Development of electrical power systems led to search for  a new mathematical methods to find the values of PID (Proportional-Integral-Derivative) controller. The goal of the paper is to improve the performance of the overall system, through improved the frequency deviation and the voltage deviation characteristics using PID controller, so in this paper are proposed three methods of artificial intelligence techniques for designing the optimal values of PID controller of Load-Frequency-Control (LFC) and Automatic-Voltage-Regulator (AVR), the first is the Firefly Algorithm (FA), the second is the Genetic Algorithm (GA) and the third is the Particle Swarm Optimization (PSO), in addition to these three methods use the conventional (Ziegler–Nichols, Z-N). The FA, GA and PSO are used to obtain the optimal parameters of PID controller based on minimized different various indices as a fitness function, these fitness functions namely Integral-Time-Absolute-Error (ITAE) and Integral-Time-Square-Error (ITSE). Comparison between the results obtained show that FA has better performance to control of frequency deviation and terminal voltage than GA and PSO, so the results observed the FA is more effectual and reliable to determine the optimal values of PID controller.

Induction machines are a crucial type of electrical equipment utilized as motors in various industries and single-phase forms in household applications. They make up more than 80% of the industrial motors used today. This paper presents a dynamic simulation of three-phase induction machines based on the (d-q) model, providing a clear and easy-to-understand explanation of the behavior of the induction motor in the synchronous reference frame. The simulation is implemented using SIMULINK/MATLAB software, and full-order model analysis is conducted for transient analysis. However, modeling induction machines as part of power system analysis can be done with a less detailed model than the full-order one. In this paper, a reduced-order model is employed to simulate the induction motor using MATLAB/SIMULINK. The results of the reduced-order system and the full-order framework are compared to investigate the model's limits, and the computational benefits of saving time during large power system simulations justify the relative decrease in accuracy. The use of a reduced-order model results in a significant reduction in computation time when simulating large and very large-scale power systems, with a much higher accuracy in transient analysis than the conventional steady-state model.

In this paper, frequency oscillation has been present as it is an important issue in power systems, especially when it is joined to the new trend to use alternative energies as an energy source, as well as to be a big risk for the inter-connection of the modern electric power networks. Wind farm acts as alternative energy and connected to two buses on the Iraqi power system. Because the low-frequency oscillation monitoring needs be accurate and fast, the main objective is to propose a novel online monitoring system consisting of phasor measurement unit's (PMU) with artificial intelligence neural network (PMU-NN). The location of the phasor measurement units has been optimized using (graph-theoretic procedure algorithm) and the function for the artificial intelligence (NN) is radial basis function (RBFNN). The data information from phasor measurement units is the inputs to the artificial intelligence system then predictions are made Information on low-frequency oscillation (target). The MATLAB toolboxes (PSAT & NN) used to obtain results. Finally, from the results, the validity of the proposed (PMU-NN) system has been proven and tested on the Iraqi power grid (24 bus) in several cases and several places on the network and the comparison was made with the analysis model.