Abstract
In our world of today developing incredibly fast, load frequency control (LFC) is an indispensable and vital element in increasing the standard of living of a country by providing a good quality of electric power. To this end, rapid and notable development has been recorded in LFC area. However, researchers worldwide need for the existence of not only effective but also computationally inexpensive control algorithm considering the limitations and difficulties in practice. Hence, this paper deals with the introduction of (1 + PD)-PID cascade controller to the relevant field. The controller is simple to implement and it connects the output of 1 + PD controller with the input of PID controller where the frequency and tie-line power deviation are applied to the latter controller as feedback signals also, which is the first attempt made in the literature. To discover the most optimistic results, controller gains are tuned concurrently by dragonfly search algorithm (DSA). For the certification purpose of the advocated approach, two-area thermal system with/without governor dead band nonlinearity is considered as test systems initially. Then single/multi-area multi-source power systems with/without a HVDC link are employed for the enriched validation purpose. The results of our proposal are analyzed in comparison with those of other prevalent works, which unveil that despite its simplicity, DSA optimized (1 + PD)-PID cascade strategy delivers better performance than others in terms of smaller values of the chosen objective function and settling time/undershoot/overshoot of the frequency and tie-line power deviations following a step load perturbation.


















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- \(S\) :
-
Speed governor regulation parameter
- \({T}_{rs}\) :
-
Hydro turbine speed governor reset time constant
- \(B\) :
-
Frequency bias constant
- \({T}_{rh}\) :
-
Hydro turbine speed governor transient droop time constant
- \({T}_{g}\) :
-
Speed governor time constant
- \({T}_{w}\) :
-
Nominal starting time of water in penstock
- \({T}_{t}\) :
-
Steam turbine time constant
- \({c}_{g}\)0:
-
Gas turbine valve positioner
- \({K}_{ps}\) :
-
Power system gain constant
- \({b}_{g}\) :
-
Gas turbine constant of valve positioner
- \({T}_{ps}\) :
-
Power system time constant
- \({X}_{c}\) :
-
Lead time constant of gas turbine speed governor
- \({T}_{12}\) :
-
Tie line power coefficient
- \({Y}_{c}\) :
-
Lag time constant of gas turbine speed governor
- \(\Delta {P}_{ref}\) :
-
Incremental change in controller output
- \({T}_{cr}\) :
-
Gas turbine combustion reaction time delay
- \(\Delta {P}_{g}\) :
-
Incremental change in governor valve position
- \({T}_{f}\) :
-
Gas turbine fuel time constant
- \(\Delta {P}_{t}\) :
-
Incremental change in turbine output power generation
- \({T}_{cd}\) :
-
Gas turbine compressor discharge volume-time constant
- \(\Delta {P}_{D}\) :
-
Incremental change in load demand
- \({K}_{dc}\) :
-
Gain constant of HVDC link
- \(\Delta f\) :
-
Incremental change in area frequency
- \({T}_{dc}\) :
-
Time constant of HVDC link
- \(\Delta {P}_{tie}\) :
-
Incremental change in tie-line power
- \({K}_{T}\) :
-
Participation factor for thermal unit
- \({ACE}_{i}\) :
-
Area control error
- \({K}_{H}\) :
-
Participation factor for hydro unit
- \({T}_{sg}\) :
-
Speed governor time constant of thermal unit
- \({K}_{G}\) :
-
Participation factor for gas unit
- \({K}_{r}\) :
-
Reheat gain constant
- \({K}_{p}\) :
-
Controller proportional gain
- \({T}_{r}\) :
-
Reheat time constant
- \({K}_{i}\) :
-
Controller integral gain
- \({T}_{gh}\) :
-
Hydro turbine speed governor main servo time constant
- \({K}_{d}\) :
-
Controller derivative gain
References
Ali ES, Abd-Elazim SM (2011) Bacteria foraging optimization algorithm based load frequency controller for interconnected power system. Int J Electr Power Energy Syst 33(3):633–638
Panda S, Mohanty B, Hota PK (2013) Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems. Appl Soft Comput 13:4718–4730
Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30:607–616
Farahani M, Ganjefar S, Alizadeh M (2012) PID controller adjustment using chaotic optimisation algorithm for multi-area load frequency control. IET Control Theory Appl 6(13):1984–1992
Konar G, Mandal KK, Chakraborty N (2014) Two area load frequency control of hybrid power system using genetic algorithm and differential evolution tuned PID controller in deregulated environment transactions on engineering technologies. Springer
Omar M, Soliman M, Abdel Ghany AM, Bendary F (2013) Optimal tuning of PID controllers for hydrothermal load frequency control using ant colony optimization. Int J Elect Eng Inform 5(3):348–360
Çelik E (2020) Improved stochastic fractal search algorithm and modified cost function for automatic generation control of interconnected electric power systems. Eng Appl Artif Intell 88:103407
Guha D, Roy PK, Banerjee S (2016) Load frequency control of large scale power system using quasi-oppositional grey wolf optimization algorithm. Int J Eng Sci Technol 19(4):1693–1713
Shiva CK, Shankar G, Mukherjee V (2015) Automatic generation control of power system using a novel quasi-oppositional harmony search algorithm. Int J Electr Power Energy Syst 73:787–804
Guha D, Roy PK, Banerjee S (2017) Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band. Appl Soft Comput 52:160–175
Hota PK, Mohanty B (2016) Automatic generation control of multi source power generation under deregulated environment. Int J Electr Power Energy Syst 75:205–214
Hasanien HM, El-Fergany A (2017) Symbiotic organisms search algorithm for automatic generation control of interconnected power systems including wind farms. IET Gener Transm Distrib 11(7):1692–1700
Guha D, Roy PK, Banerjee S (2018) Symbiotic organism search algorithm applied to load frequency control of multi-area power system. Energy Syst 9(2):439–468
Guha D, Roy P, Banerjee S (2017) Quasi-oppositional symbiotic organism search algorithm applied to load frequency control. Swarm Evol Comput 33:46–67
Gozde H, Taplamacioglu MC, Kocaarslan İ (2012) Comparative performance analysis of artificial bee colony algorithm in automatic generation control for interconnected reheat thermal power system. Int J Electr Power Energy Syst 42(1):167–178
Guha D, Roy PK, Banerjee S (2018) Application of backtracking search algorithm in load frequency control of multi-area interconnected power system. Ain Shams Eng J 9:257–276
Sahu RK, Panda S, Padhan S (2015) A hybrid firefly algorithm and pattern search technique for automatic generation control of multi area power systems. Int J Electr Power Energy Syst 64:9–23
Guha D, Roy PK, Banerjee S (2016) Load frequency control of interconnected power system using grey wolf optimization. Swarm Evol Comput 27:97–115
Mohanty B, Panda S, Hota PK (2014) Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system. Int J Electr Power Energy Syst 54:77–85
Singh SP, Prakash T, Singh VP, Babu MG (2017) Analytic hierarchy process based automatic generation control of multi-area interconnected power system using jaya algorithm. Eng Appl Artif Intell 60(2):35–44
Vrdoljak K, Peric N, Petrovic I (2010) Sliding mode based load frequency controller in power systems. Elect Power Syst Res 80(5):514–527
Dahiya P, Sharma V, Naresh R (2019) Optimal sliding mode control for frequency regulation in deregulated power systems with DFIG-based wind turbine and TCSC–SMES. Neural Comput Appl 31:3039–3056
Rosaline AD, Somarajan UK (2019) Structured H-infinity controller for an uncertain deregulated power system. IEEE Trans Indus Appl 55(1):892–906
Sondhi S, Hote YV (2014) Fractional order PID controller for load frequency control. Energy Convers Manage 85:343–353
Arya Y, Kumar N (2017) BFOA-scaled fractional order fuzzy PID controller applied to AGC of multi-area multi-source electric power generating systems. Swarm Evol Comput 32:202–218
Zamani A, Barakati SM, Yousofi-Darmian S (2016) Design of a fractional order PID controller using GBMO algorithm for load frequency control with governor saturation consideration. ISA Trans 64:56–66
Arya Y (2019) A new cascade fuzzy-FOPID controller for AGC performance enhancement of single and multi-area electric power systems. ISA Trans. https://doi.org/10.1016/j.isatra.2019.11.025
Kumar N, Tyagi B, Kumar V (2016) Deregulated multi area AGC scheme using BBBC-FOPID controller. Arab J Sci Eng 42(7):2641–2649
Ebrahim MA, Becherif M, Abdelaziz AY (2021) PID-/FOPID-based frequency control of zero-carbon multisources-based interconnected power systems underderegulated scenarios. Int Trans Elect Energy Syst 31(2):12712
Arya Y, Dahiya P, Çelik E, Sharma G, Gözde H, Nasiruddin I (2021) AGC performance amelioration in multi-area interconnected thermal and thermal-hydro-gas power systems using a novel controller. Eng Sci Technol, Int J 24(2):384–396
Arya Y, Kumar N, Dahiya P, Sharma G, Çelik E, Dhundhara S, Sharma M (2021) Cascade-IλDμN controller design for AGC of thermal and hydrothermal power systems integrated with renewable energy sources. IET Renew Power Gener 15(3):504–520
Saikia LC, Mishra S, Sinha N, Nanda J (2011) Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller. Int J Electr Power Energy Syst 33(4):1101–1108
Sahu RK, Panda S, Sekhar GTC (2015) A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems. Int J Electr Power Energy Syst 64:880–893
Sahu BK, Pati TK, Nayak JR, Panda S, Kar SK (2016) A novel hybrid LUS-TLBO optimized fuzzy-PID controller for load frequency control of multi-source power system. Int J Electr Power Energy Syst 74:58–69
Nayak JR, Shaw B, Sahu BK (2018) Application of adaptive-SOS (ASOS) algorithm based interval type-2 fuzzy-PID controller with derivative filter for automatic generation control of an interconnected power system. Int J Eng Sci Technol 21(3):465–485
Haroun AHG, Li YY (2017) A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics. ISA Trans 71(2):364–379
Fathy A, Kassem AM, Abdelaziz AY (2018) Optimal design of fuzzy PID controller for deregulated LFC of multi-area power system via mine blast algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3720-x
Nayak N, Mishra S, Sharma D, Sahu BK (2019) Application of modified sine cosine algorithm to optimally design PID/fuzzy-PID controllers to deal with AGC issues in deregulated power system. IET Gener Transm Distrib 13(12):2474–2487
Nayak PC, Mishra S, Prusty RC, Panda S (2020) Performance analysis of hydrogen aqua equaliser fuel-cell on AGC of wind-hydro-thermal power systems with sunflower algorithm optimised fuzzy-PDFPI controller. Int J Ambient Energy. https://doi.org/10.1080/01430750.2020.1839556
Khuntia SR, Panda S (2012) Simulation study for automatic generation control of a multi-area power system by ANFIS approach. Appl Soft Comput 12:333–341
Fathy A, Kassem AM (2019) Antlion optimizer-ANFIS load frequency control for multi-interconnected plants comprising photovoltaic and wind turbine. ISA Trans 87:282–296
Sharma D, Mishra S (2019) Non-linear disturbance observer-based improved frequency and tie-line power control of modern interconnected power systems. IET Gener Transm Distrib 13(16):3564–3573
Guha D, Roy PK, Banerjee S (2020) Disturbance observer aided optimised fractional-order three-degree-of-freedom tilt-integral-derivative controller for load frequency control of power systems. IET Gener Transm Distrib. https://doi.org/10.1049/gtd2.12054
Chandran K, Murugesan R, Gurusamy S, Mohideen KA, Pandiyan S, Nayyar A, Abouhawwash M, Nam AY (2020) Modified cascade controller design for unstable processes with large dead time. IEEE Access 8:157022–157036
Sivalingam R, Chinnamuthu S, Dash SS (2017) A hybrid stochastic fractal search and local unimodal sampling based multistage PDF plus (1 + PI) controller for automatic generation control of power systems. J Franklin Inst 354:4762–4783
Khamari D, Sahua RK, Panda S (2020) Adaptive differential evolution based PDF plus (1+PI) controller for frequency regulation of the distributed power generation system with electric vehicle. J Ambient Energy. https://doi.org/10.1080/01430750.2020.1783357
Padhy S, Panda S, Mahapatra S (2017) A modified GWO technique based cascade PI-PD controller for AGC of power systems in presence of plug in electric vehicles. Int J Eng Sci Technol 20(2):427–442
Dash P, Saikia LC, Sinha N (2016) Flower pollination algorithm optimized PI-PD cascade controller in automatic generation control of a multi-area power system. Int J Electr Power Energy Syst 82:19–28
Padhy S, Panda S (2017) A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles. CAAI Trans Intell Technol 2:12–25
Puja D (2015) Saikia LC, Sinha N, “Automatic generation control of multi area thermal system using Bat algorithm optimized PD–PID cascade controller.” Int J Electr Power Energy Syst 68:364–372
Satapathy P, Debnath MK, Mohanty MK, “Design of PD-PID controller with double derivative filter for frequency regulation”, IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, Delhi, India, pp. 1142–1147, 2018.
Nayyar A, Le DN, Nguyen HG (2018) Advances in swarm intelligence for optimizing problems in computer science. CRC Press
Tharwat A, Gabel T, Hassanien AE, “Parameter optimization of support vector machine using dragonfly algorithm”, International Conference on Advanced Intelligent Systems and Informatics, Springer, Cham, pp. 309–319, 2017.
Kumaran J, Sasikala J (2017) Dragonfly optimization based ANN model for forecasting India’s primary fuels’ demand. Int J Computer Appl 164(7):18–22
Wu J, Zhu Y, Wang Z, Song Z, Liu X, Wang W, Zhang Z, Yu Y, Xu Z, Zhang T, Zhou J (2017) A novel ship classification approach for high resolution SAR images based on the BDAKELM classification model. Int J Remote Sens 38(23):6457–6476
Hariharan M, Sindhu R, Vijean V, Yazid H, Nadarajaw T, Yaacob S, Polat K (2018) Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification. Comput Methods Programs Biomed 155:39–51
Rakoth S, Sasikala J (2017) Multilevel segmentation of fundus images using dragonfly optimization. Int J Computer Appl 164(4):28–32
Hemamalini B, Nagarajan V (2020) Wavelet transform and pixel strength-based robust watermarking using dragonfly optimization. Multim Tools Appl 79(7):8727–8746
Połap D, Wo´zniak M, “Detection of important features from images using heuristic approach”, International Conference on Information and Software Technologies, Springer, Berlin, Germany, pp. 432–441, 2017.
Seyedali M (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073
Venkatesh M, Sudheer G (2017) Optimal load frequency regulation of micro-grid using dragonfly algorithm. Int Res J Eng Technol 4(8):978–981
Nour EL, Kouba Y, Menaa M, Hasni M, Boudour M (2018) A novel optimal combined fuzzy PID controller employing dragonfly algorithm for solving automatic generation control problem. Electric Power Compon Syst 46(19–20):2054–2070
Çelik E (2021) Design of new fractional order PI–fractional order PD cascade controller through dragonfly search algorithm for advanced load frequency control of power systems. Soft Comput 20:1193–1217
Singh S, Ashok A, Kumar M, Garima Rawat TK (2018) Optimal design of IIR filter using dragonfly algorithm applications of artificial intelligence techniques in engineering. Springer
Aadil F, Ahsan W, Rehman ZU, Shah PA, Rho S, Mehmood I (2018) Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO). J Supercomput 74:4542–4567
Saleh AA, Mohamed AA, Hemeida AM, Ibrahim AA, “Comparison of different optimization techniques for optimal allocation of multiple distribution generation”, International Conference on Innovative Trends in Computer Engineering, Aswan, Egypt, pp. 317–323, 2018.
Wen G, Hu G, Hu J, Shi X, Chen G (2016) Frequency regulation of source-grid-load systems: a compound control strategy. IEEE Trans Industr Inf 12(1):69–78
Barisal AK (2015) Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems. Int J Electr Power Energy Syst 66:67–77
Suresh V, Sreejith S (2017) Generation dispatch of combined solar thermal systems using dragonfly algorithm. Computing 99:59–80
Rout UK, Sahu RK, Panda S (2013) Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system. Ain Shams Eng J 4(3):409–421
Sahu RK, Panda S, Rout UK, Sahoo DK (2016) Teaching learning based optimization algorithm for automatic generation control of power system using 2-DOF PID controller. Int J Electr Power Energy Syst 77:287–301
Gozde H, Taplamacioglu MC (2011) Automatic generation control application with craziness based particle swarm optimization in a thermal power system. Int J Electr Power Energy Syst 33(1):8–16
Mohanty B, Panda S, Hota PK (2014) Differential evolution algorithm based automatic generation control for interconnected power systems with non-linearity. Alex Eng J 53:537–552
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Appendices
Appendix
Nominal parameters of test systems in order
Test system-1 [1, 2, 7, 33, 71];
\(f=60\) Hz, \(B=0.425\) p.u MW/Hz, \(R=2.4\) Hz/pu, \({T}_{\mathrm{g}}=0.03\) s, \({T}_{\mathrm{t}}=0.3\) s, \({K}_{\mathrm{ps}}=120\) Hz/pu, \({T}_{\mathrm{ps}}=20\) s, \({T}_{12}=0.545\) p.u MW/rad.
\(f=60\) Hz, \(B=0.425\) p.u MW/Hz, \(R=2.4\) Hz/pu, \({T}_{\mathrm{g}}=0.2\) s, \({T}_{\mathrm{t}}=0.3\) s, \({K}_{\mathrm{ps}}=120\) Hz/pu, \({T}_{\mathrm{ps}}=20\) s, \({T}_{12}=0.444\) p.u MW/rad.
Test system-3 [19, 45, 47, 68];
\(f=60\) Hz, \(R=2.4\) Hz/pu, \({T}_{\mathrm{sg}}=0.08\) s, \({K}_{\mathrm{r}}=0.3\), \({T}_{\mathrm{r}}=10\) s, \({T}_{\mathrm{t}}=0.3\) s, \({T}_{\mathrm{gh}}=0.2\) s, \({T}_{\mathrm{rs}}=5\) s, \({T}_{\mathrm{rh}}=28.75\) s \({T}_{\mathrm{w}}=1\) s, \({b}_{\mathrm{g}}=0.05\) s, \({c}_{\mathrm{g}}=1\), \({X}_{\mathrm{c}}=0.6\) s, \({Y}_{\mathrm{c}}=1\) s, \({T}_{\mathrm{cr}}=0.01\) s, \({T}_{\mathrm{f}}=0.23\) s, \({T}_{\mathrm{cd}}=0.2\) s, \({K}_{\mathrm{T}}=0.543478\) pu, \({K}_{\mathrm{H}}=0.326084\) pu, \({K}_{\mathrm{G}}=0.130438\) pu, \({K}_{\mathrm{ps}}=68.9566\) Hz/pu MW, \({T}_{\mathrm{ps}}=11.49\) s.
Test system-4 [19, 45, 47, 49];
\(f=60\) Hz, \(B=0.4312\) pu, \(R=2.4\) Hz/pu, \({T}_{\mathrm{sg}}=0.08\) s, \({K}_{\mathrm{r}}=0.3\), \({T}_{\mathrm{r}}=10\) s, \({T}_{\mathrm{t}}=0.3\) s, \({T}_{\mathrm{gh}}=0.2\) s, \({T}_{\mathrm{rs}}=5\) s, \({T}_{\mathrm{rh}}=28.75\) s \({T}_{\mathrm{w}}=1\) s, \({b}_{\mathrm{g}}=0.05\) s, \({c}_{\mathrm{g}}=1\), \({X}_{\mathrm{c}}=0.6\) s, \({Y}_{\mathrm{c}}=1\) s, \({T}_{\mathrm{cr}}=0.01\) s, \({T}_{\mathrm{f}}=0.23\) s, \({T}_{\mathrm{cd}}=0.2\) s, \({K}_{\mathrm{T}}=0.543478\) pu, \({K}_{\mathrm{H}}=0.326084\) pu, \({K}_{\mathrm{G}}=0.130438\) pu, \({T}_{12}=0.0433\), \({K}_{\mathrm{ps}}=68.9566\) Hz/pu MW, \({T}_{\mathrm{ps}}=11.49\) s.
\(f=60\) Hz, \(B=0.4312\) pu, \(R=2.4\) Hz/pu, \({T}_{\mathrm{sg}}=0.08\) s, \({K}_{\mathrm{r}}=0.3\), \({T}_{\mathrm{r}}=10\) s, \({T}_{\mathrm{t}}=0.3\) s, \({T}_{\mathrm{gh}}=0.2\) s, \({T}_{\mathrm{rs}}=5\) s, \({T}_{\mathrm{rh}}=28.75\) s \({T}_{\mathrm{w}}=1\) s, \({b}_{\mathrm{g}}=0.05\) s, \({c}_{\mathrm{g}}=1\), \({X}_{\mathrm{c}}=0.6\) s, \({Y}_{\mathrm{c}}=1\) s, \({T}_{cr}=0.01\) s, \({T}_{\mathrm{f}}=0.23\) s, \({T}_{\mathrm{cd}}=0.2\) s, \({K}_{\mathrm{T}}=0.543478\) pu, \({K}_{\mathrm{H}}=0.326084\) pu, \({K}_{\mathrm{G}}=0.130438\) pu, \({T}_{12}=0.0433\), \({K}_{\mathrm{ps}}=68.9566\) Hz/pu MW, \({T}_{\mathrm{ps}}=11.49\) s, \({K}_{dc}=1\), \({T}_{\mathrm{dc}}=0.2\) s.
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Çelik, E., Öztürk, N., Arya, Y. et al. (1 + PD)-PID cascade controller design for performance betterment of load frequency control in diverse electric power systems. Neural Comput & Applic 33, 15433–15456 (2021). https://doi.org/10.1007/s00521-021-06168-3
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DOI: https://doi.org/10.1007/s00521-021-06168-3