Search

The continuous rise in demand for power supply has made researchers and power system engineers seek alternatives through renewable energy sources to complement the power supply in the power system grid. Wind energy conversion system (WECS) which is the means of harnessing power generation through wind is reportedly one of the most widely installed renewable alternative sources globally. Integrating WECS into the conventional power system grid results in a complex power system grid. Thus, during a disturbance or a fault period on the grid, if proper control measures are not put in place, power system instability due to power system oscillations arises. One such control measure is the damping controller which is coupled to the generating plant through its excitation system. Damping controllers help to dampen power system oscillations, but due to the dynamic nature of the power system and uncertainties inherent in a wind-integrated power grid system, fixed damping controller parameters cannot effectively dampen power system oscillations. Hence, damping controller design becomes an optimization problem. This research reviews damping controller design in a wind-integrated system using optimization techniques.

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.

Massive growth in global electrical energy demand has necessitated a genuine exploration and integration of solar and wind energy into the electrical power mix. This incorporation goes a long way in improving the cumulative generated power capacity of the power system. However, wind and solar photovoltaic (PV) are intermittent in nature, making the provisioning of a good maximum power tracking (MPPT) scheme necessary. Furthermore, the integration is characterized by synchronization challenges and introduces various modes of power system oscillations as it is converter-driven. This greatly affects the overall stability of the integrated power mix. Consequently, various technological models have been designed to address these challenges ranging from MPPT schemes, phase-lock loop (PLL), virtual synchronous generator (VSG), power system stabilizers (PSS), flexible AC transmission system (FACTS), coordinated control and artificial intelligence (AI). In this work, a multi-machine power system model is reviewed for integration stability studies. Various technical solutions associated with the integration are also reviewed. MPPT, PLL, VSG, PSS, FACTS, coordinated control, and various optimization technique schemes used for damping controller design are discussed.

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.

Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superior damping. To address this issue, a Farmland Fertility Algorithm (FFA-PSSs controller) was used to solve an optimization problem for optimal design of PSSs system parameters, and its performance efficiency was compared to GA and PSO-based PSSs controllers. In addition to PSS, flexible current transmission (FACTS) devices are widely used. PSSs controllers and FACTS devices are frequently constructed in tandem to improve the dampening efficiency of the system. In this study, an Interline Power Flow Controller (IPFC) FACTS device will be added to the PSSs controller to improve the power system’s oscillatory stability. PSSs optimal design and supplemental controller of power fluctuations for IPFC were conducted out on WSCC multi-machine test systems using a system linear model. Using time-domain simulations and quantitative analysis, the proposed IPFC model was compared to the FFA-PSSs controller in terms of performance and efficiency. The main disadvantage of this technique is the difficulty in designing a dynamic IPFC model in test systems, as well as the burden of IPFC coordinated PSSs optimization. In both PSSs design using FFA method and FFA-optimized PSS with IPFC cases, rise in the computational and simulation costs was found unavoidable. To compensate for these flaws and obtain the research contribution, this paper proposes a Neuro-Fuzzy Controller (NFC) developed as a damping controller that can take the place of the two controllers (research objectives three). The application of the NFC substitute the computational and simulation cost involved in designing multi-machine PSS and IPFC-FACTS systems simultaneously. With the availability of NFC in SIMULINK, a dynamic m

This study describes the process of interconnected multi-machine power system stabilizer (PSS) optimization using a new intelligent technique called farmland fertility algorithm (FFA) to increase the stability of IEEE three machine nine bus power system and offset the low-frequency oscillations (LFOs) during a symmetrical 100 ms three-phase fault at bus 9. The FFA-PSS controller performance is compared with two familiar classical techniques, i.e. Genetic Algorithm (GA-PSS) and Particle Swarm Optimization (PSO-PSS) to confirm the capability of the proposed technique to realize improved system stability enhancement. The Eigenvalue simulation results with FFA produce stable Eigenvalues that increase the damping ratio of the Electromechanical Modes (EMs) to more than 0.1 with smaller overshoots and time to settle which shows the effectiveness of the method for multi-machine stability improvement. Also, the phasor simulation results show that the transient responses of the system rise time, settling time, peak time and peak magnitude were all impressively improved by an acceptable amount for the interconnected system with the proposed FFA-PSS thus, was able to control the LFOs effectively and produces enhanced performance compared to the GA and PSO based PSS. Similarly, the result validates the effectiveness of the proposed FFA tuned PSS for LFO control which demonstrates robustness, efficiency, and convergence speed ability than the classical GA and PSO tuning methods.

Optimal design of interconnected multimachine power system with power system stabilizers (PSSs) enduring critical conditions is a challenging assignment due to several nonlinear dynamic device and components associated to the system. In this research, the effective application and performance assessment of genetic algorithm (GA), particle swarm optimization (PSO) and a new metaheuristic-based farmland fertility algorithm (FFA) search algorithm for designing of coordinated multi PSSs in an interconnected multimachine power systems are presented and the limitations of the existing techniques (GA and PSO) are disclosed. The PSSs design problem is transformed into an optimization problem which an eigenvalue-based objective function is developed and both the GA, PSO and the proposed FFA optimization methods are applied to search for the optimal control parameters of the PSSs that are connected to the multimachine in the power system. The PSSs are tested on an interconnected two-area ten-machine system of 39-bus New England IEEE test power system under multiple operating conditions. The optimization methods performance in terms of convergence characteristics, search computational cost, and the search quality solution are compared for justification. The eigenvalue and phasor simulation analysis results are compared so as to validate the efficiency of both optimization methods for the PSSs design thus, to control low frequency oscillations (LFOs) which improves the system dynamic stability. Also, the phasor simulation results exhibits that the transient responses of the system rise time, settling time, peak time and peak magnitude were all impressively improved by an acceptable amount for the test system with the proposed FFA-PSS. Moreover, at the end of the analysis, the FFA based PSSs design was found to converge faster with low computational cost and produces enhanced optimal PSSs parameters as compared to the other existing algorithms. Thus, the application and performa

For this study, design of proportional integral derivative (PID) PSS (PIDPSS) is proposed for damping oscillations and improve angular stability in single machine infinite bus (SMIB) power system modeled and simulated in MATLAB/SIMULINK. A new metaheuristics method called Farmland Fertility Algorithm (FFA) inspired by nature is proposed for optimal design of PIDPSS using a robust ISTSE objective function which had to be minimized. The robustness of the ISTSE objective function was tested using numerical simulation of four well-known time integral performance criteria for calculating the integral error of the two damping controllers. The two controllers are the proposed FFA PIDPSS which was compare with trial and error parameter Conventional PIDPSS (CPIDPSS) controller and well-known Differential Evolution (DE) algorithm tuned PIDPSS controller for plausible application. The phasor simulation results shows that the proposed ISTSE, the speed deviation SMIB transient response such as rise time, settling time, peak time were all significantly improved by an amount of 6.26%, 33.73%, 37.37% and 7.36% respectively by the proposed FFA approach compare to the DE method. This result validate the effectiveness of the proposed FFA tuned PIDPSS for LFO mitigation and SMIB angular stability enhancement which demonstrates robustness, efficiency and convergence speed ability than the trial and error parameter CPIDPSS and DE tuned PIDPSS method.

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.

The excessive use of power electronics devices in industrial, commercial and residential purposes have lead to the deterioration of supply current and voltage wave forms, injecting harmonic pollution on to the supply system, which no doubt generates a major concern to power system engineers. Restricted standards set by IEEE-519 or IEC-61000-3-2 necessitates that, those unwanted harmonic injected current (into the utility networks) to fall below a specified range. Present day technological development, particularly in the field of power converters, introduces the application of active power filters as a modern weapon for harmonic current mitigation and reactive power compensation leading us to IEEE norms realization. This paper depicts comprehensively, comparison in terms of total harmonic distortion (THD) and simulation results, the most effectively fast response in harmonic mitigation between fuzzy logic based active power filter (APF) control and its artificial neural network (ANN) controller counterpart. In both cases, results were developed via simulation studies under MATLAB/SIMULINK environment.