Integration of Green and Renewable Energy in Electric Power Systems

Keyhani, A.; Marwali, M.N.; Dai, Min

doi:10.1002/9780470556771

Cited by 93 publications

(79 citation statements)

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“…A typical three-phase PWM voltage inverter is used, with an L-C output filter (with inductance L inv and capacitance C inv ) and a delta-wye transformer (with resistance R T and inductance L T ) that acts as both a voltage transformer and an electrical isolation to the load. Small capacitors (denoted as C load ) are added at the load side to provide further harmonic filtering and stabilization of the load voltage [8]. According to [8], it is assumed that the PWM space vector and power converter generate ideal voltages according to the commands given by the control.…”

Section: State-space Modeling Of Three-phase Inverter Of the Microgridmentioning

confidence: 99%

“…Small capacitors (denoted as C load ) are added at the load side to provide further harmonic filtering and stabilization of the load voltage [8]. According to [8], it is assumed that the PWM space vector and power converter generate ideal voltages according to the commands given by the control. This assumption is valid due to the relatively high switching frequency of the PWM inverter [8].…”

Section: State-space Modeling Of Three-phase Inverter Of the Microgridmentioning

confidence: 99%

“…According to [8], it is assumed that the PWM space vector and power converter generate ideal voltages according to the commands given by the control. This assumption is valid due to the relatively high switching frequency of the PWM inverter [8]. The state-space model of the microgrid system in a three-phase abc reference frame is derived based on Kirchhoff 's current and voltage laws and is given by the following equations:…”

Section: State-space Modeling Of Three-phase Inverter Of the Microgridmentioning

confidence: 99%

“…Most modern DERs, such as microturbines, photovoltaic systems, fuel cells, etc., are load/grid interfaced through power electronic voltage source inverters (VSIs) [7]. According to [8], the SMG concept assumes a cluster of loads and micro-sources, operating as a single controllable system, where the inverter plays a key role for a SMG to participate in energy management and voltage and frequency control.…”

Section: Introductionmentioning

confidence: 99%

“…Upon separation from the main grid, the current operation standard prohibits the autonomous operation of the microgrid due to safety issues [9]. In [8], it was stated that "Upon separation from a utility system, if a smart grid DG stays stable, the inverter of the DG system regulates its frequency. If there is only one inverter in the DG system, load frequency control is accomplished through inverter reference frequency using the PWM technique" (p. 20).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Dual-loop Model Predictive Voltage Control/Sliding-mode Current Control for Voltage Source Inverter Operation in Smart Microgrids

Abo-Al-Ez

Ełaiw

Xia

2014

Electric Power Components and Systems

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Section: State-space Modeling Of Three-phase Inverter Of the Microgridmentioning

confidence: 99%

Section: State-space Modeling Of Three-phase Inverter Of the Microgridmentioning

confidence: 99%

Section: State-space Modeling Of Three-phase Inverter Of the Microgridmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Dual-loop Model Predictive Voltage Control/Sliding-mode Current Control for Voltage Source Inverter Operation in Smart Microgrids

Abo-Al-Ez

Ełaiw

Xia

2014

Electric Power Components and Systems

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Introducing AI applications in engineering education (PBL): An implementation of power generation at minimum wind velocity and turbine faults classification using AI

Khan,

Alam,

Rizvi

et al. 2023

Comp Applic In Engineering

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This article explores the integration of artificial intelligence (AI) applications into project‐based learning (PBL) education as a means to enhance students' education. Specifically, the implementation of AI in the context of power generation is addressed, focusing on achieving power generation at minimum wind velocity and classifying turbine faults using AI techniques. The researchers have proposed a novel generating unit which is going to generate 1 KW of electric power at a specific flow rate of air and the generated power will be stored in the battery bank through the charge controller and then the load is driven from the battery through an inverter. Iron or core losses (Hysteresis, Eddy Current losses) can be acknowledged as one of the major reasons for the inefficiency of conventional generators, therefore anovel coreless model generator was proposed which also improved efficiency and reduces drag. Wind Turbine prototype was fabricated and deployed for the testing and validation of the proposed novel design. The design produced outstanding power ratings and electrical generation characteristics compared with other existing strategies at minimal air flow. Results proved that the proposed coreless axial flux generator has the capability to produce a better power rating compared with the existing wind turbine generators. Proposed Axial flux achieved 10.73 watt power at wind velocity at around 80 rpm. At a wind velocity of 10 m/s and around 800 rpm 313–330 kwh was produced by the proposed generator while the conventional generator produced around 300 kwh. The proposed generator design performed 35% better in terms of production efficiency under load and no load conditions. Moreover, faults in turbines are very common due to the various temperatures, therefore the faults have been classified using state‐of‐the‐art AI‐based classifiers. A comparison of space vector modulation (SVM) and Naive Bayes classifiers was performed in the study to classify wind turbine faults. It was found that both classifiers performed well in achieving high accuracy. SVM achieved a slightly higher accuracy of 0.9861 compared with Naive Bayes, which achieved an accuracy of 0.967. Based on the results, it can be inferred that SVM may be a more suitable classifier for wind turbine fault classification. The case study results demonstrated the potential of AI applications in PBL education, offering students a multidisciplinary learning experience that enhances their technical knowledge, problem‐solving skills, and teamwork abilities.

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A novel islanding fault detection for distributed generation systems

Yang

Han

2013

Intl J Robust & Nonlinear

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SUMMARY This paper proposes a novel islanding fault detection method for distributed generation systems. Islanding fault detection of distributed generation systems in microgrids and smart grids is an essential security technology. The existing methods for islanding fault detection can be classified as passive methods (such as under/over voltage detection, under/over frequency detection, and voltage phase jump detection, based on natural effects of islanding) and active methods (impedance measurement, slip mode frequency shift, Sandia frequency shift and so on). Once the power consumed by a local load matches the power generated by a local inverter, the islanding phenomenon will be not obvious, and traditional passive methods may fail. In contrast, the active methods can overcome this disadvantage. However, active methods create extra disturbances that may degrade the quality of power generated by the inverter. By reducing the harmonics of the inverter output, coupled with the use of a novel filtering method, the proposed approach can be achieved without additional disturbance and does no nondetection zone islanding detection. In this paper, current harmonic compensation is applied to inverter control to minimize the inverter output harmonics, highlighting on the grid harmonics. Set‐membership filter is developed to estimate the voltage harmonics. Islanding condition is detected by the changes of the specific order harmonics. The effectiveness of the proposed method is demonstrated by simulations. A Kalman filter contrast experiment is presented to confirm that the set‐membership filter can more effectively detect islanding. Copyright © 2013 John Wiley & Sons, Ltd.

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Integration of Green and Renewable Energy in Electric Power Systems

Cited by 93 publications

References 0 publications

A Dual-loop Model Predictive Voltage Control/Sliding-mode Current Control for Voltage Source Inverter Operation in Smart Microgrids

A Dual-loop Model Predictive Voltage Control/Sliding-mode Current Control for Voltage Source Inverter Operation in Smart Microgrids

Introducing AI applications in engineering education (PBL): An implementation of power generation at minimum wind velocity and turbine faults classification using AI

A novel islanding fault detection for distributed generation systems

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