# A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System

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## Abstract

**:**

## 1. Introduction

## 2. System Model

## 3. Mathematical Modelling of Power System

#### Mathematical Model

## 4. Supplementary Control Design

#### 4.1. Neural Network (NN)

#### 4.2. Model Reference Controller

#### Controller Adaption with BP

## 5. Results and Discussion

#### 5.1. Asymmetrical Faults

#### 5.2. Symmetrical Faults

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

Parameters | Value |
---|---|

Controller training Epochs | 50 |

Controller Training sample | 100 |

Sampling Interval (sec) | 0.05 |

Maximum interval value (sec) | 20 |

Minimum interval value (sec) | 5 |

Controller training segment | 50 |

Number of hidden layers | 2 |

No. of delayed Plant Inputs | 1 |

No. of delayed Plant outputs | 1 |

## References

- Wu, W.; Chen, Y.; Fei, Y.; Zhen, H.; Zhou, B.; Wang, Z.; Chen, W. A novel damping strategy for low-frequency oscillation suppression with MMC-type unified power flow controller. In Proceedings of the IEEE International Conference on Industrial Technology, Lyon, France, 20–22 February 2018. [Google Scholar]
- Gandoman, F.H.; Ahmadi, A.; Sharaf, A.M.; Siano, P.; Pou, J.; Hredzak, B.; Agelidis, V.G. Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems. Renew. Sustain. Energy Rev.
**2018**, 82, 502–514. [Google Scholar] [CrossRef] - Kannayeram, G.; Manoharan, P.S.; Iruthayarajan, M.W.; Sivakumar, T. UPFC damping controller design using multi-objective evolutionary algorithms. Int. J. Bus. Intell. Data Min.
**2018**, 13, 52–74. [Google Scholar] [CrossRef] - Fortes, E.; Macedo, L.; Araujo, P.B.; Romero, R. A VNS algorithm for the design of supplementary damping controllers for small-signal stability analysis. Int. J. Electr. Power Energy Syst.
**2018**, 94, 41–56. [Google Scholar] [CrossRef] - Banaei, M.R.; Toloue, H.; Kazemi, F.M.; Oskuee, M.R.J. Damping of power system oscillations using imperialist competition algorithm in power system equipped by HVDC. Int. J. Ain Shams Eng. J.
**2015**, 6, 75–85. [Google Scholar] [CrossRef] - Martins, L.F.B.; Araujo, P.B.; De Vargas Fortes, E.; Macedo, L.H. Design of the PI–UPFC–POD and PSS Damping Controllers Using an Artificial Bee Colony Algorithm. J. Control Autom. Electr. Syst.
**2017**, 28, 762–773. [Google Scholar] [CrossRef] - Shahriar, M.S.; Shafiullah, M.; Rana, M.J. Stability enhancement of PSS-UPFC installed power system by support vector regression. Electr. Eng.
**2018**, 100, 1601–1612. [Google Scholar] [CrossRef] - Shojaeian, S.; Soltani, J.; Arab Markadeh, G. Damping of low-frequency oscillations of multi-machine multi-UPFC power systems, based on adaptive input-output feedback linearization control. IEEE Trans. Power Syst.
**2012**, 27, 1831–1840. [Google Scholar] [CrossRef] - Esmaili, M.R.; Khodabakhshian, A.; Bornapour, M. A new coordinated design of UPFC controller and PSS for improvement of power system stability using CPCE algorithm. In Proceedings of the IEEE Conference on Electrical Power and Energy EPEC, Ottawa, ON, Canada, 12–14 October 2016. [Google Scholar]
- Pandey, R.K.; Gupta, D.K. Knowledge domain states mapping concept for controller tuning in an interconnected power network. Int. J. Electr. Power Energy Syst.
**2016**, 80, 160–170. [Google Scholar] [CrossRef] - Tavakoli, A.R.; Seifi, A.R.; Arefi, M.M. Fuzzy-PSS and fuzzy neural network non-linear PI controller-based SSSC for damping inter-area oscillations. Trans. Inst. Meas. Control
**2016**, 40, 733–745. [Google Scholar] [CrossRef] - Moravej, Z.; Pazoki, M.; Khederzadeh, M. New Pattern-Recognition Method for Fault Analysis in Transmission Line With UPFC. IEEE Trans. Power Deliv.
**2015**, 30, 1231–1242. [Google Scholar] [CrossRef] - Mahmud, M.A.; Pota, H.R.; Hossain, M.J. Full-order nonlinear observer-based excitation controller design for interconnected power systems via exact linearization approach. Int. J. Electr. Power Energy Syst.
**2012**, 41, 54–62. [Google Scholar] [CrossRef] [Green Version] - Parimi, A.M.; Elamvazuthi, I.; Kumar, A.V.P.; Cherian, V. Fuzzy logic based control for IPFC for damping low-frequency oscillations in the multimachine power system. In Proceedings of the 2015 IEEE IAS Joint Industrial and Commercial Power Systems/Petroleum and Chemical Industry Conference (ICPSPCIC), Hyderabad, India, 19–21 November 2015. [Google Scholar]
- Singh, B.; Mukherjee, V.; Tiwari, P. A survey on impact assessment of DG and FACTS controllers in power systems. Renew. Sustain. Energy Rev.
**2015**, 42, 846–882. [Google Scholar] [CrossRef] - El-Zonkoly, A. Optimal sizing of SSSC controllers to minimize transmission loss and a novel model of SSSC to study transient response. Electr. Power Syst. Res.
**2008**, 78, 1856–1864. [Google Scholar] [CrossRef] [Green Version] - Rodríguez, O.; Medina, A.; Andersson, G. Closed-form analytical characterization of non-linear oscillations in power systems incorporating a unified power flow controller. IET Gener. Transm. Distrib.
**2015**, 9, 1019–1032. [Google Scholar] [CrossRef] - Wang, H. A unified model for analysis of FACTS Devices in Damping Power System Oscillations. Part III: Unified Power Flow Controller. IEEE Trans. Power Deliv.
**2000**, 15, 978–983. [Google Scholar] [CrossRef] - Torkzadeh, R.; Nasrazadani, H.; Aliabad, A.D. A genetic algorithm optimized fuzzy logic controller for UPFC in order to damp of low-frequency oscillations in power systems. In Proceedings of the 2014 22nd Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran, 20–22 May 2014. [Google Scholar]
- Li, Z.; Xia, Y.; Su, C.Y.; Deng, Y.; Fu, J.; He, W. Missile guidance Law Based on robust model predictive control using Neural Network Optimization. IEEE Trans. Neural Netw. Learn. Syst.
**2015**, 26, 1803–1809. [Google Scholar] [CrossRef] [PubMed] - Xu, R.; Tao, Y.; Lu, Z.; Zhong, Y. Attention-Mechanism-Containing Neural networks for high resolution remote sensing image classification. Electronics
**2018**, 10, 1602. [Google Scholar] [CrossRef] - Barone, E.R.; Salerno, V.; Siniscalchi, S.M. An introductory study on deep neural networks for high resolution areal images. AIP Conf. Proc.
**2013**, 1558, 1232. [Google Scholar] [CrossRef] - Sinniscalchi, S.M.; Salerno, V.M. Adaptation to new microphones using artificial neural networks with trainable activation functions. IEEE Trans. Neural Netw. Learn. Syst.
**2017**, 28, 1959–1965. [Google Scholar] [CrossRef] - Hautamaki, V.; Sinniscalchi, S.M.; Behravan, H.; Salerno, V.M.; Kukanov, I. Boosting universal speech attributes classification with deep Neural Network for foreign accent characterization. In Proceedings of the 16th Annual Conference of the International Speech Communication Association, Dresden, Germany, 6–10 September 2015. [Google Scholar]
- Fu, Y.; Chai, T. Neural Network Based nonlinear adaptive dynamical decoupling control. IEEE Trans. Neural Netw.
**2007**, 18, 921–925. [Google Scholar] [CrossRef] - Zeb, K.; Mehmood, C.A.; Khan, B.; Ali, S.M.; Jadoon, A.M.; Uddin, W. Fault tolerant speed regulation of induction motor using artificial neural network. In Proceedings of the IEEE Conference on Emerging Technologies, Peshawar, Pakistan, 19–20 December 2015. [Google Scholar]
- Chae, S.; Kwon, S.; Lee, D. Predicting infectious Disease Using Deep learning and Big Data. Int. J. Environ. Res. Public Health
**2018**, 15, 1596. [Google Scholar] [CrossRef] [PubMed] - Salerno, V.M.; Rabbeni, G. An Extreme learning machine approach to effective energy disaggregation. Electronics
**2018**, 7, 235. [Google Scholar] [CrossRef] - Douratsos, I.; Gomm, J.B. Neural Network based model reference adaptive control for process with time delay. Int. J. Inf. Syst. Sci.
**2006**, 3, 161–179. [Google Scholar]

**Figure 3.**Control structure for oscillation damping. MRC = model reference controller; VSC = Voltage Sources Converters; PQ = Active Power, Reactive Power; PWM = Pulse Width Modulation; PLL = Phase Lock Loop.

Controllers | 1-Φ Fault | 2-Φ Faults | 3-Φ Fault | |||
---|---|---|---|---|---|---|

ISE | IAE | ISE | IAE | ISE | IAE | |

PI | 0.0009028 | 2.432 × 10^{−7} | 0.001574 | 6.569 × 10^{−7} | 0.00253 | 1.07 × 10^{−6} |

MRC | 0.0005561 | 1.301 × 10^{−7} | 0.0005802 | 1.41 × 10^{−7} | 0.0005248 | 1.111 × 10^{−7} |

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## Share and Cite

**MDPI and ACS Style**

Uddin, W.; Zeb, N.; Zeb, K.; Ishfaq, M.; Khan, I.; Ul Islam, S.; Tanoli, A.; Haider, A.; Kim, H.-J.; Park, G.-S.
A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System. *Energies* **2019**, *12*, 3653.
https://doi.org/10.3390/en12193653

**AMA Style**

Uddin W, Zeb N, Zeb K, Ishfaq M, Khan I, Ul Islam S, Tanoli A, Haider A, Kim H-J, Park G-S.
A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System. *Energies*. 2019; 12(19):3653.
https://doi.org/10.3390/en12193653

**Chicago/Turabian Style**

Uddin, Waqar, Nadia Zeb, Kamran Zeb, Muhammad Ishfaq, Imran Khan, Saif Ul Islam, Ayesha Tanoli, Aun Haider, Hee-Je Kim, and Gwan-Soo Park.
2019. "A Neural Network-Based Model Reference Control Architecture for Oscillation Damping in Interconnected Power System" *Energies* 12, no. 19: 3653.
https://doi.org/10.3390/en12193653