This paper proposes anti-windup proportional integral controller to deal with generation rate constraint problem during a frequency load control. An interconnected two-area power system is used by ...considering nonlinear constraints for thermal and hydro units combined with dish-Stirling solar thermal system (DSTS) in the first area, the nuclear power plant in the second area, and HVDC link in both areas. The imperialist competitive algorithm was used for optimal adjustment of parameters of the proposed controller. Simulations are performed in different scenarios. The proposed control scheme is compared with conventional PI controller; the effect of the presence of DSTS and HVDC link is investigated, and finally the robustness of the proposed controller against uncertain load is studied. The comparisons are made by using various performance factors of the system response such as overshoot, settling time and rise time. The results show a significant improvement in the system response and reduction in oscillation frequency of the areas and the power of the tie-line.
Due to the nonlinear behavior of grid-connected cascaded multilevel inverters (GCCMI), the use of nonlinear controllers can guarantee system stability over a wide range of operation. Therefore, ...state-space modeling is required to design nonlinear controllers. In this manuscript, a comprehensive method is proposed for the general state-space modeling of an n-level GCCMI with LCL coupling. To validate the accuracy of obtained state-space model, an experimental setup of a cascaded multilevel inverter including two H-bridges has been implemented. The outputs of the state-space model are compared with the simulation and experimental results of the GCCMI. This shows that the proposed model is compatible with a real closed-loop system. The simulations were performed using EMTDC/PSCAD software. In the following, the designed general model is used to develop a nonlinear controller based on the Lyapunov stability criteria for a multilevel shunt active power filter (SAPF). Results show that the designed controller is stable and robust in a wide range of operating point changes.
Deep convolutional neural networks (CNNs) are used for the detection of COVID-19 in X-ray images. The detection performance of deep CNNs may be reduced by noisy X-ray images. To improve the ...robustness of a deep CNN against impulse noise, we propose a novel CNN approach using adaptive convolution, with the aim to ameliorate COVID-19 detection in noisy X-ray images without requiring any preprocessing for noise removal. This approach includes an impulse noise-map layer, an adaptive resizing layer, and an adaptive convolution layer to the conventional CNN framework. We also used a learning-to-augment strategy using noisy X-ray images to improve the generalization of a deep CNN. We have collected a dataset of 2093 chest X-ray images including COVID-19 (452 images), non-COVID pneumonia (621 images), and healthy ones (1020 images). The architecture of pre-trained networks such as SqueezeNet, GoogleNet, MobileNetv2, ResNet18, ResNet50, ShuffleNet, and EfficientNetb0 has been modified to increase their robustness to impulse noise. Validation on the noisy X-ray images using the proposed noise-robust layers and learning-to-augment strategy-incorporated ResNet50 showed 2% better classification accuracy compared with state-of-the-art method.
Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but ...electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method.
•Presenting a new feature selection.•Application of two-stage forecast engine consists of RNN and ENN.•Presenting an improved stochastic algorithm for forecast engine optimization.•An effective prediction model for training the hybrid forecast engine is presented.•Testing forecast strategy over three real world engineering cases.
Object manipulation is a basic task in robotics and automation. Active manipulation by grasp is conventional approach in object manipulation. However, in many cases, grasp-less manipulation can be ...beneficial in terms of cost, minimalism and extension of workspace. On the other hand passive mechanisms are advantageous from the energy saving viewpoint. In this paper we combine these ideas to develop a dynamic passive object manipulation mechanism to achieve manipulation in more than one dimension and simultaneously change position and orientation of the object. In developed mechanism the manipulation platform is a simple inclined surface. The object is composed of two wheels with different radiuses and an axle connecting the wheels to each other. The object moves passively along a circular path on the platform. Kinematic equations of the motion are devised, dynamic analyses are performed and no-slippage conditions are extracted. Modelling in CATIA and simulations in MSC.ADAMS are performed and experimental set up is built to verify the analysis.
The process of empowering muscles in order to make them to a normal and common value is an expensive and prolonged work, in common available methods. There are some commercial exercise machines used ...for this purpose called rehabilitation systems. However, due to their insufficient motion freedom and prospect of being expensive, these machines have limited usage. Hence, it is clearly necessary that Mechatronic technologies should be used in this area. In this paper, an algorithm and an improved rule are presented for controlling a rehabilitation system of lower limbs which is implemented on a 6-Degree Of Freedom (DOF) Stewart parallel robot. Impedance control and adaptive control are used for this purpose. Estimation and optimization of control parameters will be done by artificial neural networks and genetic algorithms, respectively (intelligent strategy). Safety is guaranteed since some of controller parameters can be adapted under the stability conditions given by using Routh stability theory. Thereafter, the results of simulations are presented by defining a physiotherapy standard mode on a desired trajectory. MATLAB/SIMULINK is used for simulations. Finally, a comparative discussion between this strategy and common methods is devised.
This paper presents a model for scheduling an energy hub (EH) whi ch supplies electricity, heat and cooling demands and manages continuous and on/off controllable appliances. The characteristics of ...EH components such as energy loss and depreciation cost of electrical, heat and cooling energy storages, as well as the feasible operation region of combined heat and power units are modeled in detail. The proposed formulation is implemented on two summer and winter typical days for various cases to study the impact of energy storage, selling electricity to the grid, smart charging and discharging of electric vehicles and managing controllable appliances. The results show that using EH and managing demands result in considerable benefits for both consumer and electricity grid, and leads to flatter demand curve under time of use demand response program.
•Propose a new energy hub for supplying electricity, heat and cooling demands.•A new energy flow model among the hub's components is proposed.•The possibility of switching on/off storage systems is incorporated in model.•The model is able to manage controllable demands including electric vehicles.
A path planning method for mobile robots based on two dimensional cellular automata is proposed. The method can be applied for environments with both concave and convex obstacles. It is also ...appropriate for multi-robot problems as well as dynamic environments. In order to develop the planning method, environment of the robot is decomposed to a rectangular grid and the automata is defined with four states including Robot cell, Free cell, Goal cell and Obstacle cell. Evolution rules of automata are proposed in order to direct the robot toward its goal. CA based path planner method is afterwards modified by a colony technique to be applicable for concave obstacles. Then a layered architecture is proposed to autonomously implement the planning algorithm. The architecture employs an abstraction approach which makes the complexity manageable. An important feature of the architecture is internal artifacts that have some beliefs about the world. Most actions of the robot are planned and performed with respect to these artifacts.
•A shrinking-horizon optimization framework for energy hub scheduling in the presence of wind turbines, storage systems and flexible loads is presented.•The uncertainties of wind turbine output power ...and electricity price are considered by utilizing shrinking-horizon method.•An integrated demand response (IDR) program is also included in the model to increase system flexibility and modify the load demand curve.•A mixed-integer non-linear programming (MINLP) problem is solved to minimize the operating cost and emissions.•In addition, the problem was solved by stochastic and shrinking-horizon methods leading to more accurate and optimal results.
This paper presents a shrinking-horizon optimization framework for energy hub scheduling in the presence of wind turbines, storage systems and flexible loads. The uncertainties of wind turbine output power and electricity price are considered in the model and in order to model them, the shrinking-horizon method is utilized. The proposed hub is able to exchange power, heat and gas with the network at its input and supplies electrical, heating and cooling demands at the output. In addition, an integrated demand response (IDR) program is included in the model to increase system flexibility and modify the load demand curve. The objective function is to minimize the operating cost and emissions and is modeled as a mixed-integer non-linear programming (MINLP) problem, solved by DICOPT solver in GAMS software. The simulation results demonstrate that increasing the flexible loads reduces the purchase of power and heat from the network during peak hours and thus leads to a reduction in operating cost. Numerical results show that a 7% and 12% increase in flexible loads reduces the total operating cost by 5% and 7%, respectively. In addition, this problem was solved by stochastic and shrinking-horizon methods, the results substantiate that the use of shrinking-horizon method has led to more accurate and optimal results.
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