 Original research
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Impact of communication time delays on combined LFC and AVR of a multiarea hybrid system with IPFCRFBs coordinated control strategy
Protection and Control of Modern Power Systems volume 6, Article number: 7 (2021)
Abstract
In this paper, the impact of communication time delays (CTDs) on combined load frequency control (LFC) and automatic voltage regulation (AVR) of a multiarea system with hybrid generation units is addressed. Investigation reveals that CTDs have significant effect on system performance. A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution  artificial electric field algorithm (DEAEFA). The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature. The investigation is further extended to integration of redox flow batteries (RFBs) and interline power flow controller (IPFC) with tielines. Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.
Introduction
A modern power system incorporates a variety of power generation units interconnected together to provide high quality power to meet varying load demand. These generation units are usually grouped to form coherent groups or control areas, while all generators in control areas must run in synchrony. Each control area is intended to be associated with other control areas through tielines, where power exchanges between control areas take place. As the system load is never constant, maintaining system stability, which depends on maintaining both frequency and system terminal voltage, is the most challenging task. The control over frequency can be achieved by minimizing active power mismatch between demand and generation through regulating the generator speed governor via LFC. The system terminal voltage is regulated by generator AVR through changing the generator field excitation current.
LFC of a thermal power system was first examined in [1] and then was extended to multiarea interconnected systems with multitype generation units. Various investigations on different test systems have been carried out. References [2, 3] analyze multiarea thermal plant possessing nonreheat turbine structure with and without considering GRCs and GDB nonlinearity, respectively. Two areas of equal generation capacity with hydrothermal units of reheat turbines are considered for investigation in [4,5,6] but system nonlinearity is not considered. In [5], the investigation is extended to incorporate wind, solar photovoltaics (PV), aquaelectrolyzes and fuel cells. References [7, 8] consider the incorporation of power generation through gas and nuclear plants in conventional hydrothermal systems. However, in [1,2,3,4,5,6,7], only the LFC problem is investigated while the AVR coupling is not considered.
Many studies have been carried out on either LFC or AVR, but investigations into their combined effect have been limited. References [9, 10] analyze the combined power system model, but they limit to a single area. A twoarea combined model is investigated in [11], but the system nonlinearity is not considered. In [12], a combined threearea interconnected system with multitype generation units considering GRC and GDB is studied, but CTDs are not considered. Most work on AGC and on combined LFC and AVR of interconnected systems mainly concentrate on the nonlinearities of GRC and GDB and give lower priority to the consideration of CTDs. This motivates the examination of a twoarea system with hybrid power generation sources in the presence of CTDs with combined effect, as the effect of CTDs on a multiarea LFC and AVR combined model has not been reported.
In both LFC and AVR loops, design of secondary regulator plays a critical role in damping out the frequency and terminal voltage deviations under varying load demand. Conventional controllers such as I/PI [8, 11] /PID [2, 3, 13]/ PID with filter (PIDN) and higher order degree of freedom (DOF) [12] controllers have been reported. Intelligencebased controllers have also been implemented such as fuzzy PI/ fuzzy PID [6, 14, 15], and FAMCON tool box based controllers such as fractional order (FO)PI/ FOPID controllers [7, 16]. However, the performance of the controllers relies heavily on optimum gain values, which can be obtained by employing soft computing techniques. Algorithms such as particle swarm optimization (PSO) [17], grey wolf optimization (GWO) [2], improved GWO (IGWO) [15], teachinglearning based optimization (TLBO) [16], artificial bee colony optimization (ABC) [18], backtracking search algorithm (BSA) [19], imperialist competitive algorithm (ICA) [20], lightning search algorithm (LSA) [12], simulated annealing (SA) [11], flower pollination algorithm (FPA) [13], hybrid firefly pattern search (hFAPS) [21], HGAPSO [3] etc. have been employed to obtain controller optimum gains. However, many of these optimization algorithms have disadvantages such as slow convergence, being easily trapped into local minima, and insufficiency in asserting average equilibrium between exploitation and exploration.
The problem considered in this work is a more realistic complex problem. Small variations in algorithm parameters may lead to large disturbances in system dynamic response. Thus, a robust and sovereign optimization algorithm is needed. To address the challenges, a new strategy of DEAEFA is presented to obtain parameters of the secondary controller in LFC and AVR loops. The superiority of the DEAEFA algorithm is validated on benchmark standard test functions that will be detailed in Section 4.
The objectives of this paper are:

a)
To design a multiarea combined LFC and AVR model consists of hybrid generation sources considering GRCs and CTDs.

b)
To solve the complex realistic problem, a novel DEAEFA algorithm is presented to obtain the optimum parametric gains of the secondary controller.

c)
To test the effectiveness of the proposed algorithm, its performance is compared with other optimization methods tested on widely used interconnected systems reported in the literature.

d)
To examine the impact of AVR and CTDs, system dynamic responses are analyzed with and without considering AVR and CTDs.

e)
To further examine the coordinated performance of IPFC and RFBs with the proposed DEAEFA optimized PID for the realistic system.

f)
To manifest the robustness of the proposed coordinated control approach using sensitivity analysis.
Power system models
Test systems under investigation
Despite the nonlinear nature of realistic power systems, extensive work on linear models in LFC domain has been reported. In addition, the nonlinearities of the system with such as CTDs, GRC and GDB have been investigated. Incorporation of such nonlinearities affects system dynamic performance. Hence, study on LFC domain should be carried out to investigate the impact of nonlinearities. Various models of interconnected systems have been considered for investigation by many researchers.
Three test systems are considered in this paper including a twoarea system with equal generation capacity of thermal power plant with nonreheat turbines (test system1) in Fig. 1, a twoarea system of hydrothermal generation units with reheat turbines (test system2) in Fig. 2, and a twoarea LFC and AVR combined model system with hybrid generation sources considering CTDs and GRC (test system3) in Fig. 3. Test systems − 1 and 2 are analyzed to regulate area frequency and deviations in power exchange among control areas with the presented DEAEFAbased PID and the responses are compared with those of other reported control strategies. On test system3, investigation is carried out to analyze the combined LFC and AVR effect on simultaneous extenuation of deviations of system frequency, voltage and power exchange through a tieline. The test system models depicted in Fig. 1 [2, 17] and Fig. 2 [18] are extensively reported on in the literature, whereas the power system depicted in Fig. 3 is examined here.
Modeling of AVR coupling with LFC
The system frequency and voltage can be controlled simultaneously through a combined LFC and AVR model. AVR is coupled to LFC through cross coupling coefficients K1, K2, K3 and K4 shown in Fig. 4. In this combined model, frequency is regulated by regulating active power mismatch among generations and demands through the LFC loop while maintaining the system terminal voltage is taken care of by the AVR loop. The AVR loop consists of an amplifier, an exciter, a sensor and a generator field unit. The sensor unit continuously monitors the terminal voltage and generates an error signal which is used to change the generator field excitation after amplification. The active power mismatch among demands and generations results in frequency fluctuation. The terminal voltage is also affected by the variation in frequency as the emf of the generator stator winding is proportional to frequency. These controlling measures in the AVR loop affect generator armature terminal EMF E^{'} which subsequently influences real power generation as [22]:
where X_{S} and δ are generator reactance and rotor angle respectively.
In the event of load variation, the frequency fluctuation can be governed by changing the rotor angle ∆δ via changing the generation of real power ∆P_{e} as:
where P_{S} is the synchronizing power coefficient. The system terminal voltage V comprises the qaxis (V_{q}) and daxis (V_{d}) components which are influenced by rotor angle. Then terminal voltage is modeled as:
The factors that regularize the voltage induced in the generator are modeled as:
where K_{1}, K_{2}, K_{3} and K_{4} are the coefficients that link the AVR with the LFC control loop. The time and gain constants of the subsystem parameters are provided in the Appendix.
Communication time delay
Modern interconnected power systems are equipped with large numbers of phase measuring units (PMUs) to facilitate the communication between different centers and areas. Usually, several signals are transmitted from generation and transmission systems to load dispatch centers or control centers and from these centers to the generating stations. The transmission and receipt of signals among these centers and stations may affect system stability. As the heart of LFC is the secondary controller which generates the command control signal by taking the area control error (ACE) signal as input, these CTDs can cause delays in input signals to the controllers and consequently delays in command control signal generation. Therefore, alteration of the generator operating set points can be delayed resulting in increased mispatch between demand and generation. This can affect system stability. Thus, CTDs need be taken into consideration to avoid system instability. Thus, here the impact of CTDs on combined frequency and voltage stabilization is analyzed considering the communication delays in test system3. The communication delay \( {e}^{{s\tau}_d} \) considered in this work is the transport delay, which is expressed by Taylor series expansion as [23]:
Controllers and optimization
Controller structure
The combined model system is equipped with a classical PID controller as secondary regulator, since almost 90% of manufacturing industries are still using this for controlling purpose because of design simplicity and efficiency. The input signals to these controllers are ACE signals while the parametric gains are tuned using DEAEFA with respect to the error squared over the integral (ISE) objective function given in (6). The output control signals ∆P_{C} ∆P_{C} from these secondary controllers are fed to the generating plants. The schematic representation of PID tuning in the considered combined system is depicted in Fig. 5.
The PID controller gains in the two areas are optimized using the proposed algorithm subjected to constraints.
DEAEFA searching strategy
DE was proposed in [24] and belongs to the category of a stochastic search method. In DE, the initial population is randomly generated within predefined limits while the next generation’s new population is generated by making use of mutation, recombination and selection operators. A recombination operator then adjudicates the population continuously to drive towards the best solution, whereas the mutation operator tries to disseminate the population in uncovered search space to locate the best optimum solution. In [25] the performance of DE is tested on several benchmark functions, and this reveals that the DE algorithm is efficient in solving nonlinear and multimodal objective functions. The benefits of DE include the potential of generating new population utilizing targets and mutant vector properties and the feature of elitism to avoid destroying the best solution when creating the next generation. However, weakness in local searching, failure in maintaining average equilibrium between exploitation and exploration, and having a tendency towards slow and premature convergence limit the application boundary.
The AEFA algorithm was proposed in [26] and was inspired by the concept of electrostatic force. In the AEFA algorithm, the charged particles act as searching agents, while the attraction and repulsion forces between these particles result in the moving of objects in search space. Hence, the positions of these charged particles are taken as problem solutions and the particle with the highest charge is believed to be best individual who attracts other charged particles and slowly moves in search space. Initialization of the AEFA is quite simple and requires only a few initial parameters. However, although the AEFA algorithm can locate near optimal solutions with high convergence speed by exerting equilibrium between exploitation and exploration, it is inferior to DE in global convergence and ease of use. The main drawback of the AEFA algorithm is its way of adjusting the step size in updating particle velocity and position. This may lead to untimely convergence, since the update of velocity and position in AEFA mainly relies on repulsion and attraction of charged particles.
Searching for the best optimal solution for a more realistic nonlinear power system needs proper initialization of initial parameters. Small deviations in initial parameters can lead to large variations in algorithmic efficiency. The problem formulated in this work, i.e., multiarea LFC and AVR combined model having hybrid generating sources considering CTDs, is a complex one, and thus a new and efficient algorithm is required. Hence, a new DEAEFA optimization is presented in this work achieved by the complementary performance of DE and AEFA algorithms in overcoming the disadvantages of individual ones and to effectively make use of individual benefits.
The proposed DEAEFA algorithm as depicted in Fig. 6 combines the evolutionary concept of DE with the charged particlebased searching strategy of the AEFA algorithm. The DEAEFA algorithm has two levels, i.e., the DE level and the AEFA level. Throughout the searching process, half of the individuals obtain a solution using the DE strategy while the other half uses the AEFA searching mechanism. Thus, the total information of each population is shared among every individual agent. The individual with best fitness value then acquires the chance of getting into optimization of the next generation. Hence, this proposed approach inherits the efficiency of searching procedure while also assuring global convergence.
The procedural flow of the DEAEFA algorithm is as follows:
Step 1: Randomly initialize the initial parameters in DE and AEFA algorithms.
Step 2: Initialize the population of DE and AEFA individually.
Step 3: Calculate cost function value of each population and consider the population sets which give the highest fitness values as the global best parameters.
Step 4: From Step 3, only the population with the highest fitness value persists and other individuals are rejected.
Step 5: The searching mechanism of the DE and AEFA algorithms moves on to the persisted individuals as mentioned in Step 4.

AEFA phase: Particle velocities and positions are updated as given in [26].

DE phase: Mutation, recombination and selection operations are performed.
Step 6: Inspect the deduced solutions. If stopping criteria are met, stop the iterations and display the best optimal solution. Otherwise go to Step 3.
The execution of the proposed DEAEFA algorithm is examined on several standard benchmark functions and the outcomes on Himmelblau’s function given in (7) are noted in Table 1. It is emphasized that the function value with the proposed algorithm is improved by 94.6% with DE and 79.88% with AEFA, while the obtained optimum values with the proposed DEAEFA hardly deviate from standard global minimum values. The convergence characteristics of the optimization algorithms tested on Himmelblau’s function are compared in Fig. 7. It reveals that the DEAEFA convergence start with a beneficial objective value and the final beneficial value is obtained after a very low number of iterations when compared to other methods. To validate the efficacy of the presented approach, it is tested on a sphere function given in (8) for 100 trials. Figure 8 shows the variations of function values at initial and final positions under DE, AEFA and DEAEFA algorithms for the 100 trials. It is observed from Fig. 8 that the final function values are below their average values for most of the trials under the proposed DEAEFA method.
Interline power flow controller
IPFC focuses on compensation of multiple lines at given substation. In general, IPFC engages many DC to AC converters allowed with a DC link in common. With the provision of DC link common arrangement, IPFC facilitates control over active power among DC link and own transmission line in addition to independent reactive series compensation. Hence, the operational performance of the entire interconnected system is improved by IPFC incorporation. IPFC is superior to other thyristor and SSSCbased controllers. The structure of IPFC used in this work is shown in Fig. 9.
The change in tieline power exchange for the IPFC controller is expressed as:
The impact of the IPFC on power flow through the tieline can be modelled as:
The incremental change in power injected by the IPFC (∆P_{IPFC}) into the line is to compensate the line power flow, and so that oscillations in the tieline can be mitigated effectively.
Redox flow batteries (RFBs)
RFBs are electrochemical rechargeable energy storage devices (ESDs) suited for a wide range of applications. In RFBs, sulphuric acid is used as the electrolyte solution which has vanadium ions and fills the reactor tank. The reactor tank has two compartments separated by a membrane. Each compartment is equipped with a pump to facilitate the circulation of the electrolyte through battery cells. The battery charging and discharging process is through reductionoxidation (redox) reaction. The efficiency of RFBs increases as the charging/discharging cycle period becomes shorter, while RFBs are not aged by frequent usage and have a quick response equivalent to superconducting magnetic energy storage devices. In general, ESD charges under normal loading conditions and delivers the energy back to the system when there is sudden rise in load. This can be done effectively and instantly through RFBs because of their quick response characteristics. Thus, RFBs can play a key role in sustaining system frequency by regulating the real power mismatch between control areas, and are recommended in power systems to improve the quality of power generated through hybrid energy sources. The transfer function model of RFBs implemented in this work is from [27] and described in Eq. (11). The parameters of RFBs are listed in the Appendix.
Results and discussion
Test system1 dynamic analysis
In this sub section, a twoarea with equal generation capacity of thermal power plant having a nonreheat turbine structure (test system1) is considered. The test system1 as depicted in Fig. 1 and the pertinent data in the Appendix is designed in SIMULINK. A classical PID controller is used and is simulated for 1% step load perturbation (SLP) on area1 at t = 0 s. Responses of the system are analyzed in terms of deviations in area1 frequency Δf_{1} ∆f_{1}, tieline power ΔP_{tie12} and area2 frequency Δf_{2}. The controller gains are tuned with DEAEFA optimization and are tabulated in Table 2 while the corresponding system dynamic variations are depicted in Fig. 10.
The performance of the presented DEAEFA strategy is compared with other optimization algorithmbased controllers that are available such as PSO [17], GWO [2], BSA [19] and HGAPSO [3] tuned PID controllers. From Fig. 10, it is clear that the proposed control approach offers better results in settling time (T_{s}) and diminishing oscillation. The proposed controller is further examined quantitatively and the calculated objective index values are noted in Table 2. As can be seen, the performance index values are significantly reduced with the proposed strategy compared to other approaches, because of the combined inherent qualities of the DE and AEFA algorithms in the presented strategy.
Test system2 dynamic analysis
The effectiveness of the presented DEAEFA algorithmbased controller is manifested by the test system of two equal areas consisting of a reheat turbine structure of hydrothermal units, a system which is widely described in the literature. The test system2 transfer function model is rendered in Fig. 2. The dynamics of system behavior are analyzed by inducing 1% SLP in area1 at t = 0 s, and the responses are analyzed in terms of Δf_{1}, ΔP_{tie12} ∆f_{1} and Δf_{2} ∆f_{2} as shown in Fig. 11.
The system dynamics of the DEAEFA optimized PID controller are compared with those of other techniques such as hFAPS [21], IMC [4], ICA [20] and ABC [18] tuned PID controller. Figure 11, shows that oscillations and peak magnitudes are reduced with the proposed DEAEFAbased controller, compared to others. This is because of the exploitation and exploration of capabilities possessed by the DEAEFA searching mechanism. The optimum controller gains and the corresponding numerical results of system responses are tabulated in Table 3.
Test system3 dynamic analysis
Dynamic analysis of the LFC and AVR combined model without considering CTDs
The conventional PID controller is incorporated in both LFC and AVR loops of test system3 with hybrid generation sources as secondary controller without considering the CTDs. The parameters of the controllers are obtained separately with optimization algorithms of DE, AEFA and DEAEFA algorithms subjected to the ISE function given in (6). The optimized controller parameters under different optimizations are noted in Table 5 and respective system dynamics are compared in Fig. 12 by subjecting area1 to 1% SLP. The characteristics of the responses of settling time (Ts), peak undershoot (PU) and overshoot (PO) are enumerated in Table 4. From Table 4 and Fig. 12, it reveals that the PU, PO and Ts of the responses under the proposed DEAEFAoptimized PID controller are less than others and also the objective function value of DEAEFA algorithm is improved by 52% and 43% compared to those of DE and AEFA algorithms, respectively.
Dynamic analysis of LFC and AVR combined model considering CTDs
Responses of the multiarea LFC and AVR combined model are analyzed by applying area1 with 1% SLP while considering CTDs. The time delay parameter (Td) is normally in the range of 0–1 s and a value of 0.25 s is used in this work. The controller parametric gain values are optimized with the proposed DEAEFA algorithm to have satisfactory operation in regulating variations in frequency and tieline power flow. System responses with the presented controller are also compared with those of DE and AEFA optimization algorithms. The controller optimal values are given in Table 5 and the accompanying dynamical system behaviors are compared in Fig. 13. The numerical results depicted in Fig. 13 are noted in Table 4 along with objective function values. The ISE index value of the presented DEAEFA approach is improved by 73% and 48% compared to those of DE and AEFA, respectively.
System responses with and without considering AVR and CTDs comparison
To investigate the impact of AVR coupling and CTDs on load frequency control, test system3 is considered with and without AVR coupling and CTDs. In each case test system3 is analyzed by applying area1 with 1% SLP under the control of the proposed DEAEFA optimized PID. The dynamic system responses are compared in Fig. 14. From Fig. 14, it is seen that CTDs and the AVR loop are exerting the most significant impact on LFC. The impact of AVR coupling on LFC can be seen through (1) while the effect of CTDs on controlling frequency, voltages and power flow via the tieline is due to the lag in transmission and receiving of control signals among various units and load dispatch centers. By considering these CTDs the system dynamic responses are disturbed more than the case without considering CTDs. Thus, in order to investigate the system dynamics in a practical manner, CTDs need to be considered and the controller designed to withstand these deviations in system dynamics. Figure 15 compares the settling time of the dynamic responses in the LFC and AVR combined system with and without considering CTDs. It can be clearly seen that with CTDs responses are settled after a longer period. Hence, CTDs need to be considered to avoid performance deterioration.
Coordinated control strategy of IPFC and RFBs in a combined model considering CTDs
To further extenuate variations in frequency, terminal voltage and tieline power, the combined model under investigation is installed with an IPFC in the tieline and placing RFBs in both areas. Initially, RFBs are placed in both areas without an IPFC and the DEAEFA tuned PID is used as a secondary controller with the system subjected to 1% SLP in area1. The controller parameters and optimum gain and time constant parameters of RFBs are listed in Table 6, and the variations are compared in Fig. 16. IPFC is then connected in the tieline while RFBs remain in areas 1 and 2. The optimum parametric gain values of the controller are again shown in Table 6. Responses of the system are analyzed for the same disturbances and the respective dynamics are depicted in Fig. 16. The numerical values of the responses outlined in Fig. 16 are noted in Table 7. From Table 7 and Fig. 16, it is seen that the respective area frequency, voltage and tieline deviations under load disturbances are greatly minimized and quickly reach the steady state values under the IPFC and RFBs coordinated control strategy along with the efficacy performance of the presented controller. Figure 17 compares the response settling time with and without considering IPFC and RFBs. It shows that the responses are settled smoothly through the proposed coordinated control mechanism.
Sensitivity analysis
To test the proposed coordinated regulating strategy robustness in mitigating variations in responses of the LFC and AVR combined model of test system3, sensitivity analysis is performed. In the sensitivity analysis, parameters such as loading and tieline synchronizing coefficient are varied at the level of ±50% from nominal values. The dynamic behaviors of the system when it is subjected to a load variation of ±50% of nominal loading are shown in Fig. 18. The synchronizing tieline coefficient value is then varied by ±50% from its nominal value and the responses obtained by applying area1 with 1% SLP are compared in Fig. 19. The system is also tested by applying load in both areas and the accompanying dynamical behavior is demonstrated in Fig. 20. The numerical results for the responses in sensitivity analysis are shown in Table 8. On examining Figs. 18, 19, 20, and Table 8 it is seen that the deviations are not significantly changed even in the case of large parametric variations. Thus it is concluded that the controller parameter gains are not required to be changed even when the system parameters such as synchronizing tieline coefficient have large variations or when a wide range of disturbances is applied to the system. This means the controller gain parameters optimized with the proposed DEAEFA algorithm along with coordinated control of IPFC and RFBs are robust. Finally, area1 is applied with random loading to validate the robustness of the presented control strategy and the results are shown in Fig. 21.
Conclusion
In this paper, the impact of CTDs on frequency and voltage control of a multiarea LFC and AVR combined model of an interconnected system is studied. A conventional PID controller is used as secondary controller whose parameters are optimized with the DEAEFA algorithm. The superiority of DEAEFA is demonstrated by comparing it with other algorithms and standard optimizing benchmark functions. Furthermore, IPFC and RFBs are installed to mitigate system deviations under load disturbance. The investigation reveals that the coordinated performance of IPFC and RFBs can significantly diminish system deviations and quickly drive the dynamic responses to steady state under load disturbance. Sensitivity analysis reveals the robustness of the controller settings and time and gain constants of IPFC and RFBs optimized with DEAEFA algorithm. Therefore, the same system can be implemented even when the system parameters and load are subjected to wide variations.
Availability of data and materials
The data used for developing SIMULINK models is given in Appendix. No other data is available.
Abbreviations
 AVR:

Automatic voltage regulator
 LFC:

Load frequency control
 CTDs:

Communication time delays
 PMUs:

Phase measuring units
 GRC:

Generation rate constraints
 ISE:

Integral square error
 DE:

Differential evolution
 AEFA:

Artificial electric field algorithm
 RFBs:

Redox flow batteries
 IPFC:

Interline power flow controller
 ESDs:

Energy storage devices
 PU:

Peak undershoot
 T_{s} :

Settling time
 PO:

Peak overshoot
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Contributions
Developed the twoarea combined LFC and AVR of interconnected power system with hybrid generating sources considering communication time delays (CTDs). No literature is available on analysis of combined LFC and AVR of interconnected system with CTDs. Presented a methodology to damp out the frequency and voltage deviations of an interconnected system simultaneously. DEAEFA algorithm is proposed to optimized PID controller is presented for combined LFC and AVR model. The superiority of proposed DEAEFA algorithm is demonstrated by tested on standard benchmark functions. The performance of DEAEFA algorithm is validated with other optimization algorithms reported in literature by conducting investigations on widely used test systems. Effect of coordinated performance of IPFC and RFBs in combined effect is investigated. Sensitivity analysis is performed to demonstrate the robustness of proposed coordinated control strategy. The author(s) read and approved the final manuscript.
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Appendix
Appendix
Parameters of the power system models
Test system1: P_{r} (Rated power) = 2000 MW, f (System frequency) = 60 Hz, B_{i} (Area bias parameter) = 0.425P.u.MW/Hz, Tt_{i} =0.3 s, T_{gi} =0.08 s, K_{Pi} =120, T_{Pi} =20s, T_{12} =0.545 s, R_{i} =2.4 Hz/p.u. MW,.
Test system2: P_{r}= 2000MW, f= 60Hz, R_{1}= 2 Hz/p.u. MW, R_{2}= 2.4 Hz/P.u.MW, T_{ti} =0.3 s, K_{Pi}= 120, T_{Pi}= 20s, T_{gi} =0.08 s, T_{12}= 0.0707 puMW/rad, T_{RHi}= 48.7 s, T_{GHi}= 0.513 s, T_{wi}= 1s, B_{i}= 0.425P.u.MW/Hz.
Test system3: P_{r} (Rated power) = 2000 MW, f= (frequency) = 60 Hz, B_{i} = 0.045P.u.MW/Hz, H =5, D =0.0145, T_{12} (Tieline synchronizing time constant) = 0.545 s, K_{PS} = 1/D, T_{PS} = 2H/Df, Thermal plant: Kre (steam reheat turbine constant) = 0.3, τ_{gr}, τ_{re}, τ_{Tr} (governor, reheater, turbine time constants) = 0.08 s,10s,0.3 s, R_{t}, R_{h}, R_{g}= 2.4 Hz/P.u. Hydro plant: τ_{h} (hydro governor time constant) = 0.3 s, τ_{rs} (reset time) = 5 s, τ_{w} (starting time of water into penstock) = 0.025 s, Gas Plant: X = (governor lead time) = 0.6 s, Y = (governor lag time) = 1 s, a,b,c = (valve position constants) = 1 s,0.05 s,1 s, τ_{CR} (Time delay of combustion reaction) = 0.01 s, τ_{F} =0.23 s, τ_{CD} =0.2 s.Wind plant: τ_{W1}, τ_{W2}= 0.6 s,0.041 s, K_{w1}, K_{w2} (wind plant gain constants) = 1.25,1.4. Diesel plant: K_{D} =16.5, τ_{d1}, τ_{d2}, τ_{d3} τ_{d4} (time constants of diesel engine) = 1 s,2 s,0.025 s,3 s. Solar PV: τ_{PV} =(solar PV time constant) = 1.8 s. K_{RFBs} = 1, T_{RFBS} = 0.9, T_{IPFC} = 0.0450, AVR: (Exciter constants) K_{E}= 1, τ_{E}= 0.4 s, K_{G}= 0.8, τ_{G}= 1.4 s, (amplifier constants) K_{A}= 10, τ_{A}= 0.1 s, (sensor constants) K_{S}= 1, τ_{S}= 0.05 s, Ps =1.5, K_{1}= 0.2, K_{2}=0.1, K_{3}= 0.5, K_{4}= 1.4.
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Kalyan, C.H.N.S., Rao, G.S. Impact of communication time delays on combined LFC and AVR of a multiarea hybrid system with IPFCRFBs coordinated control strategy. Prot Control Mod Power Syst 6, 7 (2021). https://doi.org/10.1186/s4160102100185z
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DOI: https://doi.org/10.1186/s4160102100185z
Keywords
 Combined LFCAVR
 Communication time delays (CTDs)
 IPFCRFBs strategy
 DEAEFA algorithm