Power Electronic Converter Configurations Integration with Hybrid Energy Sources – A Comprehensive Review for State-of the-Art in Research

Krishnamurthy, Kumar; Padmanaban, Sanjeevikumar; Blaabjerg, Frede; Neelakandan, Ramesh Babu; Ramanathan, Prabhu

doi:10.1080/15325008.2019.1689457

Cited by 20 publications

(6 citation statements)

References 83 publications

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“…The workflow of the proposed MSLTM model to analyze the power utilization [36], [37] and predict the stability of the SG dataset is depicted in Figure 2. First, the electricity grid dataset from different power generating units is aggregated.…”

Section: Proposed Multi-dimensional Lstm Modelmentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches. INDEX TERMS Multidirectional long short-term memory (MLSTM), machine learning (ML), smart grid (SG), cyber physical systems (CPS).

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Section: Proposed Multi-dimensional Lstm Modelmentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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“…Fig. 23 illustrates the non-isolated MIC, which is comprised of a buck and buck-boost fused multi-input DC-DC converter and an inverter [76]. In this topology, the rectified WT output and PV output are fed as inputs to the MIC.…”

Section: Multi-input Coupled Hresmentioning

confidence: 99%

Recent Advances of Wind-Solar Hybrid Renewable Energy Systems for Power Generation: A Review

Roy

Zhao

et al. 2022

IEEE Open J. Ind. Electron. Soc.

136

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A hybrid renewable energy source (HRES) consists of two or more renewable energy sources, such as wind turbines and photovoltaic systems, utilized together to provide increased system efficiency and improved stability in energy supply to a certain degree. The objective of this study is to present a comprehensive review of wind-solar HRES from the perspectives of power architectures, mathematical modeling, power electronic converter topologies, and design optimization algorithms. Since the uncertainty of HRES can be reduced further by including an energy storage system, this paper presents several hybrid energy storage system coupling technologies, highlighting their major advantages and disadvantages. Various HRES power converters and control strategies from the state-of-the-art have been discussed. Different types of energy source combinations, modeling, power converter architectures, sizing, and optimization techniques used in the existing HRES are reviewed in this work, which intends to serve as a comprehensive reference for researchers, engineers, and policymakers in this field. This article also discusses the technical challenges associated with HRES as well as the scope of future advances and research on HRES.INDEX TERMS Hybrid renewable energy sources, hybrid energy storage system, optimization, Power converter, photovoltaic power, wind turbine.

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“…PV modules are often connected in series or parallel formation to make a string, which is then hooked up to form an array, in order to obtain high layered DC voltage. Because the entire array of PV is controlled at one site for grid connection, these installations are classified as centralized PV systems [4,12,45,46]. PV systems require high electrical performance overall, but are seen to be useless for renewable power collecting in a variety of non-ideal situations [47].…”

Section: Detailed Description Of Mlpe Distributed Pv Systemmentioning

confidence: 99%

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

et al. 2022

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Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.

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Power Electronic Converter Configurations Integration with Hybrid Energy Sources – A Comprehensive Review for State-of the-Art in Research

Cited by 20 publications

References 83 publications

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Recent Advances of Wind-Solar Hybrid Renewable Energy Systems for Power Generation: A Review

Modular Level Power Electronics (MLPE) Based Distributed PV System for Partial Shaded Conditions

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