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Identification and controlling of non‐linear and complex dynamical systems have been a major concern and a serious topic of study in the field of adaptive control systems. Various techniques based on artificial intelligence have been used for this purpose. For modelling and control of plants, it is essential to optimize the values of the parameters associated with the respective technique. Searching the right or optimum value of the associated parameters is known as optimization. The intelligent water drop (IWD) algorithm is an optimization algorithm derived from the natural intelligence of water drop, which always chooses the shortest possible path while travelling from rivers and lakes towards oceans and seas. This paper introduces a new way of visualizing and understanding the IWD algorithm and presents its respective application in the field of modelling and controlling of non‐linear dynamic systems with the help of illustrative examples. Fuzzy type‐1 and recurrent fuzzy systems have been optimized using this algorithm. Its learning ability is compared with that of the grasshopper algorithm, a particle swarm optimization (PSO) based algorithm and the gradient descent method. The examples show the superiority of the IWD algorithm over the other optimization techniques. [ABSTRACT FROM AUTHOR]

Ball and beam is a popular benchmark problem in control engineering. Various control strategies have been proposed on ball & beam system in literature, In this paper, hybrid optimization algorithms have been implemented on PID controller to control ball position and beam angle. Hybrid algorithms combine exploration and exploitation ability of individual algorithm and find optimized value of performance index. In this paper, two hybrid algorithms namely PSO-GSA and PSO-GWO are used to tune controller parameters which in turn improve the system performance. Simulation results show effective and efficient improvement in system performance with these hybrid algorithms. To analyze the performance of these algorithms, time domain parameters and mean square error (MSE) has been taken as performance index. A comparative study of these algorithms with that of individual algorithms namely PSO, GWO, GSA has also been done. [ABSTRACT FROM AUTHOR]

This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial conditions. [ABSTRACT FROM AUTHOR]

Impedance mismatching between different phases of a multiphase transformer is generally observed e.g., in a three-phase to seven-phase transformer, due to an unequal number of turns in different coils. This mismatching introduces error in the study of per phase equivalent circuit diagrams as well as induces an imbalance in output voltages and currents. Therefore, it is a challenging task to develop a per-phase equivalent circuit for the secondary and primary sides (In some cases) too. This paper proposes an artificial intelligence optimization technique like PSO based modeling of the per-phase equivalent circuit of the secondary side. This paper deals with the modeling and simulation of a three-phase to seven-phase power transformer using Artificial Intelligence technique like particle swarm optimization (PSO) and Genetic Algorithm (GA). The proposed model is optimized through PSO and GA algorithms and tested for minimum voltage error in each phase. The proposed model is designed and the objective function is optimized by PSO & GA in MATLAB environment. It is found that the optimized model can be effectively implemented as a per-phase equivalent circuit for the secondary side. [ABSTRACT FROM AUTHOR]

In this paper, nature inspired search algorithms, namely particle swarm optimization (PSO) and genetic algorithm (GA) are used to design fractional order proportional and integral (FOPI) and artificial neural network (ANN) controller based distribution static compensator (DSTATCOM) and electronic load controller (ELC) for power quality improvement. Improvement in power quality is achieved using DSTATCOM and an ELC. DSTATCOM is designed using FOPI and ANN based controllers, as opposed to conventional PI controllers which are comparatively less efficient. PSO and GA techniques are employed to determine the optimal parameters for the controllers. The improvement in the performance of the ANN and FOPI as compared to PI controller for the DSTATCOM and ELC is validated using MATLAB based modeling and simulations. Linear consumer loads were used to perform a comparative study in terms of maximum percentage error. Further, we analyzed the system for a nonlinear load and demonstrated decrease in the harmonic distortion. [ABSTRACT FROM AUTHOR]

In this paper, we have solved one of the widely popular problems in the domain of control systems – distance optimization using two separate swarm intelligence algorithms – particle swarm and ant colony optimization. The problem is successfully solved with both algorithms and subsequently quantitative comparison between their performances is done. Simulated results show the more recently developed ant colony optimization algorithm to be better and more robust of the two. The requisite simulations are carried out and results are obtained in the MATLAB environment. [ABSTRACT FROM AUTHOR]

Expansion planning of distribution system is the most significant tool which deal with the continuous increasing load demand. The main motive of the expansion planning is the minimization of the investment and operation cost of distribution network equipment which consider the installation/reinforcement cost of substation, feeders and Distribution Generation. In this paper, price and load uncertainties are taken in to expansion planning which gives the robust and reliable expansion planning. These uncertainties are molded as Normal Probability Distribution Function. By using Monte Carlo Simulation uncertainties are added in to planning. A 72 bus (Kian-pars Ahvaz 11 KV a practical distribution network in Iran) distribution network is used for case study of expansion planning. This multistage dynamic expansion planning problem is resolved by the Quantum Particle Swarm Optimization. The proposed algorithm is compared with the standard Particle Swarm Optimization and results shows the superiority of proposed algorithm over PSO. [ABSTRACT FROM AUTHOR]

With socio-economical development, power distribution systems are forced to work with both heavy as well as light loads, which causes voltage instability and power loss. This paper develops a Particle Swarm Optimization (PSO) based algorithm to find an optimal place and parameter of Unified Power Flow Controller (UPFC) to improve voltage profile with minimum system loss. UPFC is a most versatile Flexible Alternating Current Transmission System (FACTS) controller, which can control bus voltage; flow of active as well as reactive power through lines. The novelty of this work is to fix the location of UPFC at base load to make power distribution system more secure and efficient, by tuning its parameters at all possible load conditions. The developed methodology is employed in IEEE 30-bus system with different intensities of the load. Results prove the success of developed technique and verify the potential of UPFC for improvement of system stability with mitigation of losses. [ABSTRACT FROM AUTHOR]

In this paper, the fractional-order (FO) proportional-integral-derivative (PID) (FO-PID) controller is designed aiming at load-frequency control (LFC) for an isolated hybrid power system, involving a solar photovoltaic generator and a diesel engine generator. The FO-PID controller is a PID controller which has fractional order for integral and derivative. This paper engages' particle swarm optimization (PSO) algorithm to carry out the above mentioned LFC for the considered wind-biomass isolated hybrid power system. A comparison of FO control strategy with conventional based controller techniques is made. The FO-PID controller outperforms the conventional PID controllers. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid of Neural Network (NN) and Fast Traversal Filter (FTF) based controller in each area is used to determine the optimal parameters of Load Frequency Control (LFC) of a realistic two area power system. The two area power system is modeled considering the various non -linearities like governor dead band, generation rate constraint (GRC) and boiler dynamics. Input to the controller i.e. the error signal is divided into two parts- linear and non- linear. The linear part of the input signal is minimized by the FTF algorithm, whereas the non- linear part is minimized by the NN algorithm. The output of the controller is the sum of the outputs of NN and FTF networks. The proposed hybrid controller requires less number of samples for training of weights, thus making the system fast. This is highly desirable in power quality problems. The various components of power system are reduced to transfer functions and the system performance is analyzed for 1% step load perturbation in area1 with different controllersproportional and integral (PI), neural network (NN) and NN+FTF based controllers. The simulations demonstrate the fast and smooth performance of the power system with the proposed controller. Simulated results evince the superiority of the proposed hybrid controller. [ABSTRACT FROM AUTHOR]