Power management system for RTG crane using fuzzy logic controller

Pietrosanti, Stefano; Alasali, Feras; Holderbaum, William

doi:10.1016/j.seta.2020.100639

Cited by 11 publications

(16 citation statements)

References 22 publications

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“…The analysis shows that the equivalent conversion method of the horizontal motion mechanism of the crane's cart and trolley is the same as that expressed by Eqs. (4) and (5). According to Eq.…”

Section: Equivalent Load Torque and System Dynamic Equation Of Crane's Hoist Motor Drag Systemmentioning

confidence: 99%

“…The motor and drive system of the running or lifting mechanism constitute the motor drive system of the crane. In the life cycle of the crane, 97%-98% of the operating cost is used to maintain the electric charge of the crane motor drive system [5,6]. Thus, effective control of the crane motor drive system is an important means to improve the crane automation level, energy consumption, and crane operation efficiency [7,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

Zhong

Ma²,

Hao

et al. 2021

IEEE Access

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To allow the hoisting motor drive system of a crane to track a load torque quickly, a linearization method was used to transform a motor nominal dynamics model into two decoupled linear rotor speed and flux linkage subsystems. The method based on the theory of differential geometry was a precise feedback method. Two active disturbance rejection controllers (ADRCs) with identical structures were designed for the rotor speed and flux linkage subsystems. The extended state observer of the ADRC could estimate the unmodeled dynamics of the motor, the variation of motor parameters due to heating, and the unknown disturbances of the motor system to determine the total disturbances of the system. A closed-loop system with ADRC and an open-loop system were compared. The motor's full-load starting time was reduced by about 50%. When the motor operated smoothly at different load rates and the rated load was suddenly applied, the electromagnetic torque fluctuation range did not exceed 20 N• m. The rotor flux was always stable at the reference value. The motor speed decreased, but the amount of decrease did not exceed 7 rad/s. The closed-loop system had a significant energy-saving effect during the motor's starting process. The power saving rate was about 55%-59% if the motor started with a light load. The power saving rate could reach 71% if the motor started with a heavy load. The ADRC system could accurately estimate the unknown model of the rotor speed and flux linkage subsystems, and adapt to parameter variations of the motor stator and rotor resistance in the range of ±10%. Motor: Te, TL Electromagnetic and load torques. TN Nominal torque. ET Electromagnetic torque tracking error. np Motor's pole logarithm. Je Rotational inertia of the rotor. n Rotor speed. ω Electric angular velocity. ωr Mechanical angular velocity. α, β Static two-phase coordinate axis.

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Section: Equivalent Load Torque and System Dynamic Equation Of Crane's Hoist Motor Drag Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

Zhong

Ma²,

Hao

et al. 2021

IEEE Access

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“…However, the proposed optimization algorithms in References 19‐23 have only targeted solving single‐objective function problems and needed an understanding about power generation with faultless in the RES. To overcome these limitations, the researchers in References 24‐27 proposed hybrid optimization algorithms using self‐learning techniques with heuristic algorithms. For example, the authors in References 24 and 26, developed a hybrid optimization algorithm by using the fuzzy logic method and PSO to diminish the fuel operational cost and transmission power loss.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 27 to address the OPF problem, a self‐learning technique was used to develop a hybrid optimization model based on the fuzzy clustering model and wavelet mutation strategy. However, the research studies in References 24‐27 assumed the perfect power generation knowledge for the RES without considering the volatile and non‐smoothed generation nature of RES units or investigating the benefits of estimating the RES generations profiles, which have a substantial influence on the performance of an electrical grid. Furthermore, due to the model complexity with different optimization techniques and the highly computational cost of these models, the hybrid optimization algorithms 24‐27 suffer when the OPF problem includes multiobjective functions (very complex) or high‐dimensional problems.…”

Section: Introductionmentioning

confidence: 99%

An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

Alasali

Nusair

Obeidat

et al. 2021

Int Trans Electr Energ Syst

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Summary An optimal power management solution is a potential tool to produce cost‐effective and environmentally friendly power supply using renewable energy sources (RESs) for the electrical power network. Therefore, the article introduces a novel optimization algorithm inspired by the vitality, namely, Manta Ray Foraging Optimization (MRFO), to figure out both multi‐ and single‐objective problems of optimal power flow (OPF) incorporating stochastic RES. The OPF problems are designed by considering four different objective functions: transmission power loss, emission index, fuel operational costs, and voltage deviation. The stochastic and volatile nature of RES increases the complexity of the OPF issue. In this study, a new MRFO algorithm and some modern metaheuristic algorithms were used to settle the issue of OPF, enhance the energy efficiency, and environmental and cost performance of the power network. The test cases, with and without RES, different RES locations on the network, increase in the load, and outages of some transmission lines, are considered by addressing the challenge of the proposed OPF. These cases are tested with bus systems as 30 and 118, and the outcome from the suggested MRFO is compared with six metaheuristic optimization algorithms. Moreover, OPF challenges are successfully settled by the MRFO algorithm and outperform the proposed metaheuristic optimization methods.

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“…If the load torque driven by the AC induction motor (hereinafter referred to as motor) is unknown and has a wide load-torque range, when selecting the motor, the rated power of the motor is generally determined according to the actual maximum load torque driven [1], [2]. For instance, to adapt to the hoisting load of a crane with a wide load-torque range and because the crane motion is typically started with a load, the motor's rated power is generally greater than the actual operating power, especially during a working cycle [3], [4]. Furthermore, the motor of the lifting mechanism usually operates with a light load, with no load, or with generating electricity [5].…”

Section: Introductionmentioning

confidence: 99%

Principle of Optimal Voltage Regulation and Energy-Saving for Induction Motor With Unknown Constant-Torque Working Condition

Zhong

Ma²,

Hao

2020

IEEE Access

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To achieve optimal voltage regulation and energy savings of an AC induction motor operating under constanttorque and variable-working-load conditions, and consider a motor's T-type and Γ-type equivalent circuit, the copper and iron losses of the stator were regarded as invariable losses that were only related to the stator voltage, and the copper losses of the rotor were regarded as variable losses that varied with the load torque. The total electrical loss formula and the optimal voltage regulation formula for the motor were deduced. The formulas revealed that the total electrical loss of the motor was affected by the stator voltage and the rotor load torque, and the optimal voltage regulation changed exponentially with the load torque of the motor. The calculation results of an engineering example showed that when s = 0.01-0.03 and the motor operated stably, the motor load torque error was not more than 1.8 Nꞏm based on the exact and approximate solutions of the slip s. The total electrical loss showed almost no calculation error when the working voltage of the motor was higher than 230 V. The optimal voltage regulation and its error increased with the increase in the motor load torque, but within the working voltage range of the motor, the optimal voltage regulation error did not exceed 6 V. The total electrical loss with the optimal voltage regulation mode and a variable load torque was less than the total electrical loss of a 380-or 220-V constant driving mode. With the increase in the load rate, the total electrical loss increased. When the motor's load did not exceed the medium load and the motor operated with optimal voltage regulation, the energy-saving effect was significant. In particular, when the motor operated without a load, the total electrical loss was almost 0. INDEX TERMS Induction motor, constant-torque working condition, equivalent circuit, total electrical loss, optimal voltage regulation, energy-saving.

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Power management system for RTG crane using fuzzy logic controller

Cited by 11 publications

References 22 publications

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

An analysis of optimal power flow strategies for a power network incorporating stochastic renewable energy resources

Principle of Optimal Voltage Regulation and Energy-Saving for Induction Motor With Unknown Constant-Torque Working Condition

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