligent power consumption technologies to improve utilization rate of distribution network equipment are proposed.

Power Hearing Better Tech and Funding Fuel Search for Intelligent Life Beyond Earth

这是一篇有关外星智能探索的最新报道。Are we alone in thegalaxy?从早期的无线电信号到现代的大型望远镜阵,人类对地外文明探索的脚步从未停止过。科技空前发展、资金相对充足,笑容不仅洋溢在科学家的脸上,希望也会在每一个相信地外文明存在的人心中升起。