Control of urban rail transit equipped with ground-based supercapacitor for energy saving and reduction of power peak demand

Gao, Zongyu; Fang, Jianjun; Zhang, YiNong; Jiang, Lan; Sun, Dong; Guo, Wei

doi:10.1016/j.ijepes.2014.11.019

Cited by 35 publications

(7 citation statements)

References 22 publications

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“…The algorithm used is independent of the system parameters and disturbances, so sliding mode control can be considered for the control of vehicle supercapacitor control systems. A hybrid vehicle supercapacitor system based on DC-DC electricity and batteries was proposed in Gao et al [7] for energy recovery and utilization. A regenerative braking energy storage system for subway networks based on supercapacitors (SC) was proposed in Shetty et al [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

Han¹,

Zhou²,

Shi³

et al. 2022

Computer Systems Science and Engineering

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Urban rail trains have undergone rapid development in recent years due to their punctuality, high capacity and energy efficiency. Urban trains require frequent start/stop operations and are, therefore, prone to high energy losses. As trains have high inertia, the energy that can be recovered from braking comes in short bursts of high power. To effectively recover such braking energy, an onboard supercapacitor system based on a radial basis function neural networkbased sliding mode control system is proposed, which provides robust adaptive performance. The supercapacitor energy storage system is connected to a bidirectional DC/DC converter to provide traction energy or absorb regenerative braking energy. In the Boost and Buck modes, the state-space averaging method is used to establish a model and perform exact linearization. An adaptive sliding mode controller is designed, and simulation results show that it can effectively solve the problems of low energy utilization and large voltage fluctuations in urban rail electricity grids, and maximise the recovery and utilization of regenerative braking energy.

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Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

Han¹,

Zhou²,

Shi³

et al. 2022

Computer Systems Science and Engineering

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“…Although experimental measured data were the base in these two papers, no model for trains is proposed. In [22,23] a primitive model of train power supply system is presented applying ordinary circuit elements. Although this technique reduces the complexity of the problem, a comparison is necessary between the model's outputs and measured data to approach a proper approximation and error reduction which seems to be missing in this research.…”

Section: Introductionmentioning

confidence: 99%

Applying a hybrid model considering the interaction between train and power system for energy consumption surveys

Haghbin¹,

Khsoravani²,

Tehrani³

et al. 2022

IET Intelligent Trans Sys

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Power supply system plays an important role in the operation of urban rail systems. It has to provide high-quality energy for trains continuously. Train's energy consumption is varied directly with operation conditions such as train's headway, train configuration, line characteristic and many other factors. Therefore, using a powerful simulation tool for estimating energy consumption considering these factors seems inevitable. This paper tries to develop a hybrid model in which the train model is based on acquired data from real measurement and an adapted network electrical model. Train empirical model is based on neural network on the basis of online data measured in line one of Shiraz metro and used for training of model. Then benefiting from optimization algorithms, a novel model of energy supply system of trains, including Traction Substations (TSS) and Overhead Catenary System (OCS), is proposed. Finally, the behaviour of trains during operation in 15-min headway was simulated and the results under same conditions were compared with practical measurements. Currently, this model helps to determine power system behaviour during different operation conditions, predicting energy consumption for different headways, Train's driver behaviour during acceleration, deceleration and braking and also operation risk analysis during traction substations outages.

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“…The authors of (Ghasemi et al, 2015; Xiao et al, 2011; Ploeg et al, 2013) using the data of the previous and following trains, a multiple trains cooperative controller is developed to keep the tracking interval at a certain distance. The authors of (Gao et al, 2015; Shi‐Gen et al, 2015) proposed a cooperative control method of a train platoon to ensure the stability of multiple train tracking operations. A topic that deals with the difficulty of commanding a multi‐agent robotic system to achieve a shared purpose, such as search, exploration, surveillance, and rescue missions.…”

Section: Introductionmentioning

confidence: 99%

Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints

2022

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The stability of each train, high control accuracy, and minimum safe separation distance are important indexes to measure the performance of the cooperative control system of multiple trains. In this article, aiming at the problem of low accuracy of multiple trains cooperative control with nonlinear running resistance and external disturbance, the distributed cooperative robust adaptive control scheme for multiple trains with RBFNN position output constraints based on train running curve tracking is proposed. Multiple different control techniques are offered for different trains, and that they are based on local knowledge of position, speed, and acceleration. The leading train's speed and position precisely match the planned operation curve, while the following train keeps the tracking interval at the minimum safe distance between two trains. In order to reduce the influence of the uncertainty of basic resistance parameters and external interference on the cooperative control of multiple trains, the parameter uncertainties are compensated by adding a robust adaptive law to the multiple trains control based on position output constraints. The lumped exogenous disturbances (additional resistance, external interference, measurement noise, etc.) are estimated using an RBFNN approximator for the unknown term of the cooperative system. The stability of the cooperative operation of multiple trains is confirmed using the Lyapunov stability theorem. The performance of the proposed scheme was evaluated by the cooperative control system of multiple trains in predecessor following (PF) and bidirectional control (BC) modes.

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Control of urban rail transit equipped with ground-based supercapacitor for energy saving and reduction of power peak demand

Cited by 35 publications

References 22 publications

Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

Adaptive Sliding Mode Control Method for Onboard Supercapacitors System

Applying a hybrid model considering the interaction between train and power system for energy consumption surveys

Cooperative robust adaptive control of multiple trains based on RBFNN position output constraints

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