Abstract:This paper investigates how to increase the efficiency of a photovoltaic/energy storage generation unit supplying dynamic loads by regulating and managing both the photovoltaic generator and the storage battery charge-discharge modes. The proposed photovoltaic/energy storage unit is proposed to supply an induction motor driven industrial pump with controlled speed and/or a DC motor driven water pump. An optimal proportional-integral-derivative control based on an Artificial Bee Colony Optimization algorithm is… Show more
“…The supply of unstable power can result in an excessive charging voltage or an insufficient charging current, and can also lead to battery over-discharge when there is continuously no power supply to the battery because of long-term insufficient sunlight [6]. Therefore, this study employed an enhanced four-stage charging method, in which a maximum power point tracking (MPPT) [14,15] was combined with the conventional stage charging method with the addition of a pre-charging stage [13,[16][17][18].…”
Section: Charge and Discharge Control Strategymentioning
To overcome the unstable photovoltaic input and high randomness in the conventional three-stage battery charging method, this paper proposes a charging control strategy based on a combination of maximum power point tracking (MPPT), and an enhanced four-stage charging algorithm for a photovoltaic power generation energy storage system. This control algorithm ensures that the charging process is not affected by fluctuations in the photovoltaic power. The discharge bus waveform, push–pull discharge load switching waveform, push–pull circuit efficiency, and voltage and current regulation accuracies of the system were investigated. The experimental results show that the charging process is consistent with the designed four-stage charging control algorithm, the voltage and current regulation accuracies satisfy the charging requirements, the busbar remained stable during the battery charging and discharging switch, and the battery balancing effect was good.
“…The supply of unstable power can result in an excessive charging voltage or an insufficient charging current, and can also lead to battery over-discharge when there is continuously no power supply to the battery because of long-term insufficient sunlight [6]. Therefore, this study employed an enhanced four-stage charging method, in which a maximum power point tracking (MPPT) [14,15] was combined with the conventional stage charging method with the addition of a pre-charging stage [13,[16][17][18].…”
Section: Charge and Discharge Control Strategymentioning
To overcome the unstable photovoltaic input and high randomness in the conventional three-stage battery charging method, this paper proposes a charging control strategy based on a combination of maximum power point tracking (MPPT), and an enhanced four-stage charging algorithm for a photovoltaic power generation energy storage system. This control algorithm ensures that the charging process is not affected by fluctuations in the photovoltaic power. The discharge bus waveform, push–pull discharge load switching waveform, push–pull circuit efficiency, and voltage and current regulation accuracies of the system were investigated. The experimental results show that the charging process is consistent with the designed four-stage charging control algorithm, the voltage and current regulation accuracies satisfy the charging requirements, the busbar remained stable during the battery charging and discharging switch, and the battery balancing effect was good.
“…Any micro grid needs some renewable sources and/or storage energy units [8][9][10]. Hydromatrix power generation is one of renewable energy sources.…”
The performance of controlled hybrid renewable hydro matrix wheels/ wind in micro grid application during the variations of water speed and load is described and investigated. This hybrid contained three wheels and three wind turbines modeled in MATLAB Simulink. This presented hybrid model consists of a water wheel and a generator in the main channel, wind turbine drives an induction generator, battery, variable static load, DC link, and control unit. Appropriate controllers are used to maintain the DC-link voltage constant at its desired value with variations of load, water and wind speed. The obtained simulated result shows that the studied hybrid system with the proposed controller and the storage system have a better performance of load voltage and current waveform compared with the case of not using an energy storage unit and the case of without a controller under the water, wind speed and load variations.
“…Also, algorithms based on artificial intelligence techniques such as fuzzy logic [13][14][15][16][17][18][19] and neural networks [20][21][22] have been used, as well as the implementation of optimization algorithms such as glowworm swarm [23], ant colony [24,25] and bee colony [26][27][28]. These algorithms are part of soft computing techniques and have the advantage of being easily implemented using embedded systems.…”
Abstract:The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.
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