Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control

Esram, Trishan; Kimball, Jonathan W.; Krein, P.T.; Chapman, P.L.; Midya, P.

doi:10.1109/tpel.2006.880242

Cited by 467 publications

(160 citation statements)

References 36 publications

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“…The switching ripple caused by the power transistor is used in [16], whereas [17] relies on the oscillation of the instantaneous power transferred to the grid. Yet, the level of this inherent ripple may not suffice, as it depends on characteristics of the inverter, such as the switching frequency and the size of the filter capacitors [16], [17]. For this reason, standard MPPT algorithms, like P&O and INC, perturb the converter duty cycle to introduce additional variation of the operating point [18].…”

Section: Ripple Control Modulementioning

confidence: 99%

Power Reserves Control for PV Systems With Real-Time MPP Estimation via Curve Fitting

Batzelis

Kampitsis

Papathanassiou

2017

IEEE Trans. Sustain. Energy

156

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-In order for a PV system to provide a full range of ancillary services to the gird, including frequency response, it has to maintain active power reserves. In this paper, a new control scheme for the dc/dc converter of a two-stage PV system is introduced, which permits operation at a reduced power level, estimating the available power (maximum power point -MPP) at the same time. This control scheme is capable of regulating the output power to any given reference, from near-zero to 100% of the available power. The proposed MPP estimation algorithm applies curve fitting on voltage and current measurements obtained during operation to determine the MPP in real time. This is the first method in the literature to use the non-simplified singlediode model for the determination of the MPP and the five model parameters while operating at a curtailed power level. The developed estimation technique exhibits very good accuracy and robustness in presence of noise and rapidly changing environmental conditions. The effectiveness of the control scheme is validated through simulation and experimental tests using a 2 kW PV array and a dc/dc converter prototype at constant and varying irradiance conditions. Index Terms -Active power control, curve fitting, linearized converter model, maximum power point tracking (MPPT), photovoltaic (PV), power reserves, single-diode model.

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Section: Ripple Control Modulementioning

confidence: 99%

Power Reserves Control for PV Systems With Real-Time MPP Estimation via Curve Fitting

Batzelis

Kampitsis

Papathanassiou

2017

IEEE Trans. Sustain. Energy

156

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“…The MPP can be tracked through different MPPT algorithms that control the switching converter in order to obtain the maximum power under all conditions [4][5][6][7][8][9][10][11][12][13][14][15]. There are various methods, some of them are based on the well-known principle of perturb and observe (P&O) [7][8], on sliding mode control method [9][10], Ripple Correlation Control (RCC) [11], artificial neuronal networks or fuzzy based algorithms [12][13][14], amongst others.…”

Section: Fig 1 Structure Of the Grid-connected Pv Systemmentioning

confidence: 99%

“…There are various methods, some of them are based on the well-known principle of perturb and observe (P&O) [7][8], on sliding mode control method [9][10], Ripple Correlation Control (RCC) [11], artificial neuronal networks or fuzzy based algorithms [12][13][14], amongst others.…”

Section: Fig 1 Structure Of the Grid-connected Pv Systemmentioning

confidence: 99%

Backstepping Controller Design to Track Maximum Power in Photovoltaic Systems

Martín

Vázquez

2014

Automatika

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Original scientific paperThis work presents a new control method to track the maximum power point of a grid-connected photovoltaic (PV) system. A backstepping controller is designed to be applied to a buck-boost DC-DC converter in order to achieve an optimal PV array output voltage. This nonlinear control is based on Lyapunov functions assuring the local stability of the system. Control reference voltages are initially estimated by a regression plane, avoiding local maximum and adjusted with a modified perturb and observe method (P&O). Thus, the maximum power extraction of the generating system is guaranteed. Finally, a DC-AC converter is controlled to supply AC current in the point of common coupling (PCC) of the electrical network. The performance of the developed system has been analyzed by means a simulation platform in Matlab/Simulink helped by SymPowerSystem Blockset. Results testify the validity of the designed control method.Key words: Backstepping, Buck-boost converter, Maximum power point (MPP), Photovoltaic system Sinteza backstepping regulatora za praćenje maksimalne proizvodnje energije u fotonaponskim sustavima. Ovaj rad predstavlja novu metodu upravljanja za slije enje točke maksimalne snage fotonaponskog (PV) sustava. Dana je sinteza backstepping regulatora za primjenu u silazno-uzlaznom DC-DC pretvaraču za postizanje optimalnog izlaznog napona PV-a. Ova je nelinearna metoda upravljanja zasnovana na Ljapunovim funkcijama osiguravajući tako lokalnu stabilnost sustava. Upravljačke reference napona prvo su estimirane korištenjem regresijske ravnine izbjegavajući lokalne maksimume, a zatim podešene tzv. modificiranom perturbiraj i uoči metodom (P&O). Prema tome, zagarantirano je maksimalno izvlačenje energije iz sustava proizvodnje. Naposlijetku, DC-AC pretvaračem upravlja se na način da osigurava željena izmjenična struja u točki zajedničkog spoja (PCC) elektroenergetske mreže. Ponašanje razvijenog sustava analizirano je kroz simulacije provedene u Matlab/Simulink okruženju uz korištenje SymPowerSystem biblioteke.Ključne riječi: Backstepping, silazno-uzlazni pretvarač, točka maksimalne proizvodnje energije (MPP), fotonaponski sustav

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“…Tey and Mekhilef improved this algorithm by modulating the duty cycle of the dc-dc converter to accelerate the MPP process [12]. Other popular approaches include Parasitic Capacitance, Voltage based peak power tracking (VMPPT), Current based peak power tracking (CMPPT), fuzzy logic controller, Neural network, and Ripple correlation control (RCC) [13], [14], [15], [16]. They, however, have their own limitations that require further investigation, e.g, fuzzy-based controllers perform well under varying atmospheric conditions, but their effectiveness depends on the expert knowledge; VMPP and CMPP trackers are fast to reach the MPP, but the output power is influenced by load characteristics, environmental factors (insulation and temperature), and the type of tracker used.…”

Section: Introductionmentioning

confidence: 99%

Management of solar energy in microgrids using IoT-based dependable control

Phung

Villefromoy

2017

2017 20th International Conference on Electrical Machines and Systems (ICEMS)

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Abstract-Solar energy generation requires efficient monitoring and management in moving towards technologies for netzero energy buildings. This paper presents a dependable control system based on the Internet of Things (IoT) to control and manage the energy flow of renewable energy collected by solar panels within a microgrid. Data for optimal control include not only measurements from local sensors but also meteorological information retrieved in real-time from online sources. For system fault tolerance across the whole distributed control system featuring multiple controllers, dependable controllers are developed to control and optimise the tracking performance of photovoltaic arrays to maximally capture solar radiation and maintain system resilience and reliability in real time despite failures of one or more redundant controllers due to a problem with communication, hardware or cybersecurity. Experimental results have been obtained to evaluate the validity of the proposed approach.

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Dynamic maximum power point tracking of photovoltaic arrays using ripple correlation control

Cited by 467 publications

References 36 publications

Power Reserves Control for PV Systems With Real-Time MPP Estimation via Curve Fitting

Power Reserves Control for PV Systems With Real-Time MPP Estimation via Curve Fitting

Backstepping Controller Design to Track Maximum Power in Photovoltaic Systems

Management of solar energy in microgrids using IoT-based dependable control

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