• Proceedings of the KIPE Conference
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• 2019.07a
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• pp.213-215
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• 2019

태양광 어레이는 일사량과 온도에 의해 출력 특성이 변화하여 전역 최대 전력 점 추종 기법(GMPPT, Global Maximum Power Point Tracking)을 필요로 한다. 기존의 P&O, IncCond 등의 알고리즘은 음영이 발생한 태양광 어레이에서 전역 최대 전력 점을 추종하지 못한다. 본 논문에서는 태양광 어레이의 전압, 전류와 전력의 상관관계를 이용하여 전역 최대 전력점을 추종하는 기법을 제안한다. 제안된 제어기법은 3kW 태양광 인버터 시스템을 구성하여 시뮬레이션을 통해 타당성을 검증하였다.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.38-45
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• 2021

The photovoltaic module has the characteristic of changing its output characteristics depending on the amount of radiation and temperature, where the arrays that connect them in series and parallel also have the same characteristics. These characteristics require the MPPT technique to find the maximum power point. Existing P&O and IncCond cannot find the global maximum power point (GMPP) for partial shading. Moreover, in the case of Improved-GMPPT and Enhanced Search-Skip-Judge-GMPPT, GMPP due to partial shading can be found, but the variation in the open voltage during temperature fluctuations will affect the operation of the Skip and will not be able to perform accurate MPPT operation. In this study, we analyzed the correlation between voltage, current, and power under solar module and array conditions. We also proposed a technique to find the maximum power point even for temperature fluctuations using not only the amount of radiation but also the temperature coefficient. The proposed control technique was verified through simulations and experiments by constructing a 2.5 kW single-phase solar power generation system.

• Journal of Power Electronics
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• v.18 no.3
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• pp.841-852
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• 2018

This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1248-1258
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• 2019

This paper presents a moth-flame optimization (MFO)-based maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The MFO algorithm is a new optimization method that exhibits satisfactory performance in terms of exploration, exploitation, local optima avoidance, and convergence. Therefore, the MFO algorithm is quite suitable for solving multiple peaks of PV systems under partial shading conditions (PSCs). The proposed MFO-MPPT is compared with four MPPT algorithms, namely the perturb and observe (P&O)-MPPT, incremental conductance (INC)-MPPT, particle swarm optimization (PSO)-MPPT and whale optimization algorithm (WOA)-MPPT. Simulation and experiment results demonstrate that the proposed algorithm can extract the global maximum power point (MPP) with greater tracking speed and accuracy under various conditions.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.7-8
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• 2014

본 논문에서는 보편적으로 태양광 시스템에 적용되는 MPPT(Maximum Power Point Tracking) 기술들이 가지는 단점을 상호 보완하는 하이브리드 MPPT 제어 알고리즘을 제안하였다. 널리 사용되고 있는 MPPT 기술들을 비교하여 분석하였고, 이를 바탕으로 각 기술들의 단점을 상호 보완할 수 있는 요소들을 추출하여 MPPT 알고리즘에 적용하였다. 구현된 알고리즘은 최대전력점으로부터 동작점이 떨어져 있는 경우 빠르게 추적할 수 있는 속응성을 가지며, 정상상태에서는 안정도를 높이고, 국부적인 최대전력점(LMPP; Local Maximum Power Point) 발생 시 이를 감지하여 전체 특성 곡선의 최대전력점(GMPP; Global Maximum Power Point)을 찾아가도록 하였다. 또한 시뮬레이션을 통해 그 특성을 확인하였다.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1327-1337
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• 2017

Photovoltaic (PV) systems are influenced by disproportionate impacts on energy production caused by frequent mismatch cases. The occurrence of multiple maximum power points (MPPs) adds complexity to the tracking process in various PV systems. However, current maximum-power point tracking (MPPT) techniques exhibit limited performance. This paper introduces an enhanced simulated annealing (ESA)-based GMPPT technique against multiple MPP issues in P-V curve with different PV system structures. The proposed technique not only distinguishes global and local MPPs but also performs rapid convergence speed and high tracking accuracy of irradiance changing and restart capability detection. Moreover, the proposed global maximum power tracking algorithm can be applied in the central converter of DMPPT and hybrid PV system to meet various application scenarios. Its effectiveness is verified by simulation and test results.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1390-1391
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• 2011

태양광 발전 시스템에서의 최대 전력점 추적(MPPT, Maximum Power Point Tracking)제어를 실험을 하기 위해 부분선형 다이오드 모델을 이용한 태블로 해석을 통해 태양광 발전 시스템의 태양전지 모듈의 특성을 시뮬레이션 하였다. 태양전지 모듈의 V-I 특성과 태양전지 패널을 직렬-병렬 어레이로 연결 시, 부분 그늘 문제(Partial Shading Problem)의 지역 최대 점(global peak)을 시뮬레이션을 통해 확인하였다.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1805-1818
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• 2018

This paper proposes a novel hybrid maximum power point tracking (MPPT) algorithm combining a Whale Optimization Algorithm (WOA) and the conventional Perturb & Observation (P&O) to track/extract the highest amount of power from a solar photovoltaic (SPV) system working under partial shading conditions (PSCs). The proposed hybrid algorithm is based on a WOA which predicts the initial global peak (GP) and is followed by P&O in the final stage to achieve a quicker convergence to a GP. Thus, this hybrid algorithm overcomes the computational burden encountered in a standalone WOA, grey wolf optimization (GWO) and hybrid GWO reported in the literature. The conventional algorithm searches for the maximum power point (MPP) in the predicted region by the WOA. The proposed MPPT technique is modelled and simulated using MATLAB/Simulink for simulating an environment to check its effectiveness in accurately tracking the MPP during the GP region. This hybrid algorithm is compared with a standalone WOA, GWO and hybrid GWO. From the simulating results, it is shown that the proposed algorithm offers high tracking performance and that it increases the output power level of a SPV system under partial shading. The algorithm also verified experimentally on various PSCs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.281-292
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• 2016

The proposed scheme is based on the modified perturb and observe (P&O) algorithm combined with the sliding mode technique. A modified P&O algorithm based sliding mode controller is developed to study the effects of partial shade, temperature, and insolation on the performance of maximum power point tracking (MPPT) used in photovoltaic (PV) systems. Under partially shaded conditions and temperature, the energy conversion efficiency of a PV array is very low, leading to significant power losses. Consequently, increasing efficiency by means of MPPT is particularly important. Conventional techniques are easy to implement but produce oscillations at MPP. The proposed method is applied to a model to simulate the performance of the PV system for solar energy usage, which is compared to the conventional methods under non-uniform insolation improving the PV system utilization efficiency and allowing optimization of the system performance. The modified perturb and observe sliding mode controller successfully overcomes the issues presented by non-uniform conditions and tracks the global MPP. Compared to MPPT techniques, the proposed technique is more efficient; it produces less oscillation at MPP in the steady state, and provides more precise tracking.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.