Huang Xiang

Bio: Huang Xiang is an academic researcher. The author has contributed to research in topics: Smart grid & Cloud computing. The author has an hindex of 4, co-authored 7 publications receiving 42 citations.

Massive real-time data load simulation testing cloud platform for smart power grid, and testing method of cloud platform

Abstract: The invention discloses a massive real-time data load simulation testing cloud platform for a smart power grid, and a testing method of the cloud platform. The cloud platform comprises a virtualization unit, a central controller module and agent modules, wherein the virtualization unit virtualizes a plurality of virtual machines; the central controller module is arranged in the virtual machine to form a central controller; the agent modules are stored in the central controller in forms of virtual machine mirror image documents, and activated to form agency servers after the central controller is applied to start the virtual machines; the central controller applies the cloud platform for enough virtual machines according to testing plans in different scales; and agency server simulation monitoring devices send massive monitoring data to the servers, and test the performance of the servers. According to the cloud platform and the testing method, powerful computing resources of cloud computing are depended on; the massive virtual machines are applied from the cloud platform; the massive data of the smart power grid is tested by the agency server simulation monitoring devices; and the cloud platform has the benefits of simple structure, low input cost, convenience in use, wide testing range and good testing effect.

Smart power grid line loss detection method and system

Abstract: The invention discloses a smart power grid line loss detection method and system. The smart power grid line loss detection method includes the steps of obtaining monitoring data of a smart power grid, classifying monitoring values according to serial numbers of monitoring devices to obtain a plurality of monitoring sets, extracting the monitoring values within corresponding set time periods in the monitoring sets through a time coordination mechanism according to sampling time of the monitoring values, computing and outputting line loss values corresponding to the monitoring sets according to the monitoring values within the corresponding set time periods in the monitoring sets and a preset spatial-temporal model, enabling the lowest common multiples of sampling periods corresponding to all the monitoring values in the corresponding monitoring sets to serve as duration of the set time periods to extract the monitoring values, and synchronously computing the line losses through the extracted monitoring values within the same set time periods to guarantee the computing accuracy. The monitoring data of the smart power grid are obtained, coherent processing is carried out on the monitoring data, then the line losses are computed, the practical line loss condition can be truly reflected, and compared with a traditional smart power grid line loss detection method, the computing accuracy is improved, and the detection error is reduced.

Exploiting application-level similarity to improve SSD cache performance in Hadoop

Abstract: To boost the performance of massive data processing, solid-state drives (SSDs) have been used as a kind of cache in the Hadoop system. However, most of existing SSD cache management algorithms are ignorant of the characteristics of upper-level applications. In this paper, we propose a novel SSD cache management algorithm called DSA, which can exploit the application-level data similarity to improve the SSD cache performance in Hadoop. Our algorithm takes both temporal similarity and user similarity in querying behaviors into account. We evaluate the effectiveness of our proposed DSA algorithm in a small-scale Hadoop cluster. Our experimental results show that our algorithm can achieve much better performance than other well-known algorithms (e.g., LRU, FIFO). We also clearly point out the underlying tradeoff between cache performance and SSD deployment cost, and identify a number of key factors that affect SSD cache performance. Our findings can provide useful guidelines on how to effectively integrate SSDs into Hadoop.

Cloud computing in electric vehicles charging control and dispatch optimization

Abstract: Charging of large-scale electric vehicles will have a significant impact on the grid so that their charging schedule needs to be optimized. The traditional centralized scheduling method requires high performance in information exchange and computing. Based on the analysis of the features of electric vehicle charging load and the research of cloud computing, we propose a cloud computing based schedule platform for optimal charging of electric vehicles. In addition, the function and implementation of the three basic modules of the platform (the data collection module, the cloud computing center and the control center) are exposed. The platform performs well in optimal scheduling for electric vehicles. In addition, the platform can also make full use of the computing resource and reduce the communication congestion.

Power grid large-scale topological structure construction method and system

Abstract: The invention provides a power grid large-scale topological structure construction method and system. A stored power grid topological incidence matrix table is read to obtain sparse matrixes, MapReduce is utilized to process the sparse matrixes so as to obtain a stored power grid topological node analysis result, a power grid topological island connecting relation is calculated to obtain a power grid topological network result, and finally a power grid large-scale topological structure is established according to the power grid topological network result. Strict processing computation and processing process are adopted in the whole process, and accurate establishment is ensured. By utilizing a MapReduce expansibility advantage, the matrix computation complexity is simplified, a computation ending condition is optimized, quickness of large-scale sparse matrix computation is ensured, the large-scale power grid topological structure is quickly computed so as to facilitate accurate analysis on topological nodes and topological islands of a large power grid, and finally the power grid large-scale topological structure is efficiently and accurately established.

Impacts and Utilization of Electric Vehicles Integration Into Power Systems

Abstract: Large scale of plug-in electric vehicles(PEVs) integration will pose inevitable impacts on the planning and operation of power system in the future.The literature reviews were carried out on PEVs integration on power system including PEVs charging load modeling,simulation and calculation,PEVs' impacts on power system,and control and utilization of PEVs charging and discharging.Firstly,key factors when analyzing the charging load were pointed out.Additionally,the impacts of PEVs integration on generation expansion,grid operation,charging facilities and distribution system planning were discussed.The current research on coordinated charging of PEVs and "vehicle-to-grid(V2G)" operation were reviewed as well.Finally,possible future research areas were presented.

Robot and cloud-assisted multi-modal healthcare system

Abstract: With the rise of the robot and cloud computing technology, human-centric healthcare service attracts widely attention in order to meet the great challenges of traditional healthcare, especially the limited medical resources. This paper presents a healthcare system based on cloud computing and robotics, which consists of wireless body area networks, robot, software system and cloud platform. This system is expected to accurately measure user's physiological information for analysis and feedback, which is assisted by the robot integrated with various sensors. In order to improve the practicability of multi-media transmission in the healthcare system, this paper proposes a novel scheme to deliver real-time video contents through an improved UDP-based protocol. Finally, the proposed approach is evaluated with experimental study based on a real testbed.

Data monitoring method of remote wireless meter reading system

Abstract: The invention relates to the technical field of wireless meter reading and provides a data monitoring method of a remote wireless meter reading system The data monitoring method comprises the following steps that an acquisition terminal acquires electric meter data in fixed time and transmits the electric meter data to a concentrator; the concentrator generates an electric meter data index table according to a set electric meter number list, and stores the electric meter data in the index table and transmits the meter reading data to a data server by use of a front-end server; the data server gathers the meter reading data transmitted by the concentrator; a monitoring server calculates a dynamic line loss rate according to the meter reading data in the data server and determines whether the dynamic line loss rate exceeds a preset value; a promoting alarm is generated when the dynamic line loss rate exceeds the preset value, and when the dynamic line loss rate does not exceed the preset value, a line loss analytic statistical form is generated according to the calculated dynamic line loss rate The data monitoring method of the remote wireless meter reading system realizes monitoring on the whole remote wireless meter reading system, and consequently, the reliability of the whole remote wireless meter reading system is improved

Machine learning‐based charge scheduling of electric vehicles with minimum waiting time

Abstract: In order to reduce the greenhouse gas emission and limit the rise in global temperature, the trend in automotive industry is changing rapidly and most of the manufacturers are moving towards the electrification of vehicles. Computational intelligence and machine learning play a very important role in the field of electric vehicles (EVs) due to the necessity of automatic control in battery charging and port accessibility. Due to the limited ranges of EVs, they have to be charged periodically during their travels and its charging will take more time. As the number of EVs increases, suitable charging infrastructure having many charging stations and co‐ordination of scheduling the charging vehicles from charging stations are necessary. As charging stations have less number of fast charging ports, accessing these fast charging ports needs proper planning. The major challenge of an EV is to identify the charging station with a fast charging port which is on route to the destination with minimum waiting time. This article deals with the application of machine learning in selecting a charging station with available fast charging port and minimum waiting time.