PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach. Doi: 10.28991/ESJ-2022-06-02-01 Full Text: PD

An intelligent hybrid short-term load forecasting model for smart power grids

An accurate load forecasting is always particularly important for optimal planning and energy management in smart buildings and power systems. Millions of dollars can be saved annually by increasing a small degree of improvement in prediction accuracy. However, forecasting load demand accurately is a challenging task due to multiple factors such as meteorological and exogenous variables. This paper develops a novel load forecasting model, which is based on a feed-forward artificial neural network (ANN), to predict hourly load demand for various seasons of a year. In this model, a global best particle swarm optimization (GPSO) algorithm is applied as a new training technique to enhance the performance of ANN prediction. The fitness function is defined and a weight bias encoding/decoding scheme is presented to improve network training. Influential meteorological and exogenous variables along with correlated lagged load data are also employed as inputs in the presented model. The data of an ISO New England grid are used to validate the performance of the developed model. The results demonstrate that the proposed forecasting model can provide significantly better forecast accuracy, training performances and convergence characteristics than contemporary techniques found in the literature. (C) 2016 Elsevier Ltd. All rights reserved

Towards Enabling Probabilistic Databases for Participatory Sensing

Participatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for world-wide applications, without spending cost to buy dedicated sensors. Despite of this benefit, the data collected from human sensors are inherently uncertain due to no quality guarantee from the participants. Moreover, the participatory sensing data are time series that not only exhibit highly irregular dependencies on time, but also vary from sensor to sensor. To overcome these issues, we study in this paper the problem of creating probabilistic data from given (uncertain) time series collected by participatory sensors. We approach the problem in two steps. In the first step, we generate probabilistic times series from raw time series using a dynamical model from the time series literature. In the second step, we combine probabilistic time series from multiple sensors based on the mutual relationship between the reliability of the sensors and the quality of their data. Through extensive experimentation, we demonstrate the efficiency of our approach on both real data and synthetic data

Probabilistic Schema Covering

Schema covering is the process of representing large and complex schemas by easily comprehensible common objects. This task is done by identifying a set of common concepts from a repository called concept repository and generating a cover to describe the schema by the concepts. Traditional schema covering approach has two shortcomings: it does not model the uncertainty in the covering process, and it requires user to state an ambiguity constraint which is hard to define. We remedy this problem by incorporating probabilistic model into schema covering to generate probabilistic schema cover. The integrated probabilities not only enhance the coverage of cover results but also eliminate the need of defining the ambiguity parameter. Both probabilistic schema covering and traditional schema covering run on top of a concept repository. Experiments on real-datasets show the competitive performance of our approach

Recent geodynamic characteristics of the Southern Central coast and the relations with geological hazards

Recent geodynamic characteristics of the Southern Central coast are analyzed on the basis of vertical and horizontal displacement velocities along active fault zones. The horizontal displacement velocity varies in magnitude from this fault system to another fault system, from 0.11–0.3 mm/year on the strike-slip - normal faults to 0–0.058 mm/year on the strike-slip faults and normal faults. The subsidence velocity changes complicatedly, different from one fault to another fault, depending on the mechanism of faults. On the continental shelf, most of the values of high subsidence’s velocity are related to the normal and strike-slip faults. Subsidence activities make the sea level increase highly, the subsidence activity makes the sea level rise at structures that fall close to the shore, reach about 0.2–0.48 mm/year in late Pleistocene - Holocene. The increase of sea level directly affects the intensity of erosion, flood, salinity and land loss events in coastal lowlands. Slippage of the seabed, earthquakes, volcanoes are geological hazards directly related to the geodynamic regime of the Southern Central coast

Bootstrapping Uncertainty in Schema Covering

Schema covering is the process of representing large and complex schemas by easily comprehensible common objects. This task is done by identifying a set of common concepts from a repository called concept repository and generating a cover to describe the schema by the concepts. Traditional schema covering approach has two shortcomings: it does not model the uncertainty in the covering process, and it requires user to state an ambiguity constraint which is hard to define. We remedy this problem by incorporating probabilistic model into schema covering to generate probabilistic schema cover. The integrated probabilities not only enhance the coverage of cover results but also eliminate the need of defining the ambiguity parameter. Experiments on real-datasets show the competitive performance of our approach

Modeling of parallel power MOSFETs in steady-state

In high-power applications, multiple power MOSFETs are connected in parallel and treated as a single switch in order to handle much larger total currents. In this paper, a parallel power MOSFETs model from the turnoff state until they reach their steady state is introduced. The model represents the relationship between each power MOSFET's gate voltage and the current distribution among them. The study's key purpose is to use the model for dealing with the asymmetry in sharing current and power loss between these semiconductor devices during the steady state region.Comment: 10 pages, 7 figures, The 2023 INTERNATIONAL SYMPOSIUM ON ADVANCED ENGINEERING (ISAE2023