Internet of Things-Based Control of Induction Machines: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Anastasios P. Stamelos
,
Stylianos A. Papazis
,
Athanasios Papoutsidakis
,
Maria G. Ioannides
,
Michael E. Stamatakis
,
Erofili E. Stamataki

The Internet of Things (IoT) is introduced in systems with electrical machines, such as in electric drive systems, wind energy generating systems, and small and special machines, to remote monitor and control the operation for data acquisition and analysis. These systems can integrate with the equipment and retrofit the existing installations. At the end of the control loops there are always motors, or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion. With the IoT-based control of induction machine systems operators can remotely monitor parameters and obtain accurate real-time feedback during fast changing duty cycle operation. Thus, IoT creates multipurpose instruments in the remote control of induction machines. 

  • Internet of Things
  • electric drive systems
  • wind energy conversion systems
  • induction machines
  • microprocessors
  • sensors

1. Introduction

The technology of Internet of Things (IoT) connects online to the internet and integrates devices. The IoT networks different kinds of physical objects equipped with electronic modules, acquires and transmits data, allows access to devices in remote areas, and the users can monitor their operation in real time. The development of IoT technology led to the emergence of new systems which are monitoring and controlling remotely complex structures. Operators can remotely monitor multipart installations, without human presence in the space where systems are sited. The interaction between machines, actuators, systems, sensors, and displays, has developed smart manufacturing applications which generate big volumes and diversity of data. Nevertheless, at the end of the control loops there are always motors or actuators, of big or small ratings, of rotating or linear movements, electrical or nonelectrical, which must produce the motion.
Recent reports present the advancement of IoT from an interdisciplinary viewpoint, like hardware and software of computers, control systems, sensors, and networks, and for a wide range of sectors. The IoT entered in to modern life by connecting smart devices, technologies, and applications. The principles of operating IoT, and its enabling of communication technologies, internet communication protocols, cloud computing, and big data analytics are applications and research topics of interest [1].
Thus, new emerged industrial applications established the Industrial Internet of Things (IIoT) [2] that connects different components of industrial equipment using sensors, and with the use of software, transmits large amounts of data. The acquired data are processed creating smartness. Related topics of smart processing are IoT-assisted manufacturing, which uses cloud computing, big data, and ICT [3]. The introduction of IIoT in industrial sectors stimulates industry growth, involving smart machines with embedded sensors, connectivity, and managing acquired data for various applications with electrical machines. IIoT connected systems allow the operators to real-time monitor the status of electrical machines, give fast feedback and regulation, and thus, optimize production processes in factories. Already, applications of IoT found in places in many sectors of technology and life, are installed in embedded devices, in smart grids, for environmental monitoring in agriculture, in construction of buildings, in automotive industries, and in health care [4].
Applications of IoT in smart grids of energy generating systems are parts of the Internet of Energy (IoE), which has roles in monitoring, remote control and management of installations, and equipment of electric generators, such as in Wind Energy Conversion Systems (WECS) of Renewable Energy Sources (RES) generation units. Because renewables depend on climate and weather conditions, the size and quality of energy produced from renewables are variable, and moreover, have high production costs. Thus, the various RES electricity generation devices, whether connected to electricity grids or stand-alone electricity systems, must provide for the possibility of improving the quality of the power produced [5]. Other weaknesses are meeting the increasing demand of electric power by RES in a reliable and efficient way. In order to achieve consumer demand for electricity when produced by hybrid schemes with RES, fossil fuels, and backup storage, new low-cost power generation schemes need to be further explored [5][6].
Along with advances in WECS design, construction, and sizes, the wind energy industry has also progressed due to Industry 4.0, which improves performance, processes, and computing power, making it easier to acquire and process data for automation, optimization, predictive maintenance, and new applications [4Mal]. Other aspects of IoT-enabled energy systems are architectures and functionalities of smart grid systems, with focus on communication, networking, computing, sensing, and security, which make them more robust, secure, efficient, and reliable, [7].
The energy sector change from a centralized to a decentralized system, by unifying big and small energy producers and RES producers, and using ICT and IoT technology, must reach net-zero emissions sustainability goals [8]. In various enterprises, by using actuators and sensors to collect and analyze real-time data, IIoT modifies the operation of machines and devices so that they do not require human intervention. Then, based on data collected and stored in real time, IIoT supports decision making, and uses this data for periodic assessment and maintenance forecasting [9]. Thus, applications through IIoT contribute to the automation of industrial production, the management of business processes, and the transportation of goods and people. The IIoT offers the improvement of human labor conditions, or even unmanned labor, especially in conditions where people can be exposed to hazardous agents and environments, such as toxic, explosive, or radioactive environments, in mining, underwater, or at heights [9]. The United States energy management system included grid surveillance with specifics on: weather and the impact of climate changes on energy generation and demand; forecasting of peak loads; renewable energy reliability and changeability; generation increase, decision analysis, and IoE integration [10].
Operation and Maintenance (O&M) are significant parts of the lifecycle costs of manufacturing machinery and of wind plants. In offshore wind farms, the condition monitoring instrumentation is used for fault identification and improvement of performance [11]. The state-of-the-art in O&M comprises offshore wind plants, WECS condition monitoring, deterioration models, fault diagnosis, prognosis, aspects of planning O&M, using artificial intelligence, data processing, integrating technology, automation, digitalization in the offshore maintenance sector, and debating the benefits, limitations, and application of these approaches with IoT and Industry 4.0 [11].
As far as developments in building infrastructure are concerned, energy installations using IoT are notable [12][13]. Some of the elements of IoT are installed from the design phase and concern building control devices, such as sensors, hardware and software, monitoring and data collection to predict energy consumption [14], supplying appliances with electricity, optimizing energy use, and ultimately improving the quality of life of residents [12][13].
In industrial plants, induction motors (IMs) are installed as varying speed-varying torque actuators, with good performance and reliability under many operating states and duty cycles. Modelling approaches of control configurations include mathematical equations, numerical analysis, computer simulations, results for the evaluation of induction motor parameters, the behavior obtained when different control techniques are applied [15], and the basic components for online monitoring of electrical equipment with electrical machines using IoT [16].
Among the important areas of IoT use related to electrical induction machines are process control with remote operation and status monitoring, equipment fault diagnosis, and power generation control. There are other fields such as education, buildings, and medicine as well. An early detection can be based on an online automatic fault diagnostic technique for the maintenance of induction motors using the IoT [17]. The benefits of induction motor controlled systems with IoT are optimized performance, reduced costs for O&M, increased reliability, and accuracy in prediction of failures.
To enable the introduction of WECS in the IoE and the conversion of wind energy, WECS technologies and their design are transformed into decentralized structures, methods, and technologies to cover electricity generation, and depending on load requirements and weather fluctuations, to include the characteristics of cyber-physical systems: security, remote control, networking, life cycle growth, and sustainability [18]. There is a tendency to deepen knowledge of double-output induction generator DOIG control systems operating in WECS. Thus, WECS equipped with DOIG have the advantages of operating with variable rotational speed input and constant frequency output and applying the techniques of maximum power point tracker MPPT, control the generated voltage, frequency, active, and reactive power [19]. The dynamic states of power systems embedded with smart grids and instabilities due to unpredictable wind and load changes, which can cause temporary load rejection, can be addressed using artificial intelligence and forecasting to predict component failures, reduce diagnostic time, and increase accuracy and stability for DOIG [19].
The development of IoT has raised new issues for energy conversion to power IoT components and devices, such as nanogenerators, with lightweight simple construction and to make them self-driving [20]. Nanogenerators can be piezoelectric, triboelectric, or pyroelectric. Thus, the sensors connected to the IoT can be powered using nanogenerators instead of conventional power supplies.
In Table 1 are the selected generic topics regarding electrical machines, working as induction motors and as induction generators, and their IoT-based specialized control systems for industrial applications and for WECS in energy generation power plants.
Table 1. Generic topics regarding electrical machines with IoT-based control systems.
  Generic Topics Specifics and Technical Aspects Citations Reference Number
Induction Motors Online monitoring performance of electrical motors using IoT. Suitability of IoT components. 2 [16]
Online automatic diagnostic techniques and early failure detection. Condition monitoring of maintenance with IoT. 10 [17]
Induction Generators WECS to cover load demands and generation requirements related to weather conditions. Technologies that allow WECS for IoE: remote control, networking, safety. 117 [18]
WECS with DOIG in smart grids, load-rejection from instabilities of wind and load demand. Prognostic diagnosis, increased accuracy, and stability. 6 [19]
Small machines Nanogenerators, lightweight and self-driving. Energy conversion devices for IoT sensors, powered by nanogenerators to replace power supplies. 0 [20]

2. Control System of Induction Machine with IoT

The parameters of electrical equipment with electrical machines, which can be sensed and measured, are voltages, currents, energy, power factor, frequencies, vibration, and temperature. The IoT-based real-time monitoring acquires data remotely, visualizes accurately the data and parameters which are not easy to be accessed, and improves the operating process. Various aspects of electric drive control have already been investigated and experimentally tested, such as scalar control of cage rotor induction motors [21][22] and double fed induction motors [23], using Raspberry Pi microcomputers [24], Arduinos microcontrollers [24], software, and IoT connection, and their published results have been published to be used for further analysis and comparison [24].
Programmable logic controllers (PLCs) are installed in systems of electrical machines with industrial automation, to control the fast-changing duty cycles, surge voltages, and over-currents caused during the succession of stop and restart [25]. Moreover, PLCs are installed in industries to optimize automation operation, increase quality, reliability, and reduce production costs. Also, PLC-controlled protective relays manage failures such as phase losses, over-currents, power surges, uneven phase shift of supply voltages, uneven current phase shift, and uncontrolled frequent stop and restart [15].
In various applications, electric machines are installed and function in two modes using similar basic equipment, but in different settings and connections. They can operate either as electric motors for industrial loads [21][22][24] or as electric generators [26][27][28], which are preferred in RES installations [27], and mainly in WECS [28].
Power plants using renewables and IoT are considered as part of the emerging Internet of Renewable Energy Sources (IoRE), which includes networking, control, productivity, security, and sustainability [29], with lower emissions [30][31][32], optimization of economics with multiple criteria [33], depending on load demand [34], and with energy constraints [6]. However, because electricity produced from renewables is of variable quantity, power quality, and high cost, these important problems must be solved to meet electricity demand with optimized efficiency [34][35]. Despite the technological development so far, new RES schemes for continuous energy supply, of sufficient quantity, and at lower cost, are still being explored. Various energy installations connected to the IoT for renewable energy industry applications have been described in the works [36][37][38].
IoT has been installed in electric machine applications with electric drive control: the induction motor control system that communicates with the IoT, obtains feedback data from sensors, and receives commands over the internet [24][39]. The project [40] designs, implements, and tests an application for monitoring induction motors via IoT, including remote control of their operation, receiving commands, and transmitting feedback information and measurements over the internet to a database. This research was developed as part of broader experiments conducted in the authors’ Electric Driver Control Laboratory. For many years this laboratory has been engaged in the study of remote control of the performance of electric machines via the Internet [24][39][40].
The rotor cage induction motor IM is most installed in industrial processes, while the double fed induction machine (DFIM) can be installed both in EDS for slip power recovery units [23][41], and in WECS for generation of electric power from wind [28][42][43]. Operating as either a motor or a generator, the induction machine’s drive system adjusts the electromechanical energy conversion direction, switching between converting mechanical energy from electrical power or generating electrical power from mechanical input. This bidirectional energy flow necessitates the use of power converters, either rectifier-inverter or cycloconverters, to interface with the three-phase AC network.
Figure 1 illustrates the architecture of an IoT-based control system for induction machines. This setup is structured and based on the single-board microcomputer Raspberry Pi (RPI) as the central electronic device. The RPI functions as a server and interfaces with a web application hosted in the Azure cloud, with a network of sensors connected to the single-board microcontrollers SBMA1 and SBMA2, and with the rectifier/inverter. Moreover, the RPI hosts a static web page directly accessible through an IP address of the operator. The RPI acts as a hub between these systems, enabling data exchange among sensors, inverter, induction motor or generator, and load.
Energies 17 00645 g001
Figure 1. General structure of IoT-based control system for Induction Machines. The arrows show the flow of data.
The following main issues need to be resolved when developing controlled IoT-based electrical machine systems:
  • The connectivity of power equipment to the internet, communication with servers, software, and databases. The units directly connected are electric motors, power converters, microcontrollers, sensors, measuring instruments. Other modules are connected indirectly via a gateway, and there is two-way communication with the backend system to register devices and collect data.
  • The data management: the acquisition, conversion, storage, retrieval, analysis, processing, security, visualization, and presentation of data in real time.
The diagram from Figure 1 shows an IoT-based control system for induction machines. The central electronic device is the Raspberry Pi (RPI), which communicates with:
  • Azure cloud web page
  • Sensors connected to single-board microcontrollers SBMA1 and SBMA2
  • Rectifier/inverter which drives the induction motor
  • Static control web page for direct access of operator
The RPI acts as a bridge between these elements, facilitating data exchange between the sensors, inverter, induction machine or generator, and the load. It is used in this IoT-based control system for induction machines for several reasons:
  • Power efficiency: The RPI is a low-power device, which is important for embedded systems like this one. This means that it can run on a small battery or even be powered directly from the induction machine itself.
  • Computational power: The RPI is a powerful device that can handle the computational tasks required for controlling the induction machine and collecting data from the sensors. This makes it a more versatile option than a simpler microcontroller.
  • Ease of use: The RPI is a relatively easy-to-use device, which makes it a good choice for developers who are not familiar with embedded systems programming. There are a large number of resources available for programming the RPI, and it can be easily connected to a variety of sensors and actuators.
  • Versatility: The RPI can be used for a variety of other tasks besides controlling induction machines, such as running web servers, hosting applications, and running data analysis software. This makes it a good investment for developers who need a versatile platform.
This system enables remote monitoring and control of induction machines, making it possible to optimize performance, reduce energy consumption, and improve overall efficiency. Here is a more detailed explanation of each component in the diagram:
  • Raspberry Pi (RPI): A single-board microcomputer that serves as the central electronic device in the system. It is responsible for collecting data from the sensors, communicating with the cloud and web applications, and controlling the rectifier/inverter.
  • Azure cloud web page: A web page hosted in the Azure cloud that provides a user interface for monitoring and controlling the induction machine. It can be used to access, visualize, and analyze data from the system.
  • Sensors: Sensors that measure various parameters of the induction machine, such as speed, currents, and voltages.
  • Single-board microcontrollers, SBMA1 and SBMA2: Single-board microcontrollers that interface with the sensors and send data to the RPI.
  • Rectifier/inverter: A power converter that converts AC voltage to DC and DC voltage to AC. It is used to drive the speed and direction of the induction machine.
  • Static web page at the operator’s IP: A web page that can be directly accessed by the operator to monitor and control the induction machine. The static web page serves two main purposes in the IoT-based control system for induction machines. (1) Real-time monitoring: The static web page provides a real-time view of the induction machine’s performance parameters. This information can be used by operators to monitor the health and condition of the machine, and to identify any potential problems. (2) Remote control: The static web page can also be used to remotely control the induction machine. Operators can use the web page to set the machine’s speed, and other parameters. This capability is particularly useful for operators who are not physically located near the machine. It offers a more straightforward and accessible interface for operators who may not require the advanced features of the cloud-based interfaces. In addition, the static web page can serve as a backup communication channel in case the internet connection is lost. This ensures that operators can still monitor and control the induction machine even if they are unable to access the cloud-based interfaces. Overall, the static web page plays a crucial role in the overall functionality of the IoT-based control system for induction machines. It provides real-time monitoring and remote-control capabilities to operators, while also complementing the dynamic functionality of the cloud-based interfaces.
Overall, the IoT-based control system for induction machines provides a comprehensive solution for remote monitoring and control of these devices. It can be used to improve performance, reduce energy consumption, and enhance overall efficiency.

2.1. Structure of Electric Drive System of Induction Motor with IoT

The induction motor IM can be controlled from the stator side, by varying the voltage and frequency [21][22]. During motor operation, three-phase IMs serve duty cycles with frequent changes in load and speed, including cut-off and reconnection. Thus, controllers must monitor the performance of IM [41]. In the case when the electrical drive system (EDS) of the induction machine with IoT performs control remotely via the internet, it uses sensors and generates data via the internet from the feedback of the control system.
In Figure 2, the diagram of the electric drive system of the induction motor with IoT-based control is shown. This is the architecture of the control system designed in the Laboratory of Electric Drives. The design and operation were introduced and described in detail in publication [40]. This configuration (Figure 2) establishes the remote-control system of a three-phase induction motor with IoT using: one single-board microcomputer Raspberry Pi (RPI), two Single-Board Microcontrollers (SBMA1 and SBMA2), a database for the managed data with Structured Query Language (SQL) [44], and a web application in Azure cloud platform [45].
  • The control system from Figure 2 consists of the following structural modules:
  • The three-phase induction motor is supplied by one digitally controlled three-phase rectifier-inverter, which controls the stator frequency and produces motion of rotation, which is transmitted to mechanical load.
  • The control system: one Control Webpage, one supervisory computer with access to the Control Webpage, one single-board microcomputer Raspberry Pi (RPI) linked to the rectifier-inverter which acquires the data from sensors through single-board microcontrollers SBMA1/SBMA2, and software. The RPI has the role of the local server which communicates with database by a web application in Azure cloud with an SQL database for data storage, data processing, and remote control.
  • Data acquisition: The rotational speed sensor and load current sensor of the induction motor transmits data to microcontrollers SBMA1 and SBMA2, respectively.
  • Networks: The IoT-based control system for induction machines has installed wired and wireless networks, including internet, local area Wi-Fi networks, and serial connections. The Internet serves to the users of the static web page hosted on the RPI. The system uses internet to connect the RPI to the Azure cloud web page. Bluetooth connectivity is established between RPI and microcontrollers. The RPI communicates with the rectifier/inverter via a serial connection; the sensors transmit data to the microcontrollers through serial connection.
 
Figure 2. Electric drive system of Induction Motor with IoT-based control.
Dashed lines show connections to the internet with Wi-Fi network; block diagram arrows show the bi-directional sending/receiving of data between server-rectifier/inverter, server-supervisory computer, and one-directional sending of data between sensor single-board microcontrollers, Arduino SBMA1/SBMA2; solid lines show the three-phase power supply and the three-phase connection between rectifier/inverter and induction motor; the orange thick double line is the coupling shaft between induction motor and load.
  • The remote control of the induction machine (IM) is initiated from the web interface, where the RPI receives commands and translates them into signals for the inverter using the Modbus serial communications protocol [46][47].
Remote control of the IM over the internet uses the supervisory computer and the microcomputer RPI. The RPI acts as an intermediary between the supervisory computer and the inverter, translating control commands from the former into signals that the inverter can understand. To facilitate this communication, a Modbus card is employed to establish a serial connection between the RPI and the inverter. The Modbus card is necessary because the inverter utilizes the Modbus serial communications protocol, a standardized communication protocol commonly used in industrial settings to exchange data between devices [46][47].
Hardware units are external: a speed encoder for speed feedback and a current sensor for load current feedback [40]. The software runs on the supervisory computer through the control website, which is accessible at the IP address reserved for laboratory experiments. The programmed functions in the software code are commands of the operator, selected from the website such as:
  • the frequency command for forward rotation
  • the reverse direction command to rotate backwards
  • the command to start the rotation
  • the end of rotation command
The software monitors the speed during load changes, while the inverter, controlled by the software, executes the frequency commands [40]. Analogue speed and current signals are converted into digital signals by two microcontrollers, Arduino SBMA1 and SBMA2, and transmitted to the microcomputer RPI. The SBMA1 receives the speed signal from the speed encoder and sends it to the RPI, using the Wi-Fi connection and software. The RPI reads the required frequency from the operator’s website and the actual rotation frequency of the IM, calculates the difference between the two frequencies and generates the error signal, which is usually non-zero, as a result of which the RPI reads the error signal, changes the frequency that the converter has, and, accordingly, corrects speed. Thus, the error signal is converted into a control signal sent by the RPI to the digital input of the inverter and corrects the frequency. The required frequency and also, the actual frequency calculated from the feedback signal, are displayed on the operators’ website. Digitized signals from measurements are stored in the Azure cloud in the SQL database.

2.2. Structure of Double Output Induction Generator with IoT-Based Control System for WECS

The cage rotor construction of induction machines is not suitable for generator operation, because it needs self-excitation which must be produced by an additional bank of capacitors [43]. On the other hand, the wound rotor induction machine can be supplied from the stator and from the rotor windings, and thus, received the name of doubly fed induction machine (DFIM). A thorough investigation has been carried out for the DFIM: using the feature of doubly controlled machine, the DFIM has increased consideration in wind generating power plants in WECS. Many times, the DFIM is named double output induction generator (DOIG), or double fed induction generator (DFIG) [27][28].
The DOIG is frequently installed in wind power plants, where it controls the generated voltage and frequency and the active and reactive power.
The components of WECS are the turbine, the induction generator, the mechanical coupling, and the controllers. The wind turbine converts the wind energy to kinetic and the DOIG converts the kinetic energy to electrical energy. Wind turbines can generate electrical energy during strong winds, but it is fluctuating [27].
Some wind turbines cannot follow the wind speed and rotate only at a single speed. There are two ways of operating at a fixed speed: to use a synchronous generator or an induction generator. Fixed speed turbines have the disadvantage of a lagging power factor of the grid-connected induction generator and demand large capacitor banks to compensate for it [43]. The energy from the wind is a function of wind speed, and the energy captured by the turbine propeller is a function of rotor speed and wind speed, thus fixed speed wind turbines fail to capture the maximum power. In contrast, variable speed DOIG can recover the maximum energy of the wind.
In the cage induction generator configuration, the power is generated when the shaft is rotating above the synchronous speed only. In the configuration with DOIG, electric power is generated at all speeds: higher than synchronous speed and lower than synchronous speed, too. Thus, the WECS with DOIG functioning at variable speed have some advantages [28]:
  • The rotor excitation voltage and frequency permit a bi-directional flow of electric power in the rotor, and a slip power recovery, instead of being lost by heating the windings. This increases generated energy, and efficiency too, are a consequence of an increased quantity of converted wind power.
  • At variable wind speed, the rotor excitation voltage controls the DOIG and generates constant frequency at the demanded value of the grid. Also, the active power is generated from the wind power at any time instant, under or above synchronous speed. The DOIG provides control of reactive power, close to unitary power factor, and eliminates the need for capacitor banks. Power quality improves because DOIG connects and disconnects from the grid, without high transient currents during cut-in or cut-out. The instabilities of wind generate higher order harmonics [48][49], which impacts the generated power quality, especially in fast transient states of the wind, and also, in weak or isolated autonomous grids [35][48]. With the DOIG, the higher order harmonics are reduced due to the high switching frequency of the power converter in the rotor, and the levels of the 5th and 7th harmonics are lower [50][51]. Causes of generated distortion and noise at different operating points can be the negative or close to zero power values when wind speed is above cut-in speed or with scattered data [48][51].
  • The power converter in the rotor has lower ratings because the rotor draws up to 35% of the rated power of DOIG.
  • Mechanical loading reduces because the operation at variable speeds produces lower stress transferred through the coupling components than in the case of fixed speed generators.
A variable speed WECS is shown in Figure 3, in an experimental configuration [27][28][39], with the induction generator in DOIG connection. The wind turbine which drives the rotor shaft of DOIG is emulated in laboratory by a variable speed dc motor. The wind turbine rotates the DOIG in the same direction as the air gap synchronous magnetic field. The rotor windings of DOIG receive excitation voltage fed by a digital rectifier-inverter from the three-phase power supply. The inverter controls the excitation voltage and frequency in the rotor. The stator windings are connected to the three-phase power supply and send the generated electric power to the grid. The control of rotor voltage and frequency during fluctuating speeds of the wind turbine is required in order to generate constant volts and hertz in the stator windings.
Energies 17 00645 g003
Figure 3. Wind Energy Conversion System with Double Output Induction Generator and with IoT-based control.
At speeds ω lower than synchronous ω1, ω < ω1, the slip power is supplied to the rotor and the generated power is sent to the grid. At speeds higher than synchronous speed, ω > ω1, the slip power is recovered from the rotor, is sent back to the excitation source and the generated power is sent from stator to grid [28]. Controlling the rotor voltage and frequency for rotor excitation results in the control of voltage and frequency generated at the stator side. The generated active power at the stator has a linear variation with the excitation frequency of the rotor [28]. At speeds higher than the synchronous speed, ω < ω1, the efficiency increases when there are increases in the wind speed.
Thus, in wind power plants, the preferred electrical machine is the DOIG, because of its capability to regulate the unexpected wind speed fluctuations using the slip excitation frequency control technique [27][28].
Figure 3 is the block diagram of the DOIG driven by a wind turbine and with the controlled system over the internet. The stator windings of the DOIG are connected to the three-phase power supply via a voltage three-phase transformer, while the rotor windings are connected to an ac/dc/ac rectifier/inverter [39].
The slip frequency control consists of injecting at the rotor windings of DOIG a three-phase voltage at slip frequency f2, as in Equation (1)
f2 = f1 − f   
ω2 = ω1 − ω   
where:
  • f1 is the power network frequency and produces the synchronous speed of air-gap magnetic field ω1
  • f is the angular rotational frequency of the shaft, which is imposed by the driving force of the wind, and is computed from the mechanical rotational speed of rotor ω,
  • f2 is the slip frequency of the voltage injected in the rotor windings and produces the rotational speed ω2 of the magnetic field of the rotor
Therefore,
f2 = s ∙ f1   
where s is the slip of the DOIG. A three-phase transformer adjusts the voltage magnitude of the grid at 380 V [27][42].
The DOIG has a bidirectional flow of power in the rotor: at super-synchronous speeds ω > ω1 the rotor sends power to the grid; at sub-synchronous speeds ω < ω1 the rotor absorbs power from grid. Thus, the DOIG works well at both sub-synchronous and super-synchronous speeds, within a speed range of ±35%·ω1. The power converter in the rotor regulates the voltage amplitude, frequency, and phase angle [27], and the generated frequency at the stator side is constant, even if the turbine turns at a variable speed ω [28]. The ratings of the power converter are up to 35% of the rated power of the DOIG, the losses are lower, the efficiency is higher, and the investment of initial capital for installed equipment is reduced. By using the DOIG technology, the ratings of wind park installations reduce, the efficiency improves, and it reduces the duration of the return of invested capitals [28]. Because the various magnitudes in WECS are difficult to access and measure, they can be estimated using the state space model of the DOIG wind turbine and using a nonlinear observer, a linearization near the operating point [27], or using Lyapunov stability theory [52].
Dashed lines show connection to the internet with a Wi-Fi network; block diagram arrows show the bi-directional sending and receiving of data between server and rectifier/inverter and server and supervisory computer; other block diagram arrows show the one-directional sending data between sensors and single board microcontrollers Arduino, SBMA1/SBMA2; solid lines show the three-phase power supply and the three-phase connections between rectifier/inverter and DOIG; the thick double line is the coupling shaft of the wind turbine and DOIG.
As mentioned above, some modules are present in both configurations of the electric drive system with induction motor (Figure 2) and of the power generation system with DOIG (Figure 3): the rectifier/inverter, the induction machine, the supervisory computer, the operator website, the microcomputer RPI, the two microcontrollers Arduino SBMA1 and SBMA2, the speed sensors, the current sensors, and the Wi-Fi Internet connection.

This entry is adapted from the peer-reviewed paper 10.3390/en17030645

References

  1. Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, A. Internet of Things: A Survey on Enabling Technologies, Protocols and Applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376.
  2. Milić, S.D.; Babić, B.M. Toward the Future—Upgrading Existing Remote Monitoring Concepts to IIoT Concepts. IEEE Internet Things 2020, 7, 11693–11700.
  3. Zhong, R.Y.; Xu, X.; Klotz, E.; Newman, S.T. Intelligent manufacturing in the context of industry 4.0: A review. Engineering 2017, 3, 616–630.
  4. Malik, P.K.; Sharma, R.; Singh, R.; Gehlot, A.; Satapathy, S.C.; Alnumay, W.S.; Pelusi, D.; Ghosh, U.; Nayak, J. Industrial Internet of Things and its Applications in Industry 4.0: State of The Art. Comput. Commun. 2021, 166, 125–139.
  5. Pali, B.S.; Vadhera, S. Renewable energy systems for generating electric power: A review. In Proceedings of the 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, India, 4–6 July 2016; pp. 1–6.
  6. Papazis, S.A. Integrated Economic Optimization of Hybrid Thermosolar Concentrating System Based on Exact Mathematical Method. Energies 2022, 15, 7019.
  7. Abir, S.M.A.A.; Anwar, A.; Choi, J.; Kayes, A.S.M. IoT-enabled smart energy grid: Applications and challenges. IEEE Access 2021, 9, 50961–50981.
  8. Biegańska, M. IoT-Based Decentralized Energy Systems. Energies 2022, 15, 7830.
  9. Wójcicki, K.; Bieganska, M.; Paliwoda, B.; Górna, J. Internet of Things in Industry: Research Profiling, Application, Challenges and Opportunities—A Review. Energies 2022, 15, 1806.
  10. Sakib, N.; Hossain, E.; Ahamed, S.I. A Qualitative Study on the United States Internet of Energy: A Step towards Computational Sustainability. IEEE Access 2020, 8, 69003–69037.
  11. Rinaldi, G.; Thies, P.R.; Johanning, L. Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review. Energies 2021, 14, 2484.
  12. Elsisi, M.; Tran, M.-Q.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F. Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings. Sensors 2021, 21, 1038.
  13. Yaïci, W.; Krishnamurthy, K.; Entchev, E.; Longo, M. Recent Advances in Internet of Things (IoT) Infrastructures for Building Energy Systems: A Review. Sensors 2021, 21, 2152.
  14. de Oliveira Cavalcanti, G.; Pimenta, H.C.D. Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review. Energies 2023, 16, 5753.
  15. Sengamalai, U.; Anbazhagan, G.; Thamizh Thentral, T.M.; Vishnuram, P.; Khurshaid, T.; Kamel, S. Three Phase Induction Motor Drive: A Systematic Review on Dynamic Modeling, Parameter Estimation, and Control Schemes. Energies 2022, 15, 8260.
  16. Kulkarni, P.P.; Patil, M.; Shibi, S.; Patle, M.; Kale, R. Review on Online Monitoring of Electrical Machine using IoT. In Proceedings of the 2019 International Conference on Nascent Technologies in Engineering (ICNTE), Navi Mumbai, India, 4–5 January 2019; pp. 1–4.
  17. De Jesús Rangel-Magdaleno, J. Induction Machines Fault Detection: An Overview. IEEE Instrum. Meas. Mag. 2021, 24, 63–71.
  18. Moness, M.; Moustafa, A.M. A Survey of Cyber-Physical Advances and Challenges of Wind Energy Conversion Systems: Prospects for Internet of Energy. IEEE Internet Things J. 2016, 3, 134–145.
  19. Behara, R.K.; Saha, A.K. Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review. Energies 2022, 15, 6488.
  20. Vinoth Kumar, K.; Omer, M.; Singh, S.S.; Nayrah, M.A. A Review on Triboelectric Nanogenerators (TENGs) using Internet of Things. In Proceedings of the 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS), Bengaluru, India, 21–22 December 2021; pp. 1–5.
  21. Ioannides, M.G.; Papazis, S.A.; Ioannidou, F.G. Implementation of scalar control scheme for variable frequency induction motor actuator system. Sens. Actuators A Phys. 2003, 106, 306–309.
  22. Lysenko, O.A. Sensorless Scalar Asynchronous Electric Drive for Pressure Stabilization of the Pumping Unit. In Proceedings of the 2021 Dynamics of Systems, Mechanisms and Machines (Dynamics), Omsk, Russia, 9–11 November 2021; pp. 1–5.
  23. Ioannides, M.G.; Tegopoulos, J.A. Generalized optimization slip power recovery drives. IEEE Trans. Energy Convers. 1990, 5, 91–97.
  24. Stamelos, A.P.; Papoutsidakis, A.; Vikentios, V.; Papazis, S.A.; Ioannides, M.G. Experimental Educational System of AC Electric Drives with Internet of Things. In Proceedings of the XXIII International Conference on Electrical Machines ICEM 2018, Alexandroupoli, Greece, 3–6 September 2018; pp. 1497–1502.
  25. Kowsalya, M.; Pradeep, R.; Elakya, A.; Gunapriya, D. PLC Based Critical Composition Control for Industries. In Proceedings of the 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 25–26 March 2022; pp. 1636–1639.
  26. Ansari, A.A.; Dyanamina, G. Fault Ride-Through Operation Analysis of Doubly Fed Induction Generator-Based Wind Energy Conversion Systems: A Comparative Review. Energies 2022, 15, 8026.
  27. Ioannides, M.G. State space formulation and transient stability of the double output asynchronous generator. IEEE Trans. Energy Convers. 1993, 8, 732–738.
  28. Ioannides, M.G.; Tuduce, R.; Cristea, P.-D.; Papazis, S.A. Wind power generating systems based on double output induction machine: Considerations about control techniques. In Proceedings of the 20th International Conference on Systems, Signals and Image Processing (IWSSIP 2013), Bucharest, Romania, 7–9 July 2013; pp. 103–107.
  29. Igbinovia, F.O.; Krupka, J. The Prospect of the Internet of Renewable Energy (IoRE) in Electricity Networks. In Proceedings of the 2019 IEEE International Symposium on Technology and Society (ISTAS), Medford, MA, USA, 15–16 November 2019; pp. 1–6.
  30. Wang, Z.; Ma, Y.; Sun, Y.; Tang, H.; Cao, M.; Xia, R.; Han, F. Optimizing Energy Management and Case Study of Multi-Energy Coupled Supply for Green Ships. J. Mar. Sci. Eng. 2023, 11, 1286.
  31. Durlik, I.; Miller, T.; Cembrowska-Lech, D.; Krzemińska, A.; Złoczowska, E.; Nowak, A. Navigating the Sea of Data: A Comprehensive Review on Data Analysis in Maritime IoT Applications. Appl. Sci. 2023, 13, 9742.
  32. Stamatakis, M.E.; Ioannides, M.G. State Transitions Logical Design for Hybrid Energy Generation with Renewable Energy Sources in LNG Ship. Energies 2021, 14, 7803.
  33. Papazis, S.A.; Ioannides, M.G.; Fotilas, P.N. An information system for the multiple criteria assessment of renewable energy power plants. Wind Eng. 2000, 24, 81–99.
  34. Papazis, S.A.; Bakos, G.C. Generalized model of economic dispatch optimization as an educational tool for management of energy systems. Adv. Electr. Comput. Eng. 2021, 21, 75–86.
  35. Beniwal, R.K.; Saini, M.K. Analysis of PQ Disturbances in Renewable Grid Integration System Using Non-Parametric Spectral Estimation Approach. In Innovations in Computational Intelligence and Computer Vision. Advances in Intelligent Systems and Computing; Sharma, M.K., Dhaka, V.S., Perumal, T., Dey, N., Tavares, J.M.R.S., Eds.; Springer: Singapore, 2021; Volume 1189.
  36. Elsisi, M.; Tran, M.-Q.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F. ‘Robust design of ANFIS-based blade pitch controller for wind energy conversion systems against wind speed fluctuations. IEEE Access 2021, 9, 37894–37904.
  37. Hosain Motlagh, N.; Mohammadrezaei, M.; Hunt, J.; Zakeri, B. Internet of Things (IoT) and the Energy Sector. Energies 2020, 13, 494.
  38. Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE Internet Things 2014, 1, 22–32.
  39. Ioannides, M.G.; Stamelos, A.; Papazis, S.A.; Papoutsidakis, A.; Vikentios, V.; Apostolakis, N. IoT monitoring system for applications with renewable energy generation and electric drives. Renew. Energ. Power Qual. J. 2021, 19, 565–570.
  40. Ioannides, M.G.; Koukoutsis, E.B.; Stamelos, A.P.; Papazis, S.A.; Stamataki, E.E.; Papoutsidakis, A.; Vikentios, V.; Apostolakis, N.; Stamatakis, M.E. Design and operation of Internet of Things-based monitoring control system for induction machines. Energies 2023, 16, 3049.
  41. Ioannidou, M.G.; Tegopoulos, J.A. Performance of a double fed induction motor with controlled rotor voltage magnitude and phase angle. IEEE Trans. Energy Convers. 1987, 2, 301–307.
  42. Pattnaik, M.; Kastha, D. Control of Double Output Induction Machine Based stand alone Variable Speed Constant Frequency generator with nonlinear and unbalanced loads. In Proceedings of the IEEE PES General Meeting, Minneapolis, MN, USA, 25–29 July 2010; pp. 1–8.
  43. Idjdarene, K.; Rekioua, D.; Rekioua, T.; Tounzi, A. Performance of an Isolated Induction Generator Under Unbalanced Loads. IEEE Trans. Energy Convers. 2010, 25, 303–311.
  44. ISO/IEC 9075-1:2016, Information Technology-Database Languages-SQL Part 1: Framework (SQL/Framework). Available online: https://www.iso.org/standard/63555.html (accessed on 17 January 2023).
  45. Pierleoni, P.; Concetti, R.; Belli, A.; Palma, L. Amazon, Google and Microsoft Solutions for IoT: Architectures and a Performance Comparison. IEEE Access 2020, 8, 5455–5470.
  46. Modbus Tools. Available online: https://www.modbustools.com/modbus.html (accessed on 24 January 2023).
  47. Hersent, O.; Boswarthick, D.; Elloumi, O. Chapter 5 ModBus. In The Internet of Things: Key Applications and Protocols, 2nd ed.; Wiley: Hoboken, NJ, USA, 2012; ISBN 978-1-119-99435-0.
  48. Ioannides, M.G. Determination of frequencies in autonomous double output asynchronous generator. IEEE Trans. Energy Convers. 1992, 7, 747–753.
  49. Tierielnyk, S.; Lukovtsev, V. Emergency Prevention Control as a Means of Power Quality Improvement in a Shipboard Hybrid Electric Power System. Energies 2024, 17, 398.
  50. Klakeel, T.; Anantharaman, M.; Islam, R.; Garaniya, V. Effectiveness of Current Technology in GHG Reduction—A literature Survey. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 2023, 17, 171.
  51. Liang, G.; Su, Y.; Wu, X.; Ma, J.; Long, H.; Song, Z. Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty. Renew. Energy 2023, 216, 118965.
  52. Barra, A.; El Haouti, K.; Ouadi, H.; Bensassi, B. Non-Linear Control of a Wind Energy System. In Proceedings of the 2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), Casablanca, Morocco, 12–13 December 2019; pp. 1–7.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback