Changes In System Parameters Research Articles

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

Minimum detectable changes based on linear Bayesian filters

This article proposes a method to assess the minimum detectable changes in system parameters based on Bayesian inference. The approach considers the monitored parameters as random variables, and uses linear Bayesian filters to update them based on system observations. To determine the detectable changes in them, a special quality of Kalman filter is exploited, that is, the fact that the posterior variance is independent of the observations of the changed state, and that it can be calculated based on previous knowledge. To enable an automated change diagnosis, a decision rule is introduced, based on the posterior’s probability of exceeding a user-defined threshold. This way, the user can specify an allowable probability of exceedance, and subsequently, this value can be converted into an equivalent detectable change. To cope with large systems and multiple system observations, a functional Kalman Filter is applied, which enables an analytical solution to the Bayesian inverse problem. For proof of concept, three numerical case studies related to structural health monitoring are presented, including a 6-DOF mass-and-spring system, an offshore tower subject to marine growth, and a reinforced concrete bridge affected by seismic damage. The case studies highlight that no data from the changed state is required to accurately evaluate the detectable change. Secondly, the detectability can be evaluated for any measurement quantity (vibrations, inclinations) or extracted damage-sensitive feature (natural frequencies, mode shapes), provided it is sensitive to changes in the system parameters. Furthermore, the detectability can be assessed for various damage scenarios and a wide range of monitored systems, provided that numerical models are available, e.g. physics-based models or surrogate models.

Does a New Electric Vehicle Manufacturer Have the Incentive for Battery Life Investment? A Study Based on the Game Framework

Motivated by the electric vehicle battery life performance, we studied the optimal investment decision-making behaviour of duopoly automakers. Based on the framework of game theory, this paper explores the influence of various parameters in the static game and dynamic game on the results, in combination with consumers’ preference for the battery life of electric vehicles. In the static game, a smaller investment coefficient is more beneficial to a firm that adopts an investment strategy rather than a firm that does not. When the investment coefficient increases, the difference between the two manufacturers will become smaller. The change of parameters in the dynamic game system may lead to complex dynamic phenomena, and the system will experience period-doubling bifurcation and N-S bifurcation from a stable state into a chaotic state. It will also significantly impact the basins of attraction, which affect the decision-makers’ initial choice. Consequently, we can use the control method to return the unstable system to stability. Based on these findings, some management insights and suggestions are presented.

Hybrid Active-Passive Shielding of Magnetic Field of Overhead Power Lines with Triangular Phase Conductors Arrangements

The aim of the work is to design a robust hybrid active-passive magnetic field shielding system created by overhead power lines with a triangular arrangement of phase wires, designed to increase the shielding efficiency of the initial magnetic field in residential premises to the level of sanitary standards and reduce the system's sensitivity to changes in system parameters. To achieve this goal, the following tasks were solved: the structure of a hybrid system of active-passive shielding was developed, and the synthesis of a system consisting of a double-circuit active and multi-circuit passive parts was performed. The synthesis of the system is reduced to solving a vector game, in which the vector payoff is calculated based on the solutions of the Maxwell equations in the quasi-stationary approximation using the COMSOL Multiphysics software. The solution of a multicriteria game is found on the basis of algorithms for optimizing a multi-swarm of particles from Pareto optimal solutions, taking into account binary preference relations. The most important results are theoretical and experimental studies of the effectiveness of the synthesized hybrid active-passive shielding system for the magnetic field created by overhead power lines with a triangular arrangement of phase wires in the shielding zone. The significance of the results obtained lies in the fact that in the course of designing a hybrid active and multiloop passive screens, the parameters of passive and active screens were calculated; as well as practical recommendations are given on the reasonable choice of the spatial arrangement of a multi-loop passive shield and two shielding windings of a robust system of hybrid shielding of the magnetic field created by overhead power lines with a triangular arrangement of phase wires.

Adaptive Model Predictive Control Scheme Based on Non-Minimal State Space Representation

The model predictive control (MPC) technique is widely employed in process industries as a control scheme. The quality of the model used greatly influences the performance of the MPC. In time-varying systems, the plant model plays a critical role in determining the controller’s performance, as the controller’s control action relies on an adaptive model. Therefore, updating the system parameters rapidly and symmetrically in time-varying systems becomes necessary. To address this need, in the proposed work, a non-minimal state space model of a time-varying system is utilized for parameter estimation, and these parameters are updated at every sampling instant using a multi-innovation recursive least squares (MIRLS) scheme, which enables the timely updates of system parameters. We have explored various extensions of the recursive least square (RLS) scheme, such as the multi-innovation recursive least squares (MIRLS) method. This extension aims to achieve a higher convergence rate for parameter estimation. Furthermore, we have focused on the parameter estimation of a non-minimal state space time-varying system, where the system parameters change at each time interval. Additionally, we have incorporated a time-varying objective function into the MPC formulations, which enables adaptability to change the system dynamics. To demonstrate the applicability of our proposed approach, we have conducted simulation experiments using a benchmark time-varying model. These experiments showcase the effectiveness and benefits of our proposed methodology in dealing with time-varying systems.

Solitons in fourth-order Schrödinger equation with parity-time-symmetric extended Rosen-Morse potentials

We investigate the fourth-order nonlinear Schrödinger equation modulated by parity-time-symmetric extended Rosen-Morse potentials. Since the imaginary part of the potentials does not vanish asymptotically, any slight fluctuations in the field can eventually cause the nonlinear modes to become unstable. Here we obtain stable solitons by adding the constraints of coefficients, which make the imaginary part of the potentials component vanish asymptotically. Furthermore, we get other fundamental stable single-hump and double-hump solitons by numerical methods. Then we consider excitations of the soliton via adiabatical change of system parameters. The results we obtained in this work provide a way to search for stable localized modes in parity-time-symmetric extended Rosen-Morse potentials with fourth-order dispersion.

Determination of the Fleet Size of AGVs with AGV Pools Using a Genetic Algorithm and Artificial Intelligence

The utilization of low-cost AGVs in the industry is increasing every day, but the efficiency of these systems is low due to the lack of a central management system. Low-cost AGVs’ main characteristic is navigation via magnetic sensors, which they follow via magnetic tape on the ground with a low-level automation system. The disadvantages of these systems are mainly due to only one circuit assignment and the lack of system intelligence. Therefore, in this study, AGV pools were employed to determine the required AGV number. This study begins by calculating the required AGV number for each AGV circuit combination assigned to every parking station by the time window approach. Mathematical-solution-based mixed integer programming was developed to find the optimum solution. Computational difficulties were handled with the development of a genetic-algorithm-based approach to find the solutions for complex cases. If production requirements change, system parameters can be changed to adapt to the production requirements and there is a need to determine the number of AGVs. It was demonstrated that AGVs and pool combinations did not lead to any loss in production due to the lack of available AGVs. It was shown that the proposed approach provides a fleet size which requires five fewer AGVs, with a 29% reduction in the number of AGVs. The effects of system parameter changes were also investigated with artificial neural networks (ANNs) to estimate the required AGVs in the case of production requirement changes. It is necessary to determine the effect of the change in system parameters on the number of AGVs without compromising on computational cost and time, especially for complex systems. Thus, in this study, an artificial neural network (ANN), the response surface method (RSM), and multiple linear regression (MLR) techniques were used to examine the effects of the system parameter changes on the AGV number. In the present case, the ANN obtained the solution at a good rate with reduced computational costs, time, and correction errors compared to the GA, at 0.4% (ANN), 7% (RSM), and 24% (MLR). The results show that the ANN provides solutions which can be used in workshops to determine the number of AGVs and also to predict the effect of changes in system parameters.

Application of genetic algorithm and terminal sliding surface to improve the effectiveness of the proportional–integral controller for the direct power control of the induction generator power system

The proportional–integral regulator is one of the most famous and widely used linear controllers in the industrial field because of its easy adjustment of the results, simplicity, and inexpensive. The negative of this controller is that it is affected by the change of system parameters, which causes several problems, including the high value of ripples and low quality of the power/current. To reduce this defect, a new proportional–integral regulator is suggested based on the use of the terminal sliding surface technique and genetic algorithm. This suggested nonlinear controller is discussed for the first time in this work to use it to ameliorate the quality of energy output from a dual-rotor wind power system using an induction generator. The use of a new controller to regulate the generator energy will inevitably lead to an increase in power errors, power ripples, and harmonic distortion of current especially if the system parameters are changed. Also, the traditional direct power control based on the proposed nonlinear controllers of the induction generator is a robust control, does not need the mathematical form of the generator, is simple, and does not need a specialist. Moreover, the suggested method is applied to the rotor inverter of the 1.5 MW generator using Matlab software. The suggested technique takes full advantage and the power regulation objective of the suggested power system is confirmed by the numerical results compared to the conventional technique and other published controls.

Adaptive Neural-Fuzzy controller design combined with LQR to control the position of gantry crane

As the world grows, the demand for transporting goods is increasing, the number of goods in factories and ports is increasing, to transport all these goods, cranes are indispensable. In fact, currently, crane rigs working in factories and ports operate with low stability, when working or the phenomenon of swaying of the load occurs, leading to inaccurate positioning, loss of safe transportation of goods. To overcome these shortcomings, the paper proposes the design of a neural-fuzzy adaptive controller combined with an LQR controller (ANFIS-LQR) to control the forklift's position in the shortest time to achieve the desired exact position. At the same time, we want to control the deflection angle of the load so that the vibration when working is minimal. To check and evaluate the quality and stability of the system; the proposed design controller is simulated on MATLAB/Simulink software in the case of changes in system parameters and noise affecting the gantry crane system. To evaluate the superiority of the paper compared with published works, the author compares ANFIS-LQR with other published control methods such as DE-PID, Fuzzy-PD, Fuzzy dual and Fuzzy sliding, the simulation results show that the neural-fuzzy adaptive controller combined with the proposed LQR controller works well t_xlvt=2.1s , t_xlgt=3.5s, 0max=0.3(rad).

РЕГУЛЯРНА ТА СКЛАДНА ПОВЕДІНКА МАЯТНИКОВОЇ СИСТЕМИ У МАГНІТНОМУ ПОЛІ

The dynamics of an oscillatory dissipative system consisting of two connected pendulums in a magnetic field is considered. The connection of these pendulums is realized by some elastic element. The inertial components of pendulums vary widely, and the mass ratio is chosen in analytical investigation as a small parameter. For approximate calculations of magnetic forces, the Padé approximation which best satisfies the experimental data, is used. Such approximation permits to describe the magnetic excitation with good accuracy. The presence of external influences in the form of magnetic forces and various types of other loads that exist in many engineering systems leads to a significant complication in the analysis of vibration modes of nonlinear systems. Nonlinear normal modes (NNM) are analysed in the system where one mode is connected and the other is localized. These modes are constructed by the multiple scales method. It is studied as the regular, as well the complex behaviour when changing system parameters, including the pendulums mass ratio, the coupling coefficient, the magnetic impact intensity coefficient, and the distance between the axis of rotation and the center of gravity. The influence of these parameters is studied at both small and not small initial angles of the pendulums. The analytical solution is compared with results of numerical simulation which is based on the Runge–Kutta method of the fourth order, where initial values of variables defined in the analytical solution are used. Numerical simulation, which includes construction of phase diagrams and trajectories in the configuration space, permits to estimate the system dynamics which can be as regular, as well irregular one. The mode stability is studied by the numerical-analytical test which is a numerical realization of the Lyapunov stability criterion. Here the mode stability is determined by analysis of orthogonal deviations from the mode trajectory in the system configuration space. Keywords: connected pendulums, magnetic forces, nonlinear normal modes, multiple scales method.

Ionospheric electric potential as an alternative indicator of solar effect on the lower atmosphere

We have explored the possibility of applying the ionospheric electric potential (EP) as a parameter describing the effects of solar activity on the troposphere. We calculated EP, using the semi-empirical model, where the potential spatial distribution is determined by solar wind, interplanetary magnetic field parameters, and the geomagnetic activity index AL. We have carried out a comparative analysis of EP and the commonly used geomagnetic activity indices in a high-latitude region for 1975–2019. It has been shown that EP can be used as an indicator of solar activity since it describes both short-period disturbances and long-term variations. The revealed similar trends in long-term EP variations and near-surface temperature suggest that the changes in climate system parameters are induced by slower changes in the Sun’s large-scale magnetic field. The performed analysis of EP and near-surface temperature correlation maps has revealed that the atmospheric static stability conditions have an effect on spatial distribution of tropospheric response to solar impact.

Электрический потенциал ионосферы — альтернативный индикатор солнечного воздействия на нижнюю атмосферу

We have explored the possibility of applying the ionospheric electric potential (EP) as a parameter describing the effects of solar activity on the troposphere. We calculated EP, using the semi-empirical model, where the potential spatial distribution is determined by solar wind, interplanetary magnetic field parameters, and the geomagnetic activity index AL. We have carried out a comparative analysis of EP and the commonly used geomagnetic activity indices in a high-latitude region for 1975–2019. It has been shown that EP can be used as an indicator of solar activity since it describes both short-period disturbances and long-term variations. The revealed similar trends in long-term EP variations and near-surface temperature suggest that the changes in climate system parameters are induced by slower changes in the Sun’s large-scale magnetic field. The performed analysis of EP and near-surface temperature correlation maps has revealed that the atmospheric static stability conditions have an effect on spatial distribution of tropospheric response to solar impact.

AdapSafe: Adaptive and Safe-Certified Deep Reinforcement Learning-Based Frequency Control for Carbon-Neutral Power Systems

With the increasing penetration of inverter-based renewable energy resources, deep reinforcement learning (DRL) has been proposed as one of the most promising solutions to realize real-time and autonomous control for future carbon-neutral power systems. In particular, DRL-based frequency control approaches have been extensively investigated to overcome the limitations of model-based approaches, such as the computational cost and scalability for large-scale systems. Nevertheless, the real-world implementation of DRLbased frequency control methods is facing the following fundamental challenges: 1) safety guarantee during the learning and decision-making processes; 2) adaptability against the dynamic system operating conditions. To this end, this is the first work that proposes an Adaptive and Safe-Certified DRL (AdapSafe) algorithm for frequency control to simultaneously address the aforementioned challenges. In particular, a novel self-tuning control barrier function is designed to actively compensate the unsafe frequency control strategies under variational safety constraints and thus achieve guaranteed safety. Furthermore, the concept of meta-reinforcement learning is integrated to significantly enhance its adaptiveness in non-stationary power system environments without sacrificing the safety cost. Experiments are conducted based on GB 2030 power system, and the results demonstrate that the proposed AdapSafe exhibits superior performance in terms of its guaranteed safety in both training and test phases, as well as its considerable adaptability against the dynamics changes of system parameters.

Dissipative consensus for two-time-scale semi-Markov jump multi-agent systems against false data injection attacks via a dynamic event-triggered scheme

This paper investigates the dynamic event-triggered dissipative consensus for two-time-scale semi-Markov jump multi-agent systems with external disturbances and false data injection attacks. Two-time-scale semi-Markov jump models are established to describe the two-time-scale phenomenon and the abrupt change of system parameters. Moreover, a dynamic event-triggered scheme in which the threshold parameter can be adjusted appropriately with the state fluctuations is adopted to reduce unnecessary data transmission and make more rational use of limited network resources. Some sufficient conditions rely on the Lyapunov analysis method are developed to ensure the Q,S,R-dissipative consensus can be achieved. Eventually, a numerical example is presented to corroborate the effectiveness of the obtained theoretical results.

Acoustic Manipulation of Particles in Microfluidic Chips with an Adaptive Controller that Models Acoustic Fields

Acoustic manipulation is a technique that uses sound waves to move particles, droplets, or cells. Closed‐loop control methods based on complex, time‐varying acoustic fields have been demonstrated, but usually require accurate models of the acoustic fields or many training experiments for successful manipulation. Herein, a new adaptive control method is proposed for the acoustic manipulation of single and multiple particles inside microfluidic chips. The method is based on online machine learning of the acoustic fields. Starting with no knowledge of the fields, the controller can manipulate particles even on the first attempt, and its performance improves in subsequent attempts, yet can still readapt if the models are invalidated by a sudden change in system parameters. The controller can generalize: it can use information learned from one task to improve its performance in other tasks. Despite the machine‐learning nature of the controller, the internal models of the controller have a physical interpretation and correspond to the experimentally observed acoustic fields. The online adaptiveness of the controller should make it easier to use in practical applications, such as particle and cell sorting, microassembly, labs‐on‐chips, and diagnostic devices, as the method does not require extensive training or prior models.

Improvement of Power Quality Using Reduced Switch Count Eleven-Level Inverter in Smart Grid

This article proposed a hybrid control topology to cascaded multilevel-inverter (CMLI) using 11-level for the grid-tied photovoltaic generation system. The proposed hybrid technique combines an archerfish hunting optimiser (AHO) and Spike Neural Network (SNN); hence, it is called the AHO-SNN technique. The proposed control technique is to maintain the regulation of power or maximal energy conversion of the solar subsystem and reduce total harmonic distortion (THD). Here, the AHO is utilised to construct the optimum control signal dataset. The best control signals are estimated using a data set performed by SNN. The resultant control signals will regulate insulated-gate-bi-polar-switches (IGBT) of Cascaded MLI. The proposed AHO-SNN control topology determines the converter switching states by constructing the operating modes of the generation system. The system parameter changes and outside disturbances are optimally minimised with this control strategy. The proposed AHO-SNN control is done in MATLAB platform, and it evaluated their performance by using existing methods, like War Strategy Optimization Algorithm (WSO) Fuzzy Wavelet Neural Network (FWNN), Side-Blotched Lizard Algorithm (SBLA), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Memetic Fire-Fly Algorithm (MFA). The result shows that the proposed approach based on THD is less than existing approaches.

Systemic immune parameters after prior radiation therapy in patients receiving immune checkpoint inhibitors

IntroductionPreclinical studies have demonstrated the ability of radiation therapy (RT) to augment immune response and tumor control by immune checkpoint inhibitors (ICI). However, numerous clinical trials combining RT and ICI have yielded relatively disappointing results. To improve understanding of optimal use of these therapies, we assessed systemic immune effects of prior RT in patients receiving ICI. Methods and MaterialsPre- and post-ICI blood samples were collected from patients enrolled in a prospective immunotherapy biospecimen protocol. Mutiplex panels of 40 cytokines and 120 autoantibodies (Ab) were analyzed. We identified differences in these parameters according to receipt, timing, and type of prior RT. We calculated P values using the Pearson product-moment correlation coefficient and false discovery rate (FDR) using the Benjamini-Hochberg Procedure. ResultsAmong 277 total patients, 69 (25%) received RT in the 6 months prior to ICI initiation. Among RT-treated patients, 23 (33%) received stereotactic RT, and 33 (48%) received curative intent RT. There was no significant difference in demographics or type of immunotherapy between patients according to prior RT exposure. Baseline complement C8 Ab and MIP-1d/CCL15 were significantly higher among patients with prior RT. For MIP-1d/CCL15, only prior stereotactic RT was associated with significant differences. ConclusionsPrior RT is associated with few changes in systemic immune parameters in patients receiving ICI. The underlying mechanisms and optimal approach to harnessing the potential synergy of RT and ICI require further prospective clinical investigation.

An auto-tuned droop coefficient-based controller for microgrid system to enhance grid resilience during blackouts

This paper presents a reliable auto-tuned droop coefficient-based controller (ATDC) for a microgrid (MG) system for enhancing grid resilience during blackouts. Existing power system networks experience blackouts due to inevitable failures such as overloading on lines, failure of a protection system, and imbalance in the power lines. The consequences of blackouts can be resolved by adapting to the concept of resilient MGs with high dynamic stability for power restoration. However, impedance parameters of power lines will change during blackouts, this eventually affects system's performance. Therefore, synchronization of MG sources to the utility grid and providing power supply to the critical loads during power system blackouts are the key factors to be resolved by maintaining system within permissible limits. The proposed ATDC-based controller adapts to changes in system parameters and auto-tunes the droop coefficients to provide proper control coordination between the each of distributed generator (DG) in MG. The proposed ATDC-based controller is designed in silico, and its effectiveness is tested under various conditions of MG operations. Results are compared with conventional and improved droop controller strategies. The test results demonstrate that the proposed controller maintains grid supply frequency within IEEE-1547 standards while ensuring appropriate power-sharing between load and MG sources.

Current Transformer Saturation Detection Method Based on Artificial Neural Network

When current transformer is saturated, mainly due to the presence of an exponentially decaying DC component in the fault current, its secondary current has a distinctive distorted waveform which significantly differs from its primary (true) waveform. It leads to an underestimation of the secondary current value calculated by the relay protection compared to its true value. Thus, in its turn, results in trip time delay or even in a relay protection devices operation failure, since its settings and algorithms are calculated and designed on the assumption that the secondary current waveform is sinusoidal and proportional to the primary. And since, when using classical electromagnetic current transformer, it is impossible to exclude the possibility of its saturation, the detection of such abnormal condition is an urgent technical problem. The article proposes to use an artificial neural network for this purpose, which, together with the traditional method of saturation detection based on adjacent secondary current samples comparison, allows implementing a fast and reliable current transformer saturation detector. The article details the stages of the practical implementation of such an artificial neural network. The MATLAB-Simulink environment was used for assess the proposed saturation detector operation. The experiments that had been performed confirmed that proposed method provides fast and accurate saturation detection within the wide range of the power system and current transformer parameters change.

A Six-Switch Mode Decoupled Wireless Power Transfer System with Dynamic Parameter Self-Adaption

For the fully resonant wireless power transfer (WPT) system, the high coupling of the converter and the resonant network introduced many problems, such as frequency splitting, the power curve peak limit, and the strict switch strategy. To solve these problems, this paper proposed a new six-switch topology based on the full–-bridge converter. With the unique structures containing two capacitor-isolated switches and a source-isolated diode, the system decouples the converter and the resonant network, and its modes have been decoupled, called the independent power injection and free resonance WPT (IPIFR–WPT) system. The capacitor-isolated switches and the source-isolated diode make the converter operate only when the voltage on the primary capacitor is equal to the source voltage, and the source will be isolated by the diode when the capacitor voltage is great than the source, which provides a wide time margin for the switches of the converter to turn on in advance. In this margin, the operation point is self-determined the same whenever the switches turn on so that the system’s performance is consistent. Based on this characteristic, the system can self-adapt a dynamic change in system parameters, with at least 15% tolerance for the coupling coefficient and 14% for the load resistance.