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

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1140-1141
• /
• 2008

The paper proposes a novel control algorithm for tracking maximum power of PV generation system. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point is accurately tracked. The paper proposes a Fuzzy-Neuro control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. Fuzzy-Neuro has the advantages which are depicted both high performance and robustness in Fuzzy control and high adaptive control in Neural Network. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.

• Advances in Energy Research
• /
• v.8 no.3
• /
• pp.185-202
• /
• 2022

This research work focuses to design and simulate a 200W solar power system with electrical power conservation scheme as well as thermal power conservation modeling to improve power extraction from solar power plant. Many researchers have been already designed and developed different methods to extract maximum power while there were very researches are available on improving solar power thermally and mechanically. Thermal parameters are also important while discussing about maximizing power extraction of any power plant. A specific type of coolant which have very high boiling point is proposed to be use at the bottom surface of solar panel to reduce the temperature of panel in summer. A comparison between different maximum power point tracking (MPPT) technique and proposed MPPT technique is performed. Using this proposed Thermo-electrical MPPT (TE-MPPT) with Deep Learning Algorithm model 40% power is conserved as compared to traditional solar power system models.

• Journal of IKEEE
• /
• v.22 no.2
• /
• pp.460-475
• /
• 2018

In the variable-speed wind energy system, to achieve maximum power point tracking (MPPT), the wind turbine should run close to its optimal angular speed according to the wind speed. Non-linear control methods that consider the dynamic behavior of wind speed are generally used to provide maximum power and improved efficiency. In this perspective, the mechanical power is estimated using Kalman filter. And then, from the estimated mechanical power, the wind speed is estimated with Newton-Raphson method to achieve maximum power without anemometer. However, the blade shape and air density get changed with time and the generator efficiency is also degraded. This results in incorrect estimation of wind speed and MPPT. It causes not only the power loss but also incorrect wind resource assessment of site. In this paper, the adaptive maximum power point tracking control algorithm for wind turbine system based on the estimation of wind speed is proposed. The proposed method applies correction factor to wind turbine system to have accurate wind speed estimation for exact MPPT. The proposed method is validated with numerical simulations and the results show an improved performance.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.25 no.1
• /
• pp.1-5
• /
• 2020

Solar power generation systems require maximum power point tracking (MPPT) control to operate PV panels at their maximum power point (MPP). Most conventional MPPT algorithms are based on the slope-tracking concept. A typical slope-tracking method is the perturb and observe (P&O) algorithm. The P&O algorithm measures the current and voltage of a PV panel to find the operating point of the voltage at which the calculated power is maximized. However, the measurement error of the sensor causes irregularity in the calculation of the generated power and voltage control. This irregularity leads to the problem of not finding the correct MPP operating point. In this work, the power output of a PV panel based on the P&O algorithm is simulated by considering the insolation profiles from typical clear and cloudy weather conditions and the errors of current and voltage sensors. Simulation analysis suggests the optimal control period and perturbation voltage of MPPT to maximize its target efficiency under real weather conditions with sensor tolerance.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.27 no.4
• /
• pp.297-304
• /
• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.958-959
• /
• 2015

This paper proposes adaptive incremental conductance(A-IC) algorithm for maximum power point tracking(MPPT) control of photovoltaic. Conventional Perturbation & Observation(PO) and IC MPPT control algorithm generally uses fixed step size. A small fixed step size will cause the tracking speed to decrease and tracking accuracy of the MPP will decrease due to large fixed step size. Therefore, this paper proposes N-IC MPPT algorithm that adjust automatically step size according to operating conditions. To improve tracking speed and accuracy, when operating point is far from maximum power point(MPP), step size uses maximum value and when operating point is near from MPP, step size uses variable step size that adjust according to slope of P-V curve. The validity of MPPT algorithm proposed in this paper prove through compare with conventional IC MPPT algorithm.

• Journal of Power Electronics
• /
• v.11 no.2
• /
• pp.218-227
• /
• 2011

The subject of variable step size maximum power point tracking (MPPT) algorithms has been addressed in the literature. However, most of the addressed algorithms tune the variable step size according to two variables: the photovoltaic (PV) array voltage ($V_{PV}$) and the PV array current ($I_{PV}$). Therefore, both the PV array current and voltage have to be measured. Recently, maximum power point trackers that arc based on a single variable ($I_{PV}$ or $V_{PV}$) have received a great deal of attention due to their simplicity and ease of implementation, when compared to other tracking techniques. In this paper, two methods have been proposed to design a variable step size MPPT algorithm using only a single current sensor for stand-alone battery storage PV systems. These methods utilize only the relationship between the PV array measured current and the converter duty cycle (D) to automatically adapt the step change in the duty cycle to reach the maximum power point (MPP) of the PV array. Detailed analyses and flowcharts of the proposed methods are included. Moreover, a comparison has been made between the proposed methods to investigate their performance in the transient and steady states. Finally, experimental results with field programmable gate arrays (FPGAs) are presented to verify the performance of the proposed methods.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.11b
• /
• pp.153-156
• /
• 2001

Maximum Power Point Tracking(MPPT) is used in wind power generation systems to maximize wind power turbin output power, irrespective of wind speed conditions and of the load electrical characteristics. In this paper we do the equivalent modeling the mechanical energy of wind power turbine according to wind speed into the synchronous generator. We analyse the equivalent modeling output part of rectifier into DC/DC converter input part theoretically. We design a control algorithm for variable voltage according to wind speed intensity and density so that load voltage of chopper is controlled steadily using the maximum power point tracking (MPPT) control method. We analyse a battery charging characteristics and a charging circuit for power storage enabling the supply of stable power to the load. We design a system and do the modeling of it analytically so that it supplies a stable power to the load by constructing a DC-AC inverter point. Also we design a charging circuit usable in actual wind power generation system of 30kW and confirm its validity.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.11a
• /
• pp.153-156
• /
• 2001

Maximum Power Point Tracking(MPPT) Is used in wind power generation systems to maximize wind power turbin output power, irrespective of wind speed conditions and of the load electrical characteristics. In this paper we do the equivalent modeling the mechanical energy of wind power turbine according to wind speed into the synchronous generator. We analyse the equivalent modeling output part of rectifier into DC/DC converter input part theoretically. We design a control algorithm for variable voltage according to wind speed intensity and density so that load voltage of chopper is controlled steadily using the maximum power point tracking(MPPT) control method. We analyse a battery charging characteristics and a charging circuit for power storage enabling the supply of stable power to the load. We design a system and do the modeling of it analytically so that it supplies a stable power to the load by constructing a DC-AC inverter point. Also we design a charging circuit usable in actual wind power generation system of 30kW and confirm its validity.