Optimal Control Parameters for a UPFC in a Multimachine Using PSO

The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the conventional techniques. This paper presents an approach using particle swarm optimization (PSO) for the design of optimal conventional controllers for a UPFC in a multimachine power system. Simulation results are presented to show the effectiveness of the proposed PSO based approach for the design of optimal conventional controllers for a UPFC in a multimachine power system

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (Rt), maximum percentage overshoot (MPOS), settling time (Ts), and steady state error (Ess) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems

Optimal tuning of fractional order controllers for dual active bridge-based DC microgrid including voltage stability assessment

In this article, three evolutionary search algorithms: particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithms (GA), have been employed to determine the optimal parameter values of the fractional-order (FO)-PI controllers implemented in the dual active bridge-based (DAB) DC microgrid. The optimum strategy to obtain the parameters of these FO-PI controllers is still a major challenge for many power systems applications. The FO-PI controllers implemented in the DAB are used to control the DC link voltage to the desired value and limit the current flowing through the converter. Accordingly, the investigated control system has six parameters to be tuned simultaneously; Kp1, Ki1, ¿1 for FO-PI voltage controller and Kp2, Ki2, ¿2 for FO-PI current controller. Crucially, this tuning optimization process has been developed to enhance the voltage stability of a DC microgrid. By observing the frequency-domain analysis of the closed-loop and the results of the subsequent time-domain simulations, it has been demonstrated that the evolutionary algorithms have provided optimal controller gains, which ensures the voltage stability of the DC microgrid. The main contribution of the article can be considered in the successful application of evolutionary search algorithms to tune the parameters of FO-based dual loop controllers of a DC microgrid scheme whose power conditioner is a DAB topology.Peer ReviewedPostprint (published version

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods

Solar array fed synchronous reluctance motor driven water pump : an improved performance under partial shading conditions

An improved performance of a photovoltaic (PV) pumping system employing a synchronous reluctance motor (SynRM) under partial shading conditions is proposed. The system does not include the dc-dc converter that is predominantly being utilized for maximizing the output power of the PV array. In addition, storage batteries are also not contained. A conventional inverter connected directly to the PV array is used to drive the SynRM. Further, a control strategy is proposed to drive the inverter so that the maximum output power of the PV array is achieved while the SynRM is working at the maximum torque per Ampere condition. Consequently, this results in an improved system efficiency and cost. Moreover, two maximum power point tracking (MPPT) techniques are compared under uniform and partial shadow irradiation conditions. The first MPPT algorithm is based on the conventional perturbation and observation (P&O) method and the second one uses a differential evolution (DE) optimization technique. It is found that the DE optimization method leads to a higher PV output power than using the P&O method under the partial shadow condition. Hence, the pump flow rate is much higher. However, under a uniform irradiation level, the PV system provides the available maximum power using both MPPT techniques. The experimental measurements are obtained to validate the theoretical work

Microgrid, Its Control and Stability: The State of The Art

Some of the challenges facing the power industries globally include power quality and stability, diminishing fossil fuel, climate change amongst others. The use of distributed generators however is growing at a steady pace to address these challenges. When interconnected and integrated with storage devices and controllable load, these generators operate together in a grid, which has incidental stability and control issues. The focus of this paper, therefore, is on the review and discussion of the different control approaches and the hierarchical control on a microgrid, the current practice in the literature concerning stability and the control techniques deployed for microgrid control; the weakness and strength of the different control strategies were discussed in this work and some of the areas that require further research are highlighted

Simulation-based coyote optimization algorithm to determine gains of PI controller for enhancing the performance of solar PV water-pumping system

In this study, a simulation-based coyote optimization algorithm (COA) to identify the gains of PI to ameliorate the water-pumping system performance fed from the photovoltaic system is presented. The aim is to develop a stand-alone water-pumping system powered by solar energy, i.e., without the need of electric power from the utility grid. The voltage of the DC bus was adopted as a good candidate to guarantee the extraction of the maximum power under partial shading conditions. In such a system, two proportional-integral (PI) controllers, at least, are necessary. The adjustment of (Proportional-Integral) controllers are always carried out by classical and tiresome trials and errors techniques which becomes a hard task and time-consuming. In order to overcome this problem, an optimization problem was reformulated and modeled under functional time-domain constraints, aiming at tuning these decision variables. For achieving the desired operational characteristics of the PV water-pumping system for both rotor speed and DC-link voltage, simultaneously, the proposed COA algorithm is adopted. It is carried out through resolving a multiobjective optimization problem employing the weighted-sum technique. Inspired on theCanis latransspecies, the COA algorithm is successfully investigated to resolve such a problem by taking into account some constraints in terms of time-domain performance as well as producing the maximum power from the photovoltaic generation system. To assess the efficiency of the suggested COA method, the classical Ziegler-Nichols and trial-error tuning methods for the DC-link voltage and rotor speed dynamics, were compared. The main outcomes ensured the effectiveness and superiority of the COA algorithm. Compared to the other reported techniques, it is superior in terms of convergence rapidity and solution qualities

Modeling and Controlling a Hybrid Multi-Agent based Microgrid in Presence of Different Physical and Cyber Components

This dissertation starts with modeling of two different and important parts of the distribution power systems, i.e. distribution line and photovoltaic (PV) systems. Firstly, it studies different approximation methods and develops a new approach for simplification of Carson\u27s equations to model distribution lines for unbalanced power flow and short circuit analysis. The results of applying the proposed method on a three-phase unbalanced distribution system are compared with different existing methods as well as actual impedance values obtained from numerical integration method. Then steady state modeling and optimal placing of multiple PV system are investigated in order to reduce the total loss in the system. The results show the effectiveness of the proposed method in minimizing the total loss in a distribution power system.;The dissertation starts the discussion about microgrid modeling and control by implementing a novel frequency control approach in a microgrid. This study has been carried out step by step by modeling different part of the power system and proposing different algorithms. Firstly, the application of Renewable Energy Sources (RES) accompanied with Energy Storage Systems (ESS) in a hybrid system is studied in the presence of Distributed Generation (DG) resources in Load Frequency Control (LFC) problem of microgrid power system with significant penetration of wind speed disturbances. The next step is to investigate the effect of PHEVs in modelling and controlling the microgid. Therefore, system with different penetrations of PHEVs and different stochastic behaviors of PHEVs is modeled. Different kinds of control approaches, including PI control as conventional method and proposed optimal LQR and dynamic programming methods, have been utilized and the results have been compared with each other. Then, Multi Agent System (MAS) is utilized as a control solution which contributes the cyber aspects of microgrid system. The modeled microgrid along with dynamic models of different components is implemented in a centralized multi-agent based structure. The robustness of the proposed controller has been tested against different frequency changes including cyber attack implications with different timing and severity. New attack detection through learning method is also proposed and tested. The results show improvement in frequency response of the microgrid system using the proposed control method and defense strategy against cyber attacks.;Finally, a new multi-agent based control method along with an advanced secondary voltage and frequency control using Particle Swarm Optimization (PSO) and Adaptive Dynamic Programming (ADP) is proposed and tested in the modeled microgrid considering nonlinear heterogeneous dynamic models of DGs. The results are shown and compared with conventional control approaches and different multi-agent structures. It is observed that the results are improved by using the new multi-agent structure and secondary control method.;In summary, contributions of this dissertation center in three main topics. Firstly, new accurate methods for modeling the distribution line impedance and PV system is developed. Then advanced control and defense strategy method for frequency regulation against cyber intrusions and load changes in a microgrid is proposed. Finally, a new hierarchical multi-agent based control algorithm is designed for secondary voltage and frequency control of the microgrid. (Abstract shortened by ProQuest.)

Optimal controllers design for voltage control in Off-grid hybrid power system

Generally, for remote places extension of grid is uneconomical and difficult. Off-grid hybrid power systems (OGHPS) has  renewable energy sources integrated with conventional sources. OGHPS is very significant as it is the only source of electric supply for remote areas. OGHPS under study  has Induction generator (IG) for wind power generation, Photo-Voltaic source with inverter, Synchronous generator (SG) for Diesel Engine (DE) and load. Over-rated PV-inverter has capacity to supply reactive power.  SG of  DE  has Automatic voltage regulator for excitation control to regulate terminal voltage. Load and IG demands reactive power, causes reactive power imbalance hence voltage fluctuations in OGHPS. To manage reactive power for voltage control, two control structures with Proportional–Integral controller(PI), to control  inverter reactive power and  SG excitation by automatic voltage regulator are incorporated.  Improper tuning of controllers lead  to oscillatory and sluggish response. Hence in this test system both controllers need to be tune optimally. This paper proposes novel intelligent computing algorithm , Enhanced Bacterial forging algorithm (EBFA) for optimal reactive power controller for voltage control in OGHPS. Small signal model of OGHPS with proposed controller is  tested for different disturbances. simulation results  are compared  with conventional  method , proved the effectiveness of EBFA