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Published in Philip A. Laplante, Comprehensive Dictionary of Electrical Engineering, 2018
distribution management system (DMS) a system that helps manage the status of the distribution network, crews and their work flow, system safety, and abnormal conditions. distribution switchboard a switchboard used in the distribution system, typically within a building. distribution transformer a transformer designed for use on a power distribution system (typically 2.4 kV to 34.5 kV) to supply electrical power to a load at the proper utilization voltage. disturbance a sudden change or a sequence of changes in the components or the formation of a power system. Also called fault. disturbance decoupling of generalized 2-D linear systems given the second generalized 2-D Fornasini-Marchesini model with disturbances E xi+1, j+1 = A1 xi+1, j + A2 xi, j+1 + B1 u i+1, j + B2 u i, j+1 + H1 z i+1, j + H2 z i, j+1 yi j = C xi j
Power system automation
Published in Mini S. Thomas, John D. McDonald, Power System SCADA and Smart Grids, 2017
Mini S. Thomas, John D. McDonald
Distribution automation/distribution management systems (DA/DMS) include substation automation, feeder automation, and customer automation. The additional features incorporated in distribution automation will beFault identification, isolation, and service restorationNetwork reconfigurationLoad management/demand responseActive and reactive power controlPower factor controlShort-term load forecastingThree-phase unbalanced power flowInterface to customer information systems (CISs)Interface to geographical information systems (GISs)Trouble call management and interface to outage management systems (OMSs)
Wireless Communication Systems in the Smart Grid
Published in K. R. Rao, Zoran S. Bojkovic, Bojan M. Bakmaz, Wireless Multimedia Communication Systems, 2017
K. R. Rao, Zoran S. Bojkovic, Bojan M. Bakmaz
The smart meter is the bridge between user behavior and power consumption metering. The distribution management system (DMS) is required for analyzing, controlling, and providing enough useful information to the utility. The SG is also composed of legacy remote terminal units (RTUs) that can perform sensor network gateway functions acting as intermediate points in the medium voltage network. The sensor network gateway is the bridge between the sensor network and the back-end system. Therefore, it provides necessary interfaces to other sensor nodes as well as interfaces to existing ICT infrastructures.
Supply chain-oriented permutation flowshop scheduling considering flexible assembly and setup times
Published in International Journal of Production Research, 2023
Kuo-Ching Ying, Pourya Pourhejazy, Chen-Yang Cheng, Ren-Siou Syu
The recent advent of Industry 4.0 is revolutionising research in various fields; scheduling is no exception to this trend, particularly because it helps integrate the physical and decisional aspects of production planning, and facilitates autonomous manufacturing within decentralised supply chain systems (Rossit, Tohmé, and Frutos 2019). Distributed manufacturing systems (DMS) play a central role in the industry 4.0-based supply chains. DMS helps improve product quality, the company’s reputation, supply chain cost/time (Wang et al. 1997), and, overall, the competitiveness of the corporate (Renna 2013). Besides, distributed manufacturing has implications for sustainability, i.e. it reduces pollutions due to less global transports and helps the development of small and regional economies (Rauch, Dallasega, and Matt 2016). Given globalisation and rapid technological development, DMSs are decisive to control the manufacturing operations across supply chains and handle the complexities involved (Cunha, Putnik, and Ávila 2000). In this situation, scheduling decisions in distributed manufacturing environments are of high relevance and should receive more attention.
Bio-Inspired Scheduling for Factory Automation in the TD-LTE System
Published in IETE Technical Review, 2022
Won Jae Ryu, Gandeva Bayu Satrya, Soo Young Shin
This paper proposes a novel scheduling TD-LTE system for factory automation. Because TD-LTE is based on a time-division duplex (TDD), which can adjust the number of uplink and downlink subframes in a frame, it can enable more efficient transmissions than FDD-LTE, which is based on FDD. TD-LTE uses one frequency band for both uplink and downlink transmissions, thereby rendering it cheaper than FDD-LTE. This implies that the application of TD-LTE to factory automation is a better alternative than FD-LTE [17]. In Korea, 2.3 GHz for WiBro is considered idle as its development has been discontinued, and its service in Korea has been terminated due to the declining number of users [18,19]. Note that 2.3 GHz is a frequency band for TD-LTE that can be applied to factory automation as a solution for industrial areas. For efficient scheduling of data from nodes, deadline monotonic scheduling (DMS), genetic algorithm (GA), particle swarm optimization (PSO), and firefly algorithm (FA) have been adopted. DMS is an algorithm that schedules tasks according to their deadlines. A task with the shortest deadline is assigned the highest priority. In this system, data transmission with the shortest deadline is assigned the highest priority [20]. Because industrial data transmission has periodicity and deadlines, DMS can be a solution for factory automation.
Planning methods and decision support systems in vehicle routing problems for timber transportation: a review
Published in International Journal of Forest Engineering, 2023
Jean-François Audy, Mikael Rönnqvist, Sophie D’Amours, Ala-Eddine Yahiaoui
Another distinction in TTVRPs is related to deterministic and stochastic problems governed by certainty and uncertainty of the available information at the time of planning. In deterministic problems, all the data are assumed to be known with certainty, while in stochastic problems some parameters (e.g., vehicle traveling time or supply/demand volumes) are random variables assumed with known probability distributions (Berbeglia et al. 2010). Nearly all literature on the TTVRPs focused on deterministic problems. The dynamic TTVRPs presented earlier in Rönnqvist and Ryan (1995), Rönnqvist et al. (1998), and Amrouss et al. (2017) are known to be dynamic and deterministic problems. In them, data are revealed dynamically in real-time and become known to DMs through communication technologies (e.g., mobile phones or communication devices, and global positioning systems) at the time of decision-making (Pillac et al. 2013). Few contributions are found addressing stochastic TTVRPs, except McDonald et al. (2001a), (2001b) and Marques et al. (2014), using mainly simulation-based planning methods. In McDonald (2001a), a transport network simulation model is used to compare three approaches of dispatching trucks to loggers, in which truck arrivals, processing times, loading/unloading times, and actual traveling times are assumed to be random variables with known probability distributions. McDonald et al. (2001b) presents a simulation approach to evaluate the potential benefits of sharing log truck resources among a group of loggers allowing dispatching trucks in a shared logistics system. Approximately 400 trucks are used per day with a group of ten loggers serving three types of assortments to be transported to three different destination mills respectively. The stochastic nature of assortment outcomes from harvesting is estimated by a set of probabilities. The random truck arrivals, processing times, and delays are also considered. Marques et al. (2014) presents a hybrid optimization and simulation approach for tactical harvesting and transportation planning and operational logistics and truck delivery scheduling considering the uncertainties of truck arrivals, delays, queuing, and processing. An optimization model with a three-phase procedure presented in Marques et al. (2012) is used to generate candidate schedules. A set of discrete-event simulation models are constructed to evaluate the performances of the candidate solutions with stochastic event occurrence, to obtain optimal or near-optimal solutions. The method has been tested in a real case of a paper mill log yard operations in Portugal. These problems can also be categorized as dynamic and stochastic TTVRPs because the random variables in these problems are revealed dynamically. Without loss of generality, we devote the literature review on the main body of static and deterministic TTVRPs in the first part of the Results section.