Optimal FOPID Error Voltage Control Dead-Time Compensation Based on FOPI current control for PMSM Servo System

The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation (PID) controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization (MCIWO) and particle swarm optimization (PSO) algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.

This paper presents a comparative assessment of the current control methods (CCM) of switch-mode converters for photovoltaic (PV) applications. In this paper, average current control, current programmed control, hysteresis current control and nonlinear carrier control methods are addressed considering input fluctuations and load variations for PV systems. Dynamic responses of PV systems are investigated and harmonic analysis is performed. Performances of the above current controllers are examined through simulation and the results are presented. The results show that the selection of different current control techniques depends on the working conditions and the area of applications.

In this paper, two fractional order proportional integral controllers are designed to improve the performance and robustness for a class of fractional order systems which can better model many real systems such as bioengineering systems. For comparison between the fractional order proportional integral controllers and the traditional integer order PID (IOPID) controller, the IOPID controller is also designed following the same proposed tuning specifications. Unfortunately, the designed IOPID controller can not meet the required spcification. However, the designed fractional order proportional integral (FOPI) and fractional order [proportional integral] (FO[PI]) controllers both can satisfy all the three specifications proposed. From the simulation results presented, it can be seen that both the FOPI and FO[PI] controllers designed work efficiently. Furthermore, we can observe that the FO[PI] controller outperforms the FOPI controller following the proposed design schemes for the fractional order systems considered.

Automatic generation control (AGC) and load frequency control (LFC) are very important parts of the power systems in order to provide reliable and quality power supply to the end users. Load variations can lead to the deviation generated power frequency which ultimately can destabilize the entire power system. Here comes the LFC for rescue by maintain the power frequency at desired value. In LFC, area controllers are used to perform this task. In this work, three area interconnected LFC system has been used for simulation purpose. Fractional order proportional integral (FOPI) controllers were used as an area controller in order to mitigate the effect of load variation in power system. In order to optimize the parameters of FOPI controllers, three optimization algorithms namely genetic algorithm (GA), particle swarm optimization (PSO) and water cycle algorithm (WCA) have been used. Performance comparison between optimized FOPI controllers using these algorithms has also been performed. Comparative analysis shows that the WCA optimization performed better than the other two GA and PSO algorithms. LFC system was simulated on MATLAB with the load disturbance of 0.01 p.u. in all areas at the same time.KeywordsLoad frequency controlOptimizationITAEPower systemsWater cycle algorithm

A nonlinear hybrid electric vehicle (HEV), taken as a challenge due to its uncertainty in speed control, is controlled using cascade control loop of FO-PI (Fractional order PI) and FO-PD (Fractional order PD) controllers in this research. HEVs have a combination of mechanical and electrical subsystems arranged in cascade configuration. Mechanical subsystem consists of dynamics of vehicles and electrical subsystem comprises of Electronic Throttle Control System (ETCS). The other controllers evaluated are fractional order PID (FOPID), classical PID and PI cascaded with PD (PI-PD). The gains of the proposed and other controllers are tuned by Particle Swarm optimisation algorithm (PSO). Performance is examined for tracking of speed profile trajectory, rejection of disturbance phenomenon. Different performance parameters such as IAE (Integral of Absolute Error), OS (Overshoot), Rise time, Settling time are measured for examining behaviour of different controllers designed for controlling the speed of HEVs. The IAE of FOPI-FOPD tuned by PSO is 17.077 is least amongst the IAE of FOPID, PI-PD, PID tuned by PSO with values 22.771, 25.250, 29.134 respectively for set point tracking profile. The results show that FOPI-FOPD controller tuned by PSO has superior performance as compared to other competitive controllers in controlling the speed of HEVs.

In the islanding micro-grid operation mode, due to the lack of support from the large power grid, the voltage of the bus and each node in the network is completely supported by the cooperation of the micro-grid inverters in the grid. Therefore, the control performance of the micro-grid inverter determines the quality of the power supply voltage. This paper proposes an improved AC and DC islanding micro-grid fixed frequency current control method with variable topology. First, the neural network algorithm improved by particle swarm optimization is used as the basis to optimize the coordinated compensation control of the micro-grid to obtain the fitness value of the objective function under the positive and negative sequence potentials. Then, a control strategy for the improved AC/DC hybrid micro-grid is proposed, and the decoupling and coordinated control strategy for the output compensation of the dual filters in the islanding mode is designed. Finally, simulations and experiments verify the improved AC/DC hybrid microgrid with variable topology and achieve the goal of constant-frequency current control.

This paper presents an optimal Direct Torque Control (DTC) strategy for Doubly Fed Induction Generator based wind turbine. The proposed strategy is considered based on Particle Swarm Optimization (PSO) Algorithm. The PSO is found to be robust and fast in solving nonlinear problems. Motivation for application of PSO approach is to overcome the limitation of the conventional controllers design, which cannot guarantee satisfactory control performance when designed by trial and error. In this work PSO algorithm was used to adjust the hysteresis torque and flux comparators bandwidths and to tune the parameters of Variable Gain PI (VGPI) controller designed for Maximum Power Point Tracking (MPPT) speed control of wind turbine to ensure high performance torque control. The optimal control strategy is considered in detail and it is shown that the use of the optimized controllers reduces rotor flux and electromagnetic torque ripples and improves the system dynamic performances. The effectiveness of the proposed direct torque control based on PSO algorithm (optimal DTC) method is illustrated through simulations on 1.5 MW DFIG based wind turbine. Simulation results illustrate the improved performances of optimal DTC compared with the conventional DTC.

The computation of optimal control variables for a two-stage steel annealing process (SAP) which comprises of one or more furnaces is proposed in this paper. The heating and soaking furnaces of the steel annealing line form two-stage hybrid systems. Three algorithms including particle swarm optimization (PSO) with globally and locally tuned parameters (GLbest PSO), a parameter free PSO algorithm (pf-PSO), and a PSO like algorithm via extrapolation (ePSO) are considered to solve this optimal control problem for the two-stage steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost, and job completion time obtained through these three methods are compared with each other and those obtained via conventional PSO (cPSO) with time varying inertia weight (TVIW) and time varying acceleration coefficient (TVAC). From the results obtained through the four algorithms considered, the efficacy and validity of each algorithm are analyzed.

For all first order plus time delay (FOPTD) systems, a fractional order PI (FOPI) or a traditional integer order PID (IOPID) controller can be designed to fulfill three design specifications: gain crossover frequency, phase margin, and a flat phase constraint simultaneously. In this paper, a guideline for choosing feasible or achievable gain crossover frequency and phase margin specifications, and a new FOPI/IOPID controller synthesis are proposed for all FOPTD systems. Using this synthesis scheme, the complete feasible region of the gain crossover frequency and phase margin can be obtained and visualized in the plane. With this region as the prior knowledge, all combinations of the phase margin and gain crossover frequency can be verified before the controller design. Only if the combination is chosen from this achievable region, the existence of the stabilizing and desired FOPI/IOPID controller design can be guaranteed. Especially, it is interesting to compare the areas of these two feasible regions for the IOPID controller and the FOPI controller. This area comparison reveals, for the first time, the potential advantages of one controller over the other in terms of achievable performances. As a basic step, a scheme for finding the stabilizing region of the FOPI/IOPID controller is presented first, and then a new scheme for designing a stabilizing FOPI/IOPID controller satisfying the given gain crossover frequency, phase margin and flat phase constraint is proposed in details. Thereafter, the complete information about the feasible region of gain crossover frequency and phase margin is collected. This feasible region for the FOPI controller is compared with that for the traditional IOPID controller. This area comparison shows the advantage of the FOPI over the traditional IOPID clearly. Simulation illustration is presented to show the effectiveness and the performance of the designed FOPI controller comparing with the designed IOPID controller following the same synthesis in this paper.

Voltage and Frequency control are the two fundamental control problems in power systems. Unlike frequency control, voltage control is complicated by the fact that reactive power can't travel far distances from its source of generation. Due to this distributed nature of reactive power, voltage control is usually performed in decentralized manner. Typically, voltage control problem is divided into a three-level hierarchical structure namely primary, secondary and tertiary voltage control.;The aim of this thesis is to present an optimal secondary voltage control by decomposing a large power system into small subsystems called voltage control areas (VCAs) using the fast community detection algorithm. Each VCA is self-sufficient in satisfying its reactive power demand. A load bus, called pilot point/bus, is selected in each VCA as a representative of the voltage profile of the whole area. Singular value decomposition of Fast Decoupled Load Flow (FDLF) Jacobian is used to optimally control the voltages of these pilot buses.;The presented approach is tested on two standard IEEE test power systems i.e. 9-Bus and 39-Bus systems. The computational time comparison of the fast community detection algorithm with another algorithm called original-GN algorithm is also presented. Through simulation results, it is shown that the presented optimal voltage control (Opt-VC) is a better approach compared to sensitivity based voltage control (Sen-VC).

This paper presents a new approach to optimal voltage and reactive power control based on a particle swarm optimization (PSO). The proposed method switches one of controller by PSO and considers control times to the objective function. Therefore, this method can give the control process. The methods which give the control process have not been proposed in the algorithm of PSO. The effectiveness of the proposed method is demonstrated by practical 15-bus and 118-bus systems.

The paper describe about the power flow control in bipolar HVDC transmission system (Rihand to Dadri). Rihand to Dadri Bipolar HVDC Transmission line Practical data is been used in this paper, the length of the transmission line is 816 kms 400 kV1500 MA. Thus for power flow control either we go current control or voltage control method. In this paper we go for current control method there for current control method is implemented by controlling the firing angle of the rectifier and inverter station. Thus the two converter station is controlled by rectifier controller on the rectifier side and inverter controller on the inverter side. A master controller has been designed for controlling the station controller (rectifier & inverter controller) Genetic algorithm and particle swarm optimization algorithm are optimization technique which are used to optimize the controller parameter for carrying out best result to control the power flow and comparing both the optimization technique through which algorithm we get the best results.

Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In these approaches, the only benefit of additional processors is an increased swarm size. However, in many cases this is not efficient when scaled to very large swarm sizes (on very large clusters). Current methods cannot answer well the question: “How can 1000 processors be fully utilized when 50 or 100 particles is the most efficient swarm size?” In this paper we attempt to answer that question with a speculative approach to the parallelization of PSO that we refer to as SEPSO. In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; that is, SEPSO is a new method of parallelization and not a new PSO algorithm or variant. However, given this new parallelization model, we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six compared to previous parallelization techniques.

The paper presents two types of one cycle current control method for Triple Active Bridge(TAB) phase-shifted DC-DC converter integrating Renewable Energy Source(RES), Energy Storage System(ESS) and a output dc bus. The main objective of the current control methods is to control the transformer current in each cycle so that dc transients are eliminated during phase angle change from one cycle to the next cycle. In the proposed current control methods, the transformer currents are sampled within a switching cycle and the phase shift angles for the next switching cycle are generated based on sampled current values and current references. The discussed one cycle control methods also provide an inherent power decoupling feature for the three port phase shifted triple active bridge converter. Two different methods, (a) sampling and updating twice in a switching cycle and (b) sampling and updating once in a switching cycle, are explained in this paper. The current control methods are experimentally verified using digital implementation technique on a laboratory made hardware prototype.