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The flyback converter (a) the flyback converter topology with snubber circuit for PV application. (b) The flyback converter configuration when Q f is turned on, σ = 1. (c) The flyback converter configuration when Q f is turned off, σ = 0.
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Finite control set model predictive control (MPC) is a model-based control method that can include multi-objective optimization, constrained control, adaptive control, and online auto-tuning of weighting factors all in a single controller that exhibits fast dynamic tracking. This paper utilizes the model-based framework of MPC to develop a sensorle...
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... overview of the proposed ASC-MPPT for the flyback converter is demonstrated in Fig. 1. The flyback converter is presented in this paper for illustration as it provides electrical isolation, making it suitable for local-area dc micro-grid use [32]. Also, the low component count and low cost supplemented with a high voltage gain make it ideal for PV moduleintegrated topologies ...
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... controller relies on measurements of two voltage sensors: the PV input voltage (v PV ) and the MPPT converter output voltage (v o ), as shown in Fig. 1. Measuring the output voltage does not require additional sensors as the output voltage is already monitored in many applications, including microgrids [34], battery charge controllers [35], grid-connected inverters [36], and load monitoring [37]. Without loss of generality, the load shown in Fig. 1 is the model-based formulation of ...
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... converter output voltage (v o ), as shown in Fig. 1. Measuring the output voltage does not require additional sensors as the output voltage is already monitored in many applications, including microgrids [34], battery charge controllers [35], grid-connected inverters [36], and load monitoring [37]. Without loss of generality, the load shown in Fig. 1 is the model-based formulation of whatever is connected to the MPPT converter output ...
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... ) based on the voltage measurements. The input voltage v PV at the next step is also estimated at the different states of the system (switch on and switch off). These signals are then used within the MPC cost function to determine the reference MPPT voltage and the MPPT perturbation size. The switching state that minimizes the cost function g (in Fig. 1) is applied to the flyback dc-dc converter. Without loss of generality and a slight modification in the system model, the proposed technique could be applied to other converter topologies, and supply power to a dc-bus of an inverter or a dc ...
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... the flyback converter in Fig. 1. Discontinuous conduction mode (DCM) maximizes the ripple PV current; hence, the flyback converter is analyzed in continuous conduction mode (CCM). The state equations are derived based on the switching of the converter: when the switch is closed (σ = 1) and when the switch is open (σ = ...
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... observer model for the PV current can be obtained by analyzing the converter ( Fig. 1(a)) in continuous conduction mode during the two switching states σ ∈ {0, 1}. Using Kirchhoff's current law when the primary switch is closed (σ = 1), shown in Fig. 1(b), the input capacitor current i Cin (t ) can be written ...
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... observer model for the PV current can be obtained by analyzing the converter ( Fig. 1(a)) in continuous conduction mode during the two switching states σ ∈ {0, 1}. Using Kirchhoff's current law when the primary switch is closed (σ = 1), shown in Fig. 1(b), the input capacitor current i Cin (t ) can be written ...
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... The sixth and fifth strategy is implementing a comprehensive monitoring system that enables real-time performance tracking and fault detection. Detection of shading events or anomalies in the system can trigger appropriate actions, such as module cleaning or maintenance, to maintain optimal performance [73]. The seventh strategy is advanced control strategies can be employed to optimize the dynamic performance of PV systems under shading. ...
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... To overcome this problem, research [10][11][12][13][14] successfully modified the algorithm and produced more stable power. In addition, researches [15][16][17][18][19] utilizing more advanced algorithms such as the Predictive-MPPT Model to produce a faster response in achieving MP. ...
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... Constant Voltage [40] 79.50% Short Circuit Pulse [40] 89.70% Open Circuit Voltage [40] 93.70% LCASF [32] 95.00% LCA [32] 96.00% Proposed Current Sensorless V 99.51% FSCC [33] 97.50% Current Sensorless [41] 92.10% ASC-MPPT [42] 96.20% Hybrid FOCV-SCAM [29] 99.70% SC-MPC-MPPT [43] 99.40% ...
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... In these research papers [19][20][21][22] the performance of the MPC design procedure for DC-DC and DC-AC converters applied to a PV system was analyzed. The authors in [19] presented a continuous control set MPC designed for a DC-DC buck converter used in a MPPT of a PV module, while in [20] an adaptive MPC for current sensorless MPPT in PV systems was evaluated. ...
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... Next, the resonant frequency and damping resistor can be calculated by using Eqs. (20), (21) [40,41]. ...
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... "Grid-connected" PV systems generate the peak power at afternoon, and so, more energy is fed into the utility grid that covers a high portion of the energy demand. Hence, the large amount of energy demand is satisfied at peak times for covering the preferred high energy demand at the beginning and the end of the day (Metry and Balog 2020). ...
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... Voltage regulation and an MPPT method based on MPC for a two-stage grid-connected PV system were introduced in [31]. The work in [32] proposed a current sensor-less model predictive control of an MPPT algorithm. A new MPPT technique of a PV module based on MPC was presented in [33]. ...
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... However, both techniques are computationally intensive due to the use of non-linear transformations and linearization procedures [16,17]. A current sensorless method based on finite control set model predictive control (MPC) with auto-tuning is developed in [18]. MPC is a powerful technique, but its applicability is limited because of its complexity of implementation, mainly in small scale PV systems. ...
... and its derivative isė =ẋ −ẋ (18) = (A − LC)e + Dφ − LEw A new extended system z is introduced, which is formed by the estimated state and the estimation error, and its dynamics are as followṡ ...
Photovoltaic (PV) systems are among the most used alternatives for electrical power generation from renewable sources. To ensure that PV systems make the most of the available solar energy, maximum power point tracking (MPPT) schemes must be implemented, which usually require voltage and current sensors to track the PV power. This paper presents the design of a robust observer using the Attractive Ellipsoid Method to achieve a precise estimation of PV current under parametric uncertainty and output perturbations. The application of such an observer enables the PV generation system to operate in a current sensorless mode, which reduces the overall cost of the system and enhances its reliability. The convergence of the observer is guaranteed by solving an optimization problem which generates the optimal gains using Linear Matrix Inequalities (LMI). To prove the effectiveness of the proposed sensorless scheme, simulations are performed in Matlab under test profiles based on the EN50530 standard and parameter uncertainty conditions, obtaining an accurate estimation which is used for MPPT operation.
