Fault diagnosis in multi-machine power systems using the Derivative-free nonlinear Kalman Filter

In this paper a new approach to parametric change detection and failure diagnosis for interconnected power units is proposed. The method is based on a new nonlinear filtering scheme under the name Derivative-free nonlinear Kalman Filter and on statistical processing of the obtained state estimates, according to the properties of the statistical distribution. To apply this fault diagnosis method, first it is shown that the dynamic model of the distributed interconnected power generators is a differentially flat one. Next, by exploiting differential flatness properties a change of variables (diffeomorphism) is applied to the power system, which enables also to solve the associated state estimation (filtering) problem. Additionally, statistical processing is performed for the obtained residuals, that is for the differences between the state vector of the monitored power system and the state vector provided by the aforementioned filter when the latter makes use of a fault-free model. It is shown, that the suitably weighted square of the residuals’ vector follows the statistical distribution. This property allows to use confidence intervals and to define thresholds that demonstrate whether the distributed power system functions as its fault-free model or whether parametric changes have taken place in it and thus a fault indication should be given. It is also shown that the proposed statistical criterion enables fault isolation to be performed, that is to find out the specific power generators within the distributed power system which have exhibited a failure. The efficiency of the proposed filtering method for condition monitoring in distributed power systems is confirmed through simulation experiments

DTC based on SVM for induction motor sensorless drive with fuzzy sliding mode speed controller

By using the direct torque control (DTC), robust response in ac drives can be produced. Ripples of currents, torque and flux are oberved in steady state. space vector modulation (SVM) applied in DTC and used for a sensorless induction motor (IM) with fuzzy sliding mode speed controller (FSMSC) is studied in this paper. This control can minimize the torque, flux, current and speed pulsations in steady state. To estimate the rotor speed and stator flux the model reference adaptive system (MRAS) is used that is designed from identified voltages and currents. The FSMSC is used to enhance the efficiency and the robustness of the presented system. The DTC transient advantage are maintained, while better quality steady-state performance is produced in sensorless implementation for a wide speed range. The drive system performances have been checked by using Matlab Simultaion, and successful results have been obtained. It is deduced that the proposed control system produces better results than the classical DTC

Artificial Neural Network Active Power Filter with Immunity in Distributed Generation

With an electrical grid shifting toward Distributed Generation (DG), the emerging use of renewable energy resources is continuously creating challenges to maintain an acceptable electrical power quality thought-out the grid; Therefore, in an energy market where loads are becoming more and more sensitive in a distributed generation filled with polluting nonlinear loads, power quality improvement devices such Active Power Filters (APFs) have to evolve to meet the new standards, since theirs conventional control strategies can't properly operate when multiple power quality problems happens at once, even the one using AI based control as it will be proven in this paper. In this paper a neural network based Active Power Filter will be tested in a DG environment where both current and voltage harmonics, along with fast frequency variation occurs, we will see how the PLL can downgrade its performances enormously under such hostile conditions, We propose to solve this problem by replacing the conventional PLL with a nonlinear least square (NLS) frequency estimator, this novel NLS-ADALINE SAPF is immune in high DG penetration environment, as it will be tested and validated experimentally on an Opal-RT OP5600 FPGA based real-time simulator

A Nonlinear Optimal Control Approach for a Lower-Limb Robotic Exoskeleton

The use of robotic limb exoskeletons is growing fast either for rehabilitation purposes or in an aim to enhance human ability for lifting heavy objects or for walking for long distances without fatigue. The paper proposes a nonlinear optimal control approach for a lower-limb robotic exoskeleton. The method has been successfully tested so far on the control problem of several types of robotic manipulators and this paper shows that it can also provide an optimal solution to the control problem of limb robotic exoskeletons. To implement this control scheme, the state-space model of the lower-limb robotic exoskeleton undergoes first approximate linearization around a temporary operating point, through first-order Taylor series expansion and through the computation of the associated Jacobian matrices. To select the feedback gains of the H-infinity controller an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control loop are proven through Lyapunov analysis. Finally, to implement state estimation-based feedback control, the H-infinity Kalman Filter is used as a robust state estimator

Comparative study of the reliability of MPPT algorithms for the crystalline silicon photovoltaic modules in variable weather conditions

AbstractThe crystalline silicon photovoltaic modules are widely used as power supply sources in the tropical areas where the weather conditions change abruptly. Fortunately, many MPPT algorithms are implemented to improve their performance. In the other hand, it is well known that these power supply sources are nonlinear dipoles and so, their intrinsic parameters may vary with the irradiance and the temperature. In this paper, the MPPT algorithms widely used, i.e. Perturb and Observe (P&O), Incremental Conductance (INC), Hill-Climbing (HC), are implemented using Matlab®/Simulink® model of a crystalline silicon photovoltaic module whose intrinsic parameters were extracted by fitting the I(V) characteristic to experimental points. Comparing the simulation results, it is obvious that the variable step size INC algorithm has the best reliability than both HC and P&O algorithms for the near to real Simulink® model of photovoltaic modules. With a 60Wp photovoltaic module, the daily maximum power reaches 50.76W against 34.40W when the photovoltaic parameters are fixed. Meanwhile, the daily average energy is 263Wh/day against 195Wh/day

Simulations Based on Experimental Data of the Behaviour of a Monocrystalline Silicon Photovoltaic Module

International audienceThe performance of monocrystalline silicon cells depends widely on the parameters like the series and shunt resistances, the diode reverse saturation current, and the ideality factor. Many authors consider these parameters as constant while others determine their values based on the I-V characteristic when the module is under illumination or in the dark. This paper presents a new method for extracting the series resistance, the diode reverse saturation current, and the ideality factor. The proposed extraction method using the least square method is based on the fitting of experimental data recorded in 2014 in Ngaoundere, Cameroon. The results show that the ideality factor can be considered as constant and equal to 1.2 for the monocrystalline silicon module. The diode reverse saturation current depends only on the temperature. And the series resistance decreases when the irradiance increases. The extracted values of these parameters contribute to the best modeling of a photovoltaic module which can help in the accurate extraction of the maximum power

A comprehensive assessment of MPPT algorithms to optimal power extraction of a PV panel

The electrical energy produced by photovoltaic (PV) process is inexhaustible, developable everywhere and clean. Whatever the conditions, it is desirable to extract the biggest amount of power from the solar panel. This is achieved with the use of a Maximum Power Point Tracking (MPPT) algorithm. Fluctuations in weather conditions (irradiation and temperature) strongly degrade the performance of the photovoltaic module's energy conversion and therefore all the power cannot be transferred to the load. The objective is to study and compare different approaches of MPPT algorithms to evaluate the power extracted under the standard test conditions and varying weather conditions. Results of the performance with all these algorithms are compared under different operating conditions. The results show that the Fuzzy Logic Controller (FLC) is the fastest in terms of stabilization and is followed respectively by Fractional Short-Circuit Current (FSCC), Fractional Open-Circuit Voltage (FOCV), Perturb and Observe (P&O), Incremental Conductance (INC) and Hill Climbing (HC) algorithms. The FLC also gives the best results in extracting, followed by P&O INC, HC, FSCC and FOCV algorithms