Search Results (5)

Recently, there has been an explosive generation of streaming data in various fields such as IoT and network attack detection, medical data monitoring, and financial trend analysis. These domains require precise and rapid analysis capabilities by minimizing noise from continuously generated raw data. In this paper, we propose SPinDP (Stream Purifier in Distributed Platform), an open source-based high-speed stream purification platform, to support real-time stream purification. SPinDP consists of four major components, Data Stream Processing Engine, Purification Library, Plan Manager, and Shared Storage, and operates based on open-source systems including Apache Storm and Apache Kafka. In these components, stream processing throughput and latency are critical performance metrics, and SPinDP significantly enhances distributed processing performance by utilizing the ultra-high-speed network RDMA (Remote Direct Memory Access). For the performance evaluation, we use a distributed cluster environment consisting of nine nodes, and we show that SPinDP’s stream processing throughput is more than 28 times higher than that of the existing Ethernet environment. SPinDP also significantly reduces the processing latency by more than 2473 times on average. These results indicate that the proposed SPinDP is an excellent integrated platform that can efficiently purify high-speed and large-scale streams through RDMA-based distributed processing. Full article

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle’s velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information. Full article

Transformers are generally considered to be the costliest assets in a power network. The lifetime of a transformer is mainly attributable to the condition of its solid insulation, which in turn is measured and described according to the degree of polymerization (DP) of the cellulose. Since the determination of the DP index is complex and time-consuming and requires the transformer to be taken out of service, utilities prefer indirect and non-invasive methods of determining the DP based on the byproduct of cellulose aging. This paper analyzes solid insulation degradation by measuring the furan concentration, recently introduced methanol, and dissolved gases like carbon oxides and hydrogen, in the insulating oil. A group of service-aged distribution transformers were selected for practical investigation based on oil samples and different kinds of tests. Based on the maintenance and planning strategy of the power utility and a weighted combination of measured chemical indicators, a neural network was also developed to categorize the state of the transformer in certain classes. The method proved to be able to improve the diagnostic capability of chemical indicators, thus providing power utilities with more reliable maintenance tools and avoiding catastrophic failure of transformers. Full article

High distributed generation (DG) penetration makes the traditional method of equalizing the distribution power system (DPS) to the PQ load bus in the risk assessment of the transmission power system (TPS) no longer applicable. This paper proposes a risk assessment method for an integrated transmission–distribution system that considers the reactive power regulation capability of the DGs. Based on the DG’s characteristics and network constraints, the regulation capacity is mapped to the boundary buses of the distribution networks. Coordinating the relationship between reactive power and active power, the utilization of the regulation capacity is maximized to reduce the load shedding in the fault analysis of the TPS. Simulation results in the integrated transmission–distribution system illustrate that the effective use of the regulation capacity of the DPS can reduce the risk of the TPS. The method can be applied to the reactive power sources planning and dispatching of power system. Full article

Distribution networks (DNWs) are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP) strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN) needs to consider choices, primarily distributed generation (DG), network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP) in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs) based on mult-objective optimizations (MOOs) in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT) have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at the multi-objective-based frameworks, are also presented in this paper. Full article