Automatic Generation Control of Interconnected Power System by using Optimization Technique

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Automatic Generation Control of Interconnected Power System by using Optimization Technique

Mohana Venkata Sai Kumar Munagapati

Electrical and Electronics Engineering Bapatla Engineering College,


Sai Leela Prasad Kandula

Electrical and Electronics Engineering Bapatla Engineering College,


Satya Dinesh Madasu

Electrical and Electronics Engineering Bapatla Engineering College,


Abstract: Any disturbances in the load demand leads to change in system frequency variations. In order to overcome such circumstances Automatic. Generation Control (AGC) is adopted. Various controllers like Proportional Integral Derivative (PID), Proportional Integral (PI), integral(I), and intelligent Controllers like Fuzzy Logic Controllers are employed as secondary control to solve AGC problems. However, the controllers alone are not enough to solve the problems, and therefore requiring further improvement by optimization of the gains and parameters of the controller, for better and efficient operation of the powersystem.

Keywords – Automatic Generation Control (AGC), PID Controller, Non linearity, Moth flame optimization technique path shows a conceptual model of transverse orientation[1].


    Automatic generation control is an interconnected power system plays major role to maintain system frequency stable in real life scenario. Load may not be constant, load will changes either increase or decrease based on the consumer needs, in this scenario we should maintain system frequency constant and quality output to the consumers need. For all these effects of the system

    ,AGC will regulate the power system network and PID controller is to reduce the frequency deviations occurs in the system PID controller the gains and parameters like Proportional gain(Kp),Integral gain(Ki) and Derivative gain(Kd) are occurs, so we have to optimize or tune those gains and parameters for desired optimal values for better and quality output for the use of consumer needs[3]. For the optimizing or tuning the gains and parameters of the controller we are introducing the optimization technique called moth flame optimization is a novel nature-inspired optimization pattern[2].

    The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation[1]. Moths are fancy insects, which are highly similar to the family of butterflies. Basically, there are over 160,000 various species of this insect in nature. They have two main milestones in their lifetime: larvae and adult. The larvae are converted to moth by cocoons. The most interesting fact about moths aretheir special navigation methods in night? They have been evolved to fly in night using the moon light. They utilized a mechanism called transverse orientation for navigation. In this method, a moth flies by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling long distances

    Despite the effectiveness of transverse orientation, we usually observe that moths fly spirally around the lights. In fact, moths are tricked by artificial lights and show such behavior[4]. This is due to the inefficiency of the transverse orientation, in which it is only helpful for moving in straight line when the light source is very far. When moths see a human-made artificial light, they try to maintain a similar angle with the light to fly in straight line. Since such a light is extremely close compared to the moon, however, maintaining a similar angle to the light source causes a useless or deadly spiral fly path for moths We mathematically model this

    behavior and propose an optimizer called Moth-Flame Optimization (MFO) algorithm.



    For various load disturbances (0.1,0.5) to the proposed model with PI, PID controllers obtain the desired results[3]. Comparison between time, frequency, tie line power under the various loading conditions along with controllers have been depicted in plots.

    Table 1:For PI controller


    Figure 1: T vs f1:for 0.1 load

    Table 2:For PID controller

    Figure 2:Tvs f1:for 0.5 load

    Figure 3:Tvs f2 : for 0.1 load

    Figure 4:Tvs f2:for 0.5 load

    Figure 5:Tvstie:for 0.1 load

    Figure 6:Tvstie:for 0.5 load


Finally, controller parameters are tuned by using moth flame optimization algorithm for proposed model two area interconnected system for obtaining optimal values[5]. when comparing both PI and PID controllers the settling time of PID controller is more effective than PI controller in proposedmodel[6].


  1. Mirjalili,Seyedali."Moth-flame optimization algorithm:A novel nature-inspired heuristic paradigm." Knowledge-based systems 89 (2015) 228-249.

  2. Bozorg-Haddad, Omid, ed. Advanced optimization by nature- inspired algorithms. Singapore: Springer,2018.

  3. Elgerd, OlleIngemar. "Electric energy systems theory: an introduction." (1982).

  4. Frank, Kenneth D., C. Rich, and T. Longcore. "Effects of artificial night lighting on moths." Ecological consequences of artificial night lighting (2006):305-344.

  5. Yldz, Betül Sultan, and Ali RzaYldz. "Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes." Materials Testing 59.5 (2017):425-429.

  6. Zawbaa, Hossam M., et al. "Feature selection approach based on moth-flame optimization algorithm." 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016.

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