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This study detects oscillations in the control loop and separates them from others by implementing supervised machine learning on generalized and normalized statistical variables. Oscillations in the control loops can result in high variability of performance, increase the costs, increment defects and potential hazards in the future. Valve stiction is one of the most important reasons for oscillatory behaviour in the process industry. The detection of this non-linear parameter becomes even more complex in the presence of other oscillating factors, such as poor controller tuning and external disturbances. The proposed method is based on a six-step algorithm. After preparing the data, the best classifier is selected from three trained classifiers Naïve Bayes, support vector machine and K -nearest neighbours' separators. Finally, the decision tree will automatically detect and classify oscillating factors in the control loop. This method is independent of the process model, and through the decision tree, it determines the probability of occurrence of each oscillation factor in the loop. The resulting system was tested on benchmark industrial data to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel control system structure is introduced for generating a sinusoidal wave on the differential boost inverter terminals using inverse model approach. To address the high‐frequency resonance issues, an active damping technique is proposed. Proportional‐resonant controllers are designed for tracking and harmonic rejection. Pulling direct and constant current from photovoltaic panels as the DC source is essential in maximum power point tracking. Therefore, a decoupling controller is utilised to suppress current oscillations. An innovative combination of tracking controller, harmonic rejector, and decoupling controller is proposed in order to provide a sinusoidal voltage output for linear and nonlinear loads with low total harmonic distortion. The proposed method reduces the number of controllers by half compare to conventional methods. In addition to simulation, experimental results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article uses the Bernstein‐type operators as the universal approximator to present an observer‐based robust adaptive controller for chaos synchronization. The lumped uncertainties including, un‐modeled dynamics and external disturbances, are modeled with this potent mathematic tool. It is shown that using the Bernstein‐type operators as basis functions and tuning the polynomial coefficients by the adaptive laws calculated in the stability analysis, uniform ultimate boundedness of the observer estimation error and synchronization error can be assured. To analyze the performance of the proposed controller scheme in terms of transient response behavior and robustness, the Duffing–Holmes oscillator is considered as the simulation testbed. A set of two different experiments are conducted to evaluate the efficiency of the introduced control approach. The performance of the proposed approach is also compared with RBFNN as a powerful approximation method. Unlike neural network/fuzzy methods, which require system states as inputs to estimate functions and construct a regressor vector, the proposed method is not dependent on the system states. Furthermore, there are more adjustable parameters in the regressor vector of the RBFNN (such as the width and center of the Gaussian units), and assigning the best values for these parameters is tedious and time‐consuming work due to the repeated trial and error. [ABSTRACT FROM AUTHOR]

Improving reliability is one of the most critical problems in power distribution networks. Optimal placement of automatic switches improves system reliability. Practical constraints such as maneuver points and the capacity of neighboring feeders are often overlooked. Reliability indices may also be improved by optimizing maneuver point locations considering the capacities of neighboring feeders. In this paper, a new perspective on the problem of recloser placement in the presence of switch disconnectors (sectionneurs) with the ability to disconnect under load is proposed. The limitations of transmission power capacity at maneuver points are also considered in the proposed method. In addition, by proposing a Markov model, the possibility of malfunction of reclosers is considered. The proposed method is applied to two case studies including Roy Billinton Test System (RBTS) and the real distribution network in Mazandaran, Iran. Real system data are collected during the years 2016–2021. The problem is solved for various scenarios. In the case of 12 fully reliable reclosers, taking into account the optimal maneuver points and without any capacity limitations, compared to the allocation of maneuver points at the endpoints of the feeders show a %4.37 increase in the reliability of the real system. Even by considering practical capacity limitations in the maneuver points, this improvement is %3.47. Also, the results show a %6.684 decrease in the system reliability by considering malfunction in the case of 12 reclosers. This underscores the importance of taking into account the reclosers malfunctions in optimal switch placement for better decision making in practice. [ABSTRACT FROM AUTHOR]

In this paper, a new robust control strategy is proposed to regulate the output voltage of a quasi‐resonant converter. The proposed approach completely eliminates the switching losses and the electromagnetic interferences due to high current changing rates. The circuit structure is simple and affordable and does not have many auxiliary switches. The regulated output voltage of a buck converter must be robust to input voltage variations. Moreover, the controller must be robust to the circuit parameter uncertainties. In this paper, a new control strategy with nonlinear structure is used with the advantage of robustness, and turn‐on and turn‐off zero current switching. The robustness of the converter to the input voltage variations and the parameter uncertainties is investigated. The new proposed control strategy can be implemented using different controller types. Therefore, a comparison between the proposed nonlinear controller and a conventional PI controller, which is most widely used in practice, is performed. The simulation results show the efficiency and robustness of the proposed method. Finally, the simulation results are verified by the experimental tests. [ABSTRACT FROM AUTHOR]

This article presents a new way to achieve reduced-order controllers using an integrated controller-plant dynamics approach. The proposed method combines the controller and plant dynamics to achieve specific overall control functionalities. The method is applied to a grid-connected inverter that conventionally uses second-order proportional-resonant (PR) and proportional-integrating (PI) transfer functions. The proposed approach replaces a third-order PR+PI function with a first-order PI function without compromising its features. The approach is to integrate the dynamics of the inverter and its output filter with the PI function to achieve a PR functionality. The proposed approach not only obviates the computational and digital implementation challenges pertaining to the PR compensators, it also results in a generally more robust and stable operation of the inverter in practical grid conditions. Detailed theoretical analysis, simulations, comparisons, and laboratory experiments are presented to elucidate various aspects of the proposed method. [ABSTRACT FROM AUTHOR]

The negative aspects of overusing fossil fuels for producing electricity attract the attention of engineers society to use renewable energies. Power electronic converters play an important role in the transmission and usage of renewable energy in the industry. Boost converters are a major part of devices in which convert renewable energies into usable energy for various industries or appliances. In this article, to control a boost converter with parametric uncertainty, a new robust‐optimal controller is designed. The proposed control method is designed based on merging two conventional control theory: the robust control theory and linear quadratic regulator technique. This method consists of two nested loops. The inner loop composed of a state feedback controller which works as active damping to improve the transient state. The outer loop consists of a PI controller to track the desired voltage and reject disturbance. The coefficients of both controllers are computed by a robust‐optimal control technique. To evaluate the proposed control method, a well‐tuned single‐loop PI controller and a quantitative feedback theory based PID controller are compared with the proposed method. As shown by simulations, the proposed method has a more reliable performance in dealing with parameters uncertainties such as load changes and input voltage drops. [ABSTRACT FROM AUTHOR]

To use photovoltaic energy, voltage source inverters are playing an important role. The most of local AC loads such as AC motors need higher voltage than solar panel output voltage. Differential boost inverter produces an AC voltage that is greater than the DC input voltage in a single power stage. This inverter consists of two DC‐DC bilateral boost converters in which work via time‐variant duty cycles to produce sinusoidal voltage, and this feature causes complexity in control design. To deal with this complexity, new modeling so‐called generalized averaged model is used to achieve a linear time‐invariant model. In this paper, a nested control loop is designed. This control strategy consists of two control loops in which the inner loop is designed by linear quadratic regulator theory to displace poles in proper location and the second loop uses a PI‐PR controller to track desired voltage and reject disturbances. To evaluate the proposed control method, the sliding mode controller is applied and compared as well. As shown by simulations, the proposed control strategy has more reliable performance and provides higher robustness in comparison with sliding mode control technique in rigid situations such as dealing with nonlinear load, parameters values changes, and external disturbances. [ABSTRACT FROM AUTHOR]

In this paper, a new perspective is presented for prediction of chaotic time series by combining the phase space reconstruction and fuzzy approach. Before applying time series to the predictor system, the reconstruction parameters, including embedding dimension and time delay, are determined in an off-line manner by using nearest neighbor and mutual information methods. Then, the structure of the fuzzy system is specified and the input number of fuzzy system is set to the embedding dimensions. According to the embedding dimension and time delay, the phase space is reconstructed point-by-point at the entry of the fuzzy system. Fuzzy system is composed of two separated parts: predictor and tracker. The predictor part forecasts the next point for new entry with fine-tuned parameters using the last step, and the tracker part adjusts the parameters for the next step. This adjustment is done iteratively. The proposed method is compared with some references' results. Simplicity and appropriate speed with sufficient accuracy are the advantages of this method. [ABSTRACT FROM AUTHOR]

This study represents a method for estimating the state of charge (SOC) of lithium-ion batteries using radial basis function (RBF) networks and square-root unscented Kalman filter (KF). The RBF network is trained offline by sampled data from the battery in the charging process. This type of neural network finds the non-linear relation which is required in the state-space equations. The state variables include the battery terminal voltage and the SOC, at the previous sample and the present sample, respectively. The proposed method is tested experimentally on a lithium-ion battery with 1.2 Ah capacity to estimate the actual SOC of the battery. The experimental results of the proposed method show some advantages, which include: (i) it is not very sensitive to determine, precisely, the measurement and process noise covariance matrices such as Kalman filter and (ii). It contains lower noise on the output, in comparison with Adaptive extended Kalman filter (EKF). [ABSTRACT FROM AUTHOR]