Search

The synthesis of gold nanoparticles is dependent on both the concentration of trisodium citrate dihydrate and the time that it interacts with tetrachloroauric acid. A wide range of gold nanoparticles with various sizes and dispersity can be produced based on control variables, such as time of reaction and acid concentration, using a similar approach to that of the Turkevich model. In this model, the pH of the solution decreases slightly throughout the reaction (0.005 unit/min) due to the chemical interactions between trisodium citrate dihydrate and tetrachloroauric acid. Dicarboxy acetone is formed during citrate oxidization, resulting in gold nuclei formation over time. In addition, gold nanoparticle nucleation causes pH fluctuation over time based on gold nanoparticle sizes. An inverse correlation (coefficient of smaller than −0.97) was calculated between the pH and reaction time at different ratios of trisodium citrate dihydrate to tetrachloroauric acid. Regression analysis was used to develop a model for the prediction of the size of gold nanoparticles ranging from 18 to 38 nm based on the concentration of trisodium citrate dihydrate and the reaction time.

This article introduces a new configuration of the doubly-fed induction generator (DFIG) based wind energy conversion system (WECS) employing only a reduced-size rotor side converter (RSC) in tandem with a supercapacitor. In the proposed structure, the grid side converter (GSC) utilized in conventional DFIG-based WECSs is successfully eliminated. This is accomplished by employing the hydraulic transmission system (HTS) as a continuously variable and shaft decoupling transmission unit. This transforms the conventional constant-ratio drives by providing an opportunity to control the power flow through the generator's rotor circuit regardless of the wind turbine's shaft speed. This feature of HTS can be utilized to control the RSC power and ultimately regulate the supercapacitor voltage without a need for GSC. The proposed system is investigated and simulated in MATLAB Simulink at various wind speeds to validate the results and demonstrate the dynamic performance of the system.

This paper introduces a systematic and comprehensive method to design a constrained fuzzy model predictive control (MPC) cooperated with integral sliding mode control (ISMC) based on the Takagi-Sugeno (T-S) fuzzy model for uncertain continuous-time nonlinear systems subject to external disturbances. The proposed controller benefits from the robustness, optimality, and practical constraints considerations. The robustness against the uncertainties and matched external disturbances is achieved by the proposed ISMC without iterative calculation for obtaining the robust invariant set. The MPC schemes are designed separately based on the both quadratic and non-quadratic Lyapunov functions. By the proposed MPC, the states of the system reach the desired values in the optimal, constrained, and robust manner against the unmatched external disturbances. New linear matrix inequalities (LMIs) conditions are proposed to design both the proposed MPC schemes. Also, the practical constraints on the control signals are guaranteed in the design procedure based on the invariant ellipsoid set. To evaluate the effectiveness of the suggested strategy, some simulation and experimental tests were run.

Industrial Internet of Things (IIoT) is an architecture that facilitates the feasibility of the distributed control of the modernized industrial systems mainly through the Internet of Things and cloud computing. This article proposes an optimal scheduling framework for the real-time operation of smart microgrids in the IIoT environment using an average consensus-based algorithm. The introduced framework suggests a fog layer as a complementary layer of IIoT to reduce latency and provide local computation and data storage for the proposed industry. Security of the system against probable attacks is the other concern that should be observed. To this end, this article sets out to evaluate the impact of a particular type of attack called the denial of service on the performance of the proposed method. Accuracy, feasibility, and fast response of the scheme are demonstrated through simulation results on a microgrid test system in the presence of dispatchable and nondispatchable generation units with heterogeneous devices.

Employment of a specific technology in the conversion of wind energy to electrical power highly influences the cost and reliability of power generation. To help the selection of the best drivetrain technology, this article presents a thorough review of three major wind turbine drivetrains, namely gearbox, direct drive, and hydrostatic. Although the gearbox drivetrain has offered a mature and applicable technology, some inherent difficulties and manufacturing of specific generators and advancements in fluid power machinery resulted in new technologies that are true competitors of the gearbox. The cost, performance, and functionality of these drivetrains are now comparable with the gearbox peers. This article presents a comprehensive review of three different types of the drivetrain and their generators. This article particularly helps researchers and the engineers in the field with new insight into all technologies.

This paper presents the dynamics and control of a new wind power plant configuration where multiple turbines are connected to a shared drivetrain. A hydrostatic transmission system is utilized to collect and transfer the harvested mechanical energy of individual turbines to the generator. The interconnected wind turbines are controlled individually to achieve maximum power point tracking (MPPT) while regulating the generator speed. In the first control loop, the pump displacement is adjusted accordingly to satisfy the MPPT. In the second control loop, the electrical load on the generator is controlled to balance the torque and reach a stable frequency operation. Simulation results show the performance of the control system operation. It also demonstrates an increase and improved smoothness of a power generation profile.

Hydraulic wind power transfer systems deliver the wind energy from multiple wind turbines to a centralized generator through a hydraulic medium, which effectively reduces the equipment and maintenance costs. In this system, the main generator should be remained at a constant rotational velocity regardless of the rotor speed to provide a fixed electrical voltage to the grid. To achieve this, a proportional valve is used to control the amount of flow between the main and auxiliary generators. In this paper, a controller is designed, simulated, and implemented to regulate the flow in the proportional valve. The control system is tested in MATLAB/Simulink and experimentally validated using dSPACE 1104.

Integration of Compressed Air Energy Storage (CAES) system with a wind turbine is critical in optimally harvesting wind energy given the fluctuating nature of power demands. Here we consider the design of a CAES for a wind turbine with hydrostatic powertrain. The design parameters of the CAES are determined based on simulation of the integrated system model for a combination of these parameter values, namely the compression ratios of the air compressors and the expanders and the air tank size. The results of the simulations were used to choose the best design parameters, which would produce the best stable performance through increased energy output of the integrated CAES and wind turbine based on the intermittent wind profile. Simulation results for a 600 kW rated power wind turbine with integrated CAES indicate that increasing the tank size and compression ratio will improve the overall power quality through increased energy output up to a limit beyond which the power quality exhibits only marginal improvement.

This paper introduces an effective piecewise linearization technique to obtain an estimation of nonlinear models when their input-output domains include multidimensional operating points. The algorithm of a forward adaptive approach is introduced to identify the effective operating points for model linearization and adjust their domains for the maximum coverage and the minimum model linearization error. The technique obtains a minimum number of linearized models and the continuity of their domains. The algorithm also yields global minimum model linearization error. The introduced algorithm is formulated for a wind power transfer system for a 2-D set of input domains. The linearization error can be arbitrarily minimized in exchange for a higher number of models. The results demonstrate a significant improvement in the linearization of nonlinear models.

In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a Lithium-Ion battery with LiCoO 2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these parameter changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified model parameters were used to generate the respective battery models for both healthy and degraded batteries. These models were then validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.

Hydraulic circuits can transfer remarkable amounts of energy in the desired direction without taking large space. To implement this technology for harvesting the energy of wind appropriately, models of the system are required. Hydraulic wind power technology has the benefits of eliminating expensive and bulky variable ratio gearbox and its costly maintenance, while enabling the integration of multiple wind turbines in a single generation unit. In this paper, the dynamics of different hydraulic elements are studied, nonlinearities are taken into account, pressure dynamics in different parts of the system are studied, and the motor load effects are considered. Based on these considerations, a novel nonlinear state-space representation of the system is introduced. Results of the mathematical model and the experimental data are compared to verify the proposed model. The comparison demonstrated that the mathematical model captures all major characteristics of the hydraulic circuit and can model the system behavior under different operating conditions.

In this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion $(\hbox{LiFePO}_{4})$ cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure.

Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.

In this paper, a model based condition monitoring technique is developed for lithium-ion battery condition monitoring. Here a number of lithium-ion batteries are cycled using two separate over discharge test regimes and the resulting shift in battery parameters is recorded. The battery models are constructed using the equivalent circuit methodology. The condition monitoring setup consists of a model bank representing the different degree of parameter shift due to overdischarge in the lithium ion battery. Extended Kalman filters (EKF) are used to maintain increased robustness of the condition monitoring setup while estimating the terminal voltage of the battery cell. The information carrying residuals are generated and evaluation process is carried out in real-time using multiple model adaptive estimation (MMAE) methodology. The condition evaluation function is used to generate probabilities that indicate the presence of a particular operational condition. Using the test data, it is shown that the performance shift in lithium ion batteries due to over discharge can be accurately detected.

Multiple-model adaptive estimation (MMAE) is a well-known technique used for model matching of deterministic parameter systems. This technique can be used in fault diagnosis by allocating a model to each type of fault. In each contingency, the model that represents the behavior of the actual system can indicate the type of fault occurrence. Kalman filters are generally used in modeling and residual-signal generation of time-invariant systems. Slowly time-varying parameter systems, however, require a system identification unit in addition to the model-matching core. This paper utilizes the least square forgetting-factor technique in parameter identification of slowly time-varying systems and combines it with MMAE for fault-diagnosis applications in microelectromechanical-systems (MEMS) lateral comb resonators (LCRs). Prescheduled faults were designed for simulations and experimentally examined in real-time implementations of estimation-based diagnosis technique for two fabricated MEMS LCRs. It is shown that the application of a system identification unit significantly increases the performance of the fault diagnosis in MEMS devices.

This paper proposes a sliding mode extremum seeking control (SM-ESC) for maximum power point tracking (MPPT) in variable speed wind energy conversion system, which includes the permanent magnet synchronous generator (PMSG), the uncontrolled rectifier, boost converter, battery and the DC constant power load (CPL). The presented MPPT control method integrates the theory of sliding mode control and the extremum seeking control. It refrains from some disadvantages in traditional wind MPPT methods, such as detecting the gradient of output power vs. rotor speed, longer transient response, high frequency noise and larger oscillations of output power. The specific working principle and adaptive step size setting of the MPPT controller are also analyzed based on the SM-ESC algorithm. Numerical simulation results demonstrate accurate operation and robustness of the MPPT algorithm in each operating condition.

A Multiple Model Adaptive Estimation (MMAE) based approach of fault diagnosis for Li-Ion battery is illustrated in this paper. Electrochemical modelling approach is integrated with MMAE for fault diagnosis. This real physics based model of Li-ion battery (with Li-Co-O2 cathode chemistry) with nominal model parameters is considered as the healthy battery model. Battery fault conditions such as aging, overcharge and over discharge causes significant variations of parameters from nominal values and can be considered as separate models. Output error injection based Partial Differential Algebraic Equation (PDAE) observers are used to generate the residual voltage signals. These residuals are then used in MMAE algorithm to detect the ongoing fault conditions of the battery. Simulation results show that the fault conditions can be detected and identified accurately which indicates the effectiveness of the proposed battery fault detection method.

Hydrostatic transmission systems (HTS) have shown potential in replacing gearbox in conventional wind turbines. However, the general perception about these systems is that they suffer from low efficiencies, specifically at low wind speeds. This paper presents a novel technique that can improve the annual energy production (AEP) beyond that of a conventional wind turbine. By optimizing the operating conditions and the design of the wind turbine, the performance and efficiency of a HTS can be improved. A side-by-side comparison with the conventional wind turbines is provided to highlight the benefits of the proposed methodology. One of the findings of this research is that, rotor, hydrostatic pump, motor and their operations' planning must be optimized together to achieve higher AEP. The reconfigured turbines are shown to provide up to 8 percent AEP increase for a 750 kW plant and up to 10 percent increase for 1500 kW plants using the proposed drivetrain configurations.

This paper proposes a standalone distributed photovoltaic system which includes two independently controlled solar power sources, a battery storage and a resistive load. Each of the PV panels consist of cascaded DC-DC boost converters controlled through two independent sliding mode controllers. The design and simulation of the supervisory controller are also discussed. First, maximum power point tracking (MPPT) control strategy is introduced to maximize the simultaneous energy harvesting from both renewable sources. Then, according to the power generation available at each renewable source and the state of charge in the battery, four contingencies will be considered in the supervisory controller. Moreover, power converters interfacing the source and common DC bus will be controlled as voltage sources under a Pi-sliding mode controller. Numerical simulations demonstrate accurate operation of the supervisory controller and functionality of the MPPT algorithm in each operating condition.

This paper introduces a standalone hybrid power generation system consisting of solar and wind power sources and a DC load. A supervisory control unit, designed to execute maximum power point tracking (MPPT), is introduced to maximize the simultaneous energy harvesting from overall power generation under different climatic conditions. Two contingencies are considered and categorized according to the power generation from each energy source, and the load requirement. Simulation results demonstrate effectiveness of the controllers and functionality of the maximum power point tracking algorithm in each operating condition for both solar and wind power sources.

Hydraulic wind power transfer systems allow collecting of energy from multiple wind turbines into one generation unit. They bring the advantage of eliminating the gearbox as a heavy and costly component. The hydraulically connected wind turbines provide variety of energy storing capabilities to mitigate the intermittent nature of wind power. This paper presents an approach to make wind power become a more reliable source on both energy and capacity by using energy storage devices, and investigates methods for wind energy electrical energy storage. The survey elaborates on three different methods named 'Battery-based Energy Storage', Pumped Storage Method, and 'Compressed Air Energy Storage (CAES)'.

Hydraulic wind power transfer systems deliver the captured energy by the blades to the generators differently and through an intermediate medium i.e. hydraulic fluid. This paper develops a control system for a nonlinear model of hydraulic wind power transfer systems. To maintain a fixed frequency electrical voltage by the system, the generator should remain at a constant rotational speed. The fluctuating wind speed from the upstream applies considerable disturbances on the system. A controller is designed and implemented to regulate the flow in the proportional valve and as a consequence the generator maintains its constant speed compensating for low wind speed and high wind speed disturbances. The controller is applied to the system by utilizing MATLAB/Simulink.

This paper presents a control approach for a single switch inverter. The inverter exhibits non-minimum phase behavior in some of the operating modes. A dual feedforward controller was employed with an adaptive PI controller to obtain perfect tracking performance. The controller successfully isolates the non-minimum phase part of the system from the minimum phase. Both separated minimum phase and non-minimum phase sub-systems were used in the dual feedforward scheme to generate desired references. The non-minimum phase dynamics are transformed to minimum phase by using an inverse system transfer function as parallel compensator. The adaptive PI controller was used to track the voltage references.

In this paper, a gearless hydraulic wind energy harvesting and transfer system is mathematically modeled and verified by experimental results. The energy is harvested by a low speed-high torque wind turbine connected to a high displacement hydraulic pump, which is connected to hydraulic motors. The quality of transferred power from the wind turbine to the generator is important to maintaining the systems power balance, and frequency droop control in grid-connected applications, and to ensure that the maximum output power is obtained. The gearless wind power transfer technology may replace the current energy harvesting system to reduce the cost of operation and increase the reliability of wind power generation.