Showing papers in "Journal of Control, Automation and Electrical Systems in 2019"

Security Against Communication Network Attacks of Cyber-Physical Systems

Abstract: Communication networks are commonly used to connect sensors, actuators, and controllers to monitor and control cyber-physical systems (CPS) The use of communication networks increases the vulnerability of the CPS to cyber attacks that can drive the system to reach unsafe states One of the most powerful cyber attacks is the so-called man-in-the-middle attack, where the intruder can observe, hide, create or replace information in the attacked network channel In a previous paper, we have introduced the definition of NA-Safe controllability, that is related with the capability of detecting intrusions and preventing damages caused by man-in-the-middle attacks in the sensor and/or control communication channels in supervisory control systems In this paper, we extend our previous work as follows: (i) we prove the correctness of the NA-Safe controllability verification algorithm; (ii) we show how to implement the security module against cyber attacks; (iii) we show that NA-Safe controllability is a necessary and sufficient condition for the existence of the security module; and (iv) we present a practical example to illustrate the results of the paper

A New Controller for DC-DC Converters Based on Sliding Mode Control Techniques

Abstract: A new general sliding mode controller is proposed for a DC–DC converter that can regulate the output voltage. Due to the nature of some non-minimum phase converters, an indirect method is used to control the output voltage. A robust nonlinear controller is employed that uses the output voltage error integral and provides zero steady-state error. The proposed method was simulated in MATLAB/Simulink, and the controller, buck converter, boost, buck/boost, and flyback responses were determined. The proposed sliding mode control can cover the operating range of load variations, input voltage, and other parameters to provide robust and steady output voltage.

Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Abstract: The paper introduces an adaptive strategy to effectively control a nonlinear dual-arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method, the proposed algorithm first allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodeled dynamics of the dual-arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot’s dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN–BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.

A New Cascade Fuzzy Power System Stabilizer for Multi-machine System Stability Enhancement

Abstract: This paper introduces a new robust controller with cascaded fuzzy blocks as a power system stabilizer (CFPSS) to enhance damping during low-frequency oscillations. This CFPSS is designed to act as a nonlinear lead–lag PSS with a given number of compensation blocks. To demonstrate the efficiency and the robustness of this proposed stabilizer, simulation results performed on the IEEE three-generator nine-bus multi-machine power system subjected to a three-phase short-circuit fault have been carried out. The parameters of the proposed PSS and those of the conventional IEEE linear lead–lag PSS have been tuned by a recently developed optimization technique (krill herd algorithm). The robustness of this novel CFPSS is proved, by optimizing the parameters of the two PSSs for one operating point (normally loaded system) and applying them to other operating points (case of heavy and low loads) with some key parameters variation. The obtained results have shown the superiority and the robustness of the CFPSS comparatively to the conventional IEEE lead–lag PSS in terms of oscillations damping over a wide range of operating conditions and against parametric variation. The same conclusions have been drawn in the case of a large power system (IEEE 16-machine, 68-bus test system) characterized by its local and inter-area oscillations modes.

Speed Control of a Separately Excited DC Motor Using New Proposed Fuzzy Neural Algorithm Based on FOPID Controller

Abstract: The main goal of this paper is to control the speed of a separately excited DC motor (SEDM) with a new proposed fuzzy neural (FN) controller. This proposed method is used to adjust the fractional order proportional integral derivative (FOPID) parameters of the controller. Also the proposed control diagram solves the problem of parameter setting of the FN controller more effectively with use of particle swarm optimization (PSO) algorithm. In simulation with MATLAB 2017b, 250 series of data were used: 175 series of data, equivalent to 70% for training the designed neural network, and about 75 series, equivalent to 30% used to test and validate the neural network. The results show that the proposed method has a lower rise time and settling time for controlling the speed of SEDM in comparison with other methods such as Ziegler–Nichols, Cohen–Coon, PSO, genetic algorithm, artificial bee colony, artificial neural network, fuzzy logic controller and adaptive neuro-fuzzy interference system for PID and FOPID controllers.

Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic

Abstract: Autonomous Unmanned Aerial Vehicles (UAVs) become an important field of research in which multiple applications can be designed, such as surveillance, deliveries, and others. Thus, studies aiming to improve the performance of these vehicles are being proposed: from new sensing solutions to more robust control techniques. Additionally, the autonomous UAV has challenges in flight stages as the landing. This procedure needs to be performed safely with a reduced error margin in static and dynamic targets. To solve this imperative issue, many applications with computer vision and control theory have been developed. Therefore, this paper presents an alternative method to train a multilayer perceptron neural network based on fuzzy Mamdani logic to control the landing of a UAV on an artificial marker. The advantage of this method is the reduction in computational complexity while maintaining the characteristics and intelligence of the fuzzy logic controller. Results are presented with simulation and real tests for static and dynamic landing spots. For the real experiments, a quadcopter with an onboard computer and ROS is used.

Dynamic Analysis of Grid-Connected Droop-Controlled Converters and Synchronverters

Abstract: The widespread utilization of micro-sources connected to the power grid, especially in microgrid applications, has led to the development of many different techniques to allow the parallel operation of these sources without communication links. Most of the proposed techniques aim to emulate the behavior of a synchronous generator, as it is the case of the droop control and the synchronverter. The goal of this paper is to develop a unified framework for the study of the dynamics of these two different approaches and to do a comparative analysis of their behavior using steady-state and small-signal models. Even though the mechanics of realization of the two approaches are different, it is shown that their models can be unified in a form that explicitly demonstrates their similarities and differences. The paper presents relationships between the equivalent parameters of the two systems that affect particular dynamic behaviors. Furthermore, it is shown that the differences in their small-signal models are restricted to a single $$2 \times 2$$ matrix. In fact, the $$2 \times 2$$ matrix can be appropriately selected to model different control techniques such as virtual synchronous generators or virtual synchronous machines. The results are validated via MATLAB/Simulink simulations and a hardware-in-the-loop with the micro-source control running in a TMS320F28335 Texas Instruments microcontroller.

Allocation of Static Var Compensator in Electric Power Systems Considering Different Load Levels

Abstract: This paper proposes an approach to determine the optimal location of static var compensators (SVCs) in electric power systems in order to improve voltage profile and minimize active power losses. A multi-scenario framework that includes different load levels with different time periods is considered in this approach. The problem is formulated as a mixed-integer nonlinear programming problem using an optimal power flow (OPF). The SVC value and location are modeled as a variable susceptance inside the bus admittance matrix and as a binary decision variable, respectively. The problem is solved using the branch and bound algorithm associated with the OPF. Studies and simulations were conducted on the IEEE 118-bus test system considering variations in both the objective function and the amount of SVCs to be allocated. Analysis of results demonstrate that the performance of the power system can be effectively enhanced due to the optimal allocation of SVC equipment if considering different load levels with different time periods for the allocation of SVCs, rather than allocate the SVCs separately.

A Novel Moving Average Forecasting Approach Using Fuzzy Time Series Data Set

Abstract: In this study, we develop a novel moving average forecasting approach based on fuzzy time series data set. The main objective of applying this moving average approach in develop method is to provide better results and enhance the accuracy in forecasted output by reducing the fluctuation in time series data set. The developed method is to define the universe of discourse and partition into equal length of intervals which is based on the average-length method. Further, triangular fuzzy sets are defined and obtain a membership grade of each moving average historical datum rather than actual datum of historical fuzzy time series data. Here, the fuzzification process of moving average historical data to their maximum membership grades obtained into corresponding triangular fuzzy sets. The general suitability of developed model has been examined by implementing in the forecast of student enrollments data at the University of Alabama. Further, the market price of State Bank of India share at Bombay Stock Exchange, India, has also been implemented in the forecast. The developed method of moving average fuzzy time series provides an improved forecasted output with least root mean square error and average forecasting errors which shows that our developed method is more superior than other existing models available in the literature based on fuzzy time series data.

A Potential Function for the Power Flow in DC Microgrids: An Analysis of the Uniqueness and Existence of the Solution and Convergence of the Algorithms

Abstract: The power flow equations in DC microgrids are nonlinear due to the presence of constant power terminals. In this context, a rigorous demonstration of the convergence and uniqueness of the solution for Newton’s method is required. This problem is particularly important in islanded microgrids, where the power flow method determines the equilibrium point, which in turn is used for other analyses such as stability, optimal operation, and reliability. In this paper, we present a new concept associated with power flow equations, namely the potential function of the power flow. This function allows transforming the power flow problem into an optimization model and uses convex analysis for determining its convergence and the uniqueness of the solution. Being a scalar function, the potential of the power flow can give valuable geometrical insights on the problem. In addition, the optimization approach can be used to solve the power flow problem considering inequality constraints. Simulation results demonstrate the applicability of this approach in practice.

Motion Analysis and Control of Three-Wheeled Omnidirectional Mobile Robot

Abstract: Omnidirectional mobile robots are holonomic vehicles that can perform translational and rotational motions independently and simultaneously. The paper provides a detailed mathematical analysis of the motion of a three-wheeled omnidirectional mobile robot leading to the kinematics of the robot. The motion of the robot can be divided into three types, pure rotation, linear motion and rotation around a point of a nonzero radius. The paper also addresses the problem of trajectory tracking, where the robot has to track the desired trajectory while tracking the desired orientation; to do so; a fuzzy controller has been designed. A comparison made between the proposed controller and another from the literature showed that the fuzzy controller with a minimal number of fuzzy rules (only four rules) is more efficient and more accurate. Furthermore, the paper proposes a simple approach to solve the kinematic saturation problem, namely that the control outputs must be within the range of the admissible control. A simulation platform was carried out using MATLAB to demonstrate the effectiveness of the proposed approach.

A New Method to Estimate Induction Machine Parameters from the No-Load Startup Transient

Abstract: The paper proposes a new method to estimate the electrical parameters of three-phase induction machines based only on the voltages and currents of the stator acquired during a no-load startup test. The method is based on the instantaneous impedance and on the rotor speed, both estimated according to procedures detailed in the paper. Parameter variations with the speed are determined based on the concept of data windowing and using a single-cage model. Theoretical and practical considerations are presented and discussed, with the method being validated through simulations and also through experimental results obtained for eleven machines with power from 5.5 to 75 kW. The most relevant characteristic of the proposed method is that the variation of the resistance and leakage inductance of the rotor during the transient, caused mainly by skin effect, can be effectively and systematically assessed. The practical results proved that the method has great potential to advantageously replace traditional no-load and blocked-rotor tests as it requires less time and has much lower associated costs.

Mobile Robot Localization Based on the Novel Leader-Based Bat Algorithm

Abstract: This work proposes a new approach to the well-known method bat algorithm for solving the mobile robots global localization problem. The proposed method is leader-based bat algorithm (LBBA). The LBBA uses a small number of better micro-bats as leaders to influence the colony in the search for the best position, dealing satisfactorily with ambiguities during the localization process. The tests covered different scenarios aiming at comparing the proposed algorithm with other methods, such as the standard BA, the particle swarm optimization and particle filter. The results outperformed the compared methods, presenting a fast response and errors below the intended tolerance. The algorithm was tested in the robot kidnapping scenario and shows fast recovery in both simulation and in a real environment. In addition, the proposed technique showed 21% lower average error when compared with an algorithm that presents a variable quantity of particles, i.e. the adaptive Monte Carlo localization algorithm.

An Observer-Based Controller for Congestion Control in Data Networks

Abstract: This paper investigates the design of an observer-based controller to overcome the congestion problem in the TCP/IP networks where the available link bandwidth is modeled as a time-variant disturbance. Then, a detailed description is discussed to establish a linearized mathematical model in order to ensure the robust stability of the saturated delayed system and satisfy the $$H_{\infty }$$ performance. Based on a Lyapunov–Krasovskii (L–K) functional, a generalized sector condition, and the Finsler’s Lemma, the proposed new design methodology leads to a quite simple LMI condition that is numerically tractable with any convex optimization algorithm. An effective iterative optimization algorithm is adopted to estimate the largest possible region of initial conditions as will be seen in the numerical examples where the results are compared to PI, RED, and REM controllers.

Data-Driven Control Design by Prediction Error Identification for Multivariable Systems

Abstract: This paper deals with data-driven control design in a model reference framework for multivariable systems. Based on a single batch of input–output data collected from the process, a fixed structure controller is estimated without using a process model, by embedding the control design problem in the prediction error identification of an optimal controller. This is an extension of optimal controller identification (OCI) for multivariable systems. Even though the multiple-input multiple-output (MIMO) formulation is extended from its single-input single-output version in a natural way, the solution of the optimization problem is rather complex due to the special structure the inverse of the controller assumes in its MIMO version. Comparisons between the OCI and the virtual reference feedback tuning—a well-known data-driven control method—are provided, showing the efficiency of the OCI controller estimate. We also explore the case where the batch of design data is collected in closed loop. Simulated and experimental results show the efficiency of the proposed methodology.

A Modified perturb-and-observe-based Maximum Power Point Tracking Technique for Photovoltaic Energy Conversion Systems

Abstract: One of the most widely used maximum power point tracking (MPPT) methods in photovoltaic systems (PVSs) is perturb and observe (PO (2) the tracking deviation under sudden change as well as gradual variation in the irradiation conditions. This paper proposes a simple modified P&O-based MPPT method for PVSs in which the perturbation size is adaptively determined in such a way that the convergence speed to the MPP is increased and the undesirable oscillations around the MPP are decreased. In addition, the direction of the perturbations is determined on the basis of incremental conductance MPPT method so that the proposed algorithm is capable to extract the maximum power from the PVS, even under sudden or gradual variation in solar irradiation. The validity and effectiveness of the proposed algorithm are investigated using time-domain simulations in the MATLAB® software environment.

Discriminating Between Loss of Excitation and Power Swings in Synchronous Generator Based on ANN

Abstract: This paper presents a newly designed scheme based on neural networks to detect loss of excitation (LOE) in synchronous generators. The proposed scheme uses more accurate mechanism and needs fewer parameters in order to achieve fast and reliable detection of LOE. Furthermore, being able to discriminate between LOE and stable power swings is a major concern to enhance the performance of traditional LOE protection. Therefore, the designed network is trained to discriminate between both cases clearly. For training and testing the proposed neural network, MATLAB program has been used for simulation. In addition, by using comparison analysis between the designed network and the previous ones and the traditional MHO relay, the results ensure that the proposed scheme has more secure and fast characters in detecting and discriminating LOE.

Dynamics, Circuitry Implementation and Control of an Autonomous Helmholtz Jerk Oscillator

Abstract: In this paper, an autonomous three-dimensional Helmholtz-type oscillator is designed based on conversion of an autonomous Helmholtz two-dimensional oscillator to a jerk oscillator. For a suitable choice of the parameters, the proposed autonomous Helmholtz jerk oscillator can generate Hopf bifurcation, bistable period-2 limit cycles, two types of one-scroll chaotic attractors and coexistence between period-3 limit cycle and one-scroll chaotic attractors. Using a weak modulation of a parameter of the proposed Helmholtz jerk oscillator, it is possible to destroy the coexisting attractors found and transform the proposed Helmholtz jerk oscillator to period-3 oscillations. Moreover using experiments and OrCAD-PSpice software, circuit implementation of the proposed autonomous Helmholtz jerk oscillator is realized in order to check the one-scroll chaotic attractors and the coexisting attractors found during the numerical simulations. Numerical and experimental/OrCAD-PSpice results have a good qualitative agreement. Finally, by adding two new parameters in the proposed autonomous Helmholtz jerk oscillator, it is possible to control the amplitude of the attractor and the largest Lyapunov exponent.

NARX Model Identification Using Correntropy Criterion in the Presence of Non-Gaussian Noise

Abstract: In past years, the system identification area has emphasized the identification of nonlinear dynamic systems. In this field, polynomial nonlinear autoregressive with exogenous (NARX) models are widely used due to flexibility and prominent representation capabilities. However, the traditional identification algorithms used for model selection and parameter estimation with NARX models have some limitation in the presence of non-Gaussian noise, since they are based on second-order statistics that tightly depend on the assumption of Gaussianity. In order to solve this dependence, a novel identification method called simulation correntropy maximization with pruning (SCMP) based on information theoretic learning is introduced by this paper. Results obtained in non-Gaussian noise environment in three experiments (numerical, benchmark data set and measured data from a real plant) are presented to validate the performance of the proposed approach when compared to other similar algorithms previously reported in the literature, e.g., forward regression with orthogonal least squares and simulation error minimization with pruning. The proposed SCMP method has shown increased accuracy and robustness for three different experiments.

Robust Observer-Based Actuator and Sensor Fault Estimation for Discrete-Time Systems

Abstract: In this paper, a robust fault estimation method based on unknown input observer (UIO) is proposed to estimate states, actuator and sensor faults simultaneously in a discrete-time system. The UIO is designed by using an $$H_\infty $$ technique, which is developed to both maintain the estimation error stable and reduce the disturbances that cannot be decoupled. In the first part of this paper, the observer is addressed for discrete-time linear systems subjected to sensor noise and process disturbances. In sequence, the method is extended to handle Lipschitz nonlinear systems. The proposed method is validated through two numerical examples, and a comparison between the proposed techniques and Extended Kalman Filter is presented. The results show the proposed approach as a better observer in terms of state and fault estimation, and process disturbance and sensor noise rejection.

An Algorithm for Optimal Placement of Voltage Sag Monitors

Abstract: Voltage sags are disturbances that deserve special attention in power quality (PQ) area, given its frequent occurrences. Their constant monitoring is, therefore, essential to diagnose its causes and mitigate economic losses of electric utility customers. However, the cost of a monitoring system may be excessive if not evaluated strategically. In this context, this work presents an algorithm for the installation of PQ monitors at strategic points of electric power distribution systems in order to diagnose voltage sags. Observability area concept and binary particle swarm optimization method were used to evaluate the problem. A sensitivity analysis was also performed, in which the influence of several parameters, such as fault resistance, system loading, detection threshold, fault type, and system expansion, was evaluated. The algorithm was validated in a Brazilian distribution system and in IEEE 34-bus system. The results indicated that the algorithm was able to detect voltage sags throughout the system using monitors at few buses, reducing the cost of the monitoring system.

Validation of Control Platform Using TMS320F28027F for Dynamic Voltage Restorer to Improve Power Quality

Abstract: A real-time control platform progressed for the power quality (PQ) change in a conveyance framework has been displayed in this paper. A DSP-based control platform is demonstrated for voltage sag compensation for dynamic voltage restorer in conjunction with a voltage source inverter (VSI) to enhance PQ. The output of VSI is controlled by activating IGBTs by SPWM control technique. A driver circuit is developed along with a filter circuit to compensate load voltage when sag occurs in a distribution system supplying a sensitive load of single-phase induction motor. The developed control platform has been asserted with a DSP processor TMS320F28027F, and its effectiveness has been shown by carrying a few experiments.

Adaptive and Exact Linearization Control of Multicellular Power Converter Based on Shunt Active Power Filter

Abstract: Most research work on active power filter (APF) uses constant DC bus voltage. However, variable nonlinear load might affect the stability of these APFs and increase the power switching loss and voltage stress on switches. In this work, the contribution is twofold. First, regulate the DC bus voltage of APF, adaptively, with the nonlinear load variations and generate the reference of the harmonic currents based on the instantaneous power theory. Second, track this reference to mitigate harmonics of the grid-side current by the exact linearization control of three-phase multicellular power converter with reduced rating power switches and reduced power switching loss under variable nonlinear load. Simulation results with comparison study with classical topology of power converter demonstrate that the adaptive exact linearization control enhances the power quality with a minimum power switching loss and reduces the total harmonic distortion of grid-side current to a value that satisfies the limits of IEEE standard. Moreover, the DC bus voltage changes, adaptively, according to different values of nonlinear load power.

Multivariable State-Space Recursive Identification Algorithm Based on Evolving Type-2 Neural-Fuzzy Inference System

Abstract: In this paper, a novel approach for state-space evolving type-2 neural-fuzzy identification of multivariable dynamic systems is proposed. According to adopted methodology, conditions for creating and merging clusters are used to perform the structural adaptation of the neural-fuzzy model. The center and shape of each cluster are estimated, defining all rules in the interval type-2 neural-fuzzy inference system. The degree of uncertainty on the shape of type-2 membership functions is computed through an extended Kalman filter-based learning mechanism. Once the type-2 membership functions (upper and lower membership values) are estimated, the fuzzy Markov parameters are computed from experimental data, and for each incoming information, the parameters of state-space linear models in the consequent proposition of inference system are recursively estimated. The efficiency and applicability of the proposed methodology are demonstrated through experimental results of modeling of an industrial dryer.

Dynamic Analysis of a Snap Oscillator Based on a Unique Diode Nonlinearity Effect, Offset Boosting Control and Sliding Mode Control Design for Global Chaos Synchronization

Abstract: This paper focuses on the behavior of a specific class of oscillator (snap) under a unique diode effect by checking the complex dynamic of the proposed oscillator. From linear and nonlinear analysis methods, the numerical integration of the system such as, fixed-point and stability analysis, bifurcation diagrams, Kaplan–Yorke dimension, Lyapunov exponent spectrum, frequency spectra, Poincare section and cross section of basins of attraction reveals that oscillator is chaotic and the chaotic robustness of the system depends on parameters changing. To the best knowledge of the authors, the system is the simplest in its category but the study revealed some interesting phenomena: asymmetric coexisting attractors, antimonotonicity phenomenon and even period-doubling bifurcation. These phenomena confer oscillating the quality of multistable or resistance to attacks in engineering application of data encryption. Furthermore, an offset boosting operation of a variable is used to control attractors and a robust sliding mode control for control engineering application of the system is designed to achieve global chaos synchronization based on Lyapunov stability theory. An appropriate electronic circuit or analog simulator is designed; PSpice simulations demonstrate feasible the proposed snap.

Identification of Corrosive Substances and Types of Corrosion Through Electrochemical Noise Using Signal Processing and Machine Learning

Abstract: Several systems in industries are subject to the effects of corrosion, such as machines, structures, and a lot of equipment. As consequence, the corrosion can damage structures and equipment, causing financial losses and accidents. Among the most common types is the localized corrosion, and it is present in most industrial processes and is the most difficult to detect. Such consequences can be reduced considerably with the use of methods of detection, analysis and monitoring of corrosion in hazardous areas, which can provide useful information to maintenance planning and accident prevention. In this work, we analyze some features extracted from electrochemical noise for the classification of different types of localized corrosion. Furthermore, we use some techniques to identify corrosive substances that may cause corrosion in materials. For both tasks, we apply signal processing and machine learning techniques. Experimental results show that the features obtained using wavelet transform and recurrence quantification analysis are effective to solve both tasks: the corrosion identification and the classification of substances. Almost all evaluated machine learning techniques achieved an average accuracy above 90%.

Direct Synthesis Based PID Controller Design for Time-Delayed Unstable Processes in Parallel Control Structure

Abstract: In this paper, a proportional–integral–derivative controller design for unstable time-delay system in the parallel control structure is proposed. The parallel control structure has two separate controllers for the set-point tracking and the load disturbance rejection. The two proposed controllers are designed through direct synthesis approach by selecting the separate closed-loop reference model for the set-point and the load disturbance response. The proposed design methods are applicable for both the low-order and the high-order unstable processes without model reduction in the higher-order process. The effectiveness of the proposed methods is demonstrated through simulation of different examples and comparison with the recently reported methods in the literature.

Design of Non-overshooting Fractional-Order PD and PID Controllers for Special Case of Fractional-Order Plants

Abstract: This study focuses on shaping the transient response of special case of fractional-order systems by using fractional-order PD (FOPD) and fractional-order PID (FOPID) controllers. For a plant with a fractional-order pole, a FOPID controller is designed in which the orders of its derivative and integral terms are considered equal to the commensurate order of the plant while for an integrating plant with a fractional-order pole, a FOPD controller is designed. The region of controller parameters is extracted to obtain a closed-loop system with a monotonically decreasing magnitude–frequency response. This leads to a non-overshooting or low-overshoot step response. The gain crossover frequency and phase isodamping conditions are employed to select appropriate controller parameters among the mentioned region. The numerical examples are provided to show the efficiency of the proposed FOPD and FOPID controllers.

Integrated Fault-Tolerant Stabilization Control for Satellite Attitude Systems with Actuator and Sensor Faults

Abstract: In this paper, the problem of integrated fault-tolerant stabilization control is studied for the attitude systems of a rigid satellite with both actuator and sensor faults. Firstly, a virtual observer is designed for the faulty attitude systems of rigid satellite in order to estimate the unknown actuator and sensor faults. Because the virtual observer includes unmeasurable information of the attitude systems, the real observer is then presented. On this basis, an integral sliding-mode fault-tolerant stabilization control approach is proposed by using the estimated fault information, and it not only suppresses the disturbance with a disturbance attenuation level $$\gamma $$ , but also eliminates the effects of actuator and sensor faults. Finally, the effectiveness of the proposed fault-tolerant stabilization scheme is demonstrated in the attitude systems of a rigid satellite subject to the time-varying actuator and sensor faults.

Maximum Penetration Level Evaluation of Hybrid Renewable DGs of Radial Distribution Networks Considering Voltage Stability

Abstract: The issues regarding generation uncertainties associated with wind energy and solar photovoltaic (PV) systems along with load demand uncertainties are considered in this paper to evaluate the maximum penetration of renewable energy resources. The nodes which are less voltage stable are considered as the most suitable locations for distributed generations (DGs) placement. For identification of these critical nodes, a voltage stability index (VSI) has been utilized. To analyze the voltage profile, power losses and system voltage stability with large penetration of the wind energy and solar PV into the distribution networks, a probabilistic-based approach has been adopted. The penetration limit depends upon the type of DG that is connected to the distribution network. Usually, the integration of DGs reduces the power losses in the network, however as penetration level increases, the power losses begins to increase. The detailed mathematical models of wind and solar PV-based renewable resources are used. The Hong’s $$2m+1$$ point estimation method combined with Cornish–Fisher expansion is adopted in this paper to conduct the probabilistic studies. The effectiveness of the method is validated through IEEE 33-node radial distribution test network for four different scenarios. The results obtained have been verified and compared with Monte Carlo simulation technique.