1) The document describes a system for integrating compressed air energy storage (CAES) with medium and low voltage grids using modern nonlinear control techniques.
2) A model reference adaptive control technique is used to control the DC-DC boost converter output voltage despite fluctuations from the CAES input. A sliding mode controller is also used for the three-phase voltage source inverter to control active and reactive power injection into the grid.
3) Experimental results are presented along with simulation results to validate the effectiveness of the controllers in maintaining stable output voltages and power flows despite uncertainties in the system.
A NOVEL CONTROL STRATEGY FOR POWER QUALITY IMPROVEMENT USING ANN TECHNIQUE FO...IJERD Editor
The proposed system presents power-control strategies of a Micro grid-connected hybrid generation
system with versatile power transfer. This hybrid system allows maximum utilization of freely available
renewable energy sources like wind and photovoltaic energies. For this, an adaptive MPPT algorithm along with
standard perturbs and observes method will be used for the system.
The inverter converts the DC output from non-conventional energy into useful AC power for the
connected load. This hybrid system operates under normal conditions which include normal room temperature
in the case of solar energy and normal wind speed at plain area in the case of wind energy. However, designing
an optimal micro grid is not an easy task, due to the fact that primary energy carriers are changeable and
uncontrollable, as is the demand. Traditional design and optimization tools, developed for controlled power
sources, cannot be employed here. Simulation methods seem to be the best solution.
The dynamic model of the proposed system is first elaborated in the stationary reference frame and
then transformed into the synchronous orthogonal reference frame. The transformed variables are used in
control of the voltage source converter as the heart of the interfacing system between DG resources and utility
grid. By setting an appropriate compensation current references from the sensed load currents in control circuit
loop of DG, the active, reactive, and harmonic load current components will be compensated with fast dynamic
response, thereby achieving sinusoidal grid currents in phase with load voltages, while required power of the
load is more than the maximum injected power of the DG to the grid. In addition, the proposed control method
of this paper does not need a phase-locked loop in control circuit and has fast dynamic response in providing
active and reactive power components of the grid-connected loads.
02 19 jan17 12566 final paper in ijeecs format(edit)IAESIJEECS
To maintain voltage stability of a power system STATCOM is better solution which can provide the required amount of reactive power under various disturbances. In previous work, STATCOM with various energy storage elements was discussed for voltage and power system stability. Apart from these previous works, this work proposes a new structure of hybrid energy storage system (HESS) for voltage stability by using battery and super capacitor. A new model of STATCOM with hybrid energy storage system is designed by using two bidirectional DC-DC converters and results are analyzed for conventional STATCOM and STATCOM with hybrid energy storage system. Results are also analyzed for STATCOM system with out any energy storage system, STATCOM with battery, STATCOM with super capacitor and STATCOM with HESS under sudden load changes by using MATLAB/Simulink.
Analysis of Six Active Power Control Strategies of Interconnected Grids with ...Power System Operation
In this paper, the generator angle stability of several active power control schemes of a
voltage-source converter (VSC)-based high-voltage DC (HVDC) is evaluated for two interconnected
AC systems. Excluding frequency control, there has been no detailed analysis of interconnected grids
depending upon the converter power control, so six different types of active power control of the
VSC-HVDC are defined and analyzed in this paper. For each TSO (transmission system operator),
the applicable schemes of two kinds of step control and four kinds of ramp-rate control with a droop
characteristic are included in this research. Furthermore, in order to effectively evaluate the angle
stability, the Generators-VSC Interaction Factor (GVIF) index is newly implemented to distinguish
the participating generators (PGs) group which reacts to the converter power change. As a result,
the transient stabilities of the two power systems are evaluated and the suitable active power control
strategies are determined for two TSOs. Simulation studies are performed using the PSS®E program
to analyze the power system transient stability and various active power control schemes of the
VSC-HVDC. The results provide useful information indicating that the ramp-rate control shows a
more stable characteristic than the step-control for interconnected grids; thus, a converter having a
certain ramp-rate slope similar to that of the other generator shows more stable results in several cases.
Design and Simulation of a Three Phase Power Converter Connected To a Distrib...IJERA Editor
The distributed generation systems based on renewable energy sources (photovoltaic, fuel cells, and storage systems such as ultra capacitors and batteries) are of great interest due to their low environmental impact and technical advantages such as improvements in voltage levels and reduced power losses when a distributed generation system is installed in radial lines. Two control algorithms were proposed to improve the grid-connected and intentional-islanding operations methods, in which the distributed generation system must detect the situation and switch from power or current to voltage as a control variable to provide constant rms voltage to the local loads. The power flow between the grid and the distributed generation is controlled by applying a power/voltage method that regulates the amplitude and the displacement of the grid voltage synthesized by the distributed generation, while a phase-locked loop algorithm is used to synchronize the grid and distributed generation.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
A NOVEL CONTROL STRATEGY FOR POWER QUALITY IMPROVEMENT USING ANN TECHNIQUE FO...IJERD Editor
The proposed system presents power-control strategies of a Micro grid-connected hybrid generation
system with versatile power transfer. This hybrid system allows maximum utilization of freely available
renewable energy sources like wind and photovoltaic energies. For this, an adaptive MPPT algorithm along with
standard perturbs and observes method will be used for the system.
The inverter converts the DC output from non-conventional energy into useful AC power for the
connected load. This hybrid system operates under normal conditions which include normal room temperature
in the case of solar energy and normal wind speed at plain area in the case of wind energy. However, designing
an optimal micro grid is not an easy task, due to the fact that primary energy carriers are changeable and
uncontrollable, as is the demand. Traditional design and optimization tools, developed for controlled power
sources, cannot be employed here. Simulation methods seem to be the best solution.
The dynamic model of the proposed system is first elaborated in the stationary reference frame and
then transformed into the synchronous orthogonal reference frame. The transformed variables are used in
control of the voltage source converter as the heart of the interfacing system between DG resources and utility
grid. By setting an appropriate compensation current references from the sensed load currents in control circuit
loop of DG, the active, reactive, and harmonic load current components will be compensated with fast dynamic
response, thereby achieving sinusoidal grid currents in phase with load voltages, while required power of the
load is more than the maximum injected power of the DG to the grid. In addition, the proposed control method
of this paper does not need a phase-locked loop in control circuit and has fast dynamic response in providing
active and reactive power components of the grid-connected loads.
02 19 jan17 12566 final paper in ijeecs format(edit)IAESIJEECS
To maintain voltage stability of a power system STATCOM is better solution which can provide the required amount of reactive power under various disturbances. In previous work, STATCOM with various energy storage elements was discussed for voltage and power system stability. Apart from these previous works, this work proposes a new structure of hybrid energy storage system (HESS) for voltage stability by using battery and super capacitor. A new model of STATCOM with hybrid energy storage system is designed by using two bidirectional DC-DC converters and results are analyzed for conventional STATCOM and STATCOM with hybrid energy storage system. Results are also analyzed for STATCOM system with out any energy storage system, STATCOM with battery, STATCOM with super capacitor and STATCOM with HESS under sudden load changes by using MATLAB/Simulink.
Analysis of Six Active Power Control Strategies of Interconnected Grids with ...Power System Operation
In this paper, the generator angle stability of several active power control schemes of a
voltage-source converter (VSC)-based high-voltage DC (HVDC) is evaluated for two interconnected
AC systems. Excluding frequency control, there has been no detailed analysis of interconnected grids
depending upon the converter power control, so six different types of active power control of the
VSC-HVDC are defined and analyzed in this paper. For each TSO (transmission system operator),
the applicable schemes of two kinds of step control and four kinds of ramp-rate control with a droop
characteristic are included in this research. Furthermore, in order to effectively evaluate the angle
stability, the Generators-VSC Interaction Factor (GVIF) index is newly implemented to distinguish
the participating generators (PGs) group which reacts to the converter power change. As a result,
the transient stabilities of the two power systems are evaluated and the suitable active power control
strategies are determined for two TSOs. Simulation studies are performed using the PSS®E program
to analyze the power system transient stability and various active power control schemes of the
VSC-HVDC. The results provide useful information indicating that the ramp-rate control shows a
more stable characteristic than the step-control for interconnected grids; thus, a converter having a
certain ramp-rate slope similar to that of the other generator shows more stable results in several cases.
Design and Simulation of a Three Phase Power Converter Connected To a Distrib...IJERA Editor
The distributed generation systems based on renewable energy sources (photovoltaic, fuel cells, and storage systems such as ultra capacitors and batteries) are of great interest due to their low environmental impact and technical advantages such as improvements in voltage levels and reduced power losses when a distributed generation system is installed in radial lines. Two control algorithms were proposed to improve the grid-connected and intentional-islanding operations methods, in which the distributed generation system must detect the situation and switch from power or current to voltage as a control variable to provide constant rms voltage to the local loads. The power flow between the grid and the distributed generation is controlled by applying a power/voltage method that regulates the amplitude and the displacement of the grid voltage synthesized by the distributed generation, while a phase-locked loop algorithm is used to synchronize the grid and distributed generation.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
A Power quality problem is an occurrence of nonstandard voltage, current or frequency that results in a
failure or a misoperation of end user equipments. Utility distribution networks, sensitive industrial loads and
critical commercial operations suffer from various types of outages and service interruptions which can cost
significant financial losses. With the increase in load demand, the Renewable Energy Sources (RES) are
increasingly connected in the distribution systems which utilizes power electronic Converters/Inverters. This
paper presents a single-stage, three-phase grid connected solar photovoltaic (SPV) system. The proposed system
is dual purpose, as it not only feeds extracted solar energy into the grid but it also helps in improving power
quality in the distribution system. The presented system serves the purpose of maximum power point tracking
(MPPT), feeding SPV energy to the grid, harmonics mitigation of loads connected at point of common coupling
(PCC) and balancing the grid currents. The SPV system uses a three-phase voltage source converter (VSC) for
performing all these functions. An improved linear sinusoidal tracer (ILST)-based control algorithm is proposed
for control of VSC. In the proposed system, a variable dc link voltage is used for MPPT. An instantaneous
compensation technique is used incorporating changes in PV power for fast dynamic response. The SPV system
is first simulated in MATLAB along with Simulink and simpower system toolboxes.
Stability Improvement in Grid Connected Multi Area System using ANFIS Based S...IJMTST Journal
Generally, the non-conventional energy sources are being extensively used in case of power electronic
converter based distribution systems. This paper mainly focuses on the wind energy system integrating with
grid connected system and also improvement of power quality features. The wind energy power plant is
modelled based on associated equations. For improving this power quality problems, this paper proposes the
concepts of shunt converter controllers. This paper also proposes the concepts of ANFIS based Static
Compensator. And also the results are compared for this cases. Thus with such a control, a balanced load
currents are obtained even in the presence of non-linear load. The experimental setup is done in Matlab and
verified the simulation results
Development of Power System Stabilizer Using Modern Optimization Approachijtsrd
The utilization of energy has increased manifold because of the industrial revolution. The development of the induction motor by Nikola Tesla in 1888 triggered the increasing demand of electrical energy in the industrial world and also its usage for artificial lighting. Higher amount of energy required for present society is provided by means of electrical energy. Highly complicated power systems have been constructed to deal with the increasing demand. The development in electric power production is focused on the interconnecting the transmission lines, generators and loads into huge integrated systems, which helps in providing reliable and quality power system to the consumers.Power system stability is an idea that focuses on the way the system adjusts with the occurrence of disorder. These disorders can result because of switching off a load or a modification in the mechanical input to the system. Because of these disturbances oscillations may happen in the system that could ultimately build up to a larger extent, resulting in the synchronous generators to go out of step and lose synchronism.Due to the importance of the power system and its stability, there are several techniques developed by various researchers for the stabilization of the power systems. The paper addresses enhanced Artificial Bee Colony ABC technique applied to design a robust power system stabilizer PSS in order to improve transient and dynamic stabilities of a turbo alternator connected to an infinite bus system comparisons wind, thermal and solar power generations the design of the proposed controller initially is formulated as an optimization problem and then enhanced ABC is employed for obtaining optimal controller parameters. The results shows that the proposed algorithm are better in performance than the conventional methods. Mr. Qamar ul Zaman | Mr. Deepak Joshi | Mr. Yuvraj Ranawat "Development of Power System Stabilizer Using Modern Optimization Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47776.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/47776/development-of-power-system-stabilizer-using-modern-optimization-approach/mr-qamar-ul-zaman
Voltage Regulation with Hybrid RES based Distributed Generation in the for Ac...IJMTST Journal
In this paper adaptive zone-based Volt/VAR management is proposed, which coordinates active
participation of DGs with conventional voltage regulation equipment. To achieve a flexible and scalable
solution while minimizing complexity and requirements for data-handling capability, DG management
systems are integrated with decentralized parts of the Volt/VAR management system in smaller
geographical zones. Coordination of DGs with conventional voltage regulation equipment is based on
predefined control hierarchies. However, to reduce requirements for data handling capability, the distribution
grid is divided into zones with individual voltage regulation and reactive support schemes. To add flexibility
and scalability, these zones can be combined into larger zones with a common Volt/VAR management
scheme. This is referred to as adaptive zoning. The results indicate that the control schemes successfully
restore voltage to within limits after disturbance of grid conditions. Adaptive zoning effectively reduces
system complexity and requirements for data handling capability, while still ensuring a grid-wide solution.
The proposed concept is implemented to hybrid RES method the simulation results are presented by using
Matlab/Simulink platform.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Isolated Wind Hydro Hybrid Generation System with Battery StorageIJMER
In this paper, a new isolated wind- hydro hybrid generation system comprising one squirrel -cage induction generator (SCIG) driven by a variable-speed wind turbine and another synchronous generator driven by constant power hydro turbine feeding three phase four wire local loads is proposed. The system utilizes two back to back connected voltage-source converters (VSCs) with a battery energy storage system at their dc link. The main objective of the control algorithm for the VSC is to achieve control of the magnitude and the frequency of the load voltage. The proposed wind-hydro hybrid system has a capability of bidirectional active- and reactive-power flow, by which it controls the magnitude and the frequency of the load voltage. The proposed electromechanical system is modelled and simulated in MATLAB using Simulink.
Modeling Optimization Voltage Index Unified Power Flow Controller Equivalent ...IJMTST Journal
This paper presents an active-reactive power control strategy for voltage source converters (VSCs) based on derivation of the direct and quadrature components of the VSC output current. The proposed method utilizes a multivariable proportional-integral controller and provides almost completely decoupled control capability of the active and reactive power with almost full disturbance rejection due to step changes in the power exchanged between the VSC and the grid. It also imposes fast transient response and zero steady-state error as compared to the conventional power control approaches. The applicability of the proposed power control strategy for providing the robust stability of the system against the uncertainties of the load parameters is also investigated. The superiority of the proposed control strategy over conventional approaches in the new condition of supplying the load is demonstrated. The theoretical aspects of the proposed multivariable-based power control strategy and the conventional approaches are reviewed and simulation results are presented in two separate sections. MATLAB/Simulink 2009a is used to simulate different scenarios of the simulation.
Grid Interconnection of Renewable Energy Sources at the Distribution Level Wi...Pradeep Avanigadda
Renewable energy resources (RES) are being increasingly
connected in distribution systems utilizing power electronic
converters. This paper presents a novel control strategy for
achieving maximum benefits from these grid-interfacing inverters
when installed in 3-phase 4-wire distribution systems. The inverter
is controlled to perform as a multi-function device by incorporating
active power filter functionality. The inverter can thus be
utilized as: 1) power converter to inject power generated from
RES to the grid, and 2) shunt APF to compensate current unbalance,
load current harmonics, load reactive power demand and
load neutral current. All of these functions may be accomplished
either individually or simultaneously. With such a control, the
combination of grid-interfacing inverter and the 3-phase 4-wire
linear/non-linear unbalanced load at point of common coupling
appears as balanced linear load to the grid. This new control
concept is demonstrated with extensive MATLAB/Simulink simulation
studies and validated through digital signal processor-based
laboratory experimental results.
Index Terms—Active power filter
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
A Power quality problem is an occurrence of nonstandard voltage, current or frequency that results in a
failure or a misoperation of end user equipments. Utility distribution networks, sensitive industrial loads and
critical commercial operations suffer from various types of outages and service interruptions which can cost
significant financial losses. With the increase in load demand, the Renewable Energy Sources (RES) are
increasingly connected in the distribution systems which utilizes power electronic Converters/Inverters. This
paper presents a single-stage, three-phase grid connected solar photovoltaic (SPV) system. The proposed system
is dual purpose, as it not only feeds extracted solar energy into the grid but it also helps in improving power
quality in the distribution system. The presented system serves the purpose of maximum power point tracking
(MPPT), feeding SPV energy to the grid, harmonics mitigation of loads connected at point of common coupling
(PCC) and balancing the grid currents. The SPV system uses a three-phase voltage source converter (VSC) for
performing all these functions. An improved linear sinusoidal tracer (ILST)-based control algorithm is proposed
for control of VSC. In the proposed system, a variable dc link voltage is used for MPPT. An instantaneous
compensation technique is used incorporating changes in PV power for fast dynamic response. The SPV system
is first simulated in MATLAB along with Simulink and simpower system toolboxes.
Stability Improvement in Grid Connected Multi Area System using ANFIS Based S...IJMTST Journal
Generally, the non-conventional energy sources are being extensively used in case of power electronic
converter based distribution systems. This paper mainly focuses on the wind energy system integrating with
grid connected system and also improvement of power quality features. The wind energy power plant is
modelled based on associated equations. For improving this power quality problems, this paper proposes the
concepts of shunt converter controllers. This paper also proposes the concepts of ANFIS based Static
Compensator. And also the results are compared for this cases. Thus with such a control, a balanced load
currents are obtained even in the presence of non-linear load. The experimental setup is done in Matlab and
verified the simulation results
Development of Power System Stabilizer Using Modern Optimization Approachijtsrd
The utilization of energy has increased manifold because of the industrial revolution. The development of the induction motor by Nikola Tesla in 1888 triggered the increasing demand of electrical energy in the industrial world and also its usage for artificial lighting. Higher amount of energy required for present society is provided by means of electrical energy. Highly complicated power systems have been constructed to deal with the increasing demand. The development in electric power production is focused on the interconnecting the transmission lines, generators and loads into huge integrated systems, which helps in providing reliable and quality power system to the consumers.Power system stability is an idea that focuses on the way the system adjusts with the occurrence of disorder. These disorders can result because of switching off a load or a modification in the mechanical input to the system. Because of these disturbances oscillations may happen in the system that could ultimately build up to a larger extent, resulting in the synchronous generators to go out of step and lose synchronism.Due to the importance of the power system and its stability, there are several techniques developed by various researchers for the stabilization of the power systems. The paper addresses enhanced Artificial Bee Colony ABC technique applied to design a robust power system stabilizer PSS in order to improve transient and dynamic stabilities of a turbo alternator connected to an infinite bus system comparisons wind, thermal and solar power generations the design of the proposed controller initially is formulated as an optimization problem and then enhanced ABC is employed for obtaining optimal controller parameters. The results shows that the proposed algorithm are better in performance than the conventional methods. Mr. Qamar ul Zaman | Mr. Deepak Joshi | Mr. Yuvraj Ranawat "Development of Power System Stabilizer Using Modern Optimization Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-1 , December 2021, URL: https://www.ijtsrd.com/papers/ijtsrd47776.pdf Paper URL: https://www.ijtsrd.com/engineering/electrical-engineering/47776/development-of-power-system-stabilizer-using-modern-optimization-approach/mr-qamar-ul-zaman
Voltage Regulation with Hybrid RES based Distributed Generation in the for Ac...IJMTST Journal
In this paper adaptive zone-based Volt/VAR management is proposed, which coordinates active
participation of DGs with conventional voltage regulation equipment. To achieve a flexible and scalable
solution while minimizing complexity and requirements for data-handling capability, DG management
systems are integrated with decentralized parts of the Volt/VAR management system in smaller
geographical zones. Coordination of DGs with conventional voltage regulation equipment is based on
predefined control hierarchies. However, to reduce requirements for data handling capability, the distribution
grid is divided into zones with individual voltage regulation and reactive support schemes. To add flexibility
and scalability, these zones can be combined into larger zones with a common Volt/VAR management
scheme. This is referred to as adaptive zoning. The results indicate that the control schemes successfully
restore voltage to within limits after disturbance of grid conditions. Adaptive zoning effectively reduces
system complexity and requirements for data handling capability, while still ensuring a grid-wide solution.
The proposed concept is implemented to hybrid RES method the simulation results are presented by using
Matlab/Simulink platform.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Isolated Wind Hydro Hybrid Generation System with Battery StorageIJMER
In this paper, a new isolated wind- hydro hybrid generation system comprising one squirrel -cage induction generator (SCIG) driven by a variable-speed wind turbine and another synchronous generator driven by constant power hydro turbine feeding three phase four wire local loads is proposed. The system utilizes two back to back connected voltage-source converters (VSCs) with a battery energy storage system at their dc link. The main objective of the control algorithm for the VSC is to achieve control of the magnitude and the frequency of the load voltage. The proposed wind-hydro hybrid system has a capability of bidirectional active- and reactive-power flow, by which it controls the magnitude and the frequency of the load voltage. The proposed electromechanical system is modelled and simulated in MATLAB using Simulink.
Modeling Optimization Voltage Index Unified Power Flow Controller Equivalent ...IJMTST Journal
This paper presents an active-reactive power control strategy for voltage source converters (VSCs) based on derivation of the direct and quadrature components of the VSC output current. The proposed method utilizes a multivariable proportional-integral controller and provides almost completely decoupled control capability of the active and reactive power with almost full disturbance rejection due to step changes in the power exchanged between the VSC and the grid. It also imposes fast transient response and zero steady-state error as compared to the conventional power control approaches. The applicability of the proposed power control strategy for providing the robust stability of the system against the uncertainties of the load parameters is also investigated. The superiority of the proposed control strategy over conventional approaches in the new condition of supplying the load is demonstrated. The theoretical aspects of the proposed multivariable-based power control strategy and the conventional approaches are reviewed and simulation results are presented in two separate sections. MATLAB/Simulink 2009a is used to simulate different scenarios of the simulation.
Grid Interconnection of Renewable Energy Sources at the Distribution Level Wi...Pradeep Avanigadda
Renewable energy resources (RES) are being increasingly
connected in distribution systems utilizing power electronic
converters. This paper presents a novel control strategy for
achieving maximum benefits from these grid-interfacing inverters
when installed in 3-phase 4-wire distribution systems. The inverter
is controlled to perform as a multi-function device by incorporating
active power filter functionality. The inverter can thus be
utilized as: 1) power converter to inject power generated from
RES to the grid, and 2) shunt APF to compensate current unbalance,
load current harmonics, load reactive power demand and
load neutral current. All of these functions may be accomplished
either individually or simultaneously. With such a control, the
combination of grid-interfacing inverter and the 3-phase 4-wire
linear/non-linear unbalanced load at point of common coupling
appears as balanced linear load to the grid. This new control
concept is demonstrated with extensive MATLAB/Simulink simulation
studies and validated through digital signal processor-based
laboratory experimental results.
Index Terms—Active power filter
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
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The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. Energies 2021, 14, 4097 2 of 18
When comparing the merits of competing ESS, CAES excels in energy storage capacity,
output power and storage duration [13]. In addition to these qualities, CAES systems are
effective across a wide range of storage capacities, and as such there is growing interest in
small-scale CAES. Micro-CAES systems utilize smaller over ground storage vessels instead
of underground salt caverns and demonstrate a more adaptable solution for integration
with distributed generation, alleviating the requirement of suitable geological conditions
that traditional CAES technologies rely upon [14]. Integrating small-scale CAES systems
with distributed renewable generation will allow for downsizing of the installed power
generation devices, peak shaving of demand and increased autonomy for the distributed
power generator [15].
The output voltage of the small power CAES varies in the range up to 300 V DC
and depends on a lot of mechanical factors which leads to deviation in DC link voltage.
To convert into standard grid voltage, it should be properly maintained by a DC-DC
boost converter. Although, in the process of step up of this variable DC link voltage
additional fast transients are introduced into the system owing to load variation. So, the
boost converter used to step up the DC voltage must be efficient and the controller should
be robust enough to maintain the DC link voltage to the desired value [16].
The basic boost converter structure exhibits non-minimum phase properties which
demand the design of indirect control schemes. The popular techniques involve first
order inductor current dynamics or second order dynamics of total stored energy which
depend on the circuit parameters. So, if the system is affected by unexpected fluctuations
from the load side or from the input or there is some parametric variation involved, the
performance of the converter deteriorates. These variations are very common in case of
renewable energy systems as well as some ESS, such as compressed air energy storage. So,
the researchers have focused on the design of robust control techniques to mitigate the
effects of these problems associated with variable voltage fed boost converter [16–18].
The adaptive control technique is quite simple and efficient when implemented for
a system whose model has variable circuit parameters with high input fluctuation and
load deviations. Application of various simple adaptive control (SAC) techniques has been
shown in different fields of engineering, namely flight control [19,20], power systems [21],
robotics [22], drug infusion [23], motor control [24] and others [25]. In the literature, it can
be observed that the SAC technique has some limitations, such as the system requirements
to be almost strictly passive (ASP) with the transfer function of the system almost strictly
positive real (ASPR). For non-ASPR system various parallel feed-forward compensators
(PFCs) can be used to make the system ASPR in order to apply SAC [19]. However, the
PFC should be sufficiently small so as to keep the behavior of the actual plant similar, yet
guarantee the system satisfy the ASPR condition to apply the adaptive control. In this
work, among the various control techniques the model reference based adaptive control
(MRAC) technique has been considered to control the DC link voltage at the output of the
boost converter and the corresponding PFC has also been designed.
Once the DC link voltage is maintained, mitigating the effects of all uncertainties
and fluctuations, this voltage is converted to AC in order to be connected to the AC grid.
A voltage source inverter (VSI) is used for its unmatched merit over the other DC–AC
converters. To reduce the unwanted harmonics produced by the high switching frequency
of the VSI, an LCL filter is connected to the output of the VSI. However, this makes
the system complex. A transformation of the system to dq reference frame is adopted
for this purpose, however, strong coupling terms make the design of controller much
complicated. Over the years of research, different decoupling strategies have been reported
in the literature. A decoupling technique based on feedback linearization theory has been
discussed in [26,27]. Though the grid current control is effective, the design is very much
complex. Another model order reduction method has been used in [28], but the grid current
is controlled indirectly in this case and a phase difference remains between the control
variable and grid voltage. The effect of parameter variation and operating point changes
3. Energies 2021, 14, 4097 3 of 18
are not taken into the control design. In this regard it is better to use a sliding mode control
technique to solve these problems [29].
The CAES based grid system also shows different uncertainties in terms of the power
injection to the grid and several disturbances in voltage due to the mechanical constraints
associated with it. So, the sliding mode control (SMC) as discussed in [29] has been
implemented in this work to successfully integrate CAES with the grid.
The presented paper develops a new integration technique of a small scale CAES to
the medium voltage grid with a two stage conversion. An improved MRAC approach is
proposed to cope with the uncertainties of the DC–DC conversion including CAES voltage
fluctuations and the loading conditions from the DC–AC stage. A smart decoupling of
the DC–AC conversion control into two independent subsystems with SMC controllers is
introduced allowing to mitigate parametric disturbances and to decouple the control of the
active and reactive powers injected into the grid.
The rest of the paper is organized as follows: Section 2 describes the overall system
configuration. The details of the compressed air energy storage is elaborated in Section 3. In
Section 4 the design of the entire system controller has been described. Finally, the correspond-
ing simulation and experimental results have been shown in Sections 5 and 6 respectively.
2. System Configuration
The layout of the total system under study has been shown in Figure 1. It comprises
of a CAES, a DC-DC boost converter, a voltage source inverter, a filter and a step-up
transformer (110 V/230 V). The output of the CAES is given to the boost converter to step
up to 450 V and this boosted voltage is converted to three phase AC and connected to
the grid.
Energies 2021, 14, x FOR PEER REVIEW 4 of 20
introduced allowing to mitigate parametric disturbances and to decouple the control of
the active and reactive powers injected into the grid.
The rest of the paper is organized as follows: Section 2 describes the overall system
configuration. The details of the compressed air energy storage is elaborated in Section 3.
In Section 4 the design of the entire system controller has been described. Finally, the
corresponding simulation and experimental results have been shown in Sections 5 and 6
respectively.
2. System Configuration
The layout of the total system under study has been shown in Figure 1. It comprises
of a CAES, a DC-DC boost converter, a voltage source inverter, a filter and a step-up
transformer (110 V/230 V). The output of the CAES is given to the boost converter to step
up to 450 V and this boosted voltage is converted to three phase AC and connected to the
grid.
Figure 1. Block diagram of the system under study.
During a power demand by the grid the CAES delivers the required amount of active
power to the grid. The grid voltage (vg,k), grid current (ig,k), inverter output current (iinvk),
filter capacitor voltage (vcf,k) at the output of the inverter (where k = phase A, B, C) and DC
link voltage (VDC) are measured using voltage and current sensors. The feedback signals
ig,k and VDC are compared to their specific references marked with the corresponding
subscript “ref” to produce the error signals, which eventually are fed to the controller to
produce the control signals to the system. The classical controller performance for the
boost converter worsens in presence of unknown fluctuation in input supply and load
due to the non-minimum phase characteristic of the boost converter.
It is important to design a power flow controller that will provide the required
amount of power to the grid from the CAES. A sliding mode control based power flow
controller for the VSI is developed for this purpose. For a specific power demand, the
controller controls the gate pulses of the VSI to deliver the required power to the grid with
maintained grid voltages.
3. Compressed Air Energy Storage Details
The micro-CAES system employed is designed around the use of an air motor. Air
Figure 1. Block diagram of the system under study.
During a power demand by the grid the CAES delivers the required amount of active
power to the grid. The grid voltage (vg,k), grid current (ig,k), inverter output current (iinvk),
filter capacitor voltage (vcf,k) at the output of the inverter (where k = phase A, B, C) and DC
link voltage (VDC) are measured using voltage and current sensors. The feedback signals
ig,k and VDC are compared to their specific references marked with the corresponding
subscript “ref” to produce the error signals, which eventually are fed to the controller to
produce the control signals to the system. The classical controller performance for the boost
converter worsens in presence of unknown fluctuation in input supply and load due to the
non-minimum phase characteristic of the boost converter.
It is important to design a power flow controller that will provide the required amount
of power to the grid from the CAES. A sliding mode control based power flow controller
for the VSI is developed for this purpose. For a specific power demand, the controller
4. Energies 2021, 14, 4097 4 of 18
controls the gate pulses of the VSI to deliver the required power to the grid with maintained
grid voltages.
3. Compressed Air Energy Storage Details
The micro-CAES system employed is designed around the use of an air motor. Air
motors, or expanders, generate work from the expansion of compressed air. Coupling air
motors with electrical generators therefore creates systems capable of converting potential
energy in the form of high-pressure air into electrical power. A number of types of air
motor are available, with the scroll-type air motor being chosen as the sub component
owing to the robust, reliable and smooth operation [11,12]. Scroll motors are comprised of
two identical interlocking scroll blades, with one fixed and the other mounted on a shaft at
an offset, such that the moving scroll can rotate eccentrically around a fixed orbit. Sealed
chambers are created by the meshing of the two scroll blades and depending on the stage
of the orbit of the moving scroll, the chamber volumes vary. When driven with compressed
air, the expansion causes work to rotate the scroll shaft.
4. Design of the System Controller
The practical circuit for the proposed work is shown in Figure 2. In this work two
robust controllers have been designed: an MRAC controller for the boost converter to
maintain the DC link voltage and one SMC controller to control the power flow from
energy storage to grid through the VSI. The design of both controllers is described in the
following subsections.
Energies 2021, 14, x FOR PEER REVIEW 5 of 20
at an offset, such that the moving scroll can rotate eccentrically around a fixed orbit. Sealed
chambers are created by the meshing of the two scroll blades and depending on the stage
of the orbit of the moving scroll, the chamber volumes vary. When driven with
compressed air, the expansion causes work to rotate the scroll shaft.
4. Design of the System Controller
The practical circuit for the proposed work is shown in Figure 2. In this work two
robust controllers have been designed: an MRAC controller for the boost converter to
maintain the DC link voltage and one SMC controller to control the power flow from
energy storage to grid through the VSI. The design of both controllers is described in the
following subsections.
Figure 2. Block diagram of the system under study.
4.1. Control of DC Link Voltage
To design the MRAC controller for the boost converter to maintain the DC link
voltage, the reference plant model for the converter is to be chosen first. According to the
circuit diagram for the boost converter given in Figure 2, the linearized state space model
around an arbitrary operating point is described as:
𝑥 =
⎣
⎢
⎢
⎡ −
𝑅
𝐿
−
1 − 𝐷
𝐿
1 − 𝐷
𝐶
−
1
𝑅 𝐶⎦
⎥
⎥
⎤
𝑥 +
𝑉
𝐿
−
𝐼
𝐶
𝑢
𝑦 = 0 1 𝑥 ⎭
⎪
⎪
⎬
⎪
⎪
⎫
(1)
where, the system states are 𝑥 = ∆𝑖 ∆𝑣 and the input is 𝑢 = ∆𝑑 . I, Vc and D are
the steady state values of the inductor current iL, output voltage vC and duty cycle d of the
transistor switch Tb around the operating point and Δ corresponds to the deviation of
these parameters from steady state. In the state equation RLoad is the resistance as seen by
boost converter and is given by 𝑅 = 𝑉 𝑃
⁄ with PDemand is the required power at
the output. So, from the state matrix it is clear that the zero of the boost converter system
is given as:
𝑧 =
𝑉 (1 − 𝐷)
𝐼
− 𝑅
𝐿
(2)
which is located at the right half of the s-plane and hence the system becomes non-
Figure 2. Block diagram of the system under study.
4.1. Control of DC Link Voltage
To design the MRAC controller for the boost converter to maintain the DC link voltage,
the reference plant model for the converter is to be chosen first. According to the circuit
diagram for the boost converter given in Figure 2, the linearized state space model around
an arbitrary operating point is described as:
.
x =
− R
L −1−D
L
1−D
C − 1
RLoadC
#
x +
VC
L
− I
C
#
u
y =
0 1
x
(1)
where, the system states are x =
∆ iL ∆vC
T
and the input is u = [∆d]. I, Vc and D are
the steady state values of the inductor current iL, output voltage vC and duty cycle d of
the transistor switch Tb around the operating point and ∆ corresponds to the deviation of
these parameters from steady state. In the state equation RLoad is the resistance as seen by
boost converter and is given by RLoad = V2
C/PDemand with PDemand is the required power at
5. Energies 2021, 14, 4097 5 of 18
the output. So, from the state matrix it is clear that the zero of the boost converter system is
given as:
z =
VC(1−D)
I − R
L
(2)
which is located at the right half of the s-plane and hence the system becomes non-minimum
phase and non-ASPR. In order to apply the MRAC control technique the system first needs
to be converted into ASPR system by adding suitable PFC to the system. The transfer function
for the model in Equation (1) is of the form:
G(s) =
Y(s)
U(s)
=
−k1s + k2
s2 + k3s + k4
(3)
where, ki (i = 1,2,3,4) is a positive constant.
For the system in Equation (1) with L = 8.2 mH, R = 82 Ohm, C = 1120 uF, RLoad = 100 Ohm,
Vc = 450 V, I = 10.12 A and D = 0.55 the root locus of the transfer function in Figure 3a
shows the non-ASPR character of the system. Since, the direct MRAC technique cannot be
applied to the system with non-minimum phase a PFC is designed to make it minimum phase.
However, in order to keep the system dynamics intact, it must be ensured that the augmented
plant output should be almost same as the actual plant output with this modification.
Energies 2021, 14, x FOR PEER REVIEW 6 of 20
where, ki (i = 1,2,3,4) is a positive constant.
For the system in Equation (1) with L = 8.2 mH, R = 82 Ohm, C = 1120 uF, RLoad = 100
Ohm, Vc = 450 V, I = 10.12 A and D = 0.55 the root locus of the transfer function in Figure
3a shows the non-ASPR character of the system. Since, the direct MRAC technique cannot
be applied to the system with non-minimum phase a PFC is designed to make it minimum
phase. However, in order to keep the system dynamics intact, it must be ensured that the
augmented plant output should be almost same as the actual plant output with this
modification.
(a) (b)
Figure 3. Root locus of the (a) uncompensated system; (b) compensated system.
To design the PFC, it must be ensured that the plant is stabilizable using any
controller. So, a PI controller is designed by root locus method to shift poles to the left side
of the s-plane as:
𝐶(𝑠) =
0.0001𝑠 + 0.03
𝑠
(4)
Now, to make the system minimum phase and of relative degree one, a PFC is designed
as inverse of a PD compensator D(s) that stabilizes the series combination of C(s)G(s). The
PFC is thus designed using the root locus method as:
𝑃𝐹𝐶 = 𝐷(𝑠) =
0.001
0.001𝑠 + 1
(5)
Figure 3b shows the root locus of the augmented system 𝐺 (𝑠) = 𝐶(𝑠)𝐺(𝑠) + 𝑃𝐹𝐶
which is now minimum phase and of relative degree one and hence the MRAC technique
can be applied to this augmented system.
0 1 2 3 4
104
-1.5
-1
-0.5
0
0.5
1
1.5
104
Real Axis (seconds-1
)
Figure 3. Root locus of the (a) uncompensated system; (b) compensated system.
To design the PFC, it must be ensured that the plant is stabilizable using any controller.
So, a PI controller is designed by root locus method to shift poles to the left side of the
s-plane as:
C(s) =
0.0001s + 0.03
s
(4)
Now, to make the system minimum phase and of relative degree one, a PFC is designed as
inverse of a PD compensator D(s) that stabilizes the series combination of C(s)G(s). The
PFC is thus designed using the root locus method as:
PFC = D(s)−1
=
0.001
0.001s + 1
(5)
6. Energies 2021, 14, 4097 6 of 18
Figure 3b shows the root locus of the augmented system G0(s) = C(s)G(s) + PFC
which is now minimum phase and of relative degree one and hence the MRAC technique
can be applied to this augmented system.
The control block diagram for the MRAC based DC link voltage control is shown in
Figure 4. The reference model for the controller is chosen as:
GREF(s) =
Ym(s)
R(s)
=
bm
s + am
(6)
where, am and bm are positive constants.
Energies 2021, 14, x FOR PEER REVIEW 7 of 20
r(t)
G(s)
Adaptative
Control
Mechanism
Control
Law
+
-
ym
ar, ax
u PI
PFC
+
+ xm
Augmented Plant
em
GREF(s)
Figure 4. Control block diagram of MRAC controller for boost converter.
The objective of the controller is to minimize a convex function 𝐽(𝑎) = 0.5𝑒𝑚
2
, where
a is the adaptation parameters (ar and ax), em is the error between the desired output ym and
augmented plant output xm and is given by:
𝑒𝑚 = 𝑥𝑚 − 𝑦𝑚 (7)
If the error is increased due to system conditions, then the MRAC modifies the
control parameters ar and ax so as to minimize the error.
The adjustment mechanism of conventional MRAC to change the control parameter
in the direction of negative gradient of J(a) is given in [25] as:
𝑑𝑎𝑟
𝑑𝑡
= −𝛾′
𝜕𝐽
𝜕𝑎𝑟
= −𝛾′
𝑒𝑚
𝜕𝑒𝑚
𝜕𝑎𝑟
𝑑𝑎𝑥
𝑑𝑡
= −𝛾′
𝜕𝐽
𝜕𝑎𝑥
= −𝛾′
𝑒𝑚
𝜕𝑒𝑚
𝜕𝑎𝑥 }
(8)
where, γ' is the adaptation gain.
The control law is taken as:
𝑢(𝑡) = 𝑎𝑟(𝑡)𝑟(𝑡) − 𝑎𝑥(𝑡)𝑥𝑚(𝑡) (9)
From (3) and (9) it can be deduced:
𝑥𝑚 =
𝐺′(𝑠)𝑎𝑟𝑟
1 + 𝐺′(𝑠)𝑎𝑥
(10)
Substituting (10) and (6) in (7) it is found that,
𝑒𝑚 =
𝐺′(𝑠)𝑎𝑟𝑟
1 + 𝐺′(𝑠)𝑎𝑥
− 𝐺𝑅𝐸𝐹(𝑠)𝑟 (11)
Taking partial derivative of em w.r.t. adaptation parameters ar and ax it can be written,
𝜕𝑒𝑚
𝜕𝑎𝑟
=
𝐺′(𝑠)𝑟
1 + 𝐺′(𝑠)𝑎𝑥
𝜕𝑒𝑚 𝐺′(𝑠)𝑥𝑚
(12)
Figure 4. Control block diagram of MRAC controller for boost converter.
The objective of the controller is to minimize a convex function J(a) = 0.5em
2, where a
is the adaptation parameters (ar and ax), em is the error between the desired output ym and
augmented plant output xm and is given by:
em = xm − ym (7)
If the error is increased due to system conditions, then the MRAC modifies the control
parameters ar and ax so as to minimize the error.
The adjustment mechanism of conventional MRAC to change the control parameter
in the direction of negative gradient of J(a) is given in [25] as:
dar
dt = −γ0 ∂J
∂ar
= −γ0em
∂em
∂ar
dax
dt = −γ0 ∂J
∂ax
= −γ0em
∂em
∂ax
)
(8)
where, γ0 is the adaptation gain.
The control law is taken as:
u(t) = ar(t)r(t) − ax(t)xm(t) (9)
From (3) and (9) it can be deduced:
xm =
G0(s)arr
1 + G0(s)ax
(10)
Substituting (10) and (6) in (7) it is found that,
em =
G0(s)arr
1 + G0(s)ax
− GREF(s)r (11)
7. Energies 2021, 14, 4097 7 of 18
Taking partial derivative of em w.r.t. adaptation parameters ar and ax it can be written,
∂em
∂ar
= G0(s)r
1+G0(s)ax
∂em
∂ax
= − G0(s)xm
1+G0(s)ax
(12)
For accurate error tracking it can be assumed that,
1 + G0
(s)ax ≈ s + am (13)
So, from (8) and (12) it can be written,
dar
dt = −γemGREFr
dax
dt = γemGREFxm
)
(14)
with, bm absorbed in γ and normalized with static gain of one.
Combining (9) and (14) the control signal to the plant is generated. When the tracking
error em increases these adaptation parameters get modified to change the control signal to
the plant and effectively reduce the error.
Alternatively, to provide similar control quality, irrespective of the operating point, an
LQR controller with integral action or a PI controller might be used with the parameters
updated in real time. It is achieved via extensive experimental look-up tables, making the
control design cumbersome and depending on the accuracy of the parameters measurement,
or via parameters’ adaptation algorithms which still do not guarantee that the dynamical
behavior is the same for all operating points.
4.2. Control of Grid Side VSI
The purpose of SMC based controller for the VSI is to control the power flow through
the VSI to the grid when there is a requirement of power from the CAES. This can be
achieved by using a PI controller also but, the major drawbacks are: there must be two
control loops for voltage and current and the power decoupling ability between the active
and reactive power is very poor in presence of disturbances in grid voltage and frequency.
So, the attenuation of the controllers for one operating point does not provide required
quality in the whole control range and also it is very sensitive to the accuracy of the
parameters. As an alternative a simplified decoupling method has been employed to
transform the system into two reduced order decoupled system and then an SMC has been
designed with Pref and Qref as the active and reactive power references respectively, for
controlling the grid currents. The d-q reference frame rotates synchronously with the grid
voltage vector and initially at t = 0 the q-axis was aligned with phase A axis vector.
The block diagram of the SMC controller is shown in Figure 5. In the system shown in
Figure 2, at the point of coupling to the grid, it can be modelled as:
Lf
2
.
idinv
.
iqinv
#
= −
Rf
2
idinv
iqinv
+
vdinv
vqinv
−
vc f d
vc f q
−
Lf
2 ω
−iqinv
idinv
Lf
2
.
idg
.
iqg
#
= −
Rf
2
idg
iqg
+
vc f d
vc f q
−
vdg
vqg
−
Lf
2 ω
−iqg
idg
Cf
.
vc f d
.
vc f q
=
idinv
iqinv
−
idg
iqg
− Cf ω
−vc f q
vc f d
(15)
where, subscript with ‘inv’, ‘g’ and ‘cf’ corresponds to inverter output, grid and filter ca-
pacitor respectively, while, ‘d’ and ‘q’ stand for d-axis and q-axis fundamental components
of the corresponding parameters respectively. This can be rearranged as a standard state
space system as:
.
x = f (x) + g(x)u (16)
8. Energies 2021, 14, 4097 8 of 18
where,
f (x) =
−
Rf
Lf
idg + 2
Lf
vc f d − 2
Lf
vdg + ωiqg
−
Rf
Lf
idinv − 2
Lf
vc f d + ωiqinv
1
Cf
idinv − 1
Cf
idg + ωvc f q
−
Rf
Lf
iqg + 2
Lf
vc f q − 2
Lf
vqg − ωidg
−
Rf
Lf
iqinv − 2
Lf
vc f q − ωidinv
1
Cf
iqinv − 1
Cf
iqg − ωvc f d
and g(x) =
0 0
2
Lf
0
0 0
0 0
0 2
Lf
0 0
with state vector as x =
x1 x2 x3 x4 x5 x6
T
=
h
idg idinv vc f d iqg iqinv vc f q
iT
,
input as u =
vdinv vqinv
T
and output as y =
idg iqg
T
. Now defining a new set
of variables as z1 = x1, z2 =
.
z1 =
.
x1, z3 =
.
z2 =
..
x1,
.
z3 =
...
x1, z4 = x4, z5 =
.
z4 =
.
x4, z6 =
.
z5 =
..
x4,
.
z6 =
...
x4 and assuming vdg and vqg as constants, system (16) can be represented as:
.
z1
.
z2
.
z3
.
z4
.
z5
.
z6
=
0 1 0 0 0 0
0 0 1 0 0 0
a1 0 0 0 0 0
0 0 0 0 1 0
0 0 0 0 0 1
0 0 0 a1 0 0
z1
z2
z3
z4
z5
z6
+
0
0
A1
x, vdg, vqg
0
0
A2
x, vdg, vqg
+
0 0
0 0
4
L2
f Cf
0
0 0
0 0
0 4
L2
f Cf
[α(x) + λ] (17)
where,
a1 = −
R3
f
L3
f
+
4Rf
L2
f Cf
+
3ω2Rf
Lf
A1(x) = −
4Rf
L2
f Cf
idinv +
2R2
f
L3
f
− 8
L2
f Cf
− 6ω2
Lf
vc f d +
3ωR2
f
L2
f
− 6ω
Lf Cf
− ω3
iqg + 6ω
Lf Cf
iqinv
−
6ωRf
L2
f
vc f q −
2R2
f
L3
f
− 4
L2
f Cf
− 2ω2
Lf
vdg +
4ωRf
L2
f
vqg
A2(x) = −
4Rf
L2
f Cf
iqinv +
2R2
f
L3
f
− 8
L2
f Cf
− 6ω2
Lf
vc f q −
3ωR2
f
L2
f
− 6ω
Lf Cf
− ω3
idg − 6ω
Lf Cf
idinv
+
6ωRf
L2
f
vc f d −
2R2
f
L3
f
− 4
L2
f Cf
− 2ω2
Lf
vqg −
4ωRf
L2
f
vdg
u =
vdinv vqinv
T
= α(x) + λ
Considering,
αd(x)
αq(x)
= −
L2
f Cf
4
A1
x, vdg, vqg
A2
x, vdg, vqg
the original 6th order system
expressed by (17) can be decoupled into two independent, identical 3rd order system as:
.
z1
.
z2
.
z3
=
0 1 0
0 0 1
a1 0 0
z1
z2
z3
+
0
0
4
L2
f Cf
λd
.
z4
.
z5
.
z6
=
0 1 0
0 0 1
a1 0 0
z4
z5
z6
+
0
0
4
L2
f Cf
λq
(18)
Once the system is decoupled the simplified system for control can be expressed as:
...
y1
...
y2
=
...
idg
...
iqg
#
= a1
idg
iqg
+ E
λd
λq
(19)
9. Energies 2021, 14, 4097 9 of 18
where, E =
4
L2
f Cf
0
0 4
L2
f Cf
=
E1 0
0 E1
.
So, the SMC need to be designed to track only one of the outputs and the other one
will be identical to it.
Energies 2021, 14, x FOR PEER REVIEW 12 of 20
λ
α +
)
(x
d
λ
dg
v qg
v
dg
i qg
i
dgref
i qgref
i
dinv
v
qinv
v
ref
abc
v _
abc
inv
i , abc
g
v , abc
cf
v ,
abc
g
i ,
( )
σ
ρ sign
−
q
λ
Figure 5. Control block diagram of SMC controller for VSI.
5. Simulation Results
The entire system control is simulated in MATLAB/SIMULINK environment using
the ideal switches and elements of Simscape toolbox to check the feasibility of the system.
The MRAC and SMC controller are designed as discussed in Section 4. The parameters
for the design are presented in Table 1.
Table 1. Simulation parameter
System Parameter Value
Lf/2 1.64 mH, 7.2 A
Cf 10 μF, 305 V
Cin 470 μF, 400 V
L 8.2 mH, 7.2 A
R 82 Ω, 150 W
Transformer turns ratio 110:230
DC-link Voltage 450 V
Grid Voltage 230 V
Switching frequency 5 kHz
Adaptation gain γ 0.8
Sliding mode gain ρ 9
5.1. DC Side Controller
The performance of the DC side controller has been investigated for different input
voltage and load conditions. Figure 6 shows the DC-link voltage for variation in the input
voltage from the CAES keeping the load constant at RLoad = 100 Ohm. The boost converter
starts operating from t = 1 s and then for variation in the CAES input voltage, the output
DC-link voltage is found to be maintained at 450 V.
Figure 5. Control block diagram of SMC controller for VSI.
As the relative degree of system in (19) is 3 the sliding surface for the tracking algo-
rithm is chosen as:
σ =
..
er + m2
.
er + m1er + m0
Z
erdτ (20)
with mi (I = 0, 1, 2) are positive constants and er is error in the output vector, which is
defined as:
eridg = idg − idgre f
eriqg = iqg − iqgre f
(21)
The reference values are computed from the following equations.
idgre f =
2(vqgQre f +vdgPre f )
3
v2
dg+v2
qg
iqgre f =
2(vqgPre f −vdgQre f )
3
v2
dg+v2
qg
(22)
The SMC λd (or λq) need to be designed such that in finite time σ = 0 which yields the
sliding variable dynamic characteristic as:
.
σ =
...
er + m2
..
er + m1
.
er + m0er
(23)
Assuming the reference currents remain constant during the control response it is
found that,
.
σ =
...
idg + m2
..
er + m1
.
er + m0er
= a1idg + E1λd + m2
..
idg + m1
.
idg + m0idg
= (a1 + m0)idg + m1
.
idg + m2
..
idg + E1λd
= F
idg,
.
idg,
..
idg
+ E1λd
(24)
10. Energies 2021, 14, 4097 10 of 18
where, F
idg,
.
idg,
..
idg
is assumed bounded, i.e.,
16. ≤ N.
To satisfy Lyapunov’s global finite-time stability criterion it is required.
.
V = σ
.
σ ≤ −
α
√
2
|σ| (25)
with α = constant 0. Selecting λd = −ρsign(σ) where ρ is a positive constant and
using (24) it can be shown:
.
σ = σ
F
idg,
.
idg,
..
idg
− E1ρsign(σ)
= σF
idg,
.
idg,
..
idg
− E1ρ|σ|
=
sign(σ)F
idg,
.
idg,
..
idg
− E1ρ
|σ|
(26)
Comparing (25) and (26) the condition for stability is found as:
ρ ≥
α
√
2E1
+
1
E1
sign(σ)F
idg,
.
idg,
..
idg
(27)
As the maximum positive value for 1
E1
sign(σ)F
idg,
.
idg,
..
idg
is N
E1
the value for ρ
should be,
ρ ≥
α
√
2E1
+
N
E1
(28)
This value for ρ is same for both the SMC λd and λq.
For any imperfection in the decoupling, (19) is modified as:
...
y1
...
y2
=
...
idg
...
iqg
#
= a1
idg
iqg
+
∆A1
x, vdg, vqg
∆A2
x, vdg, vqg
+ E
λd
λq
(29)
In that case, deriving in the same manner the condition for stability is found as:
ρd ≥ α
√
2E1
+ 1
E1
N +
28.
(30)
From (30) it can be observed that for bounded disturbance ∆A the SMC parameter ρ is
also bounded and the system will be stabilized.
With use of ‘sign()’ function in the analysis it may also create chattering problem which
can be reduced by choosing similar function ‘tanh()’ and proper value of ρ.
5. Simulation Results
The entire system control is simulated in MATLAB/SIMULINK environment using
the ideal switches and elements of Simscape toolbox to check the feasibility of the system.
The MRAC and SMC controller are designed as discussed in Section 4. The parameters for
the design are presented in Table 1.
29. Energies 2021, 14, 4097 11 of 18
Table 1. Simulation parameter.
System Parameter Value
Lf/2 1.64 mH, 7.2 A
Cf 10 µF, 305 V
Cin 470 µF, 400 V
L 8.2 mH, 7.2 A
R 82 Ω, 150 W
Transformer turns ratio 110:230
DC-link Voltage 450 V
Grid Voltage 230 V
Switching frequency 5 kHz
Adaptation gain γ 0.8
Sliding mode gain ρ 9
5.1. DC Side Controller
The performance of the DC side controller has been investigated for different input
voltage and load conditions. Figure 6 shows the DC-link voltage for variation in the input
voltage from the CAES keeping the load constant at RLoad = 100 Ohm. The boost converter
starts operating from t = 1 s and then for variation in the CAES input voltage, the output
DC-link voltage is found to be maintained at 450 V.
Energies 2021, 14, x FOR PEER REVIEW 13 of 20
Figure 6. Input CAES voltage and DC-link voltage for boost converter.
The corresponding inductor and output currents of the boost converter are shown in
Figure 7. It is seen that at the starting of the boost converter the current peak is very high
but at the later stage for variation in the voltage the current peaks are within limit. This
starting current can be limited by using a charging resistor in series with the boost
converter and once the current is within limit it is short-circuited.
Figure 6. Input CAES voltage and DC-link voltage for boost converter.
The corresponding inductor and output currents of the boost converter are shown
in Figure 7. It is seen that at the starting of the boost converter the current peak is very
high but at the later stage for variation in the voltage the current peaks are within limit.
This starting current can be limited by using a charging resistor in series with the boost
converter and once the current is within limit it is short-circuited.
The transient response of the boost converter with a standard PI controller and MRAC
controller for two loading conditions is shown in Figure 8. The gains of the PI controller are
chosen to be Kp = 0.1 and Ki = 1 for minimum settling time possible for the system under
test with acceptable ripple of 2% and without overshoot condition.
30. Energies 2021, 14, 4097 12 of 18
Figure 6. Input CAES voltage and DC-link voltage for boost converter.
The corresponding inductor and output currents of the boost converter are shown in
Figure 7. It is seen that at the starting of the boost converter the current peak is very high
but at the later stage for variation in the voltage the current peaks are within limit. This
starting current can be limited by using a charging resistor in series with the boost
converter and once the current is within limit it is short-circuited.
Figure 7. Input current and output current of the boost converter.
The transient response of the boost converter with a standard PI controller and
MRAC controller for two loading conditions is shown in Figure 8. The gains of the PI
controller are chosen to be Kp = 0.1 and Ki = 1 for minimum settling time possible for the
system under test with acceptable ripple of 2% and without overshoot condition.
Figure 7. Input current and output current of the boost converter.
es 2021, 14, x FOR PEER REVIEW 14 of 20
Figure 8. Output voltage of the boost converter for fall in input voltage with MRAC and PI
controller.
The response shows that with the MRAC controller the output voltage settles to
reference voltage in 0.121 s whereas with the PI controller, it settles in 0.528 s for a fall of
50 V in the input voltage at t = 1.71 s and RLoad = 10 Ohm. Similarly, for RLoad = 100 Ohm the
settling time for the output voltage remains almost same for the same dip in the input
voltage but, comparatively larger oscillations are added in case of PI controller. This
proves the faster operation and better damping property of the MRAC. This operation can
be made even faster with choice of higher value of adaptation parameter γ. However, the
overshoot in the output voltage also rises with it. So, an optimum value of adaptation
parameter γ as given in Table 1 is chosen for this simulation.
5.2. Grid Side Controller
The performance of the SMC controller to control the power flow through the VSI to
the grid from the CAES has also been analyzed in simulation for different power demand
scenario.
Figure 9a shows the inverter output voltage for different power flow conditions. The
0.5 1 1.5 2 2.5 3
Time (seconds)
0
100
200
300
400
MRAC, Rload
=10 Ohm
PI, Rload
=10 Ohm
MRAC, Rload
= 100 Ohm
PI, Rload
= 100 Ohm
Reference Voltage
1.7 1.8 1.9 2 2.1 2.2 2.3
350
400
450
Ts=0.528s
Ts=0.121s
Figure 8. Output voltage of the boost converter for fall in input voltage with MRAC and PI controller.
The response shows that with the MRAC controller the output voltage settles to
reference voltage in 0.121 s whereas with the PI controller, it settles in 0.528 s for a fall of
50 V in the input voltage at t = 1.71 s and RLoad = 10 Ohm. Similarly, for RLoad = 100 Ohm
the settling time for the output voltage remains almost same for the same dip in the input
voltage but, comparatively larger oscillations are added in case of PI controller. This proves
the faster operation and better damping property of the MRAC. This operation can be
made even faster with choice of higher value of adaptation parameter γ. However, the
overshoot in the output voltage also rises with it. So, an optimum value of adaptation
parameter γ as given in Table 1 is chosen for this simulation.
31. Energies 2021, 14, 4097 13 of 18
5.2. Grid Side Controller
The performance of the SMC controller to control the power flow through the VSI
to the grid from the CAES has also been analyzed in simulation for different power
demand scenario.
Figure 9a shows the inverter output voltage for different power flow conditions. The
switch S3 between inverter side and grid has been closed at t = 0.1 s. It can be seen from
Figure 9b that inverter phase-A reference voltage and the grid voltage of phase-A are
synchronized with each other for successful integration with the grid. The command for
power requirement by the grid has been given at t = 0.15 s and the grid current increases
accordingly to meet the power demand of 600 W, keeping the voltage unchanged. The
corresponding inverter currents in d-q reference frame have been shown in Figure 9c. At
t = 0.6 s the power demand rises from 600 W to 1500 W and again at t = 1.2 s it reduces
to 600 W. From Figure 9d it is clear that the inverter system is able to comply the power
demand requirement by the grid successfully.
Energies 2021, 14, x FOR PEER REVIEW 15 of 20
(a) (b)
(c) (d)
Figure 9. (a) Three phase inverter reference voltage, grid voltage and grid current; (b) inverter reference voltage and grid
voltage of phase-A, (c) Id and Iq of inverter output current; (d) active and reactive power flow to the grid.
6. Experimental Results
The proposed two stage grid tied micro-CAES system using a boost converter and a
VSI has been verified in the hardware setup in OPAL-RT platform. The hardware setup
developed for the system under study is shown in Figure 10.
OPAL-RT
Boost converter and VSI
with sensors and control
board
CAES
Figure 9. (a) Three phase inverter reference voltage, grid voltage and grid current; (b) inverter reference voltage and grid
voltage of phase-A, (c) Id and Iq of inverter output current; (d) active and reactive power flow to the grid.
6. Experimental Results
The proposed two stage grid tied micro-CAES system using a boost converter and a
VSI has been verified in the hardware setup in OPAL-RT platform. The hardware setup
developed for the system under study is shown in Figure 10.
The scroll-type air motor employed in the experimental system is an Air Squared Mfg.
1 kW device. The device is fed from a 340 L air tank charged with air at up to 20 bar from a
grid fed compressor. The air flow into the scroll is controlled by a pressure regulating valve.
The scroll-air motor is coupled with a Voltmaster AB30L 2.4 kW induction generator, whose
output voltage is then rectified to DC and used as input for the other system components.
32. Energies 2021, 14, 4097 14 of 18
The inputs and outputs to the micro-CAES system at different air pressures can be observed
in Figure 11. The input pressure to the expander is incrementally increased in steps of 1 bar.
The generator cuts in when the pressure increases to over 2.5 bar and an output voltage
from the generator is achieved. It can be observed that the output voltage from the CAES
system generator is proportional to the input pressure to the air motor.
(c) (d)
Figure 9. (a) Three phase inverter reference voltage, grid voltage and grid current; (b) inverter reference voltage and grid
voltage of phase-A, (c) Id and Iq of inverter output current; (d) active and reactive power flow to the grid.
6. Experimental Results
The proposed two stage grid tied micro-CAES system using a boost converter and a
VSI has been verified in the hardware setup in OPAL-RT platform. The hardware setup
developed for the system under study is shown in Figure 10.
OPAL-RT
DSO
Boost converter and VSI
with sensors and control
board
Controller
interface
CAES
Figure 10. Experimental setup of the grid integrated CAES system.
Figure 10. Experimental setup of the grid integrated CAES system.
Energies 2021, 14, x FOR PEER REVIEW 16 of 20
The scroll-type air motor employed in the experimental system is an Air Squared
Mfg. 1 kW device. The device is fed from a 340 L air tank charged with air at up to 20 bar
from a grid fed compressor. The air flow into the scroll is controlled by a pressure
regulating valve. The scroll-air motor is coupled with a Voltmaster AB30L 2.4 kW
induction generator, whose output voltage is then rectified to DC and used as input for
the other system components. The inputs and outputs to the micro-CAES system at
different air pressures can be observed in Figure 11. The input pressure to the expander is
incrementally increased in steps of 1 bar. The generator cuts in when the pressure
increases to over 2.5 bar and an output voltage from the generator is achieved. It can be
observed that the output voltage from the CAES system generator is proportional to the
input pressure to the air motor.
Figure 11. CAES output voltage with different air flow rate and air pressure.
The controllers have been designed in MATLAB and interfaced to the system via
OPAL-RT. The feedback signals are acquired from the actual system using voltage and
current sensors and then fed to the controllers through ADC converters.
To prove the efficacy of the MRAC the grid tied CAES system has been tested in real
time hardware setup and the DC-link voltage is compared with PI control. Figure 12a
shows that for a rise in power demand from zero to 934 W the DC-link voltage falls and
then the controller drives it back to its reference voltage after 9.2 seconds for the PI
controller, whereas the proposed MRAC controller takes only 2 seconds to settle the
voltage to its reference level as can be seen from Figure 12b. The deviation in settling time
of the simulation result and that of experimental result is due to the use of a rate limiter
in the experimental setup with slew rate of 200 for smooth operation. However, it can be
noted that the ratio of settling time for MRAC and PI control technique remains
approximately same in both simulation and experimental result.
Figure 11. CAES output voltage with different air flow rate and air pressure.
The controllers have been designed in MATLAB and interfaced to the system via
OPAL-RT. The feedback signals are acquired from the actual system using voltage and
current sensors and then fed to the controllers through ADC converters.
To prove the efficacy of the MRAC the grid tied CAES system has been tested in real
time hardware setup and the DC-link voltage is compared with PI control. Figure 12a
shows that for a rise in power demand from zero to 934 W the DC-link voltage falls and
then the controller drives it back to its reference voltage after 9.2 s for the PI controller,
whereas the proposed MRAC controller takes only 2 s to settle the voltage to its reference
level as can be seen from Figure 12b. The deviation in settling time of the simulation result
and that of experimental result is due to the use of a rate limiter in the experimental setup
with slew rate of 200 for smooth operation. However, it can be noted that the ratio of
33. Energies 2021, 14, 4097 15 of 18
settling time for MRAC and PI control technique remains approximately same in both
simulation and experimental result.
Energies 2021, 14, x FOR PEER REVIEW 17 of 20
(a) (b)
Figure 12. (a) Input and output voltage of the boost converter for a rise in power demand with PI controller; (b) input and
output voltage of the boost converter for a rise in power demand with MRAC controller.
The effect of variation in CAES voltage on the DC-link voltage and inverter current
is shown in Figure 13a,b. It is seen that the variation in the input voltage from 200 V to 150
V and vice versa has negligible effect on the output current of the system. So, the power
flow remains unaltered at 710 W for input side disturbances which proves the
effectiveness of the MRAC controller.
Input CAES Voltage
(100V/division)
DC link Voltage
(100V/division)
Input DC Current (4A/division)
Inverter Current (4A/division)
Time : (2s / division)
(a) (b)
Figure 13. (a) Inverter current (phase A) and DC-link voltage for fall in input CAES voltage; (b) inverter current (phase A)
and DC-link voltage for increase in input CAES voltage.
The inverter output should be in phase with the grid and match the frequency of the
grid voltage. Figure 14a shows that the inverter voltage and grid current waveforms have
the same frequency with the unity power factor, which satisfies the condition for the grid
integration of the inverter. The waveform for the three phase inverter current for the 934
W power delivery condition is shown in Figure 14b.
Figure 12. (a) Input and output voltage of the boost converter for a rise in power demand with PI controller; (b) input and
output voltage of the boost converter for a rise in power demand with MRAC controller.
The effect of variation in CAES voltage on the DC-link voltage and inverter current is
shown in Figure 13a,b. It is seen that the variation in the input voltage from 200 V to 150 V
and vice versa has negligible effect on the output current of the system. So, the power flow
remains unaltered at 710 W for input side disturbances which proves the effectiveness of
the MRAC controller.
Energies 2021, 14, x FOR PEER REVIEW 17 of 20
(a) (b)
Figure 12. (a) Input and output voltage of the boost converter for a rise in power demand with PI controller; (b) input and
output voltage of the boost converter for a rise in power demand with MRAC controller.
The effect of variation in CAES voltage on the DC-link voltage and inverter current
is shown in Figure 13a,b. It is seen that the variation in the input voltage from 200 V to 150
V and vice versa has negligible effect on the output current of the system. So, the power
flow remains unaltered at 710 W for input side disturbances which proves the
effectiveness of the MRAC controller.
Input CAES Voltage
(100V/division)
DC link Voltage
(100V/division)
Input DC Current (4A/division)
Inverter Current (4A/division)
Time : (2s / division)
(a) (b)
Figure 13. (a) Inverter current (phase A) and DC-link voltage for fall in input CAES voltage; (b) inverter current (phase A)
and DC-link voltage for increase in input CAES voltage.
The inverter output should be in phase with the grid and match the frequency of the
grid voltage. Figure 14a shows that the inverter voltage and grid current waveforms have
the same frequency with the unity power factor, which satisfies the condition for the grid
integration of the inverter. The waveform for the three phase inverter current for the 934
W power delivery condition is shown in Figure 14b.
Figure 13. (a) Inverter current (phase A) and DC-link voltage for fall in input CAES voltage; (b) inverter current (phase A)
and DC-link voltage for increase in input CAES voltage.
The inverter output should be in phase with the grid and match the frequency of the
grid voltage. Figure 14a shows that the inverter voltage and grid current waveforms have
the same frequency with the unity power factor, which satisfies the condition for the grid
integration of the inverter. The waveform for the three phase inverter current for the 934 W
power delivery condition is shown in Figure 14b.
The power flow reference is fed to the system in terms of the reference current Idgref
and Iqgref. Figure 15a,b shows one phase inverter current with the change in the reference
power. In Figure 15a it is shown that a step change of power delivery from 710 W to 934 W
has been made by changing the reference current Idgref from 3 A to 4 A based on (22). As a
result, the SMC controller generates the actuating signal such that the inverter current (rms)
changes from 2.15 A to 2.83 A. Similarly, for a fall in power demand from 934 W to 710 W,
the inverter current (rms) again changes from 2.83 A to 2.15 A as shown in Figure 15b. In
34. Energies 2021, 14, 4097 16 of 18
both the cases Iqgref has been kept as zero. From the experimental results it is verified that
any power demand by the grid can be delivered with faster response and high efficiency
by the grid connected CAES based system with the designed controller.
Energies 2021, 14, x FOR PEER REVIEW 18 of 20
(a) (b)
Figure 14. (a) Grid current and inverter reference voltage with unity power factor; (b) three phase inverter current for 934
W power delivery.
The power flow reference is fed to the system in terms of the reference current Idgref
and Iqgref. Figure 15a,b shows one phase inverter current with the change in the reference
power. In Figure 15a it is shown that a step change of power delivery from 710 W to 934
W has been made by changing the reference current Idgref from 3 A to 4 A based on (22). As
a result, the SMC controller generates the actuating signal such that the inverter current
(rms) changes from 2.15 A to 2.83 A. Similarly, for a fall in power demand from 934 W to
710 W, the inverter current (rms) again changes from 2.83 A to 2.15 A as shown in Figure
15b. In both the cases Iqgref has been kept as zero. From the experimental results it is verified
that any power demand by the grid can be delivered with faster response and high
efficiency by the grid connected CAES based system with the designed controller.
(a) (b)
Figure 15. (a) Inverter current for rise in Idgref, (b) inverter current for fall in Idgref.
7. Conclusions
A grid integrated compressed air energy storage system with modern non-linear
control techniques has been presented in this paper. The complete system uses a two-stage
conversion with a model reference adaptive controlled DC–DC boost converter and
sliding mode-controlled voltage source inverter to integrate the energy storage device to
the grid. The conventional control techniques require a detail knowledge of system
parameters and they respond efficiently for known disturbances only. In the presented
work the MRAC and SMC controllers can address any kind of disturbances arising in the
system due to parametric changes and operating point shifting, making the system more
robust. The total system has been simulated in the MATLAB/SIMULINK environment
with different operating conditions and the results have been presented that prove the
Figure 14. (a) Grid current and inverter reference voltage with unity power factor; (b) three phase inverter current for 934 W
power delivery.
(a) (b)
Figure 14. (a) Grid current and inverter reference voltage with unity power factor; (b) three phase inverter current for 934
W power delivery.
The power flow reference is fed to the system in terms of the reference current Idgref
and Iqgref. Figure 15a,b shows one phase inverter current with the change in the reference
power. In Figure 15a it is shown that a step change of power delivery from 710 W to 934
W has been made by changing the reference current Idgref from 3 A to 4 A based on (22). As
a result, the SMC controller generates the actuating signal such that the inverter current
(rms) changes from 2.15 A to 2.83 A. Similarly, for a fall in power demand from 934 W to
710 W, the inverter current (rms) again changes from 2.83 A to 2.15 A as shown in Figure
15b. In both the cases Iqgref has been kept as zero. From the experimental results it is verified
that any power demand by the grid can be delivered with faster response and high
efficiency by the grid connected CAES based system with the designed controller.
(a) (b)
Figure 15. (a) Inverter current for rise in Idgref, (b) inverter current for fall in Idgref.
7. Conclusions
A grid integrated compressed air energy storage system with modern non-linear
control techniques has been presented in this paper. The complete system uses a two-stage
conversion with a model reference adaptive controlled DC–DC boost converter and
sliding mode-controlled voltage source inverter to integrate the energy storage device to
the grid. The conventional control techniques require a detail knowledge of system
parameters and they respond efficiently for known disturbances only. In the presented
work the MRAC and SMC controllers can address any kind of disturbances arising in the
system due to parametric changes and operating point shifting, making the system more
robust. The total system has been simulated in the MATLAB/SIMULINK environment
Figure 15. (a) Inverter current for rise in Idgref, (b) inverter current for fall in Idgref.
7. Conclusions
A grid integrated compressed air energy storage system with modern non-linear
control techniques has been presented in this paper. The complete system uses a two-stage
conversion with a model reference adaptive controlled DC–DC boost converter and sliding
mode-controlled voltage source inverter to integrate the energy storage device to the grid.
The conventional control techniques require a detail knowledge of system parameters and
they respond efficiently for known disturbances only. In the presented work the MRAC
and SMC controllers can address any kind of disturbances arising in the system due to
parametric changes and operating point shifting, making the system more robust. The
total system has been simulated in the MATLAB/SIMULINK environment with different
operating conditions and the results have been presented that prove the better performance
of the system. Moreover, the conclusions drawn from the simulation are also tested in a
real time hardware setup with the OPAL-RT platform and validated to claim the efficacy of
the system.
Author Contributions: Conceptualization, S.K.M., S.B.K., M.D., O.K. and J.W.; methodology, S.K.M.,
S.B.K., M.K., O.K. and J.W.; software, S.K.M. and D.L.; validation, S.K.M., S.B.K., M.K., D.L., M.D.,
35. Energies 2021, 14, 4097 17 of 18
O.K. and J.W.; formal analysis, S.K.M., S.B.K., M.D., O.K. and J.W.; investigation, S.K.M., S.B.K., M.K.,
D.L. and O.K.; resources, M.K., D.L., M.D., O.K. and J.W.; data curation, S.K.M., S.B.K., M.K. and D.L.;
writing—original draft preparation, S.K.M. and M.K.; writing—review and editing, S.K.M., S.B.K.,
O.K. and J.W.; visualization, S.K.M., S.B.K., D.L., O.K. and J.W.; supervision, S.B.K., O.K. and J.W.;
project administration, S.B.K., O.K. and J.W.; funding acquisition, S.B.K., O.K. and J.W. All authors
have read and agreed to the published version of the manuscript.
Funding: This work was supported by Department of Science and Technology, India under project
grant DST/RCUK/JVCCE/2015/02 (c) and was conducted as part of the research project ‘Joint
UK-India Clean Energy Centre (JUICE)’ which is funded by the RCUK’s Energy Programme (contract
no: EP/P003605/1). The projects funders were not directly involved in the writing of this article.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data that support the findings of this study are openly available in
WRAP at http://wrap.warwick.ac.uk/154104/.
Conflicts of Interest: The authors declare no conflict of interest.
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