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The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm

  • Ihsan Mousa Jawad , Ali Qasim Abdulrasool , Abbas Q. Mohammed , Wafaa Said Majeed and Haider TH. Salim ALRikabi EMAIL logo
Published/Copyright: February 10, 2024
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Abstract

Meta-heuristic approaches evaluate the optimum size and position of flexible alternative current transmission systems power systems. The seeker optimization algorithm (SOA) technique was used to solve power engineering optimization problems with better performance than traditional approaches. This article shows the application of SOA for optimal setting and allocation of thyristor-controlled series compensators (TCSCs) in a transmission line. TCSC devices are used to improve transmission systems’ capacity and voltage profile by controlling the transmission line reactance. The IEEE 30 bus system, as well as the Iraqi Super Grid 400 kV system, is used as a test system to illustrate the technique used. Results showed that the installation of TCSC unites the aims to reduce the voltage deviation, reduce the losses of active/reactive power, and increase transmission line reserves over thermal limits. TCSC devices are very effective for the better use of existing installations without sacrificing the stability margin. SOA is intended to identify the best location and size of TCSC devices that resolve the technological difficulties of reducing the TCSC devices’ costs.

Keywords: TCSC; Thyristor; SOA

1 Introduction

In Iraq, in the previous few decades up to the present, the energy demand has increased. There is a big difference between generation and demand in almost all months of the year, in addition to the increase in emergencies due to the significant growth in loads or loss of line or the deficit in the production of generating units, and the difference in consumer habits, all of this leads to severe problems in the network, such as instability power system voltage, enhanced activity, reduced reactive power, poor energy level efficiency, and unregulated transmission line power flow [1].

Traditional solutions to all these problems, like building a new power plant or adding a new transmission line, are more challenging and complex because they have many challenges. There are several techniques proposed for enhancing the transmission infrastructure [2]. The flexible alternating current transmission system (FACTS) is one of the most successful technical solutions. FACTS technologies will improve power technology’s flexibility to support alternative current (AC) systems’ control, stability, and power transfer capacity [3].

The thyristor-controlled series compensator (TCSC) is one of the FACTS family’s prominent members and is increasingly used by modern power systems on high voltage long transmission lines. The system may be responsible for various functions for power system operation, like power flow scheduling, reducing net losses, supplying voltage support, mitigating subsynchronous resonance, damping power oscillation, and improving transient stability. TCSC devices are very effective for the better use of existing installations without sacrificing the stability margin [4]. The TCSC systems must be optimally placed in the connected networks on perfect parameters to achieve the previous performance. The following goals are considered for the optimum implementation of TCSC devices: reduce active/reactive losses, improve the stability margin, increase transmission capacity, and prevent system blackouts [5].

Methods of optimization have been regularly established in recent years. The significant contributions and achievements of it is that it continue to be available to academics, particularly in architecture and economics [6]. As a result, several intelligent strategies, such as differential evolution, are employed [710]. Jaya algorithm [11], gravitational search algorithm [12], particle swarm optimization [11,12], bacteria foraging algorithm [13], exchange market algorithm [14], and harmony search algorithm [15] have been offered to overcome the optimum power flow (OPF) problem effectively. Among the most effective optimization techniques is the seeker optimization algorithm (SOA) [16]. SOA is therefore used for solving multiple complex and nonconvex problems in several articles recently. The OPF specifies the optimal position and the size of TCSC devices to overcome technical issues and reduce the cost of TCSC device installation. The SOA technique is used to allocate the TCSC devices to transmission networking, conceived as a nonlinear optimization problem with equality limits and inequalities. In reactive power management, the SOA could be utilized with multitarget OPF to address this issue. TCSC systems are perfectly positioned to boost transmission capacity, enhance voltage profile, and expand transmission line thermal reserve.

This article analyzes the multi-objective function of lowering active/reactive losses, decreasing voltage deviation, reducing costs, and increasing TCSC units during regular and emergency operations. The SOA application concept is implemented on standardized test systems IEEE 30-bus and the Iraqi Super Grid (ISG) 400 kV system; the performance of the suggested technique is evaluated. The capability of the suggested SOA and simulation results are linked with the existing techniques in the literature. Section 2 discusses the modeling of thyristor-controlled compensator series into the power system. The remaining content is formatted accordingly. Section 3 outlines the mathematical terminology challenge, whereas Section 4 presents the recommended SOA algorithm. In Section 5, the consequences of the simulations are compared to those of alternative methods. Section 6 additionally illustrates the conclusion of the planned SOA process’s application.

2 TCSC

The first installation for TCSC (manufacturer dubbed advanced series capacitor) was implemented in the 1990s at the Kayenta substation (Arizona) to boost power transmission capabilities [17]. The TCSC consists of three main components: capacitor bank C, bypass inductor L, and bidirectional thyristors T1 and T2; the capacitor is inserted directly in series with the transmission line thyristor-controlled inductor is mounted directly in parallel with the capacitor. Therefore, no interface equipment is needed for high-voltage transformers. It is much cheaper for TCSC than other competing FACT technologies [18]. With the thyristor firing control, changing the TCSC’s sensory component is simple. TCSC models include the dynamical as well as steady-state models. It enables quicker adjustments in transmission line impedance [19]. TCSC may be utilized as an inductor or a capacitor for static modeling. As a result, the reactance of the transmission line is restricted in terms of percentages. TCSC reactance ( x TCSC ) is demonstrated as a function, including its reactance of the transmission line ( x T . L ). The practical constraints value of x TCSC to avert overcompensation of transmission line, which be illustrated as follows [17]:

(1) 0.8 x T . L x TCSC 0.2 x T . L .

Figure 1 represents TCSC equivalent circuit static modeling joined in series connection with transmission lines, considering x TCSC values possibly managed for being negative or positive depending on the objectives and restrictions within allowed limits. Several constraints to installed TCSC are selected for mounting it on every transmission line, excluding those joining any busses with two generations. Besides, the TCSCs do not place transformer’s serial. As described in the study by Sakr et al. [7], it should not be mounted with light loading lines. Moreover, for severe emergencies or economic cases, the number of TCSC devices in this research is optimized to 2 or 3. The model is used to ensure that transmission lines are not overcompensated.

Figure 1 
               Diagram shows a line between two buses with TCSC [20].
Figure 1

Diagram shows a line between two buses with TCSC [20].

3 Problem formulation

The proposed SOA will resolve multitarget OPF in reactive power management. The SOA approach may be utilized to develop solutions for the suggested fitness functions. The purpose is to determine the appropriate placements for TCSC devices as well as the amount of compensation (sizing) for TCSC to meet the minimum five values for the stated objectives.

(2) Minimize ( Obj ) = w 1 × P losses + w 2 × | Q losses | + w 3 × VD + w 4 × cost + w 5 × NT ,

where, w 1 , w 2 , , w 5 will be adjusted to meet the requirements of the various use cases.

The weighting factors w i for the i th the objective function demonstrates the comparative importance between the m objectives.

(3) 0 w i 1 , i = 1 m w i = 1

One of the aims of equation (2) is to decrease the loss of active/reactive power, which is a function of the bus’s voltage (V i , V j ), and mutual conductance and substance (G ij , B ij ) and (θ ij ) refer to phase difference between the bus voltages i and j for a whole number of buses NB:

(4) P losses = i , j NB G ij ( V i 2 + V j 2 2 V i V j cos θ ij ) ,

(5) Q losses = i , j NB B ij ( V i 2 + V j 2 2 V i V j sin θ ij ) ,

Another goal in equation (2) is for enhancing the voltage profile by lowering voltage deviations on buses by equation (6):

(6) VD = i = 1 NB | V i V ref | ,

where V i refers to voltage in bus i and V ref refers to reference voltage. The cost of TCSC device should be minimized in the power system. The cost can be calculated according to equations (7) and (8) [21]:

(7) C TCSC = 0.0015 × S TCSC 2 0.713 × S TCSC + 153.75 ,

(8) Cost = C TCSC × S TCSC .

The cost here are the accumulated costs of TCSC in line k , C TCSC is the operating cost of individually TCSC device in $/MVAr, and S TCSC is the TCSC mounted capacity in MVAr (mega volt-amperes reactive), which can be determined by equation (9)

(9) S TCSC = I L max 2 × x TCSC ,

where I L max refers to nominal transmission line current, where TCSC is combined. It is preferable for reducing the number of TCSC units. ( NT ) is the objective function due to the improved system output by a few FACTS instruments for monitoring and repair purposes, which is the main function of the objective in equation (4). The power system is limited by equality and inequity in the following ways.

3.1 The total power balance is active/reactive

The constraint of power parity must be satisfied for supply and demand equilibrium. According to equations (10) and (11), the amount of active/reactive power produced must be equal to the sum of the system’s required active/reactive power plus the power losses:

(10) i = 1 NG P G i = i = 1 NB P D i + P losses ,

(11) i = 1 NG Q G i = i = 1 NB Q D i + Q losses ,

where P G i and Q G i are active/reactive power generation in unit, respectively. P D i and Q D i refer to active/reactive power demand at bus i .

3.2 That each bus’s active/reactive balance

Equations (12) and (13) can be used to model the balance of active/reactive power for each bus:

(12) P G i = P D i + i = 1 Ncl P F ij ,

(13) Q G i = Q D i + j = 1 Ncl Q F ij i NB , i j ,

where P F ij and Q F ij refer to active/reactive power passing on bus lines i ( Ncl ), respectively.

3.3 Power inequality constraints and voltage generation limits

The generator voltage, as well as active and reactive output, should be used to minimize both the lower and higher limitations:

(14) P G i min P G i P G i max ,

(15) Q G i min Q G i Q G i max ,

(16) V G i min V G i V G i max .

Here, G i max , P G i min , Q G i max , and Q G i min are maximum and minimum for active and reactive power generation at bus i , respectively. V G i max and V G i min represent maximum and minimum generating voltage limits at bus i.

3.4 Security constraints

In this case, the transmission line loading ( S l i ) should remain within the limitations allowed for this issue, as well as lower and higher limits should likewise regulate the load-bus voltages ( V L i ) following equations (17) and (18):

(17) S l i min S l i max i Nl ,

(18) V L i min V L i V L i max i NB NG ,

where S l i max and S l i min are maximum and minimum loading for line i . V L i max and V L i min are maximum and minimum voltage values on loading bus i .

The OPF problem with TCSC integration contains the following control variables that must be taken into consideration:

  • TCSC device locations.

  • The TCSC compensation rate (level).

  • SOA parameters and considerations for weighting ( w 1 : w 5 ).

4 The SOA

SOA is a population-based stochastic search algorithm developed by Dai et al. [22]. SOA models human search behaviors based on their memories, knowledge, the reasoning of confusion, and communication. The algorithm operates on a group of solutions called the search population (or swarm) known as searchers (or agents). The overall population is divided into subpopulations of problem K. The number of variables is divided by random division into subpopulations of many applicants S. Random search is performed separately by subpopulating the maximum and minimum value controls of the variables over different fields in the specified search field. Comparable subpeople seekers form a locality where they can learn from one another and exchange information [23].

SOA uses a step length α ij ( t ) and search direction d ij ( t ) , which are individually decided for each i th seeker and each j th variable to each repetition t , where α ij ( t ) ≥ 0 and d ij ( t ) ∈ {−1, 0, 1}. The subsections describe the procedure for determining the step length and the search direction [24]:

4.1 Exploration direction option

The multitudes appear, for instance, to define their goals in cooperation. According to its conclusion, the seeker drives near its important best location as there is no information from the others. This view of the i th seeker can be stated positively (+) or negatively (−). A positive sign indicates that the seeker is inclining to its important best location. The negative sign indicates the reverse. Statistically, this can be clarified using (19), seeing P i , best ( t ) as the best seeker’s location associated with its recent location X i ( t ) :

(19) d i 1 ( t ) = sign ( P i , best ( t ) X i ( t ) ) .

Other seekers in the same region are encouraged to change their performances and collaborate to serve all regional subpopulations. It should be noted that, while this phrase should seem to point west, I believe this has the correct flow:

(20) d i 2 ( t ) = sign ( P g , best ( t ) X i ( t ) ) ,

(21) d i 3 ( t ) = sign ( P l , best ( t ) X i ( t ) ) ,

where P g , best ( t ) denotes to neighbors’ global best location and P l , best ( t ) epitomizes neighbors’ local best location in each subpopulation. Finally, the new sites are directed by the old ones so that the seekers’ future can be showed and improved. If t 1 and t 2 are the past and the future location of each seeker, respectively, another path guidance can then be deleted, and mathematically this is shown as follows:

(22) d i 4 ( t ) = sign ( X i ( t 1 ) X i ( t 2 ) ) .

The four directions, described in equations (19)–(22), are used to pick the right direction to search. The final direction value (d ij ( t ) ) can be +1, which means the right way near the most acceptable solution, −1, which means that it is not optimal, or 0, which translates that this is the best location. The value of d ij ( t ) is nominated using proportional principle as follows:

(23) d ij = 0 , r j P j ( 0 ) 1 , P j ( 0 ) r j P j ( 0 ) + P j ( 1 ) 1 , P j ( 0 ) + P j ( 1 ) r j 1 ,

where r j varies arbitrarily within [0, 1], and P j ( m ) can be intended using (24), and it signifies the proportion of the sum of “m” from the set { d i 1 , d i 2 , d i 3 , d i 4 } on each variable j and each seeker for all the four truthful guidelines:

(24) P j ( m ) = k = 1 : 4 d i , k d i , k = m / 4 .

4.2 Computation of the step length

Seekers must travel at a particular speed or step length α ij to adjust their approach to finding the best answer. Equation (25) specifies the part of the motion of fuzzy logic and stretches the step length α ij for each variable j as follows:

(25) α ij = δ j ln ( μ ij ) .

where μ ij is an arbitrary number within the variety [ μ i , 1 ] that can be considered using (26). The value of μ i fuzzy dispensation membership function deceits between the maximum ( μ max < 1 ) and minimum ( μ min = 0.0111). The regulated values can be modified and calculated problem for each seeker using (27):

(26) μ ij = rand ( μ i , 1 ) ,

(27) μ i = μ max S i S 1 ( μ max μ min ) i S .

δ represents the Bell membership function and it is described using (28) as the product of ω (a variable that linearly diminutions from 0.9 to 0.1 every running). The absolute value will be alteration between the best seeker x best and an arbitrarily nominated seeker x rand . δ is then collective between seekers via the step length function.

(28) δ = ω × | x best x rand | .

4.3 Update of the i th seeker position

The seeker’s location travels closer to the optimal from t to t + 1 using step α ij , which is directed by d ij as follows:

(29) X ij ( t + 1 ) = X ij ( t ) α ij ( t ) × d ij ( t ) .

4.4 Subpopulations sharing information

Finally, subpopulations search independently, utilizing their information for the best value. This can lead to a locally optimal solution for the subpopulations. In each iteration, data have to be shared between subpopulations. This is the equation combining a subpopulation’s worst value with the best use of the other subpopulations:

(30) X ( k ) ij , worst ( k ) = X ( k ) ij , best ( l ) , ran d j 0.5 X ( k ) ij , worst ( k ) , else .

ran d j is a random value between [0, 1], and X ( k ) ij , best ( l ) and X ( k ) ij , worst ( k ) are the j th variable of the n . The worse and good locally in k th subpopulation n , k , L = 1 , 2 , . . . , K 1 , k l . respectively

To improve population diversity, good knowledge is exchanged among the other subpopulations obtained by each subpopulation. The implementation process of the SOA technology is defined as follows in Figure 2.

Figure 2 
                  The applying SOA approach flowchart.
Figure 2

The applying SOA approach flowchart.

5 Results and discussion

The test standard system, which is 400 kV Iraqi (ISG) gird system, was suggested for this study [20].

Table 1 displays the SOA parameter settings for typical study and emergency operation of these networks using MATLAB/MATPOWER/6 analytics software; the generational numbers to obtain an optimum solution are limited to 50 iterations. Still, many variables were constant in other SOA parameters for all cases and systems, such as the weighting factor set at several values for managing all objectives.

Table 1

The parameter setting of SOA

Subpopulations 3
Individual per each population 100
Total population size 300
Generations 50
Membership function (µ) 0.011 ≤ µ ≤ 0.3
Velocity step (k) 0.1
Velocity weight function ( ω ) 0.3 ≤  ω  0.8
Weight factors W 1 = 0.75 ; W 2 = 0.15 ; W 3 = 0.05 ; W 4 = 0.03 ; W 5 = 0.02

The following four cases are used to highlight the significance of this study:

Case 1: Original data systems are operating normally.

Case 2: Outage of a critical line.

Case 3: All bus loads are increased, or only specific buses have an increase.

Case 4: Outage as a percentage of generation.

5.1 ISG system 400 kV

Another research system is the Iraqi super high voltage (SHV) grid 400 kV, which comprises 36 buses, 22 generators, 24 load buses, 84 autotransformers, and 52 transmission lines. Twenty-two generating stations have varying MW generation as well as MVAr generation/absorption capacities. Bus 20 (KUTP) is considered as the slack bus. All the data came from the Iraqi National Control Center and indicated the condition of operations during the winter season on January 1, 2017. During regular and emergency operations, the lowest and maximum bus voltage magnitudes are 0.94 and 1.05 PU, respectively.

Table 1 shows the results of solving the first instance, a regular operating case, with and without incorporating the TCSC with the SOA method (4). In Case 1, two TCSC units were introduced in lines 37 and 13 between HDTH-QIM4 and KRK4-DAL4, reducing active/reactive power losses to 34.051 MW and 287.89 MVAr. The SOA system impacts for active/reactive power losses are 38.22, 341.38, and 0.004, with a voltage deviation of 0.001. The active and reactive power losses without installation of TCSC units are 46.139 MW and 412.65 MVAr, respectively, indicating that the proposed SOA technique is highly successful in addressing the regular operation of original systems data scenario 1.

In example 2, line 20 (BGS4 -BGC4) is identified as a vital line, which causes increased system power losses and has been disabled. In this case, the activated and reactive power losses rise to 39.86 MW and 347.17 MVAr, respectively. When the TCSC units are placed on lines 16 and 35 (the line between (BGW4-BGN4) and (KUT4-NSRP), they are decreased to 38.96 MW and 345.13 MVAr, respectively. In addition, the voltage deviation has been lowered to 0.019, suggesting improved system performance.

In the following scenario, the load on all buses (case 3, a) increases by 10%. Line 20 is disconnected (case 2), resulting in a significant violation of system variables and increased system power losses. In such a state, the active/reactive power loss and voltage deviations are 68.24 and 608.75 MVAr, respectively, and 0.142. The SOA technique improved system efficiency by introducing TCSC units between lines 31 and 36 (the line between AMN4-KUTP and KUT4-AMR4. When active and reactive power loss is restricted to 68.01 MW and 601.78 MVAr, respectively, there are only two TCSC units.

In Case 3b, the load on all buses increased by 20%, while line 20 is unavailable (case 2). As indicated in Table (2), the active power losses, reactive power, and voltage deviation dropped from 85.51, 757.25, and 0.36, respectively, to 84.09, 748.80, and 0.21 with the insertion of the ideal position of the TCSC device between lines 31 and 36 (the line between AMN4-KUTP and KUT4-AMR4).

Table 2

Obtained SOA results of cases (1–4) (Iraqi (SHV) gird system 400 kV)

Cases Case 1 Case 2 Case 3.a Case 3.b Case 4
Method Without TCSC With TCSC Without TCSC With TCSC Without TCSC With TCSC Without TCSC With TCSC Without TCSC With TCSC
TCSC location 37 and 13 16 and 35 36 and 31 36 and 31 13 and 29
Line reactance (PU) 0.0239, 0.0363 0.0073, 0.0142 0.0084, 0.0435 0.0046, 0.0187 0.0239, 0.0216 0.0147, 0.0066 0.0239, 0.0216 0.0085, 0.0106 0.0363, 0.0135 0.0238, 0.0101
Compensation level (%) 69.25, 60.9 45.23, 56.51 38.5, 64.7 64.43, 51 34.43, 25.18
TCSC cost ($/h) 16.312 × 104 20.316 × 104 5.539 × 104 7.272 × 104 2.912 × 104
P losses (MW) 38.22 34.051 39.86 38.96 68.24 68.01 85.51 84.09 40.62 39.15
Q losses (MVAr) 341.38 287.89 347.17 345.13 608.75 601.78 757.25 748.80 351.95 328.04
Total VD (PU) 0.004 0.001 0.03 0.019 0.142 0.11 0.36 0.21 0.29 0.101

Case 4 introduces the outage of bus 26 generating units 14 (GKHER). To survive this outage, to some degree, the machine must be reliable. If there was a 10% reduction in the output unit at bus 26, higher power losses were observed for the system without any line flow violation. In this mode, 39.15 MW and 328.04 MVAr are lost in active and reactive power when the best TCSC device site is in lines 13 and 29. This reduction in active/reactive power losses will affect power generation in all scenarios (1–4). Figure 3 depicts the reduction in reactive power generation using the SOA approach for the Iraqi SHV grid system 400 kV.

Figure 3 
                  Reduction in reactive power generation due to the addition of the TCSC with SOA (cases 1–4).
Figure 3

Reduction in reactive power generation due to the addition of the TCSC with SOA (cases 1–4).

6 Conclusion

As a testing system, this article uses SOA algorithms for the optimum sites for TCSC devices in the IEEE 30 buses as well as the Iraqi SHV grid system 400 kV. The static TCSC units modeling was linked to a series of high-voltage AC transmission lines, which use fast-affected transmission lines. Obtaining the optimal location for connecting the equipment of TCSC has led to good and encouraging results for the use of this method in the development of electrical networks.

Among the important results obtained is overcoming the multi-problems with TCSC such as reduced active and reactive power loss, voltage variation, TCSC costs, and TCSC unit count under normal and emergency conditions. The suggested SOA algorithm’s result is compared to the DE procedure. The suggested SOA device has optimally decreased both active and reactive power loss, as well as the TCSC cost. In all case studies, the voltage profile has also been enhanced by minimizing voltage deviation, according to the survey. SOA has demonstrated a significant capacity to reduce independent factors for violation impact, particularly in emergencies. The algorithm is applied successfully and identified the best location and the size for the TCSC, which was designed to improve the margin of stability and transmission capacity, optimize transmission line efficiency by lowering active and reactive losses, and enhance voltage profile for all buses by lowering voltage deviation as well as reducing the limits of reactive power for the generating units. This SOA approach offers the advantages of reducing the size of FACTS devices, switching operations, and regulating activities to increase the equipment’s life cycle in a portion of the Iraqi network.

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

  2. Data availability statement: Most datasets generated and analyzed in this study are comprised in this submitted manuscript. The other datasets are available on reasonable request from the corresponding author with the attached information.

References

[1] Reka SS, Dragicevic T. Future effectual role of energy delivery: A comprehensive review of Internet of Things and smart grid. Renew Sustain Energy Rev. 2018;91:90–108.10.1016/j.rser.2018.03.089Search in Google Scholar

[2] Mahdad B, Bouktir T, Srairi K. The strategy of location and control of FACTS devices for enhancing power quality. IEEE Mediterranean Electrotechnical Conference; 2006. p. 1068–72.10.1109/MELCON.2006.1653284Search in Google Scholar

[3] Habur K, O’Leary D. FACTS-flexible alternating current transmission systems: For cost-effective and reliable transmission of electrical energy. Siemens-World Bank document–Final Draft Report, Erlangen. vol. 46, 2004.Search in Google Scholar

[4] Murali D, Rajaram M, Reka N. Comparison of FACTS devices for power system stability enhancement. Int J Comput Appl. 2010;8:30–5.10.5120/1198-1701Search in Google Scholar

[5] Zhang X, Tomsovic K, Dimitrovski A. Optimal allocation of series FACTS devices in large-scale systems. IET Gener Transm Distrib. 2018;12:1889–96.10.1049/iet-gtd.2017.1223Search in Google Scholar

[6] Malladi KT, Sowlati T. Biomass logistics: A review of important features, optimization modeling, and the new trends. Renew Sustain Energy Rev. 2018;94:587–99.10.1016/j.rser.2018.06.052Search in Google Scholar

[7] Sakr WS, El-Sehiemy RA, Azmy AM. Optimal allocation of TCSCs by adaptive DE algorithm. IET Gener Transm Distrib. 2016;10:3844–54.10.1049/iet-gtd.2016.0362Search in Google Scholar

[8] Li S, Gong W, Wang L, Yan X, Hu C. Optimal power flow by means of improved adaptive differential evolution. Energy. 2020;198:1–21.10.1016/j.energy.2020.117314Search in Google Scholar

[9] Warid W, Hizam H, Mariun N, Abdul-Wahab NI. Optimal power flow using the Jaya algorithm. Energies. 2016;9:678.10.3390/en9090678Search in Google Scholar

[10] Duman S, Güvenç U, Sönmez Y, Yörükeren N. Optimal power flow using gravitational search algorithm. Energy Convers Manage. 2012;59:86–95.10.1016/j.enconman.2012.02.024Search in Google Scholar

[11] Abido M. Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst. 2002;24:563–71.10.1016/S0142-0615(01)00067-9Search in Google Scholar

[12] Naderi E, Narimani H, Fathi M, Narimani MR. A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Appl Soft Comput. 2017;53:441–56.10.1016/j.asoc.2017.01.012Search in Google Scholar

[13] Panda A, Tripathy M, Barisal A, Prakash T. A modified bacterium foraging based optimal power flow framework for hydro-thermal-wind generation system in the presence of statcom. Energy. 2017;124:720–40.10.1016/j.energy.2017.02.090Search in Google Scholar

[14] Rajan A, Malakar T. Exchange market algorithm based optimum reactive power dispatch. Appl Soft Comput. 2016;43:320–36.10.1016/j.asoc.2016.02.041Search in Google Scholar

[15] Abbasi M, Abbasi E, Mohammadi-Ivatloo B. Single and multi-objective optimal power flow using a new differential-based harmony search algorithm. J Ambient Intell Human Comput. 2020;12:1–21.10.1007/s12652-020-02089-6Search in Google Scholar

[16] Shafik MB, Rashed GI, El-Sehiemy RA, Chen H. Optimal sizing and sitting of TCSC devices for the multi-objective operation of power systems using an adaptive seeker optimization algorithm. IEEE Region Ten Symposium; 2018. p. 231–6.10.1109/TENCONSpring.2018.8691948Search in Google Scholar

[17] Acha E, Fuerte-Esquivel CR, Ambriz-Perez H, Angeles-Camacho C. FACTS: Modeling and simulation in power networks. England: John Wiley & Sons; 2004.10.1002/0470020164Search in Google Scholar

[18] Rashed GI, Sun Y, Shaheen HI. Optimal location and parameter setting of TCSC for loss minimization based on differential evolution and genetic algorithm. Phys Procedia. 2012;33:1864–78.10.1016/j.phpro.2012.05.296Search in Google Scholar

[19] Saravanan M, Slochanal SMR, Venkatesh P, Abraham PS. Application of PSO technique for optimal location of FACTS devices considering system loadability and cost of installation. International Power Engineering Conference; 2005. p. 716–21.10.1109/IPEC.2005.207001Search in Google Scholar

[20] Abdulsada MA, Tuaimah FM. Power system static security assessment for iraqi super high voltage grid. Int J Appl Eng Res. 2017;12:8354–65.Search in Google Scholar

[21] Seto Wibowo R, Yorino N, Eghbal M, Zoka Y, Sasaki Y. Expected security cost‐based FACTS device allocation using hybrid PSO. IEEJ Trans Electr Electron Eng. 2011;6:331–7.10.1002/tee.20665Search in Google Scholar

[22] Dai C, Zhu Y, Chen W. Seeker optimization algorithm. In International Conference on Computational and Information Science; 2006. p. 167–76.10.1109/ICCIAS.2006.294126Search in Google Scholar

[23] Dai C, Chen W, Song Y, Zhu Y. Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization. J Syst Eng Electron. 2010;21:300–11.10.3969/j.issn.1004-4132.2010.02.021Search in Google Scholar

[24] Shaw B, Mukherjee V, Ghoshal SP. The solution of economic dispatch problems by the seeker optimization algorithm. Expert Syst Appl. 2012;39:508–19.10.1016/j.eswa.2011.07.041Search in Google Scholar

Received: 2023-04-16
Revised: 2023-07-11
Accepted: 2023-07-24
Published Online: 2024-02-10

© 2024 the author(s), published by De Gruyter

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

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  4. Application of finite element method in industrial design, example of an electric motorcycle design project
  5. Correlative evaluation of the corrosion resilience and passivation properties of zinc and aluminum alloys in neutral chloride and acid-chloride solutions
  6. Will COVID “encourage” B2B and data exchange engineering in logistic firms?
  7. Influence of unsupported sleepers on flange climb derailment of two freight wagons
  8. A hybrid detection algorithm for 5G OTFS waveform for 64 and 256 QAM with Rayleigh and Rician channels
  9. Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy
  10. Exploring the potential of ammonia and hydrogen as alternative fuels for transportation
  11. Impact of insulation on energy consumption and CO2 emissions in high-rise commercial buildings at various climate zones
  12. Advanced autopilot design with extremum-seeking control for aircraft control
  13. Adaptive multidimensional trust-based recommendation model for peer to peer applications
  14. Effects of CFRP sheets on the flexural behavior of high-strength concrete beam
  15. Enhancing urban sustainability through industrial synergy: A multidisciplinary framework for integrating sustainable industrial practices within urban settings – The case of Hamadan industrial city
  16. Advanced vibrant controller results of an energetic framework structure
  17. Application of the Taguchi method and RSM for process parameter optimization in AWSJ machining of CFRP composite-based orthopedic implants
  18. Improved correlation of soil modulus with SPT N values
  19. Technologies for high-temperature batch annealing of grain-oriented electrical steel: An overview
  20. Assessing the need for the adoption of digitalization in Indian small and medium enterprises
  21. A non-ideal hybridization issue for vertical TFET-based dielectric-modulated biosensor
  22. Optimizing data retrieval for enhanced data integrity verification in cloud environments
  23. Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria
  24. Nonlinear finite-element analysis of RC beams with various opening near supports
  25. Thermal analysis of Fe3O4–Cu/water over a cone: a fractional Maxwell model
  26. Radial–axial runner blade design using the coordinate slice technique
  27. Theoretical and experimental comparison between straight and curved continuous box girders
  28. Effect of the reinforcement ratio on the mechanical behaviour of textile-reinforced concrete composite: Experiment and numerical modeling
  29. Experimental and numerical investigation on composite beam–column joint connection behavior using different types of connection schemes
  30. Enhanced performance and robustness in anti-lock brake systems using barrier function-based integral sliding mode control
  31. Evaluation of the creep strength of samples produced by fused deposition modeling
  32. A combined feedforward-feedback controller design for nonlinear systems
  33. Effect of adjacent structures on footing settlement for different multi-building arrangements
  34. Analyzing the impact of curved tracks on wheel flange thickness reduction in railway systems
  35. Review Articles
  36. Mechanical and smart properties of cement nanocomposites containing nanomaterials: A brief review
  37. Applications of nanotechnology and nanoproduction techniques
  38. Relationship between indoor environmental quality and guests’ comfort and satisfaction at green hotels: A comprehensive review
  39. Communication
  40. Techniques to mitigate the admission of radon inside buildings
  41. Erratum
  42. Erratum to “Effect of short heat treatment on mechanical properties and shape memory properties of Cu–Al–Ni shape memory alloy”
  43. Special Issue: AESMT-3 - Part II
  44. Integrated fuzzy logic and multicriteria decision model methods for selecting suitable sites for wastewater treatment plant: A case study in the center of Basrah, Iraq
  45. Physical and mechanical response of porous metals composites with nano-natural additives
  46. Special Issue: AESMT-4 - Part II
  47. New recycling method of lubricant oil and the effect on the viscosity and viscous shear as an environmentally friendly
  48. Identify the effect of Fe2O3 nanoparticles on mechanical and microstructural characteristics of aluminum matrix composite produced by powder metallurgy technique
  49. Static behavior of piled raft foundation in clay
  50. Ultra-low-power CMOS ring oscillator with minimum power consumption of 2.9 pW using low-voltage biasing technique
  51. Using ANN for well type identifying and increasing production from Sa’di formation of Halfaya oil field – Iraq
  52. Optimizing the performance of concrete tiles using nano-papyrus and carbon fibers
  53. Special Issue: AESMT-5 - Part II
  54. Comparative the effect of distribution transformer coil shape on electromagnetic forces and their distribution using the FEM
  55. The complex of Weyl module in free characteristic in the event of a partition (7,5,3)
  56. Restrained captive domination number
  57. Experimental study of improving hot mix asphalt reinforced with carbon fibers
  58. Asphalt binder modified with recycled tyre rubber
  59. Thermal performance of radiant floor cooling with phase change material for energy-efficient buildings
  60. Surveying the prediction of risks in cryptocurrency investments using recurrent neural networks
  61. A deep reinforcement learning framework to modify LQR for an active vibration control applied to 2D building models
  62. Evaluation of mechanically stabilized earth retaining walls for different soil–structure interaction methods: A review
  63. Assessment of heat transfer in a triangular duct with different configurations of ribs using computational fluid dynamics
  64. Sulfate removal from wastewater by using waste material as an adsorbent
  65. Experimental investigation on strengthening lap joints subjected to bending in glulam timber beams using CFRP sheets
  66. A study of the vibrations of a rotor bearing suspended by a hybrid spring system of shape memory alloys
  67. Stability analysis of Hub dam under rapid drawdown
  68. Developing ANFIS-FMEA model for assessment and prioritization of potential trouble factors in Iraqi building projects
  69. Numerical and experimental comparison study of piled raft foundation
  70. Effect of asphalt modified with waste engine oil on the durability properties of hot asphalt mixtures with reclaimed asphalt pavement
  71. Hydraulic model for flood inundation in Diyala River Basin using HEC-RAS, PMP, and neural network
  72. Numerical study on discharge capacity of piano key side weir with various ratios of the crest length to the width
  73. The optimal allocation of thyristor-controlled series compensators for enhancement HVAC transmission lines Iraqi super grid by using seeker optimization algorithm
  74. Numerical and experimental study of the impact on aerodynamic characteristics of the NACA0012 airfoil
  75. Effect of nano-TiO2 on physical and rheological properties of asphalt cement
  76. Performance evolution of novel palm leaf powder used for enhancing hot mix asphalt
  77. Performance analysis, evaluation, and improvement of selected unsignalized intersection using SIDRA software – Case study
  78. Flexural behavior of RC beams externally reinforced with CFRP composites using various strategies
  79. Influence of fiber types on the properties of the artificial cold-bonded lightweight aggregates
  80. Experimental investigation of RC beams strengthened with externally bonded BFRP composites
  81. Generalized RKM methods for solving fifth-order quasi-linear fractional partial differential equation
  82. An experimental and numerical study investigating sediment transport position in the bed of sewer pipes in Karbala
  83. Role of individual component failure in the performance of a 1-out-of-3 cold standby system: A Markov model approach
  84. Implementation for the cases (5, 4) and (5, 4)/(2, 0)
  85. Center group actions and related concepts
  86. Experimental investigation of the effect of horizontal construction joints on the behavior of deep beams
  87. Deletion of a vertex in even sum domination
  88. Deep learning techniques in concrete powder mix designing
  89. Effect of loading type in concrete deep beam with strut reinforcement
  90. Studying the effect of using CFRP warping on strength of husk rice concrete columns
  91. Parametric analysis of the influence of climatic factors on the formation of traditional buildings in the city of Al Najaf
  92. Suitability location for landfill using a fuzzy-GIS model: A case study in Hillah, Iraq
  93. Hybrid approach for cost estimation of sustainable building projects using artificial neural networks
  94. Assessment of indirect tensile stress and tensile–strength ratio and creep compliance in HMA mixes with micro-silica and PMB
  95. Density functional theory to study stopping power of proton in water, lung, bladder, and intestine
  96. A review of single flow, flow boiling, and coating microchannel studies
  97. Effect of GFRP bar length on the flexural behavior of hybrid concrete beams strengthened with NSM bars
  98. Exploring the impact of parameters on flow boiling heat transfer in microchannels and coated microtubes: A comprehensive review
  99. Crumb rubber modification for enhanced rutting resistance in asphalt mixtures
  100. Special Issue: AESMT-6
  101. Design of a new sorting colors system based on PLC, TIA portal, and factory I/O programs
  102. Forecasting empirical formula for suspended sediment load prediction at upstream of Al-Kufa barrage, Kufa City, Iraq
  103. Optimization and characterization of sustainable geopolymer mortars based on palygorskite clay, water glass, and sodium hydroxide
  104. Sediment transport modelling upstream of Al Kufa Barrage
  105. Study of energy loss, range, and stopping time for proton in germanium and copper materials
  106. Effect of internal and external recycle ratios on the nutrient removal efficiency of anaerobic/anoxic/oxic (VIP) wastewater treatment plant
  107. Enhancing structural behaviour of polypropylene fibre concrete columns longitudinally reinforced with fibreglass bars
  108. Sustainable road paving: Enhancing concrete paver blocks with zeolite-enhanced cement
  109. Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model
  110. Design and simulation of photonic crystal fiber for highly sensitive chemical sensing applications
  111. Optimization and design of a new column sequencing for crude oil distillation at Basrah refinery
  112. Inductive 3D numerical modelling of the tibia bone using MRI to examine von Mises stress and overall deformation
  113. An image encryption method based on modified elliptic curve Diffie-Hellman key exchange protocol and Hill Cipher
  114. Experimental investigation of generating superheated steam using a parabolic dish with a cylindrical cavity receiver: A case study
  115. Effect of surface roughness on the interface behavior of clayey soils
  116. Investigated of the optical properties for SiO2 by using Lorentz model
  117. Measurements of induced vibrations due to steel pipe pile driving in Al-Fao soil: Effect of partial end closure
  118. Experimental and numerical studies of ballistic resistance of hybrid sandwich composite body armor
  119. Evaluation of clay layer presence on shallow foundation settlement in dry sand under an earthquake
  120. Optimal design of mechanical performances of asphalt mixtures comprising nano-clay additives
  121. Advancing seismic performance: Isolators, TMDs, and multi-level strategies in reinforced concrete buildings
  122. Predicted evaporation in Basrah using artificial neural networks
  123. Energy management system for a small town to enhance quality of life
  124. Numerical study on entropy minimization in pipes with helical airfoil and CuO nanoparticle integration
  125. Equations and methodologies of inlet drainage system discharge coefficients: A review
  126. Thermal buckling analysis for hybrid and composite laminated plate by using new displacement function
  127. Investigation into the mechanical and thermal properties of lightweight mortar using commercial beads or recycled expanded polystyrene
  128. Experimental and theoretical analysis of single-jet column and concrete column using double-jet grouting technique applied at Al-Rashdia site
  129. The impact of incorporating waste materials on the mechanical and physical characteristics of tile adhesive materials
  130. Seismic resilience: Innovations in structural engineering for earthquake-prone areas
  131. Automatic human identification using fingerprint images based on Gabor filter and SIFT features fusion
  132. Performance of GRKM-method for solving classes of ordinary and partial differential equations of sixth-orders
  133. Visible light-boosted photodegradation activity of Ag–AgVO3/Zn0.5Mn0.5Fe2O4 supported heterojunctions for effective degradation of organic contaminates
  134. Production of sustainable concrete with treated cement kiln dust and iron slag waste aggregate
  135. Key effects on the structural behavior of fiber-reinforced lightweight concrete-ribbed slabs: A review
  136. A comparative analysis of the energy dissipation efficiency of various piano key weir types
  137. Special Issue: Transport 2022 - Part II
  138. Variability in road surface temperature in urban road network – A case study making use of mobile measurements
  139. Special Issue: BCEE5-2023
  140. Evaluation of reclaimed asphalt mixtures rejuvenated with waste engine oil to resist rutting deformation
  141. Assessment of potential resistance to moisture damage and fatigue cracks of asphalt mixture modified with ground granulated blast furnace slag
  142. Investigating seismic response in adjacent structures: A study on the impact of buildings’ orientation and distance considering soil–structure interaction
  143. Improvement of porosity of mortar using polyethylene glycol pre-polymer-impregnated mortar
  144. Three-dimensional analysis of steel beam-column bolted connections
  145. Assessment of agricultural drought in Iraq employing Landsat and MODIS imagery
  146. Performance evaluation of grouted porous asphalt concrete
  147. Optimization of local modified metakaolin-based geopolymer concrete by Taguchi method
  148. Effect of waste tire products on some characteristics of roller-compacted concrete
  149. Studying the lateral displacement of retaining wall supporting sandy soil under dynamic loads
  150. Seismic performance evaluation of concrete buttress dram (Dynamic linear analysis)
  151. Behavior of soil reinforced with micropiles
  152. Possibility of production high strength lightweight concrete containing organic waste aggregate and recycled steel fibers
  153. An investigation of self-sensing and mechanical properties of smart engineered cementitious composites reinforced with functional materials
  154. Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model
  155. Experimental investigation of dynamic soil properties for modeling energy-absorbing layers
  156. Numerical investigation of the effect of longitudinal steel reinforcement ratio on the ductility of concrete beams
  157. An experimental study on the tensile properties of reinforced asphalt pavement
  158. Self-sensing behavior of hot asphalt mixture with steel fiber-based additive
  159. Behavior of ultra-high-performance concrete deep beams reinforced by basalt fibers
  160. Optimizing asphalt binder performance with various PET types
  161. Investigation of the hydraulic characteristics and homogeneity of the microstructure of the air voids in the sustainable rigid pavement
  162. Enhanced biogas production from municipal solid waste via digestion with cow manure: A case study
  163. Special Issue: AESMT-7 - Part I
  164. Preparation and investigation of cobalt nanoparticles by laser ablation: Structure, linear, and nonlinear optical properties
  165. Seismic analysis of RC building with plan irregularity in Baghdad/Iraq to obtain the optimal behavior
  166. The effect of urban environment on large-scale path loss model’s main parameters for mmWave 5G mobile network in Iraq
  167. Formatting a questionnaire for the quality control of river bank roads
  168. Vibration suppression of smart composite beam using model predictive controller
  169. Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete
  170. In-depth analysis of critical factors affecting Iraqi construction projects performance
  171. Behavior of container berth structure under the influence of environmental and operational loads
  172. Energy absorption and impact response of ballistic resistance laminate
  173. Effect of water-absorbent polymer balls in internal curing on punching shear behavior of bubble slabs
  174. Effect of surface roughness on interface shear strength parameters of sandy soils
  175. Evaluating the interaction for embedded H-steel section in normal concrete under monotonic and repeated loads
  176. Estimation of the settlement of pile head using ANN and multivariate linear regression based on the results of load transfer method
  177. Enhancing communication: Deep learning for Arabic sign language translation
  178. A review of recent studies of both heat pipe and evaporative cooling in passive heat recovery
  179. Effect of nano-silica on the mechanical properties of LWC
  180. An experimental study of some mechanical properties and absorption for polymer-modified cement mortar modified with superplasticizer
  181. Digital beamforming enhancement with LSTM-based deep learning for millimeter wave transmission
  182. Developing an efficient planning process for heritage buildings maintenance in Iraq
  183. Design and optimization of two-stage controller for three-phase multi-converter/multi-machine electric vehicle
  184. Evaluation of microstructure and mechanical properties of Al1050/Al2O3/Gr composite processed by forming operation ECAP
  185. Calculations of mass stopping power and range of protons in organic compounds (CH3OH, CH2O, and CO2) at energy range of 0.01–1,000 MeV
  186. Investigation of in vitro behavior of composite coating hydroxyapatite-nano silver on 316L stainless steel substrate by electrophoretic technic for biomedical tools
  187. A review: Enhancing tribological properties of journal bearings composite materials
  188. Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement
  189. Design a new scheme for image security using a deep learning technique of hierarchical parameters
  190. Special Issue: ICES 2023
  191. Comparative geotechnical analysis for ultimate bearing capacity of precast concrete piles using cone resistance measurements
  192. Visualizing sustainable rainwater harvesting: A case study of Karbala Province
  193. Geogrid reinforcement for improving bearing capacity and stability of square foundations
  194. Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis
  195. Adsorbent made with inexpensive, local resources
  196. Effect of drain pipes on seepage and slope stability through a zoned earth dam
  197. Sediment accumulation in an 8 inch sewer pipe for a sample of various particles obtained from the streets of Karbala city, Iraq
  198. Special Issue: IETAS 2024 - Part I
  199. Analyzing the impact of transfer learning on explanation accuracy in deep learning-based ECG recognition systems
  200. Effect of scale factor on the dynamic response of frame foundations
  201. Improving multi-object detection and tracking with deep learning, DeepSORT, and frame cancellation techniques
  202. The impact of using prestressed CFRP bars on the development of flexural strength
  203. Assessment of surface hardness and impact strength of denture base resins reinforced with silver–titanium dioxide and silver–zirconium dioxide nanoparticles: In vitro study
  204. A data augmentation approach to enhance breast cancer detection using generative adversarial and artificial neural networks
  205. Modification of the 5D Lorenz chaotic map with fuzzy numbers for video encryption in cloud computing
  206. Special Issue: 51st KKBN - Part I
  207. Evaluation of static bending caused damage of glass-fiber composite structure using terahertz inspection
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