20 research outputs found
Control of LPV Modeled AC-Microgrid Based on Mixed H2/Hâ Time-Varying Linear State Feedback and Robust Predictive Algorithm
This paper presents a robust model predictive control (RMPC) method with a new mixed
H2/Hâ linear time-varying state feedback design. In addition, we propose a linear parameter-varying model
for inverters in a microgrid (MG), in which disturbances and uncertainty are considered, where the inverters
connect in parallel to renewable energy sources (RES). The proposed RMPC can use the gain-scheduled
control law and satisfy both the H2 and Hâ proficiency requirements under various conditions, such as
disturbance and load variation. A multistep control method is proposed to reduce the conservativeness caused
by the unique feedback control law, enhance the control proficiency, and strengthen the RMPC feasible
area. Furthermore, a practical and efficient RMPC is designed to reduce the online computational burden.
The presented controller can implement load sharing among distributed generators (DGs) to stabilize the
frequency and voltage of an entire smart island. The proposed strategy is implemented and studied in a MG
with two DG types and various load types. Specifically, through converters, one type of DGs is used to control
frequency and voltage, and the other type is used to control current. These two types of DGs operate in a
parallel mode. Simulation results show that the proposed RMPCs are input-to-state practically stable (ISpS).
Compared with other controllers in the literature, the proposed strategy can lead to minor total harmonic
distortion (THD), lower steady-state error, and faster response to system disturbance and load variation
Predicting the solar maximum with the rising rate
The growth rate of solar activity in the early phase of a solar cycle has
been known to be well correlated with the subsequent amplitude (solar maximum).
It provides very useful information for a new solar cycle as its variation
reflects the temporal evolution of the dynamic process of solar magnetic
activities from the initial phase to the peak phase of the cycle. The
correlation coefficient between the solar maximum (Rmax) and the rising rate
({\beta}a) at {\Delta}m months after the solar minimum (Rmin) is studied and
shown to increase as the cycle progresses with an inflection point (r = 0.83)
at about {\Delta}m = 20 months. The prediction error of Rmax based on {\beta}a
is found within estimation at the 90% level of confidence and the relative
prediction error will be less than 20% when {\Delta}m \geq 20. From the above
relationship, the current cycle (24) is preliminarily predicted to peak around
October 2013 with a size of Rmax =84 \pm 33 at the 90% level of confidence.Comment: 7 pages, 3 figures, accepted for publication in SCIENCE CHINA
Physics,Mechanics & Astronom
A Robust Kalman Filter-Based Approach for <i>SoC</i> Estimation of Lithium-Ion Batteries in Smart Homes
Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncertainties and measurement noises. Being able to cope with model uncertainties and measurement noises is essential, since they can lead to inaccurate SoC estimations. An experiment test bench is developed, and various experiments are conducted to extract the battery model parameters. The experimental results show that the proposed method can more accurately estimate SoC compared with other Kalman filter-based methods. The proposed method can be used in optimal BMSs to promote battery performance and decrease battery operational costs in smart homes
Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation
This paper proposes centralized and distributed optimization models for V2G applications to provide frequency regulation in power systems and the electricity market. Battery degradation and dynamic EV usages such as EV driving period, driving distance, and multiple charging/discharging locations are modeled. The centralized V2G problem is formulated into the linear programming (LP) model by introducing two sets of slack variables. However, the centralized model encounters limitations such as privacy concerns, high complexity, and central failure issues. To overcome these limitations, the distributed optimal V2G model is developed by decomposing the centralized model into subproblems using the augmented Lagrangian relaxation (ALR) method. The alternating direction method of multipliers (ADMM) is used to solve the distributed V2G model iteratively. The proposed models are evaluated using real data from the Independent Electricity System Operator (IESO) Ontario, Canada. Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. The simulation also shows that including battery degradation and dynamic EV usage increases the model accuracy. By using the proposed approaches, the high cost and the low efficiency power generation units for frequency regulation can be compensated or partially replaced by EVs, which will reduce the generation cost and greenhouse gas emissions
A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes
Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncertainties and measurement noises. Being able to cope with model uncertainties and measurement noises is essential, since they can lead to inaccurate SoC estimations. An experiment test bench is developed, and various experiments are conducted to extract the battery model parameters. The experimental results show that the proposed method can more accurately estimate SoC compared with other Kalman filter-based methods. The proposed method can be used in optimal BMSs to promote battery performance and decrease battery operational costs in smart homes
Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation
This paper proposes centralized and distributed optimization models for V2G applications to provide frequency regulation in power systems and the electricity market. Battery degradation and dynamic EV usages such as EV driving period, driving distance, and multiple charging/discharging locations are modeled. The centralized V2G problem is formulated into the linear programming (LP) model by introducing two sets of slack variables. However, the centralized model encounters limitations such as privacy concerns, high complexity, and central failure issues. To overcome these limitations, the distributed optimal V2G model is developed by decomposing the centralized model into subproblems using the augmented Lagrangian relaxation (ALR) method. The alternating direction method of multipliers (ADMM) is used to solve the distributed V2G model iteratively. The proposed models are evaluated using real data from the Independent Electricity System Operator (IESO) Ontario, Canada. Simulation results show that the proposed models can aggregate EVs for frequency regulation; meanwhile, the EV owners can obtain monetary rewards. The simulation also shows that including battery degradation and dynamic EV usage increases the model accuracy. By using the proposed approaches, the high cost and the low efficiency power generation units for frequency regulation can be compensated or partially replaced by EVs, which will reduce the generation cost and greenhouse gas emissions
Evaluation of physical fitness and health of young children aged between 3 and 6 based on cluster and factor analyses
Abstract Background As life improves and sedentary time increases, young children's physical fitness gradually declines. Methods Multi-stage stratified whole cluster sampling was utilized to sample 5584 preschoolers. Young infants' morphology, function, and quality were revealed using cluster and factor analysis. Results The cluster analysis separated 3â6-year-olds into two genders: 1,551 men in group A "high physical fitness" 1,499 men in group B "low physical fitness"; 1,213 women in group A and 1,321 women in group B. Young children's fitness was measured by standing long jump(1.00), weight(1.00), and height(1.00). A cluster analysis of 3â4-year-olds classified them into three groups: 272 âmuscular strength,â 75 âaverage physical fitness,â and 250 âlow agility.â Young children's health depends on weight (1.00), height (0.57), and chest circumference (0.54). A cluster analysis of the 4â5-year-olds classified them into two groups: 1070 âbalanceâ and 806 âmuscular strength.â Young childrenâs health depends on weight (1.00), height (0.74), and chest circumference (0.71). A cluster analysis of the 5â6-year-olds divided them into three groups: 1762 âhigh physical fitness,â 384 âobese,â and 105 âlow physical fitness.â Young childrenâs physical health depends on BMI (1.00), weight (1.00), and chest circumference (1.00). Factor analysis demonstrated that muscle strength, body shape, cardiovascular variables, and physical fitness composite components affected young children's health. Conclusion Women should focus on motor function and strength, while men on flexibility. Male group B âlow physical fitnessâ should focus on strength, motor function, and balance, whereas male group A âhigh physical fitnessâ should focus on flexibility. Then, female group A âhigh physical fitnessâ should emphasize variety.2) For 3â4-year-olds, group A âmuscular strengthâ should focus on flexibility, and group C âlow agilityâ on motor function. 3) For 4â5-year-olds, group A âbalancedâ should focus on strength and motor function; 4) For 5â6-year-olds, group B âobeseâ should emphasize weight loss, and group C âlow fitnessâ should emphasize strength, motor function, and flexibility; 5) Young childrenâs physical fitness depends on muscle strength, body shape, cardiovascular factors, and physical fitness composite
Experimental Investigations into the Effects of Lithology on Acoustic Emission
In order to study how lithology affects acoustic emissions (AE), a series of tunnel rock burst simulation experiments,
monitored by acoustic emission instruments, were conducted on granite, marble and basalt. By analyzing the
characteristic parameters, this study found that AE events occur more frequently during the rock burst process on granite
and basalt. Marble remains dormant until 75% of the loading time before the peak, at which point, cracks develop rapidly
and AE events dramatically increase. During the rock burst process, the AE energy release demonstrates that low energy
is released in the incubation phase and robust energy is released during the later phase. Before the rock burst occurs,
increased in the heterogeneity index Cv values of the AE event are subject to lithology. The Cv values of granite and
basalt have an increase of about 0.2-0.4, while marble shows an increase of 1.0-1.2. The heterogeneity index Cv value of
an AE event is in line with the rock burst process
Additional file 1 of Evaluation of physical fitness and health of young children aged between 3 and 6 based on cluster and factor analyses
Additional file 1