Afshin Izadian

Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

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 LiFePO4 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.

Renewable Energy Policies: A Brief Review of the Latest U.S. and E.U. Policies

The U.S. Government and the European Union have developed policies to promote microgeneration and smart grid initiatives. Many projects and significant developments have been accomplished, although the momentum has just begun. To accelerate the implementation and to include the private sector, the U.S. states and the E.U. countries have developed their policies to improve their share of green and economic energy production. The main policies have guaranteed longterm profits for the private sector and tax credits for individuals to participate. Although incredible, these policies need to be constantly compared and modified to keep the green energy progress on track. Liquidation of the government-supported renewable energy projects and consequences for the expected sustained growth of this sector show that the government subsidies have not generated enough incentives for the private sector to decrease the cost of renewable energy manufacturing and research and development. The timely and rightful intervention of government must create a strong private sector to achieve the steady momentum of growth in technical skills and manufacturing infrastructures. This article provided a brief review of the latest relevant ideas that central and local governments have developed with the hope of creating more unified and stronger policies.

Fault Diagnosis of Time-Varying Parameter Systems With Application in MEMS LCRs

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.

Fault diagnosis of Li-Ion batteries using multiple-model adaptive estimation

In this paper a battery fault detection unit is developed using multiple model adaptive estimation technique. Impedance spectroscopy data from Li-ion cell is used along with the equivalent circuit methodology to construct the battery models. Battery faults such as over charge and over discharge cause significant model parameter variation and can be considered as separate models. Kalman filters are used to estimate the parameters of each model and to generate the residual signal. These residuals are used in the multiple model adaptive estimation technique to detect battery faults. Simulation results show that using this method the stated battery faults can be detected in real-time, thus providing an effective way of diagnosing Li-Ion battery failure.

A Hydraulic Wind Power Transfer System: Operation and Modeling

Conventional wind power plants employ a variable speed gearbox to run a generator housed on top of a tower. A new topology can remove some of the weight from the tower and centralize the wind power generation. This new topology uses a hydraulic wind power transfer system to connect several wind turbines to the generation unit. This paper demonstrates a mathematical modeling of this wind power transfer technology and its dynamic behavior. The flow response, angular velocity, and pressure of the system obtained from the mathematical model are compared with test results to demonstrate the accuracy of the mathematical model. Several speed-step responses of the system obtained from the mathematical model demonstrate a close agreement with the results from the prototype of the hydraulic wind power transfer unit.

Modeling of gearless wind power transfer

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.

Controls of hydraulic wind power transfer

The energy of wind can be transferred to the generator by employing a gearbox or through an intermediate medium such as hydraulic fluids. In this method, a high-pressure hydraulic system is utilized to transfer the energy produced from a wind turbine to a central generator. The speed control of wind driven hydraulic machinery is challenging, since the intermittent nature of wind imposes the fluctuation on the wind power generation and consequently varies the frequency of voltage. On the other hand, as the load of the generators increases, the frequency of the voltage drops. Therefore, hydraulically connected wind turbine and generator need to be controlled to maintain the frequency and compensate for the power demands. This paper introduces a closed loop gain scheduling flow control technique to maintain a constant frequency at the wind turbine generator. The governing equations of the renewable energy transfer system are derived and used to design the control system. The mathematical model is verified with a detailed model built using the SimHydraulics toolbox of MATLAB. The speed control profile obtained from a gain scheduling PI controller demonstrates a high performance speed regulation. The simulation results demonstrate the effectiveness of both the proposed model and the control technique.

Application of Kalman filters in model-based fault diagnosis of a DC-DC boost converter

This paper illustrates how Kalman filters were used in a model-based fault diagnosis of a DC-DC boost converter. A time-averaging model was used with the Kalman filters to generate residual signals. Multiple signature faults were developed in fault scenarios to identify critical variations in the elements of a power converter using the adaptive estimation technique. Results show a very precise and accurate fault diagnosis of signature faults. The fault diagnosis shows a high performance in transients and against noise in the circuit.

Modeling of hydraulic wind power transfers

The energy of wind can be transferred to the generators by using a gearbox or through an intermediate medium such as hydraulic fluids. In this method, a high-pressure hydraulic system is utilized to transfer the energy produced from a wind turbine to a central generator. In this paper, a gearless hydraulic wind energy transfer system is introduced and the dynamic model of the system is obtained. A pressure loss model is introduced to address transmission efficiency. The dynamic model is verified with a detailed model created by using the SimHydraulics toolbox of MATLAB. The comparison of the simulation results demonstrated successful validation of the mathematical model.

Multiple-model adaptive estimation of a hydraulic wind power system

Nonlinear model of hydraulic wind power system operates on a wide spectrum of operating points such as random wind speed disturbances and applied control commands. Thus, one way to linearize this model is to use multiple linear models representing the whole range of operating points. This paper introduces a minimal number of fixed linear models in a multiple model adaptive estimation (MMAE) framework to reduce the state estimation error. System parameters such as pressures of the pump and motors can be estimated while the overall error in entire operating points is reduced. The algorithm is composed of a bank of Kalman filters, each of which is modeled to match particular real world operating condition. Simulation results demonstrate that the adaptive approach can optimally estimate the state variables in a wide range of operating points.