Artificial intelligent controller-based power quality improvement for microgrid integration of photovoltaic system using new cascade multilevel inverter

Mahendravarman, I.; Elankurisil, S.A.; Venkateshkumar, M.; Ragavendiran, A.; Chin, N.

doi:10.1007/s00500-020-05120-2

Cited by 19 publications

(9 citation statements)

References 11 publications

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“…In this section, we have explained the microgrid integration with a hybrid PV/wind-based power system, which has been developed and controlled by the DNN-based MPPT algorithm [24,25]. The detailed simulation model is presented in Figure 21.…”

Section: Microgrid Integration With a Hybrid Pv/wind Power Systemmentioning

confidence: 99%

A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

Ab-BelKhair

Rahebi

Nureddin

2020

International Journal of Photoenergy

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Presently, climate change and global warming are the most uncontrolled global challenges due to the extensive fossil fuel usage for power generation and transportation. Nowadays, most of the developed countries are concentrating on developing alternative resources; consequently, they did huge investments in research and development. In general, alternative energy resources including hydropower, solar power, and wind energy are not harmful to nature. Today, solar power and wind power are very popular alternative energy sources due to their enormous availability in nature. In this paper, the photovoltaic cell and wind energy systems are investigated under various weather conditions. Based on the findings, we developed an advanced intelligent controller system that tracks the maximum power point. The MPPT controller is a must for the renewable energy sources due to unpredictable weather conditions. The main objective of this paper is to propose a new algorithm that is based on deep neural network (DNN) and maximum power point tracking (MPPT), which was simulated in a MATLAB environment for photovoltaic (PV) and wind-based power generation systems. The development of an advanced DNN controller that improves the power quality and reduces THD value for the microgrid integration of hybrid PV/wind energy system was performed. The MATLAB simulation tool has been used to develop the proposed system and tested its performance in different operating situations. Finally, we analyzed the simulation results applying the IEEE 1547 standard.

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Section: Microgrid Integration With a Hybrid Pv/wind Power Systemmentioning

confidence: 99%

A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

Ab-BelKhair

Rahebi

Nureddin

2020

International Journal of Photoenergy

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“…Based on PQ problems, MGSA optimize the number of control signal required by the converter to restore initial operating state. Mahendravarman et al 36 have presented the maximum power point tracking (MPPT) algorithm depending upon artificial intelligent controller to photovoltaic system with new cascade multilevel inverter of grid connection photovoltaic system. The suggested cascade multilevel inverter was designed along small count of electronic switches, then it could be enabled in asynchronous voltage sources, which was very appropriate to photovoltaic scheme.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

An intelligent technique for optimal power quality reinforcement in a grid‐connected HRES system: EVORFA technique

Goud

Rao

Reddy

2020

Int J Numerical Modelling

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This manuscript proposes the optimal power quality reinforcement in grid‐connected hybrid renewable energy sources like solar photovoltaic, wind turbine, battery storage using an intelligent approach. The proposed hybrid approach is the consolidation of Egyptian vulture optimization algorithm (EVOA) and random forest algorithm (RFA); hence, it is known as EVORFA technique. The major objective of this research is voltage stabilizing, power loss reduction, extenuating harmonic distortion. EVOA is mainly used to the offline way to differentiate the perfect combination and forms the dataset of proportional integral gain parameters; load current, DC‐link voltage, and voltage sources are based on reduced error objective function. In EVOA, multiple parameters are considered that is identified to the power quality (PQ) issues. The RFA predicts most optimal control signal with minimum error based on the accomplished dataset. The proposed EVORFA approach is executed in MATLAB/Simulink work site. The EVORFA approach performance is carried out in two modes, that is, simultaneous PQ reinforcement and RES power injection PRES > 0 and PQ reinforcement (PRES = 0). By then the experimental results are compared to the existing methods like gravitational search algorithm (GSA) and RFA.

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“…In the present situation, renewable energy sources are required to make the maximum power point tracking algorithm generate maximum power under various weather conditions [15]. The researchers have been focused to create different MPPT algorithms, including the incremental conductance, P&O, feedback voltage and current, fuzzy, ANN, PSO ANFIS, and other controllers [16][17][18].…”

Section: Deep Neural Network-based Mppt For Pvmentioning

confidence: 99%

“…The detailed simulation model is presented in Figure 21. In this simulation model, a 74 kW hybrid PV (50 kW) and a fuel cell (6 × 4 = 24 kW) energy system are integrated into a power microgrid with the support of a smart inverter, which is controlled by a DNN-based voltage source controller [15,20,21] as shown in Figure 22. In this control-ler, there are three major subcontrollers, which are designed including a voltage regulator, a phase lock loop, and a current regulator.…”

Section: Integration Of Microgrid With Hybrid Pv and Fuel Cell Power Systemsmentioning

confidence: 99%

Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network

Nureddin

Rahebi

Ab-BelKhair

2020

International Journal of Photoenergy

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Nowadays, the power demand is increasing day by day due to the growth of the population and industries. The conventional power plant alone is incompetent to meet the consumer demand due to environmental concerns. In this present situation, the essential thing is to be find an alternate way to meet the consumer demand. In present days most of the developed countries concentrate to develop alternative resources and invest huge money for its research and development activities. Most renewable energy sources are naturally friendly sources such as wind, solar, fuel cell, and hydro/water sources. The results of power generation using renewable energy sources only depend on the availability of the resources. The availability of renewable energy sources throughout the day is variable due to fluctuations in the natural resources. This research work discusses two major renewable energy power generating sources: photovoltaic (PV) cell and fuel cell. Both of them provide foundations for power generation, so they are very popular because of their impressive performance mechanisms. The mentioned renewable energy-based power generating systems are static devices, so the power losses are generally ignorable as compared to line losses in the main grid. The PV and fuel cell (FC) power systems need a controller for maximum power generation during fluctuations in the input resources. Based on the investigation report, an algorithm is proposed for an advanced maximum power point tracking (MPPT) controller. This paper proposes a deep neural network- (DNN-) based MPPT algorithm, which has been simulated using MATLAB both for PV and for FC. The main purpose behind this paper has been to develop the latest DNN controller for improving the output power quality that is generated using a hybrid PV and fuel cell system. After developing and simulating the proposed system, we performed the analysis in different possible operating conditions. Finally, we evaluated the simulation outcomes based on IEEE 1547 and 519 standards to prove the system’s effectiveness.

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Artificial intelligent controller-based power quality improvement for microgrid integration of photovoltaic system using new cascade multilevel inverter

Cited by 19 publications

References 11 publications

A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

A Study of Deep Neural Network Controller-Based Power Quality Improvement of Hybrid PV/Wind Systems by Using Smart Inverter

An intelligent technique for optimal power quality reinforcement in a grid‐connected HRES system: EVORFA technique

Power Management Controller for Microgrid Integration of Hybrid PV/Fuel Cell System Based on Artificial Deep Neural Network

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