A hybrid method for maximum power tracking of a small scale CAES system

Kokaew, Vorrapath; Sharkh, Suleiman M.; Moshrefi-Torbati, M.

doi:10.1109/csndsp.2014.6923799

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…The estimated speed at the MPP will exhibit a limit cycle or large dither which is not desirable. Using the fine step in [70] improves this but small fluctuations remain.…”

Section: Review Of Variable Step Size Mppt Techniquesmentioning

confidence: 99%

“…This model has already been analysed in Appendix B and reported in [13,70,16]. The inductor current in the frequency domain I L (s) therefore, is given as…”

Section: A Small Signal Modelmentioning

confidence: 99%

“…In both [5] and [70], the P&O method, in which fixed speed step changes are used, results in the estimated MPPT fluctuating or oscillating. The estimated speed at the MPP will exhibit a limit cycle or large dither which is not desirable.…”

Section: Review Of Variable Step Size Mppt Techniquesmentioning

confidence: 99%

“…In [70], a hybrid P&O method is used. The derivative of power with respect to both speed (dP/dω) and the dc-dc converter duty cycle (dP/dD) are calculated.…”

Section: Review Of Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

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Maximum Power Point Tracking of a Small-Scale Compressed Air Energy Storage System

Kokaew

Sharkh²,

Moshrefi-Torbati

2016

IEEE Trans. Ind. Electron.

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The thesis is concerned with a small-scale compressed air energy storage (SS-CAES) system. Although these systems have relatively low energy density, they offer advantages of low environmental impact and ease of maintenance.The thesis focuses on solving a number of commonly known problems related to the perturb and observe (P&O) maximum power point tracking (MPPT) system for SS-CAES, including confusion under input power fluctuation conditions and operating point dither.A test rig was designed and built to be used for validation of the theoretical work. The rig comprised an air motor driving a permanent magnet DC generator whose power output is controlled by a buck converter. A speed control system was designed and implemented using a dSPACE controller. This enabled fast convergence of MPPT.Four MPPT systems were investigated. In the first system, the air motor characteristics were used to determine the operating speed corresponding to MPP for a given pressure. This was compared to a maximum efficiency point tracking (MEPT) system. Operating at the maximum power point resulted in 1% loss of efficiency compared to operating at the maximum efficiency point. But MPPT does not require an accurate model of the system that is needed for MEPT, which also requires more sensors.The second system that was investigated uses a hybrid MPPT approach that did not require a prior knowledge system model. It used the rate of change of power output with respect to the duty cycle of the buck converter as well as the change in duty cycle to avoid confusion under input power fluctuations. It also used a fine speed step in the vicinity of the MPP and a coarse speed step when the operating point was far from the MPP. Both simulation and experimental results demonstrate the efficiency of this proposed system. The third P&O MPPT system used a fuzzy logic approach which avoided confusion and eliminated operating point dither. This system was also implemented experimentally.A speed control system improved the controllable speed-range by using a buck-boost converter instead. The last MPPT system employed a hybrid P&O and incremental inductance (INC) approach to avoid confusion and eliminate operating point dither. The simulation results validate the design.Although the focus of the work is on SS-CAES, the results are generic in nature and could be applied to MPPT of other systems such as PV and wind turbine.

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“…The estimated speed at the MPP will exhibit a limit cycle or large dither which is not desirable. Using the fine step in [70] improves this but small fluctuations remain.…”

Section: Review Of Variable Step Size Mppt Techniquesmentioning

confidence: 99%

“…This model has already been analysed in Appendix B and reported in [13,70,16]. The inductor current in the frequency domain I L (s) therefore, is given as…”

Section: A Small Signal Modelmentioning

confidence: 99%

Section: Review Of Variable Step Size Mppt Techniquesmentioning

confidence: 99%

“…In [70], a hybrid P&O method is used. The derivative of power with respect to both speed (dP/dω) and the dc-dc converter duty cycle (dP/dD) are calculated.…”

Section: Review Of Maximum Power Point Tracking (Mppt)mentioning

confidence: 99%

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Maximum Power Point Tracking of a Small-Scale Compressed Air Energy Storage System

Kokaew

Sharkh²,

Moshrefi-Torbati

2016

IEEE Trans. Ind. Electron.

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“…The operating point is known as power curves. The power curves are the relationship between several parameters contained in the SS-CAES to the power produced [22]. Some of these parameters are air-motor speed (ω), air pressure (pAir), and electrical energy generated by the generator (P) on the loading.…”

Section: Introductionmentioning

confidence: 99%

Power curves prediction using empirical data regression on small scale compressed air energy storage

Widjonarko

Soenoko

Wahyudi

et al. 2019

JMES

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The key to optimizing the system is to know the operating point of the system at the time of loading, or it is known as the power curve. However, to identify the power curve, the existing method is to model the mathematical of the system. Therefore some component characteristics need to be known and need additional observations if the component variable is unknown, and it becomes a long identification process. So, in this exploratory research will be presented the way to find out the power curve of a system without modeling mathematical of the system, but by using the polynomial regression technique. This regression technique form is using the empirical data of the power curve form parameter on SS-CAES prototype. The method is based on five approach model in which is the variation of loading sampling data to be used with the purpose is to find the best sampling of prediction. The data will be analyzed in the form of statistical parameters and the graph to show the evaluation process of this technique. From the results of the regression can be concluded that the power curve of SS-CAES can be identified with a high correlation value of 0.997 (99,745% accuracy) and the best way to take samples of data to be used in this technique is presented in the paper.

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Control and Sizing of a Hybrid Battery and Compressed Air Energy Storage system

Omsin

Sharkh

Moshrefi-Torbati

2020

2020 2nd International Conference on Smart Power &Amp; Internet Energy Systems (SPIES)

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This paper discusses the sizing and control of a hybrid energy storage system comprising a battery and a compressed air energy storage (CAES) system. The CAES system is connected to the load through a boost converter that controls the air motor's speed to achieve maximum power point tracking (MPPT). A bidirectional converter is used to connect a battery to the load and maintain the output voltage constant. The air motor and battery sizes are estimated for a typical house in the Southern region of the UK. The battery is sized to buffer load fluctuations. All system models have been simulated using MATLAB/Simulink. Two scenarios are considered: a CAES only system controlled in constant voltage mode and a hybrid system comprising CAES with an MPPT controller and a battery with a voltage controller. The results demonstrate that the power rate of air motor is estimated properly by considering the difference between the generated power and demand power. The power difference called energy deficit is used to size the battery. The performance of CAES system is improved by hybridizing with a battery; the system maintains constant voltage when the CAES operates at maximum power point (MPP). The air motor in hybrid system controlled in MPPT mode has approximately 47% greater efficiency than that of air motor controlled in voltage mode.

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A hybrid method for maximum power tracking of a small scale CAES system

Cited by 6 publications

References 12 publications

Maximum Power Point Tracking of a Small-Scale Compressed Air Energy Storage System

Maximum Power Point Tracking of a Small-Scale Compressed Air Energy Storage System

Power curves prediction using empirical data regression on small scale compressed air energy storage

Control and Sizing of a Hybrid Battery and Compressed Air Energy Storage system

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