A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition

Zhang, Wei; Zhou, Guopeng; Ni, Hao; Sun, Yong

doi:10.1109/access.2019.2950375

Cited by 38 publications

(20 citation statements)

References 31 publications

(29 reference statements)

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“…This approach, however, still suffers from slow speeds and high power loss [5], [18]. The next class of GMPPT methods involve artificial neural networks (ANN) [19]- [22]. Many of the bioinspired methods such as artificial bee colony [23], particle swarm optimization (PSO) [24], [25], gray wolfe optimization [26], [27], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Modified butterfly optimization algorithm is one of the bio-inspired algorithms that has been hybridized with a constant impedance method to improve the response time to one second [34]. Some other methods that have been combined into hybrid algorithms are PSO along with Distributed Evaluation [5], ANN with conventional and modified P&O or INC [19], [20]. However, these methods remain highly computationally intensive [35] and the accuracy of the output depends on the learning dataset and the initial conditions [29].…”

Section: Introductionmentioning

confidence: 99%

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Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Bharadwaj

2021

IEEE Access

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Global maximum power point tracking (GMPPT) refers to the extraction of the maximum power from photovoltaic (PV) modules in real time under changing ambient conditions. Due to the installation of PV systems in densely built-up areas, partial shading scenarios are commonplace. Commercially established GMPPTs suffer from low tracking speeds and inefficiency. A novel GMPPT algorithm is proposed here based on the rectangular power comparison (RPC), which exploits the fundamental relationship between the shading factor, the bypass diode voltage and the global maximum power point. The entire theoretical formulation of RPC is presented systematically for the first time. This method boasts of increased conversion speeds owing to the precomputation of the module voltage versus the shading factor correlations using the regression of diode model from the experimentally obtained bypass diode characteristics. The proposed method is simple to implement with the computational complexity of order n, which represents the number of uniquely shaded PV modules in a series string. The proposed approach addresses the much-needed intersection problem between the distributed and centralized PV systems and therefore targets PV strings which are most common in residential and small to medium scale commercial PV installations world over. The proposed approach is validated with the in-house developed prototype hardware set-up and software control implementation giving a 99% tracking efficiency with a recorded tracking time of 10 ms. The experimental results show 50 times improvement in speed and 95% increase in power gain as compared to the other popular existing methods namely scanning based GMPPT and local MPPT methods respectively, with negligible computational burden and less than 0.5% added cost to the conventional PV energy conversion system. INDEX TERMS Efficiency optimization, Global maximum power point tracking, Partial shading, Photovoltaic module measurements and Solar energy conversion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Bharadwaj

2021

IEEE Access

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“…For a large value of

Δ D

, the P&O algorithm tracks the MPP at a faster rate but has large steady‐state power oscillations at MPP, and for small values of

Δ D

the steady‐state power oscillations at MPP is low but the algorithm takes more time to track the MPP. To address this trade‐off, different methods such as optimised P&O [25], modified P&O [24, 26–28], model predictive control [29], adaptive step size based P&O (ASDFP&O) [30–35] etc. are presented in the literature.…”

Section: Introductionmentioning

confidence: 99%

Adaptive step size based drift‐free P&O algorithm with power optimiser and load protection for maximum power extraction from PV panels in stand‐alone applications

Madhu

Vyjayanthi

Modi

2021

IET Renewable Power Gen

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This paper presents an adaptive step size based drift‐free perturb and observe algorithm with power optimiser and load protection for maximum power extraction from photovoltaic panels in stand‐alone applications. The proposed algorithm consists of an adaptive step‐size technique, drift mitigation technique and power optimiser and load protection technique for fast maximum power extraction with less steady‐state power loss and protection features. The adaptive step‐size technique continuously monitors the absolute value of change in power (|ΔP|) to adjust the perturbation step size (ΔD) for fast tracking of maximum power point, and the drift mitigation technique mitigates the confusion in selecting the correct perturbation direction that exists in the conventional perturb and observe algorithm for a sudden change in irradiance conditions. Besides, the presented power optimiser and load protection uses a closed‐loop duty cycle (D) value for detecting possible underloading and overloading conditions, wherein, in case of underloading or overloading, it turns off the power optimiser to protect itself and the connected load from any damage or malfunctioning. The overall performance of the proposed algorithm, in terms of its dynamic operations, reduced steady‐state power loss, and protection features are evaluated using MATLAB simulations and verified with an experimental prototype. Comparative analysis of the proposed algorithm with state‐of‐the‐art perturb and observe techniques is carried out for validating its effectiveness and feasibility in real‐time applications.

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“…Furthermore, hybrid MPPT methods have been developed to improve the performance of the above methods, such as the modified P&O with an artificial neural network (MP&O-ANN) [28], overall distribution with PSO (OD-PSO) [29], differential evolutionary with PSO (DE-PSO) [30], P&O with PSO (P&O-PSO) [31], grey wolf optimization with P&O (GWO-P&O) [32], incremental conductance with firefly algorithm (INC-FA) [33], and cuckoo search with golden section search (CS-GSS) [34]. These methods can find the GMPP, but they are algorithmically complex and have a long computing time.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Photovoltaic Array Modeling and the MPPT Control Method under Partial Shading Conditions

Bai

Sun

Pachauri

et al. 2021

International Journal of Photoenergy

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On the basis of a five-parameter photovoltaic (PV) mathematical model, a multipeak output model of a PV array under partial shading conditions (PSCs) is obtained by MATLAB simulation. Simulation and experimental results demonstrate that the model can simulate the performance curves of the PV array under the PSCs. Optimized particle swarm optimization (OPSO) is used to control the multipeak output model that can quickly and accurately track the global maximum power point (GMPP) of PV modules under PSCs. Its main idea is to determine the initial position of particles and remove the acceleration factor and random number in traditional particle swarm optimization (PSO) algorithm. Additionally, according to the distance between two consecutive peak points, the maximum value of velocity is obtained. The advantages of the OPSO include the following: compared with the traditional PSO algorithm, the computing time is greatly shortened; and it is easy to achieve the MPPT with a low-cost microprocessor. In addition, a PV optimizer is designed to improve the output power of PV modules under PSCs, and simulation and experimentation have compared the output characteristics of PV modules in traditional control mode and optimized control mode under PSCs. The experimental results show that the PV optimizer improves the output power of the PV modules by 13.4% under the PSC.

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A Modified Hybrid Maximum Power Point Tracking Method for Photovoltaic Arrays Under Partially Shading Condition

Cited by 38 publications

References 31 publications

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Optimized Global Maximum Power Point Tracking of Photovoltaic Systems Based on Rectangular Power Comparison

Adaptive step size based drift‐free P&O algorithm with power optimiser and load protection for maximum power extraction from PV panels in stand‐alone applications

Investigation on Photovoltaic Array Modeling and the MPPT Control Method under Partial Shading Conditions

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