Abstract:One of the main renewable energy sources for the future is photovoltaic (PV) energy. Hence, working of the PV systems at maximum efficiency is taken into consideration in recent years. In this paper, for improving the performance of the global maximum power point tracking under partial shading conditions and uncertainty in parameters of DC-DC converter, a two-level adaptive control scheme is proposed. The proposed controller is capable of efficiently handling the uncertainties in the PV systems and the perturb… Show more
“…Therefore, it is apparent to improve the transient performance without the need for high adaptation gain. Similar work can be found in 14 . To the best of our knowledge no work has been done in this area dealing with such kind of problem.…”
Section: Introductionsupporting
confidence: 79%
“…For a decoupled analysis in 2-level MPPT control approach the time constant of the 1st-level must be smaller than the dynamics of environmental changes and the time constant of the 2nd-level of control must be less than the 1st-level. The significant difference between these time constants gives liberty to decouple the analyses for 1st and 2nd levels; this, simplifies the overall MPPT control design 2 , 14 . Figure 7 Representation of PV system with proposed 2-level MPPT control architecture.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…The basic idea of designing MRAC system is that the plant model output should always tracks the reference model output 17 , 18 . According to ( 5 ), the plant model for the proposed HFLAG-MRAC with positive coefficients is considered as 2 , 14 where the values of , , and can be interpreted from ( 5 ). The reference model is given by where is a positive gain and and should be chosen such that ζ = 1.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…2 is considered which is shown in Fig. 5 , in a similar way as 2 , 3 , 14 . Figure 5 Small signal model of PV power converter stage.…”
Section: Background and System Descriptionmentioning
confidence: 99%
“…The MRAC in 2-level MPPT ensures the improved transient performance of the PV system under rapid changes in the environmental conditions 2 , 14 . However, achieving improved transient performance with fast convergence in MRAC, high adaptation gain is needed.…”
Under rapidly changing environmental conditions, the model reference adaptive control (MRAC) based MPPT schemes need high adaptation gain to achieve fast convergence and guaranteed transient performance. The high adaptation gain causes high-frequency oscillations in the control signals resulting in numerical instability and inefficient operation. This paper proposes a novel high-frequency learning-based adjustable gain MRAC (HFLAG-MRAC) for a 2-level MPPT control architecture in photovoltaic (PV) systems to ensure maximum power delivery to the load under rapidly changing environmental conditions. In the proposed 2-level MPPT control architecture, the first level is the conventional ripple correlation control (RCC) that yields a steady-state ripple-free optimum duty cycle. The duty cycle obtained from the first level serves as an input to the proposed HFLAG-MRAC in the second level. In the proposed adaptive law, the adaptation gain varies as a function of the high-frequency ripple content of the tracking error. These high-frequency contents are the difference between the tracking error and its low-pass filtered version representing the fluctuations in output due to rapid changes in the environmental conditions. Thus, adjusting the adaptation gain by high-frequency content of the tracking error ensures fast convergence, guaranteed transient performance, and overall system stability without needing high adaptation gain. The adaptive law of the proposed HFLAG-MRAC is derived using the Lyapunov theory. Simulation studies, experimental analysis, and performance comparison with recent similar work validate the effectiveness of the proposed work.
“…Therefore, it is apparent to improve the transient performance without the need for high adaptation gain. Similar work can be found in 14 . To the best of our knowledge no work has been done in this area dealing with such kind of problem.…”
Section: Introductionsupporting
confidence: 79%
“…For a decoupled analysis in 2-level MPPT control approach the time constant of the 1st-level must be smaller than the dynamics of environmental changes and the time constant of the 2nd-level of control must be less than the 1st-level. The significant difference between these time constants gives liberty to decouple the analyses for 1st and 2nd levels; this, simplifies the overall MPPT control design 2 , 14 . Figure 7 Representation of PV system with proposed 2-level MPPT control architecture.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…The basic idea of designing MRAC system is that the plant model output should always tracks the reference model output 17 , 18 . According to ( 5 ), the plant model for the proposed HFLAG-MRAC with positive coefficients is considered as 2 , 14 where the values of , , and can be interpreted from ( 5 ). The reference model is given by where is a positive gain and and should be chosen such that ζ = 1.…”
Section: Proposed Methodologymentioning
confidence: 99%
“…2 is considered which is shown in Fig. 5 , in a similar way as 2 , 3 , 14 . Figure 5 Small signal model of PV power converter stage.…”
Section: Background and System Descriptionmentioning
confidence: 99%
“…The MRAC in 2-level MPPT ensures the improved transient performance of the PV system under rapid changes in the environmental conditions 2 , 14 . However, achieving improved transient performance with fast convergence in MRAC, high adaptation gain is needed.…”
Under rapidly changing environmental conditions, the model reference adaptive control (MRAC) based MPPT schemes need high adaptation gain to achieve fast convergence and guaranteed transient performance. The high adaptation gain causes high-frequency oscillations in the control signals resulting in numerical instability and inefficient operation. This paper proposes a novel high-frequency learning-based adjustable gain MRAC (HFLAG-MRAC) for a 2-level MPPT control architecture in photovoltaic (PV) systems to ensure maximum power delivery to the load under rapidly changing environmental conditions. In the proposed 2-level MPPT control architecture, the first level is the conventional ripple correlation control (RCC) that yields a steady-state ripple-free optimum duty cycle. The duty cycle obtained from the first level serves as an input to the proposed HFLAG-MRAC in the second level. In the proposed adaptive law, the adaptation gain varies as a function of the high-frequency ripple content of the tracking error. These high-frequency contents are the difference between the tracking error and its low-pass filtered version representing the fluctuations in output due to rapid changes in the environmental conditions. Thus, adjusting the adaptation gain by high-frequency content of the tracking error ensures fast convergence, guaranteed transient performance, and overall system stability without needing high adaptation gain. The adaptive law of the proposed HFLAG-MRAC is derived using the Lyapunov theory. Simulation studies, experimental analysis, and performance comparison with recent similar work validate the effectiveness of the proposed work.
The solar photovoltaic (PV) energy generation technology has grown rapidly. Herein, a novel continuous‐time Lyapunov‐based model reference adaptive control (LB‐MRAC) technique is suggested for determining the duty cycle of boost converter to maintain maximum power point (MPP) of the solar PV panel. For ensuring the fast‐tracking speed, the reference voltage of, respectively, MPP is calculated using perturb and observe (PO) and input to the MRAC technique. Using MATLAB/Simulink, the performances of the LB‐MRAC maximum power point tracking are comparatively analyzed with PO, incremental conductance, and adaptive neuro‐fuzzy inference system under diverse testing modes such as stand‐alone, partial shading, grid integrated, and real time. The proposed technique tracks MPP in just 3.7 ms having tracking efficacy between 99.15% and 99.59% under highly fluctuating irradiance and temperature with load uncertainties. In partial shading mode, four patterns (3 × 1 PV string) are devised and determined where that the suggested approach monitors the global MPP in merely 0.04 s with the highest power tracked and the least amount of shading loss. Next, the efficacy is checked in a three‐phase 50 kW grid‐integrated mode under realistic weather. Finally, the performance of the suggested continuous‐time LB‐MRAC technique is experimentally corroborated employing OPAL‐RT(OP4510) in the real‐time mode.
This study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.
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