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In this work, an isolated power system requiring a Load Frequency Control (LFC) by the application of a PID controller is designed in the MATLAB/Simulink environment. To acquire the PID gain parameters for an optimum dynamic load frequency control, several optimization procedures are applied. Genetic algorithms (GA) and Particle Swarm Optimization (PSO) technique were used to determine the proportional gain (KP), integral gain (KI), and deferential gain (KD) of the controller. Artificial Neural Network (ANN) training was is also caried out for the PID tuning and the comparative analysis of the results obtained shows that the Particle Swarm optimization (PSO) has the best performance, with an overshoot of 0.58 percent and a settling time of 0.52 seconds.

To improve the capacity of the power system damping, a power system stabilizers (PSSs) is typically the chosen option. Traditional PSSs performance fails to provide superior damping under other operating circumstances. Many adaptive control approaches have been proposed to solve this problem, but they are both difficult and expensive. Implementing a PSSs damping controller requires extensive system modeling, placing a heavy computational burden on the system, and is a time-consuming process. A Neuro-Fuzzy Controller (NFC), a damping controller that can take the place of the FFA-PSS controller, was proposed to address these shortcomings. The dynamic model of 10-machine test systems under multiple operating conditions was developed with the presence of NFC in SIMULINK. After carrying out the time-domain simulation on the New England IEEE test system, the proposed NFC model produced a 13% rotor speed and 43% rotor angle respective angle stability enhancement in G5 based on the time to settle when compared to the FFA-PSS model. Our work has led us to conclude that the simulation results with the proposed NFC model shows that the Electromechanical Modes (EMs) eigenvalues were shifted to the left in S-plane and the system damping was greatly enhanced.

Today's power system grids are massively interconnected with newer technologies and control devices integrated into the system all for enhanced efficiency and economic benefits in operation. This property makes the grid highly dynamic and diverse thus, concluding it as the biggest artificial living network on earth. To observe the power system security requirements, planning and safe operation of today's system becomes imperatively necessary. If the power system security criterion is ignored, then power blackouts will be born and the system response will distribute severe socio-economic impact, therefore, it is essential to know the dynamic security assessment (DSA) for the systems and allow it to remain in a stable state. This paper will execute a comparison of each method of transient stability index (TSI) and transient stability assessment (TSA) and how the classes of these methods are implemented. The classifications will highlight the difference between the methods and show the individual benefits and setbacks when applied on a practical large sized power system. This review paper can deliver an in-depth knowledge into the development of a rapid and reliable rotor angle transient instabilities technique towards improving DSA of a practical large-sized power system.

The Phasor Measurement Unit (PMU) is the heart of smart grid system as it provides the data such as voltage and phase angle measurements of all buses of the system and thereby maintaining the system observability. In this context, this paper summarizes the various research based on PMU for complete observability and monitoring of integrated power system. The survey indicates that most of the recent researches are focusing on optimal PMU placement (OPP) rather than design and modeling of PMU considering various cases. Moreover, the state estimation using synchrophasor technology are also presented as addition objective to obtain the optimal number of PMU that need to be installed in the system for power system analysis and economic benefits of the system. The trend of research based on synchrophasor technology are evolving for real-time power system monitoring application where it also covers for dynamic power system assessment.

Suppliers of electricity and their consumers have attained a peak stage of concern regarding the quality of electric power nowadays, with current harmonics concise injection activities looms the power networks as certain loads (principally nonlinear loads) are applied on to it. With active power filter (APF) wide knowledge and sophistication in this regard, a high performance extraction algorithm coupled with a fast switching technique must be developed in utilizing this device optimum potential. This paper introduces a new effective algorithms for the compensation of this ill effects in the power networks namely the digital hysteresis algorithm with a modification by bandwidth absence in the switching analysis (called bandless digital hysteresis algorithm) and a modified artificial neural network responsible to detect and generate the necessary compensation signal injected on to the power networks by the harmonic agents. The proposed scheme is achieved via simulation studies (under MATLAB SIMULINK environment) and effectives results obtained are discussed to verify its performance.

Proliferation of nonlinear loads /devices in power systems generates a major concern to power system engineers, courtesy of its severe contamination effects (polluting the distribution networks with current harmonics). This paper depicts artificial intelligence (AI) application on resolving the power quality problem mentioned above by using the parallel active power filter (APF) strategy in two-wire distribution systems. The proposed AI adopted is an artificial neural network (ANN) responsible to detect current harmonics for the active power filtering process. The novelty control design is an artificial neural network (ANN) adopting a modified mathematical algorithm (a modified delta rule weight-updating W-H) and a suitable alpha value (learning rate value) which determines the filters optimal operation. The proposed scheme is achieved via simulation studies (under MATLAB SIMULINK environment) and results obtained are discussed to verify its performance.