• Title/Summary/Keyword: predictive power

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A Model Predictive Tracking Control Algorithm of Autonomous Truck Based on Object State Estimation Using Extended Kalman Filter (확장 칼만 필터를 이용한 대상 상태 추정 기반 자율주행 대차의 모델 예측 추종 제어 알고리즘)

  • Song, Taejun;Lee, Hyewon;Oh, Kwangseok
    • Journal of Drive and Control
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    • v.16 no.2
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    • pp.22-29
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    • 2019
  • This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

MPC-based Two-stage Rolling Power Dispatch Approach for Wind-integrated Power System

  • Zhai, Junyi;Zhou, Ming;Dong, Shengxiao;Li, Gengyin;Ren, Jianwen
    • Journal of Electrical Engineering and Technology
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    • v.13 no.2
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    • pp.648-658
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    • 2018
  • Regarding the fact that wind power forecast accuracy is gradually improved as time is approaching, this paper proposes a two-stage rolling dispatch approach based on model predictive control (MPC), which contains an intra-day rolling optimal scheme and a real-time rolling base point tracing scheme. The scheduled output of the intra-day rolling scheme is set as the reference output, and the real-time rolling scheme is based on MPC which includes the leading rolling optimization and lagging feedback correction strategy. On the basis of the latest measured thermal unit output feedback, the closed-loop optimization is formed to correct the power deviation timely, making the unit output smoother, thus reducing the costs of power adjustment and promoting wind power accommodation. We adopt chance constraint to describe forecasts uncertainty. Then for reflecting the increasing prediction precision as well as the power dispatcher's rising expected satisfaction degree with reliable system operation, we set the confidence level of reserve constraints at different timescales as the incremental vector. The expectation of up/down reserve shortage is proposed to assess the adequacy of the upward/downward reserve. The studies executed on the modified IEEE RTS system demonstrate the effectiveness of the proposed approach.

Machine learning-based Fine Dust Prediction Model using Meteorological data and Fine Dust data (기상 데이터와 미세먼지 데이터를 활용한 머신러닝 기반 미세먼지 예측 모형)

  • KIM, Hye-Lim;MOON, Tae-Heon
    • Journal of the Korean Association of Geographic Information Studies
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    • v.24 no.1
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    • pp.92-111
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    • 2021
  • As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

Novel Control of a Modular Multilevel Converter for Photovoltaic Applications

  • Shadlu, Milad Samady
    • Transactions on Electrical and Electronic Materials
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    • v.18 no.2
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    • pp.103-110
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    • 2017
  • The number of applications of solar photovoltaic (PV) systems in power generation grids has increased in the last decade because of their ability to generate efficient and reliable power in a variety of low installation in domestic applications. Various PV converter topologies have therefore emerged, among which the modular multilevel converter (MMC) is very attractive due to its modularity and transformerless features. The modeling and control of the MMC has become an interesting issue due to the extremely large expansion of PV power plants at the residential scale and due to the power quality requirement of this application. This paper proposes a novel control method of MMC which is used to directly integrate the photovoltaic arrays with the power grid. Traditionally, a closed loop control has been used, although circulating current control and capacitors voltage balancing in each individual leg have remained unsolved problem. In this paper, the integration of model predictive control (MPC) and traditional closed loop control is proposed to control the MMC structure in a PV grid tied mode. Simulation results demonstrate the efficiency and effectiveness of the proposed control model.

Efficiency Improvement of Synchronous Boost Converter with Dead Time Control for Fuel Cell-Battery Hybrid System

  • Kim, Do-Yun;Won, Il-Kuen;Lee, Jung-Hyo;Won, Chung-Yuen
    • Journal of Electrical Engineering and Technology
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    • v.12 no.5
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    • pp.1891-1901
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    • 2017
  • In this paper, optimal control of the fuel cell and design of a high-efficiency power converter is implemented to build a high-priced fuel cell system with minimum capacity. Conventional power converter devices use a non-isolated boost converter for high efficiency while the battery is charged, and reduce its conduction loss by using MOSFETs instead of diodes. However, the efficiency of the boost converter decreases, since overshoot occurs because there is a moment when the body diode of the MOSFET is conducted during the dead time and huge loss occurs when the dead time for the maximum-power-flowing state is used in the low-power-flowing state. The method proposed in this paper is to adjust the dead time of boost and rectifier switches by predicting the power flow to meet the maximum efficiency in every load condition. After analyzing parasite components, the stability and efficiency of the high-efficiency boost converter is improved by predictive compensation of the delay component of each part, and it is proven by simulation and experience. The variation in switching delay times of each switch of the full-bridge converter is compensated by falling time compensation, a control method of PWM, and it is also proven by simulation and experience.

Reducing Common-Mode Voltage of Three-Phase VSIs using the Predictive Current Control Method based on Reference Voltage

  • Mun, Sung-ki;Kwak, Sangshin
    • Journal of Power Electronics
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    • v.15 no.3
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    • pp.712-720
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    • 2015
  • A model predictive current control (MPCC) method that does not employ a cost function is proposed. The MPCC method can decrease common-mode voltages in loads fed by three-phase voltage-source inverters. Only non-zero-voltage vectors are considered as finite control elements to regulate load currents and decrease common-mode voltages. Furthermore, the three-phase future reference voltage vector is calculated on the basis of an inverse dynamics model, and the location of the one-step future voltage vector is determined at every sampling period. Given this location, a non-zero optimal future voltage vector is directly determined without repeatedly calculating the cost values obtained by each voltage vector through a cost function. Without utilizing the zero-voltage vectors, the proposed MPCC method can restrict the common-mode voltage within ± Vdc/6, whereas the common-mode voltages of the conventional MPCC method vary within ± Vdc/2. The performance of the proposed method with the reduced common-mode voltage and no cost function is evaluated in terms of the total harmonic distortions and current errors of the load currents. Simulation and experimental results are presented to verify the effectiveness of the proposed method operated without a cost function, which can reduce the common-mode voltage.

Space Vector Modulation based on Model Predictive Control to Reduce Current Ripples with Subdivided Space Voltage Vectors (전류 리플 저감을 위한 세분화된 공간전압벡터를 이용한 모델 예측 제어 기반의 SVM 방법)

  • Moon, Hyun-Cheol;Lee, June-Seok;Lee, June-Hee;Lee, Kyo-Beum
    • The Transactions of the Korean Institute of Power Electronics
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    • v.22 no.1
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    • pp.18-26
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    • 2017
  • This paper proposes the model predictive control with space vector modulation (SVM) method for current control of voltage-source inverter. Unlike the conventional method using a limited number of voltage vectors by switching states, the proposed method can consider various voltage vectors to identify the optimized voltage vector. The various voltage vectors are obtained by subdividing existing voltage vectors. The optimized voltage vector that minimizes the cost function is selected and applied to the inverter by using the SVM. The various voltage vectors and SVM reduce current ripples in the output AC side of the inverter compared with the conventional method. The effectiveness and performance of the proposed method are verified through simulation and experiment with a three-phase two-level voltage-source grid-connected inverter.

Model Predictive Torque Control of Surface Mounted Permanent Magnet Synchronous Motor Drives with Voltage Cost Functions

  • Zhang, Xiaoguang;Hou, Benshuai;He, Yikang;Gao, Dawei
    • Journal of Power Electronics
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    • v.18 no.5
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    • pp.1369-1379
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    • 2018
  • In this paper, a model predictive torque control (MPTC) without the use of a weighting factor for surface mounted permanent-magnet synchronous machine (SPMSM) drive systems is presented. Firstly, the desired voltage vector is predicted in real time according to the principles of deadbeat torque and flux control. Then the sector of this desired voltage vector is determined. The complete enumeration for testing all of the feasible voltage vectors is avoided by testing only the candidate vectors contained in the sector. This means that only two voltage vectors in the sector need to be tested for selecting the optimal voltage vector in each control period. Thus, the calculation time can be reduced when compared with the conventional enumeration method. On the other hand, a novel cost function that only includes the dq-axis voltage errors between the desired voltage and candidate voltage is designed to eliminate the weighting factor used in the conventional MPTC. Thus, the control complexity caused by the tuning of the weighting factor is effectively decreased when compared with the conventional MPTC. Simulation and experimental investigation have been carried out to verify the proposed method.

Assessing the Impacts of Errors in Coarse Scale Data on the Performance of Spatial Downscaling: An Experiment with Synthetic Satellite Precipitation Products

  • Kim, Yeseul;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.33 no.4
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    • pp.445-454
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    • 2017
  • The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.

A High Performance Permanent Magnet Synchronous Motor Servo System Using Predictive Functional Control and Kalman Filter

  • Wang, Shuang;Zhu, Wenju;Shi, Jian;Ji, Hua;Huang, Surong
    • Journal of Power Electronics
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    • v.15 no.6
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    • pp.1547-1558
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    • 2015
  • A predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) servo systems is proposed in this paper. The PFC-based method is first introduced in the control design of speed loop. Since the accuracy of the PFC model is influenced by external disturbances and speed detection quantization errors of the low distinguishability optical encoder in servo systems, it is noted that the standard PFC method does not achieve satisfactory results in the presence of strong disturbances. This paper adopted the Kalman filter to observe the load torque, the rotor position and the rotor angular velocity under the condition of a limited precision encoder. The observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC method, called the PFC+Kalman filter method, is presented, and a high performance PMSM servo system was achieved. The validity of the proposed controller was tested via experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.