Home A SIMULINK implementation of a vector shift relay with distributed synchronous generator for engineering classes
Article Open Access

A SIMULINK implementation of a vector shift relay with distributed synchronous generator for engineering classes

  • Alcedir Luis Finkler EMAIL logo , Luana Obregon , Mauricio de Campos , Paulo Sérgio Sausen , João Manoel Lenz and Airam Teresa Zago Romcy Sausen
Published/Copyright: May 5, 2021
Become an author with De Gruyter Brill

Abstract

In recent years, the concerns regarding global warming have encouraged an increase in research on renewable energy and distributed generation. Different renewable resources are currently being used, and bioenergy is one among them. Biogas can be produced via digesters, and its energy is converted into electricity and injected into the electrical power system for supplying to meet the local or distant demands. Nevertheless, the generation of electricity via biogas on the consumer side brings new problems and challenges to the power system controller. Protection devices, such as a vector shift relay, are one of the most important components needed to connect a bioenergy system using synchronous generators into the mains. Although distributed synchronous generators are widely used and simulated in software tools, especially in MATLAB/SIMULINK, there is still a gap in technical literature detailing how to design or model a Vector Shift Relay. In view of this subject’s importance, this article aims to assist students, researchers, and engineers by proposing a step-by-step method on how to model and implement a vector shift relay in MATLAB/SIMULINK, although the methodology may easily be used in other simulation tools. A review of the topic is presented along with a detailed description of all needed blocks and expected results.

1 Introduction

Economic growth has a straightforward relationship to the necessity of energy expansion. A higher percentage of renewable energy (RE) sources has been encouraged by worries about global warming [1]. Every day, the importance of the research about RE resources as well as the importance of addressing this issue as electrical engineering discipline is growing [2]. To meet this requirement, the students and researchers need to be prepared to find, understand, and solve new problems on this subject. The engineers need to be able to solve problems about the use of the devices to convert the RE into electrical energy and share with the electrical power system [3]. This sharing is done with the use of generators connected to the consumer facilities, known as distributed generation (DG) [4]. There is a current demand of graduate specialization courses on DG, and universities should address this gap.

Lectures of DG courses, either in undergrad or graduate classes, can be performed with the support of hardware-based or software-based experiments. Hardware-based experiments are restricted due to concerns of security and cost. For the hardware-based laboratories, some difficulties can be listed as follows: for security concerns, the students are requested to perform specific tests following step-by-step procedures and are not being allowed to try different proposals; there is the necessity of space availability for the equipment that usually constrains the use of the equipment for a couple of students; and for the hardware experimental implementation the system is greatly simplified [5]. On the other hand, with the use of software programs, the students can perform the simulation of any scenario without risk of damage. The students can be induced to perform any “what-if” type of test and to learn by their own experiences [6]. Among the software programs used in research about electrical power systems, the utilization of MATLAB/SIMULINK is swiftly growing [7]. Classes and methodologies that defy and motivate students to apply their multidisciplinary knowledge into solving real problems are becoming more important, especially using computer simulation [3]. To capture the interest of the students, real problems tend to be more attractive. So, related to the worries about RE, which scenario could be proposed? What kind of troubles can be handled by the students?

Regarding RE, the energy produced with biogas obtained by the anaerobic digestion is becoming more attractive [1]. The biogas is converted to electricity using a gas engine as primary machine coupled to a synchronous or asynchronous generator [8,9]. Despite the benefits of the DGs, their use has some associated risks. When a DG is installed in a consumer facility, due to some failure in the transmission line, there is a risk of a fraction of this line losing the connection with the main grid. In this case, the local load could be fed by the DG, and this is known as an unintentional islanding event [10]. The unintentional islanding operation can bring many troubles with respect to the quality of the energy issues and also puts the staff, working in the distribution line, at risk [11]. The detection of an unintentional islanding event is known as anti-islanding protection [12]. In Brazil, one of the biggest companies that works with the electrical distribution is the Companhia Paranaense de Energia, COPEL. This company follows the standard NTC 9,05,200 [13] to guide the DGs to connect parallel to the grid. In its October 2018 revision, it is defined that the DG should use an anti-islanding method, suggesting the use of vector shift relay, or, also known as vector shift relays or vector surge relays (ANSI 78V). So, unintentional islanding detection is a quite important issue for the feasibility of the use of synchronous machines with biogas engines. Besides that, the studies related to islanding detection supply to the students of electrical engineering a good view of some issues related to the DGs as follows: worries about safety of the maintenance staff; worries about voltage and frequency stability; and a possibility to exercise the knowledge about control and protection of power systems.

Even though MATLAB/SIMULINK is being used as a powerful tool for the power system analysis, in its libraries there is non pre-built block with a vector shift relay. In a research with the key words most used in articles about anti-islanding detection the following (“islanding” and “vector shift,” “islanding” and “vector surge,” “islanding” and “vector jump,” “loss of mains” and “vector shift,” “loss of mains” and “vector surge,” “loss of mains” and “vector jump”) were found as listed in Table 1.

Table 1

Articles found related to vector shift relays

Title Author Software
Protection & control strategy for effectively interconnecting and islanding distributed energy resources during grid disturbances [28] Xavier (2019)
Implications for the rate of change of frequency on an isolated power system [29] O’Donovan et al. (2019)
Islanding detection during intended island operation of nested microgrid [30] Laaksonen and Hovila (2018) PSCAD
Wide area phase angle measurements for islanding detection – An adaptive nonlinear approach [31] Liu et al. (2016) DigSilent
Grid code compatible islanding detection schemes using traditional passive methods [32] Laaksonen (2016) PSCAD
Synchrophasor-based islanding detection for distributed generation systems using systematic principal component analysis approaches [33] Guo et al. (2015)
Islanding detection based on probabilistic PCA with missing values in PMU data [34] Liu et al. (2014)
A study on anti-islanding detection algorithms for grid-tied photovoltaic systems [35] Banu et al. (2014) SIMULINK
A composite method for islanding detection based on vector shift and frequency variation [36] Hou et al. (2010) PSCAD
Dispersed generation in MV networks: performance of anti-islanding protections [37] Delfanti et al. (2010) DigSilent
Design and implementation of an anti-islanding protection strategy for distributed generation involving multiple passive protection [38] Foss and Leppik (2009)
A practical method for assessing the effectiveness of vector surge relays for distributed generation applications [39] Freitas et al. (2005)
False operation of vector surge relays [40] Freitas and Wilsun (2004)

Software used in the simulations.

Not informed.

Current literature regarding the use of vector shift relays, given in Table 1, does not go into the details of how to implement it from scratch when using a software that does not contain this type of protection device in its default library, nor how to design it when applied to a synchronous generator. Simulation software is a powerful tool to help students, engineers, and researchers study these systems, and it is used to overcome the difficulties of safety, cost, and space that exist in implementing DG experiments. Thus, this article, giving complete step-by-step modeling of a vector shift relay applied in distributed bioenergy systems, is proposed and demonstrated in MATLAB/SIMULINK.

The organization of this article is done as follows: in Section 2 is first done a review about the standards related to the islanding detection, so that this information guides in choosing the components to the proposed diagram as well as their settings. After this, a second subsection is used to do a review about the vector shift relay working principle and to explain its implementation in MATLAB/SIMULINK. Later, each subsection is used to describe in detail each of the others blocks necessary to the simulation. Within each subsection, some suggestions of problems and possible research to be performed with the students are presented. Conclusion is given in Section 3.

2 Performing the simulation

In the simulation of a vector shift relay, the excitation system and the speed governor have a key function on the circuit and can significantly affect the obtained results. Thus, dedicated subsections are written for them. For a clear understanding, this section is divided into seven subsections. Each of them brings the most relevant information for better understanding and addresses some possible discussions to be carried on with the students in the classes.

2.1 Standards related to islanding detection

A well-established regulatory framework is needed in order to make the DG systems feasible and attractive to the investors [14]. One of the most important standards for handling this issue is the IEEE 1547-2018 standard. To guarantee the safety of workers who operate the energy distribution grid, the IEEE 1547-2018 standard states that, as soon as a loss of DG connection with the interconnected power system occurs, the DG must be able to detect this loss and automatically disconnect the generator from the system in less than 2 s [15]. The IEEE 1547.6, in the chapter “7.1.2 DR Electric power generation technologies,” classifies generator units into three types: induction generators, synchronous generators, and inverters [16]. Among these, the most concerning are the synchronous generators because they have frequency and voltage controllers that allow these generators to continue operating with nominal voltage and frequency values of the local grid, even after loss of connection with the system. Furthermore, the IEEE 1547.1 standard defines test procedures to be performed with anti-islanding protection devices to ensure safe operation [17]. In some countries, as an example of Brazil, the distribution companies make their standards to guide the requirements to connect the DGs. One of the biggest distribution energy companies in Brazil, called Companhia Paranaense de Energia, COPEL, has the standard NTC 905200 [13] to guide the DGs to connect parallel to the grid at the low voltage. In the revision of October 2018, it is defined that the DG should use an anti-islanding method, suggesting the use of vector shift relay (ANSI 78V), but, it is not informed of the setting value to this protection device. The designer responsible for the project, who connects the DG on the grid, has to define the values to be set on the protection relays. It implies a huge responsibility on the designers. How could the designer know the correct settings for this device? Would it be possible for the students to develop a procedure to help the engineers to quickly access these settings for the vector shift relays?

2.2 Working principle of vector shift relays

Islanding detection can be performed quickly using vector shift relays [18]. The vector shift relays available in the market measure the voltage of each phase and compute the duration of each cycle of the grid. Then, the duration of the last period is compared with the duration of the previous period. An increase or decrease in the duration of the period would be identified as a vector shift causing the relay to act. Thus, the typical settings for this protection ranges from 2° to 20° [19].

The basic principle of operation of a vector shift relay can be explained with the help of Figure 1 [19]. In this diagram, Ē I represents the internal voltage of the synchronous machine, Δ V ̄ represents the voltage drop in the reactance of the synchronous machine X d as a function of the current Ī s , V ̄ T represents the voltage at the coupling point between the generator and the grid, known as the point of common coupling (PCC), and Ī Gr represents the current flowing from the grid. CB is the switch that will be used to disconnect the grid, thus allowing the generator to supply the islanded load. VS Relay represents the switch that must be connected to the generator output to disconnect it when the anti-island circuit detects loss of connection to the grid.

Figure 1 
                  Basic diagram of a single generator connected parallel to the grid [19].
Figure 1

Basic diagram of a single generator connected parallel to the grid [19].

In ref. [19], it is demonstrated that during the period in which the DG operates in parallel to the grid, the load connected to the PCC will be fed by Ī s + Ī Gr . The internal voltage of the machine Ē I will be at a displacement angle to the voltage at PCC represented by V ̄ T . The angle between Ē I and V ̄ T is known as the load angle, which is represented by θ . When a loss of connection to the grid occurs, simulated by the opening of the switch CB, the load will be exclusively fed by Ī s ; thus, the voltage at the PCC will be represented by V ̄ T . In this case, there is a change in the load angle. This change is represented by Δ θ . Figure 2a represents the voltage vectors before the opening of the CB switch, whereas Figure 2b represents the voltage vectors after the opening of the switch. This displacement in the angle of the voltage at the PCC is called the vector shift.

Figure 2 
                  Vector phase diagram: (a) load angle before islanding; (b) displacement in the loading angle during islanding [41].
Figure 2

Vector phase diagram: (a) load angle before islanding; (b) displacement in the loading angle during islanding [41].

The change in the load angle of the machine can be easily assessed by measuring the displacement in the phase of the voltage at PCC. Therefore, this displacement is proportional to the change in the load angle. Figure 3 illustrates the voltage at the PCC immediately after the opening of the CB switch. For the purpose of simulation, the single line diagram of Figure 1 was implemented in SIMULINK, as illustrated in Figure 4.

Figure 3 
                  Voltage measured at PCC in the cycle of islanding event [41].
Figure 3

Voltage measured at PCC in the cycle of islanding event [41].

Figure 4 
                  Diagram of DG connected parallel to the grid.
Figure 4

Diagram of DG connected parallel to the grid.

The generator G1 is connected to the infinite bus through the switch CB. Parallely connected to the G1 there is the local load. The vector shift relay is also illustrated. For simulation purposes, as the analysis would be done in symmetrical faults, it is considered that the measures for the vector shift relay are being performed in a single phase. The configuration of the G1 and of the block of “Excitation and speed governor” is done in Sections 2.3 and 2.4.

A single line diagram of Figure 4 is shown in Figure 5.

Figure 5 
                  Single line diagram of DG connected parallel to the grid.
Figure 5

Single line diagram of DG connected parallel to the grid.

The subsystem used to simulate the vector shift relay block of Figure 4 is illustrated in Figure 6.

Figure 6 
                  Diagram of the proposed vector shift relay implementation in SIMULINK.
Figure 6

Diagram of the proposed vector shift relay implementation in SIMULINK.

In this simulation, the system was considered balanced, and only one phase was analyzed. This subsystem takes the voltage of the phase “a” as a reference and applies it to the block “zero crossing.” Thus, this block gives an impulse of unity each time the voltage crosses to zero. After this, the “off delay” block is applied to solve any problems associated with the time step of the simulation. This signal is applied to an “AND” port to check if the crossing occurred during the raising period or the falling period. Therefore, it will only compute the zero crossing in the raising period. For a better understanding of the diagram in Figure 6, a flowchart is illustrated in Figure 7. At the point “A” of Figure 6, there is a zero crossing signal reference for the beginning of each cycle. This signal is applied to four synchronized subsystems. The first one, at each falling edge will capture the current crossing time. This information will be available at the point “C.” But, before the current time is updated, the second subsystem will save the last crossing time at the rising edge in “B.” A subtraction of the current time from the last time will be done putting at the point “D” the information of the duration of each full cycle. In a similar way, the duration of each cycle will be updated at “F” as well as the duration of the past cycle will be stored in “E.” The difference between this time duration is available at “G.” This difference will be divided by the duration of the last cycle to calculate the percentage variation of the cycle time, which is available at “H.” Once a full cycle corresponds to a 360°, the variation in degrees can be obtained by multiplying the percentage by 360. This information is available at “I.” The RMS value of the voltage measured at input 1 is divided by the phase voltage. In “K” there is the voltage measured in percentage. This value is compared to a reference, in this case, 90% and, if the voltage of the phase is greater than this, the signal at “L” will be one. Otherwise, if the voltage is smaller, the signal will be zero disabling the vector shift relay. Another condition is implemented in “J” where a unity step is applied after 1.5 s. This has the function to disable the vector shift relay during the initial transient period of simulation. Past the transient period of simulation, if the voltage is greater than 90%, the module of the information of the phase displacement will be available in “M.” This value will be compared to a triggering value adjusted in the input 2. If the measured value is greater than the triggering value, the output 3 will become high at “O.” This information can be used to stop the simulation. At the output 1 will be available the information about the phase displacement measured in the instant where the simulation was stopped. In the output 2, point “N,” will be available the information about the time that the simulation was stopped. So, time information available at the output two can be used to compare with the time that CB switch was set to open. With these, the duration that the vector shift relay spent to feel the loss of main can be captured.

Figure 7 
                  Flowchart of the vector shift relay implemented in SIMULINK.
Figure 7

Flowchart of the vector shift relay implemented in SIMULINK.

For good accuracy of the results, the time step for the simulation must be considered. For a frequency of 50 Hz, if an accuracy of 0 . 2 is desired, the time step should not be greater than 1 × 1 0 5 .

2.3 Excitation control

In the IEEE 1547.1, section “5.7.1 Unintentional islanding test,” the test procedures to ensure the action of the anti-islanding detection device are described. In the section “5.7.2.5 Comments,” it is emphasized that, the characteristics of the fuel rate (speed governor) and of the excitation devices must be considered during the unintentional islanding test procedure [15]. So, these should be considered in the simulation.

The excitation control is composed of components such as the automatic voltage regulator (AVR), reactive current compensation, and power system stabilizer and limiters. The IEEE Std 421.5-2016 “Recommended Practice for Excitation System Models for Power System Stability Studies,” has a collection of the most used excitation systems in synchronous machines. The excitation system can be classified into three groups according to the current source to the field winding as Type DC, Type AC, and Type ST. Each of them is further divided into different topologies according to their implemented functions, giving a total of 43 different excitation systems. A sample data for each of them is provided in the Annex H of the standard [20]. The AVR ST1A was selected for this article. Students could also be challenged into researching the advantages and disadvantages of each topology through simulation.

Another important device to be studied is the reactive power controller. It can be used to ensure a predefined power factor (PF) value or a predefined reactive power value. For the PF, there is a trouble that needs to be handled about the nonlinear function. Some normalization methods are presented in the IEEE Std 421.5-2016 [20]. As defined in the IEEE 1547-2018, chapter 5.3.1, in the general case, the DG should be set to work with a constant PF equal to one [15]. For this simulation, a “VAR type 2,” as presented in the IEEE Std 421.5-2016, was considered with reactive power set to zero. The parameters used were available at the Annex H of the IEEE Std 421.5-2016 [20].

The excitation diagram implemented in the subsystem “Excitation and speed governor” of Figure 4 is illustrated in Figure 8.

Figure 8 
                  Excitation control diagram of the synchronous generator with ST1A and VAR Type 2.
Figure 8

Excitation control diagram of the synchronous generator with ST1A and VAR Type 2.

By convention, the generator is considered with a lagging PF when it is supplying the reactive power, and leading PF when it is consuming reactive power. Many research studies are done comparing the effects of a generator working with unity PF or lagging PF [21]. Related to this topic, the students can be encouraged to change the control from the reactive power control to PF control. The students can also be encouraged to perform some experiments by changing the PF of the generator among unity PF, lagging PF, and leading PF to check the effects of these in the voltage of the PCC. The students can be asked to implement all the limiters in the excitation control as over and under voltage, maximum and minimum reactive power, and all others suggested in the IEEE Std 421.5-2016 [20].

2.4 Speed governor

Considering the DG working with biogas, the most common topology is the use of a gas engine with a synchronous generator. For its proper operation, the speed of the synchronous machine has to be controlled. The synchronous machine can work attached to the distribution grid or islanded, and the speed governor must maintain a constant speed of the machine even in the case of islanding [22]. For this purpose, the two main methods used to control the speed of the generators, only based on local measurements, with no need of communication, are isochronous mode and droop mode. In the isochronous mode, the speed of the generator is kept constant from no load to full load, which is recommended for islanded operation. In case of parallel operation, if two generators are settled to isochronous mode, they will fight by the load and some of them will be switched off. In the droop mode, the speed is decreased as the load is increased in a proportional scale known as droop coefficient. For the operation parallel with the utility grid (infinite bus), the machines are usually set in droop mode. The usual speed droop coefficient is between 3 and 5% [8,23].

For this simulation purpose, the model used for the speed governor is illustrated in Figure 9 as a droop control. As inputs of the controller, a summing block will compare the reference speed (wref) with the measured speed of the shaft of the generator (wm). A second adding block compares the measured active power (Peo) with the commanded power (Pcommand). So, the control will try to increase the speed of the shaft until the measured power matches the commanded power with an error defined by the droop characteristics. The speed error will be applied to the control block. The dynamics of the actuator and of the gas engine are described in ref. [8,9]. For simulation purposes, the models and parameters of the control, actuator, and engine were used as described in ref. [9]. The parameters are available in the Appendix.

Figure 9 
                  Speed control of the synchronous generator.
Figure 9

Speed control of the synchronous generator.

Some tasks to the students regarding speed governor can be to analyze the different speed droop settings as reference [24]. What would be the effects of a higher or lower droop setting? Another option to defy the students could be to improve the research about nonlinear methods to adjust the droop as developed in ref. [25]. As the isochronous mode is more appropriate for islanded operation and the speed droop mode more appropriate for grid tie operation, an automatic switching method is proposed in ref. [23]. As soon as an islanding event is detected, the generator is switched from droop mode to isochronous mode. Some research to improve this method can be proposed.

2.5 Synchronous machine

The SIMULINK has the block “Synchronous machine-p.u. standard” in its library. In the simulation described here, it was considered a salient pole machine of 31,300 VA, 50 Hz, and 400 V. The parameters of the synchronous generator were set based on ref. [22] and are available in the Appendix. In MATLAB/SIMULINK, for the simulation of the synchronous machine is also necessary to add the “powergui” block. On this simulation, it was set to discrete solver with a time step of 1 × 1 0 2 s .

2.6 Infinite bus

On the settings of the infinite bus a point to be handled is the internal impedance or, the short-circuit level. Some different scenarios can be simulated considering the generators installed in the rural, urban, or suburban areas. The distance between the generator and the distribution transformer will directly affect the circuit characteristics. A research about the minimum and maximum length as well as the minimum and maximum short-circuit level and the typical X/R relation for each different scenario is done in ref. [26] and could be used to support the simulations. With this information, the students can be defied to experiment the DG in different conditions. As the coverage of all possible topologies is beyond the focus of this article, for the proposed example, the check box of internal impedance and short-circuit level of the infinite bus are unchecked. The phase-to-phase voltage is defined as 400 V and the frequency as 50 Hz.

2.7 Simulation discussion

Once everything is set as described, the simulation can be performed. The switch CB illustrated in Figure 4 is used to simulate any event that would make the DG islanded with the load. In the IEEE 1547.1, in section “5.7.2 Unintentional islanding test for synchronous generators” a procedure to be followed to ensure the proper operation of the unintentional islanding protection is described [17]. On this standard, there is a step-by-step procedure explaining how to choose the values to set the load and what conditions would be needed to simulate to ensure the proper operation of the anti-islanding device.

The phase displacement measured at the output 1 of the subsystem illustrated in Figure 6 can be plotted using the “Scope” block from SIMULINK. If more than one simulation need to be plotted in the same graphic, a “To Workspace” block from SIMULINK can be used. As an example, four simulations were performed. In the first one, the local load was set with the same power as the settled power in the DG ( P L = 1.00 P G ). A second simulation was performed with the local load settled at 95% of the power settled in the DG ( P L = 0.95 P G ). Similarly, a third with 90% ( P L = 0.90 P G ) and a fourth with local load settled as 85% of the power settled in the DG ( P L = 0.85 P G ) were performed. For all these simulations, the CB switch was set to open with 18 s. The simulation running time was set to 20 s. The phase displacement angle, obtained as a result of these four simulations is illustrated in Figure 10. For this plot, the time was decremented from 18, thus, the time illustrated in the graphics means the time after CB opening. As illustrated in Figure 4, the triggering angle of the vector shift relay was set at 1.5 degrees, so, for the simulations with a power mismatch equal to 0%, 5%, and 10%, the relay was not triggered. In the simulation with a power mismatch of 15%, the phase displacement observed was greater than the triggering angle settled value, so, the vector shift relay has actuated. Once the vector shift relay act, the phase displacement at this moment will be stored in the output 1 of the subsystem as illustrated in Figure 4. The time that this trip had occurred will be stored in the output 2. Thus, with this information, it is possible to determine how long it took for the system to detect the islanding. The output 3 of the subsystem of Figure 6 will be used to stop the simulation.

Figure 10 
                  Phase displacement signal at output of the vector shift relay block.
Figure 10

Phase displacement signal at output of the vector shift relay block.

This scenario was organized considering the studies with focus in the methods of vector shift relay to detect the unintentional islanding switching off the DG as soon as possible. In the case the user intends to analyze the operation of an islanded DG system, it is recommended to the students to do a review of the IEEE 1547.4 that describes all the tests to be performed to ensure the proper operation for some islanded area [27].

3 Conclusion

This article presented a vector shift relay implementation in MATLAB/SIMULINK with all components necessary to its simulation as a proposal for DG lectures to electrical engineering courses and researches. The choice of each component and all settings are justified based on standards or referenced articles. For each topic, possible challenges and difficulties for the students to research are related. Thus, the objective here was to provide a tool to help students and engineers in improving their self-learning abilities, problem solving, and analysis of DG systems. Finally, this article aims to contribute with a step-by-step orientation to students and engineers about unintentional islanding detection with vector shift relays applied to synchronous machine, specially due to the lack of technical literature and guides on this subject.

Acknowledgments

The authors would like to thank the Instituto Federal Farroupilha, IFFar, for their support and incentive to professional qualification, and in particular, the Institutional Qualification Incentive Program (PIIQP). The authors would like to thank the Brazilian National Research Council (CNPq) for providing a scholarship. The authors would like to thank Prof. A. M. Kulkarni and Kaustav Dey from Indian Institute of Technology, Bombay, for their attention and cooperation in the development of this study.

  1. Conflict of interest: Authors state no conflict of interest.

  2. Data availability statement: All data generated or analysed during this study are included in this published article.

Appendix

The parameters suggested for the simulations are available at Table A.

Table A

Parameters for simulation

Parameter Value Unit
R a 0.003 pu
X d 1.8 pu
X d 0.3 pu
X d 0.23 pu
X q 1.7 pu
X q 0.25 pu
T d 0.8274 Seconds
T d 0.0232 Seconds
T d 0 5 Seconds
T d 0 0.03 Seconds
T q 0.0293 Seconds
T q 0 0.07 Seconds
H 3 Seconds
T 1 0.01 Dimensionless
T 2 0.02 Dimensionless
T 3 0.2 Dimensionless
T 4 0.25 Dimensionless
T 5 0.009 Dimensionless
T min 0 pu
T max 1.1 pu
T D 0.024 Seconds

References

[1] Das CK, Ehsan MA, Kader MA, Alam MJ, Shafiullah GM. A practical biogas based energy neutral home system for rural communities of Bangladesh. J Renew Sustain Energy. 2016 Mar 2;8(2):023101; Corrected and republished from: J Renew Sustain Energy; 2016 Mar 2:8(2):029902. 10.1063/1.4942783. Search in Google Scholar

[2] Sridevi R, Kumar C, Suresh P, Ganesan S, Subramanian S, Suresh A. Development of a pedagogical framework to analyze the performance of induction machines. Int J Elect Eng Edu. 2019 Feb 20:0020720919828993. 10.1177/0020720919828993. Search in Google Scholar

[3] Stojković S, Bjekić M, Janda Ž. Educational simulation model for studying the impact of distributed generation on distribution networks using ATP-EMTP software. Int J Elect Eng Edu 2014 Oct 1;51(4):292–305. 10.7227/ijeee.0002. Search in Google Scholar

[4] Arunachalam K, Pedinti VS, Goel S. Decentralized distributed generation in India: A review. J Renew Sustain Energy 2016 Apr 5;8(2):025904. 10.1063/1.4944966. Search in Google Scholar

[5] Wei M, Zhang H, Fang T. Enhancing the course teaching of power system analysis with virtual simulation platform. Int J Elect Eng Edu. 2020 Sep 26:0020720920953434. 10.1177/0020720920953434. Search in Google Scholar

[6] Ponce P, Ibarra L, Mata O, Molina A. How to develop research skills among undergraduate engineering students to tackle current ongoing topics? A Smart-Grid case study. Int J Elect Eng Edu. 2019 Jan 3:0020720918816004. 10.1177/0020720918816004. Search in Google Scholar

[7] Ekinci S, Lale ZH, Demiroren A. A didactic procedure for transient stability simulation of a multi-machine power system utilizing SIMULINK. Int J Elect Eng Edu. 2016 Aug 4;53(1): 54–71. 10.1177/0020720915597935. Search in Google Scholar

[8] Renjit AA, Illindala MS, Lasseter RH, Erickson MJ, Klapp D. Modeling and control of a natural gas generator set in the CERTS microgrid. 2013 IEEE Energy Conversion Congress and Exposition (ECCE). Denver, USA. Piscataway: IEEE; 2013 Sep 15–19. 10.1109/ECCE.2013.6646903. Search in Google Scholar

[9] Wang L, Lin P. Analysis of a commercial biogas generation system using a gas engine – induction generator set. IEEE Trans Energy Conv. 2009 Mar;24(1):230–9. 10.1109/tec.2008.2006554. Search in Google Scholar

[10] Yu S, Yin L. Islanding detection method based on S transform and ANFIS. J Renew Sustain Energy. 2018 Oct 24;10(5):055503. 10.1063/1.5045316. Search in Google Scholar

[11] Li D, Li L, Yang C, Zhang S. A sequence perturbation based islanding detection for distributed generation with periodic code matching. J Renew Sustain Energy. 2015 Feb 20;7(1):013133. 10.1063/1.4913266. Search in Google Scholar

[12] Chen K, Wang Y, Tian S, Cheng Y, Yin C. Interference of various active islanding detection methods with positive feedback in multi-inverter power system. J Renew Sustain Energy. 2015 May 15;7(3):033103. 10.1063/1.4921368. Search in Google Scholar

[13] COPEL. Ntc905200: Acesso de micro e minigeração distribuída ao sistema da Copel (com compensação de energia), Curitiba: Companhia Paranaense de Energia Copel Distribuição S.A; 2018. Available from:https://www.copel.com/hpcopel/root/sitearquivos2.nsf/verdocatual/E59DF9E94B635F678325831D0047F719$FILE/NTC905200_Rev04102018.pdf. Search in Google Scholar

[14] Bishoge OK, Kombe GG, Mvile BN. Renewable energy for sustainable development in sub-Saharan African countries: Challenges and way forward. J Renew Sustain Energy. 2020 Sep 10;12(5). 10.1063/5.0009297. Search in Google Scholar

[15] IEEE. Std 1547-2018 (Revision of IEEE Std 1547-2003): IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces. 2018 Apr 6. 138p. 10.1109/IEEESTD.2018.8332112. Search in Google Scholar

[16] IEEE. Std 1547.6-2011: IEEE recommended practice for interconnecting distributed resources with electric power systems distribution secondary networks;2011 Sep 12. p.38. 10.1109/IEEESTD.2011.6022734. Search in Google Scholar

[17] IEEE. Std 1547.1-2005: IEEE standard conformance test procedures for equipment interconnecting distributed resources with electric power systems; 2005 Jul 1. p. 62 10.1109/IEEESTD.2005.96289. Search in Google Scholar

[18] Laaksonen H, Hovila P, Kauhaniemi K, Sirviö K. Advanced islanding detection in grid interactive microgrids. CIRED 2018 Ljubljana Workshop on Microgrids and Local Energy Communities. Ljubljana, Slovenia; 2018 Jun 7–8. 10.34890/449. Search in Google Scholar

[19] Kandakatla M, Laaksonen H, Bonela S. Advanced Vector Shift Algorithm for Islanding Detection. 23rd International Conference and Exhibition on Electricity Distribution (CIRED). Lyon, France; 2015 Jun 15–18. Search in Google Scholar

[20] IEEE. Std 421.5-2016: Recommended practice for excitation system models for power system stability studies. 2016 Aug 26. p. 207. 10.1109/IEEESTD.2016.7553421. Search in Google Scholar

[21] Aman MM, Jasmon GB, Bakar AHA, Mokhlis H. A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm. Energy 2014 Jan 17;66:202–15. 10.1016/j.energy.2013.12.037. Search in Google Scholar

[22] Kundur P. Power system stability and control. 1st ed. New York: McGraw-Hill; 1994. 10.1201/b12113-11Search in Google Scholar

[23] Silva AMB, Guimaraes GC, Chagas MLR, Tamashiro MA, Rodrigues AR, Arantes RV. Performance analysis of distributed synchronous generators with controllers equipped with switchable operating modes. IEEE Latin America Trans. 2016 May;14(5):2280–90. 10.1109/tla.2016.7530424. Search in Google Scholar

[24] Sorrentino E, Villafuerte P. Effect of the control of generators and turbines on the transient stability of a power system. IEEE Latin America Trans. 2016 Mar;14(3):1227–34. 10.1109/tla.2016.7459603. Search in Google Scholar

[25] Zhang K, Dai X. Nonlinear control of synchronous generator set with multiple controlled variables. 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies; Nanjing, China: 2008 Apr 6–9. 10.1109/DRPT.2008.4523642. Search in Google Scholar

[26] Hernando-Gil I, Shi H, Li F, Djokic S, Lehtonen M. Evaluation of fault levels and power supply network impedances in 230/400 V 50 Hz generic distribution systems. IEEE Trans Power Delivery. 2017 Apr;32(2):768–77. 10.1109/TPWRD.2016.2609643. Search in Google Scholar

[27] IEEE. Std 1547.4-2011: Guide for Design, Operation, and Integration of Distributed Resource Island Systems with Electric Power Systems; 2011 Jul 20. 10.1109/IEEESTD.2011.5960751. Search in Google Scholar

[28] Xavier J. Protection & Control strategy for effectively interconnecting and islanding distributed energy resources during grid disturbances. 72nd Conference for Protective Relay Engineers (CPRE). College Station, USA; 2019 Mar 25–28. 10.1109/CPRE.2019.8765888. Search in Google Scholar

[29] O’Donovan M, O’Callaghan E, Barry N, Connell J. Implications for the rate of change of frequency on an isolated power system. 54th International Universities Power Engineering Conference (UPEC). Bucharest, Romania; 2019 Sep 3–6. 10.1109/UPEC.2019.8893446. Search in Google Scholar

[30] Laaksonen H, Hovila P. Islanding detection during intended island operation of nested microgrid. IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). Saravejo, Bosnia and Herzegovina; 2018 Oct 21–25. 10.1109/ISGTEurope.2018.8571571. Search in Google Scholar

[31] Liu X, Kennedy J, Laverty D, Morrow DJ, McLoone S. Wide-area phase-angle measurements for islanding detection – An adaptive nonlinear approach. IEEE Power & Energy Society General Meeting. 2016 Aug;31(4):1901–11. 10.1109/tpwrd.2016.2518019 Search in Google Scholar

[32] Laaksonen H. Grid code compatible islanding detection schemes using traditional passive methods. 13th International Conference on Development in Power System Protection 2016 (DPSP). Edinburgh, UK;2016 Mar 7–10. 10.1049/cp.2016.0067. Search in Google Scholar

[33] Guo Y, Li K, Laverty DM, Xue Y. Synchrophasor-based islanding detection for distributed generation systems using systematic principal component analysis approaches. IEEE Trans Power Delivery. 2015 Dec 30(6):2544–52. 10.1109/tpwrd.2015.2435158. Search in Google Scholar

[34] Liu XA, Laverty D, Best R. Islanding detection based on probabilistic PCA with missing values in PMU data. 2014 IEEE PES General Meeting. National Harbor, USA;2014 Jul 27–31. 10.1109/PESGM.2014.6939272. Search in Google Scholar

[35] Banu IV, Istrate M, Machidon D, Pantelimon R. A study on anti-islanding detection algorithms for grid-tied photovoltaic systems. 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). Bran, Romania;2014 May 22–24. 10.1109/OPTIM.2014.6850940. Search in Google Scholar

[36] Hou M, Gao H, Lu Y, Zhang Y, Cao H, Lin Y. A composite method for islanding detection based on vector shift and frequency variation. 2010 Asia-Pacific Power and Energy Engineering Conference. Chengdu, China; 2010 Mar 28–31. 10.1109/APPEEC.2010.5448252. Search in Google Scholar

[37] Delfanti M, Falabretti D, Merlo M, Monfredini G, Olivieri V. Dispersed generation in MV networks: Performance of anti-islanding protections. Proceedings of 14th International Conference on Harmonics and Quality of Power (ICHQP). Bergamo, Italy;2010 Sep 26–29. 10.1109/ICHQP.2010.5625446. Search in Google Scholar

[38] Foss A, Leppik K. Design and implementation of an anti-islanding protection strategy for distributed generation involving multiple passive protections. 2009 IEEE Electrical Power & Energy Conference (EPEC). Montreal, Canada; 2009 Oct 22–23. 10.1109/EPEC.2009.5420963. Search in Google Scholar

[39] Freitas W, Huang Z, Xu W. A practical method for assessing the effectiveness of vector surge relays for distributed generation applications. IEEE Power Eng Soc General Meeting 2005 Jan;20(1):57–63. 10.1109/TPWRD.2004.838637. Search in Google Scholar

[40] Freitas W, Wilsun Xu. False operation of vector surge relays. IEEE Trans Power Delivery. 2004 Jan;19(1):436–8. 10.1109/tpwrd.2003.820412. Search in Google Scholar

[41] Hou M, Gao H, Liu B, Zou GB. Vector shift method for islanding detection based on simulation test. Trans Tianjin Univ. 2008 Apr 20;14(2):123–7. 10.1007/s12209-008-0022-x. Search in Google Scholar

Received: 2020-12-15
Revised: 2021-02-11
Accepted: 2021-04-12
Published Online: 2021-05-05

© 2021 Alcedir Luis Finkler et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Electrochemical studies of the synergistic combination effect of thymus mastichina and illicium verum essential oil extracts on the corrosion inhibition of low carbon steel in dilute acid solution
  3. Adoption of Business Intelligence to Support Cost Accounting Based Financial Systems — Case Study of XYZ Company
  4. Techno-Economic Feasibility Analysis of a Hybrid Renewable Energy Supply Options for University Buildings in Saudi Arabia
  5. Optimized design of a semimetal gasket operating in flange-bolted joints
  6. Behavior of non-reinforced and reinforced green mortar with fibers
  7. Field measurement of contact forces on rollers for a large diameter pipe conveyor
  8. Development of Smartphone-Controlled Hand and Arm Exoskeleton for Persons with Disability
  9. Investigation of saturation flow rate using video camera at signalized intersections in Jordan
  10. The features of Ni2MnIn polycrystalline Heusler alloy thin films formation by pulsed laser deposition
  11. Selection of a workpiece clamping system for computer-aided subtractive manufacturing of geometrically complex medical models
  12. Development of Solar-Powered Water Pump with 3D Printed Impeller
  13. Identifying Innovative Reliable Criteria Governing the Selection of Infrastructures Construction Project Delivery Systems
  14. Kinetics of Carbothermal Reduction Process of Different Size Phosphate Rocks
  15. Plastic forming processes of transverse non-homogeneous composite metallic sheets
  16. Accelerated aging of WPCs Based on Polypropylene and Birch plywood Sanding Dust
  17. Effect of water flow and depth on fatigue crack growth rate of underwater wet welded low carbon steel SS400
  18. Non-invasive attempts to extinguish flames with the use of high-power acoustic extinguisher
  19. Filament wound composite fatigue mechanisms investigated with full field DIC strain monitoring
  20. Structural Timber In Compartment Fires – The Timber Charring and Heat Storage Model
  21. Technical and economic aspects of starting a selected power unit at low ambient temperatures
  22. Car braking effectiveness after adaptation for drivers with motor dysfunctions
  23. Adaptation to driver-assistance systems depending on experience
  24. A SIMULINK implementation of a vector shift relay with distributed synchronous generator for engineering classes
  25. Evaluation of measurement uncertainty in a static tensile test
  26. Errors in documenting the subsoil and their impact on the investment implementation: Case study
  27. Comparison between two calculation methods for designing a stand-alone PV system according to Mosul city basemap
  28. Reduction of transport-related air pollution. A case study based on the impact of the COVID-19 pandemic on the level of NOx emissions in the city of Krakow
  29. Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment
  30. A new method for solving quadratic fractional programming problem in neutrosophic environment
  31. Effect of fish scales on fabrication of polyester composite material reinforcements
  32. Impact of the operation of LNG trucks on the environment
  33. The effectiveness of the AEB system in the context of the safety of vulnerable road users
  34. Errors in controlling cars cause tragic accidents involving motorcyclists
  35. Deformation of designed steel plates: An optimisation of the side hull structure using the finite element approach
  36. Thermal-strength analysis of a cross-flow heat exchanger and its design improvement
  37. Effect of thermal collector configuration on the photovoltaic heat transfer performance with 3D CFD modeling
  38. Experimental identification of the subjective reception of external stimuli during wheelchair driving
  39. Failure analysis of motorcycle shock breakers
  40. Experimental analysis of nonlinear characteristics of absorbers with wire rope isolators
  41. Experimental tests of the antiresonance vibratory mill of a sectional movement trajectory
  42. Experimental and theoretical investigation of CVT rubber belt vibrations
  43. Is the cubic parabola really the best railway transition curve?
  44. Transport properties of the new vibratory conveyor at operations in the resonance zone
  45. Assessment of resistance to permanent deformations of asphalt mixes of low air void content
  46. COVID-19 lockdown impact on CERN seismic station ambient noise levels
  47. Review Articles
  48. FMEA method in operational reliability of forest harvesters
  49. Examination of preferences in the field of mobility of the city of Pila in terms of services provided by the Municipal Transport Company in Pila
  50. Enhancement stability and color fastness of natural dye: A review
  51. Special Issue: ICE-SEAM 2019 - Part II
  52. Lane Departure Warning Estimation Using Yaw Acceleration
  53. Analysis of EMG Signals during Stance and Swing Phases for Controlling Magnetorheological Brake applications
  54. Sensor Number Optimization Using Neural Network for Ankle Foot Orthosis Equipped with Magnetorheological Brake
  55. Special Issue: Recent Advances in Civil Engineering - Part II
  56. Comparison of STM’s reliability system on the example of selected element
  57. Technical analysis of the renovation works of the wooden palace floors
  58. Special Issue: TRANSPORT 2020
  59. Simulation assessment of the half-power bandwidth method in testing shock absorbers
  60. Predictive analysis of the impact of the time of day on road accidents in Poland
  61. User’s determination of a proper method for quantifying fuel consumption of a passenger car with compression ignition engine in specific operation conditions
  62. Analysis and assessment of defectiveness of regulations for the yellow signal at the intersection
  63. Streamlining possibility of transport-supply logistics when using chosen Operations Research techniques
  64. Permissible distance – safety system of vehicles in use
  65. Study of the population in terms of knowledge about the distance between vehicles in motion
  66. UAVs in rail damage image diagnostics supported by deep-learning networks
  67. Exhaust emissions of buses LNG and Diesel in RDE tests
  68. Measurements of urban traffic parameters before and after road reconstruction
  69. The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
  70. Analysis of dangers in the operation of city buses at the intersections
  71. Psychological factors of the transfer of control in an automated vehicle
  72. Testing and evaluation of cold-start emissions from a gasoline engine in RDE test at two different ambient temperatures
  73. Age and experience in driving a vehicle and psychomotor skills in the context of automation
  74. Consumption of gasoline in vehicles equipped with an LPG retrofit system in real driving conditions
  75. Laboratory studies of the influence of the working position of the passenger vehicle air suspension on the vibration comfort of children transported in the child restraint system
  76. Route optimization for city cleaning vehicle
  77. Efficiency of electric vehicle interior heating systems at low ambient temperatures
  78. Model-based imputation of sound level data at thoroughfare using computational intelligence
  79. Research on the combustion process in the Fiat 1.3 Multijet engine fueled with rapeseed methyl esters
  80. Overview of the method and state of hydrogenization of road transport in the world and the resulting development prospects in Poland
  81. Tribological characteristics of polymer materials used for slide bearings
  82. Car reliability analysis based on periodic technical tests
  83. Special Issue: Terotechnology 2019 - Part II
  84. DOE Application for Analysis of Tribological Properties of the Al2O3/IF-WS2 Surface Layers
  85. The effect of the impurities spaces on the quality of structural steel working at variable loads
  86. Prediction of the parameters and the hot open die elongation forging process on an 80 MN hydraulic press
  87. Special Issue: AEVEC 2020
  88. Vocational Student's Attitude and Response Towards Experiential Learning in Mechanical Engineering
  89. Virtual Laboratory to Support a Practical Learning of Micro Power Generation in Indonesian Vocational High Schools
  90. The impacts of mediating the work environment on the mode choice in work trips
  91. Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
  92. Car braking effectiveness after adaptation for drivers with motor dysfunctions
  93. Case study: Vocational student’s knowledge and awareness level toward renewable energy in Indonesia
  94. Contribution of collaborative skill toward construction drawing skill for developing vocational course
  95. Special Issue: Annual Engineering and Vocational Education Conference - Part II
  96. Vocational teachers’ perspective toward Technological Pedagogical Vocational Knowledge
  97. Special Issue: ICIMECE 2020 - Part I
  98. Profile of system and product certification as quality infrastructure in Indonesia
  99. Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
  100. A review on the fused deposition modeling (FDM) 3D printing: Filament processing, materials, and printing parameters
  101. Facile rheological route method for LiFePO4/C cathode material production
  102. Mosque design strategy for energy and water saving
  103. Epoxy resins thermosetting for mechanical engineering
  104. Estimating the potential of wind energy resources using Weibull parameters: A case study of the coastline region of Dar es Salaam, Tanzania
  105. Special Issue: CIRMARE 2020
  106. New trends in visual inspection of buildings and structures: Study for the use of drones
  107. Special Issue: ISERT 2021
  108. Alleviate the contending issues in network operating system courses: Psychomotor and troubleshooting skill development with Raspberry Pi
  109. Special Issue: Actual Trends in Logistics and Industrial Engineering - Part II
  110. The Physical Internet: A means towards achieving global logistics sustainability
  111. Special Issue: Modern Scientific Problems in Civil Engineering - Part I
  112. Construction work cost and duration analysis with the use of agent-based modelling and simulation
  113. Corrosion rate measurement for steel sheets of a fuel tank shell being in service
  114. The influence of external environment on workers on scaffolding illustrated by UTCI
  115. Allocation of risk factors for geodetic tasks in construction schedules
  116. Pedestrian fatality risk as a function of tram impact speed
  117. Technological and organizational problems in the construction of the radiation shielding concrete and suggestions to solve: A case study
  118. Finite element analysis of train speed effect on dynamic response of steel bridge
  119. New approach to analysis of railway track dynamics – Rail head vibrations
  120. Special Issue: Trends in Logistics and Production for the 21st Century - Part I
  121. Design of production lines and logistic flows in production
  122. The planning process of transport tasks for autonomous vans
  123. Modeling of the two shuttle box system within the internal logistics system using simulation software
  124. Implementation of the logistics train in the intralogistics system: A case study
  125. Assessment of investment in electric buses: A case study of a public transport company
  126. Assessment of a robot base production using CAM programming for the FANUC control system
  127. Proposal for the flow of material and adjustments to the storage system of an external service provider
  128. The use of numerical analysis of the injection process to select the material for the injection molding
  129. Economic aspect of combined transport
  130. Solution of a production process with the application of simulation: A case study
  131. Speedometer reliability in regard to road traffic sustainability
  132. Design and construction of a scanning stand for the PU mini-acoustic sensor
  133. Utilization of intelligent vehicle units for train set dispatching
  134. Special Issue: ICRTEEC - 2021 - Part I
  135. LVRT enhancement of DFIG-driven wind system using feed-forward neuro-sliding mode control
  136. Special Issue: Automation in Finland 2021 - Part I
  137. Prediction of future paths of mobile objects using path library
  138. Model predictive control for a multiple injection combustion model
  139. Model-based on-board post-injection control development for marine diesel engine
  140. Intelligent temporal analysis of coronavirus statistical data
Downloaded on 27.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/eng-2021-0066/html
Scroll to top button