This paper presents an intelligent proportional-integral sliding mode control (iPISMC) for direct power control of variable speed-constant frequency wind turbine system. This approach deals with optimal power production (in the maximum power point tracking sense) under several disturbance factors such as turbulent wind. This controller is made of two sub-components: (i) an intelligent proportional-integral module for online disturbance compensation and (ii) a sliding mode module for circumventing disturbance estimation errors. This iPISMC method has been tested on FAST/Simulink platform of a 5 MW wind turbine system. The obtained results demonstrate that the proposed iPISMC method outperforms the classical PI and intelligent proportional-integral control (iPI) in terms of both active power and response time.

Direct power control of DFIG wind turbine systems based on an intelligent proportional-integral sliding mode control.

This brief presents a novel power control strategy for variable-speed wind turbines equipped with doubly fed induction generators (DFIGs). The control objective is to optimize the extracted power from wind while regulating the stator reactive power to meet grid requirements. First, in order to optimize the extracted power, an adaptive control technique is designed to drive the electromagnetic torque to follow its reference generated by the maximum power point tracking algorithm. Subsequently, aiming at satisfying reactive power requirements on the grid side, an adaptive reactive power controller is proposed to manipulate the stator reactive power to follow a given desired reactive power determined by the grid. Compared with most existing studies, we are capable of quantifying and further guaranteeing the system performance on both transient and steady-state stages. All signals in the closed-loop system are proved to be bounded via standard Lyapunov synthesis. Finally, the effectiveness of the proposed scheme is validated on a 1.5-MW DFIG-based wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulator developed by the National Renewable Energy Laboratory.

Guaranteed Performance Control of DFIG Variable-Speed Wind Turbines

This paper introduces a novel approach for dynamically detecting the lowest speed threshold at which regenerative braking is effective in electric vehicles (EVs). The control approach is based on real-time sensing of the motor controller dc link current and disabling regenerative braking when current changes direction while the motor is operating as a generator. Various factors influencing the regenerative braking capability of EVs at low speed are discussed, and the simulation studies are carried out to illustrate the effect of each factor on the displacement of the low-speed threshold. Based on the results obtained from the simulation studies, a dynamic low-speed cutoff point (LSCP) detection method is proposed. This method requires no hardware modification to the vehicle braking architecture and can be implemented solely by modifying the brake controller. The proposed method is tested on an experimental EV test platform for a predetermined drive cycle. It is shown that in comparison to considering a constant low-speed threshold during braking, the amount of energy recaptured through the regenerative braking process can be improved by taking advantage of the proposed method.

Maximizing Regenerative Braking Energy Recovery of Electric Vehicles Through Dynamic Low-Speed Cutoff Point Detection

This paper addresses the problems of state/fault estimation (FE) and fault tolerant control (FTC) for a class of Takagi–Sugeno (T–S) fuzzy systems subject to simultaneously external disturbances, sensor and actuator faults. A fuzzy-reduced-order robust state/fault estimation observer is proposed in this paper. The observer can not only estimate system state, sensor and actuator faults simultaneously, but also attenuate the influence of disturbances. Compared with the existing results, the observer has a wider application range and a lower dimension. Using the information of estimation, an observer-based fault tolerant controller is designed to compensate the fault effect and guarantee the stability of closed-loop system. In the paper, the observer and the controller are designed separately, which can avoid the coupling between them. As a result, the gain matrices of the observer and the controller can be calculated separately. At last, the simulations show the effectiveness of the proposed method.

Robust state/fault estimation and fault tolerant control for T–S fuzzy systems with sensor and actuator faults

Compared with energy storage equipment based on supercapacitors, flywheels, or lithium batteries, inverters have obvious advantages in installation space, cost, reliability, and service lifetime. For this reason, regenerative inverters are increasingly installed in the substations of urban rail transit systems. They have controllable operating characteristics, but their impact on the energy saving and railway operation cost has not been fully studied. In this paper, based on the typical application scheme of regenerative inverters, the operating characteristics of the inverter including power limitation, start working voltage, and virtual internal resistance are introduced at first. A simplified power system model containing regenerative inverters and trains has been built. The impact of operating characteristics on the amount of regenerative braking energy and its distribution is analyzed. The inverter operating characteristics are optimized by a cost function considering total energy consumption, brake shoes wear, and inverter expense. A case study based on a multiple-train scenario is presented to validate the cost-saving performance. The work is expected to provide some guides for the design and optimization of the traction power supply system with inverting substations.

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Inverter Operating Characteristics Optimization for DC Traction Power Supply Systems

This paper will present the regenerative braking quantification, design control, and simulation of a hybrid energy storage system (HESS) for an electric vehicle (EV) in extreme conditions. The EV is driven by two 30-kW permanent magnet synchronous motors. The HESS contains a Li-Ion battery and ultracapacitor (UC) storage element sources as well as a dissipative resistor. The UC will be mainly involved in braking and traction modes. The role of the resistor is to protect the dc bus and the battery according to the voltage and current constraints. The sizing of the elements takes into consideration the extreme braking conditions of the vehicle while respecting the Economic Commission for Europe Regulation No. 13 Harmonized. Controllers are being used in order to regulate the various electrical variables of the overall system. A sequential logic controller is also being introduced. The role of the sequential logic controller is to activate the different existing regulation controllers and to ensure the switching between the storage elements depending on the system states. Simulation tests will be performed covering a wide operation range for variable braking load, according to vehicle speed and road type, and extreme braking conditions.

Regenerative Braking Modeling, Control, and Simulation of a Hybrid Energy Storage System for an Electric Vehicle in Extreme Conditions

This paper addresses the robust fuzzy scheduler controller (RFSC) for nonlinear systems which is robust enough to stabilize a nonlinear system with parametric uncertainties, wind disturbance, and give an acceptable closed-loop performance in the presence of state variables unavailable for measurements. The Takagi-Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the nonlinear system. The concept of parallel distributed compensation (PDC) is employed to design fuzzy control from the TS fuzzy models. Sufficient conditions are formulated in the format of linear matrix inequalities (LMIs). The proposed controller design methodology is finally demonstrated through the model of wind energy systems (WES) with a doubly-fed induction generator (DFIG) to illustrate the effectiveness of the proposed method. The proposed algorithm maximizes the produced power and is able to maintain a stable system during the parameter uncertainties.

Robust Scheduler Fuzzy Controller of DFIG Wind Energy Systems

A fuzzy fault-tolerant control (FFTC) framework is proposed for wind-diesel-hybrid systems (WDHS) with time-varying bounded sensor faults. The algorithm utilizes fuzzy systems based on “Takagi-Sugeno” (TS) fuzzy models to represent nonlinear systems. A fuzzy proportional-integral estimation observer (FPIEO) design is proposed to achieve fault estimation of TS models with abrupt sensor faults. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability and are formulated in the format of linear matrix inequalities (LMIs) to obtain controller gains and observer gains. The proposed algorithm maximizes the produced power, minimizes the voltage ripple, and is able to maintain the system's stability during the sensor faults. A physical model of the WDHS is presented and transformed into a TS model. Then, an FFTC algorithm is developed and applied to a WDHS to demonstrate the effectiveness of this method.

Fuzzy Fault-Tolerant Control of Wind-Diesel Hybrid Systems Subject to Sensor Faults

Lung and colon cancers lead to a significant portion of deaths. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only way to improve the chances of survival and reduce cancer mortality. Using artificial intelligence in the histopathological diagnosis of colon and lung cancer can provide significant help to specialists in identifying cases of colon and lung cancers with less effort, time and cost. The objective of this study is to set up a computer-aided diagnostic system that can accurately classify five types of colon and lung tissues (two classes for colon cancer and three classes for lung cancer) by analyzing their histopathological images. Using machine learning, features engineering and image processing techniques, the six models XGBoost, SVM, RF, LDA, MLP and LightGBM were used to perform the classification of histopathological images of lung and colon cancers that were acquired from the LC25000 dataset. The main advantage of using machine learning models is that they allow a better interpretability of the classification model since they are based on feature engineering; however, deep learning models are black box networks whose working is very difficult to understand due to the complex network design. The acquired experimental results show that machine learning models give satisfactory results and are very precise in identifying classes of lung and colon cancer subtypes. The XGBoost model gave the best performance with an accuracy of 99% and a F1-score of 98.8%. The implementation and the development of this model will help healthcare specialists identify types of colon and lung cancers. The code will be available upon request. 

https://univ-angers.hal.science/hal-03846675/document

Mohamad Oueidat

Papers

Regenerative Braking Modeling, Control, and Simulation of a Hybrid Energy Storage System for an Electric Vehicle in Extreme Conditions

Robust Scheduler Fuzzy Controller of DFIG Wind Energy Systems

Fuzzy Fault-Tolerant Control of Wind-Diesel Hybrid Systems Subject to Sensor Faults

Lung and colon cancer classification using medical imaging: a feature engineering approach

Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG