Design of PI Controller for Automatic Generation Control of Multi Area Interconnected Power System Using Bacterial Foraging Optimization

Abstract

Abstract-The system comprises of three interconnected power system networks based on thermal, wind and hydro power generation. The load variation in any one of the network results in frequency deviation in all the connected systems .The PI controllers have been connected separately with each system for the frequency control and the gains (Kp and Ki ) of all the controllers have been optimized along with frequency bias (Bi) and speed regulation parameter ( Ri ) . The computationally intelligent techniques like bacterial foraging optimization (BFO) and particle swarm optimization (PSO) have been applied for the tuning of controller gains along with variable parameters Bi and Ri. The gradient descent (GD) based conventional method has also been applied for optimizing the parameters Kp , Ki ,Bi and Ri .The frequency responses are obtained with all the methods . The performance index chosen is the integral square error (ISE). The settling time, peak overshoot and peak undershoot of all the frequency responses on applying three optimization techniques have been compared. It has been observed that the peak overshoot and peak undershoot significantly reduce with BFO technique followed by the PSO and GD techniques. While obtaining such optimum response the settling time is increased marginally with bacterial foraging technique due to large number of mathematical equations used for the computation in BFO. The comparison of frequency response using three techniques show the superiority of BFO over the PSO and GD techniques. The designing of the system and tuning of the parameters with three techniques has been done in MATLAB/SIMULINK environment.Keyword -Bacterial foraging optimization; Particle swarm optimization; Gradient Descent ; Integral square error; peak overshoot; peak undershoot; settling time; PI controller I. INTRODUCTION One of the power system requirement is the stable operation in respect of voltage and frequency under varying load conditions. The variation in these parameters must lie within permissible range. The real power control help in achieving the frequency control and reactive power help in achieving the voltage control. The frequency deviation occurs in all the interconnected areas when they are connected through same tie line. The performance index has to be minimized continuously whenever there is frequency change .The performance index in the present case is chosen as the integral square error (ISE) which has to be minimized whenever there is frequency change using the computationally intelligent techniques. The load frequency controller is the proportional integral (PI) controller which is the most effective controller if it is properly tuned for proportional (Kp) and integral (Ki) gains and it is used in decentralized control mode as the interconnected areas are of unequal nature. The interconnected power system areas in most of the literature are of same nature but in actual scenario this trend is changing due to the increasing scarcity of non renewable energy generation sources. The interconn...

AGC in an Interconnected Power System of Thermal Generating Unit through Evolutionary Technique

“…To overcome the problem of primary control action, the secondary control action also called with controller, need to apply, these controllers are set for a particular operating condition and they take care of small changes condition and they take care of small changes in load demand without exceeding the prescribed limits of frequency. These control action comprises of different controller like conventional, intelligent and evolutionary technique (Kothari and Nagrath, 2003;Elgerd, 1971;Challa and Rao, 2010;Sivanagaraju and Sreenivasan, 2011;Hadi, 1999;Kumari and Jha, 2014). Four area AGC model of thermal generating system is shown Fig.…”

Impact of Spotted Hyena Optimized Cascade Controller in Load Frequency Control of Wave-Solar-Double Compensated Capacitive Energy Storage Based Interconnected Power System

“…Numerous categories of subordinate controllers such as integer order (InO), fractional order (FrO), and cascade controllers are described in the literature associated with AGC. Diverse authors have described numerous InO controllers, such as integral (I) [23], proportional-integral (PI) [24], and proportional-integral-derivative with filter (PIDN) [25], in AGC. Dual [26] or trio [27] higher grade of freedom subordinate controllers have also been examined in this arena of learning, and correspond to the InO sort.…”

“…However, the usage of typical methods such as straight pursuit, arbitrary pursuit, incline pursuit, and numerous others is pretty arduous and delivers substandard consequences, as well as craving a great number of repetitions to deliver outcomes. The optimization procedures that have previously been found in AGC knowledge are whale optimization algorithm (WOA) [17], bacterial foraging optimization (BFO) [24], cuckoo search (CS) [26], differential evolution (DE) [32], particle swarm optimization (PSO) [33], firefly algorithm (FA) [34], grey wolf optimization (GWO) [35], imperialist competitive algorithm (ICA) [36], flower pollination algorithm (FPA) [37], honey badger algorithm [38], AdaBoost algorithm [39], and improved mothflame algorithm [40]. A newly developed bio-inspired meta-heuristic algorithm titled spotted hyena optimizer (SHO) [41] is obtainable from the literature.…”

Dynamic Stability Evaluation of an Integrated Biodiesel-Geothermal Power Plant-Based Power System with Spotted Hyena Optimized Cascade Controller

“…Numerous forms of subsidiary controllers, such as integer-order (InOr), fractional-order (FrOr), and cascade controllers (CaC), are stated in literature to be linked to AGC. The usage of various InOr controllers, such as integral (I) [26], proportionalintegral (PI) [27], and proportional-integral-derivative with filter (PIDN) [28], is reflected in the literature. Two [29] or three [30] higher degrees of freedom controllers have been inspected in this ground of training, which were also of the InOr type.…”