Maximum Power Point Tracking for Photovoltaic System by Using Fuzzy Neural Network

Hameed, Waleed I.; Saleh, Ameer L.; Sawadi, Baha A.; Al‐Yasir, Yasir I. A.; Abd‐Alhameed, Raed A.

doi:10.3390/inventions4030033

Cited by 27 publications

(22 citation statements)

References 12 publications

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“…Furthermore, the comparative study of performance evaluation of QLT2FL may demonstrate with a well-known strategy under related condition, which is based hybrid fuzzy neural network (FNN) [10]. The FNN strategy performs tracking efficiency about 96%, some of the simulation result in irradiation condition about 800 W/m 2 shows the overshoot due to temperature changes.…”

Section: Simulation Results and Discussionmentioning

confidence: 98%

“…However, varying of solar irradiation conditions will cause oscillation, drift, and uncertainty problems in MPP tracking. Fuzzy neural network (FNN) [10] yield power tracking efficiency about 96%, however still suffers from overshoot when temperature changes occur. The MPP achievement can be observed from the P-V curve where its gradient close to zero.…”

Section: Introductionmentioning

confidence: 99%

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Q-Learning Hybrid Type-2 Fuzzy Logic Control Approach for Photovoltaic Maximum Power Point Tracking Under Varying Solar Irradiation Exposure

Iskandar¹,

Leksono²,

Joelianto³

2021

IJIES

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This paper presents a control strategy of maximum power point tracking and focuses on hybrid q-learning and type-2 fuzzy logic control strategy. Photovoltaics have non-linear voltage and current characteristics which influenced by temperature and exposure of solar irradiation so that the maximum power point (MPP) can change at any time. Unfortunately, operating conditions outside the MPP could reduce the efficiency of electrical power transfer from the photovoltaic system to the load. Q-learning (QL) has the potential to provide action decisions of duty cycle signal based on the state of the power gradient percentage range. The Q-learning hybrid method with Type-2 fuzzy logic control is proposed to provide correction of QL control signal by considering the uncertainty direction of power point shifting. The proposed strategy is important particularly to avoid a sudden change in solar irradiation exposure. The simulation results show that the Q-learning hybrid type-2 fuzzy logic control based MPPT response in the photovoltaic control system has tracking efficiency about 97%, rapid rise time of 0.08s, settling time of 0.23s, low overshoot and stable response for handling the change of irradiation and temperature exposure simultaneously.

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Section: Simulation Results and Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Q-Learning Hybrid Type-2 Fuzzy Logic Control Approach for Photovoltaic Maximum Power Point Tracking Under Varying Solar Irradiation Exposure

Iskandar¹,

Leksono²,

Joelianto³

2021

IJIES

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“…The PV generator can be presented by an electrical model [83], a polynomial model [84] or an efficiency model [34,85]. The proposed methodology uses the last possibility, applying the reduced Durisch's model [86], which allows the modelling of different PV cell technologies without astronomical calculations for the air mass.…”

Section: Renewable Energy Generator Modelsmentioning

confidence: 99%

Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes

et al. 2021

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The Green Deal and increased nutritional needs are driving factors in human activities nowadays. Agriculture is an essential economic sector that can profit from the application of renewable energy sources by the assimilation of off-grid, arid and barren terrains. Power supplied by hybrid systems for water pumping is a solution for overcoming the stochastic character of the renewable energy sources. This paper presents a sizing methodology for a hybrid system with wind and PV generation and water tank storage, based on the consideration of the entire energy conversion chain with energy models and a one-year operation simulation. The PV generator is modeled using a reduced Durisch’s model, while for the wind generator a piecewise interpolation is used. The methodology is applied for sites in Bulgaria with specific agricultural crops and meteorological data. Combinations of PV (different technologies) and wind (different types) generators and water tank capacities are considered and discussed. The combinations are compared on the basis of three criteria: the investment cost, the satisfaction of crop requirements and system oversizing. The possibility for the introduction of battery storage is also examined. The results show some trends in the hybrid system sizing and the possibility to apply the proposed methodology for various sites, generators and crops.

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“…Recurrent networks have feedback and are also known as feedback networks. There are several types of recurrent networks that depend on the feedback connection [12][13][14][15][16][17][18][19][20][21][22].…”

Section: Recurrent Wavelet Neural Network (Rwnns)mentioning

confidence: 99%

Wavelet Neural Networks for Speed Control of BLDC Motor

Saleh¹,

Obed²,

Qasim³

et al. 2021

Automation and Control

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In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.

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Maximum Power Point Tracking for Photovoltaic System by Using Fuzzy Neural Network

Cited by 27 publications

References 12 publications

Q-Learning Hybrid Type-2 Fuzzy Logic Control Approach for Photovoltaic Maximum Power Point Tracking Under Varying Solar Irradiation Exposure

Q-Learning Hybrid Type-2 Fuzzy Logic Control Approach for Photovoltaic Maximum Power Point Tracking Under Varying Solar Irradiation Exposure

Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes

Wavelet Neural Networks for Speed Control of BLDC Motor

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