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This research creates an optimal power grid stabilizer for four machines. Rotor speed signals are system inputs in the proposed design. To improve response, this system uses a conventional power system stabilizer (CPSS) and an optimal CPSS (OCPSS). The genetic optimization hybrid has a new structure, and the network generators use the bacterial foraging algorithm (BFA) with stabilizer system. The system set is tested by MATLAB software under various conditions to evaluate the designs. The experiment starts with a three-phase fault in line 3 that is fixed by breaking the line after 0.2 seconds. The simulation results show that after a short circuit in line 3, the proposed OCPSS design reaches damping after about a cycle of oscillation in 4 seconds. However, the conventional CPSS design achieves damping after four oscillations in six seconds. Simulations show that the proposed method is better than genetic algorithm (GA) and BFA. Power system oscillations are dampened faster and with lower amplitude when power system stabilizer (PSS) coordinate with the proposed optimization method. It also improves power system dynamics. We demonstrate with the proposed OCPSS stabilizer that advanced optimization systems can maximize system control capacity by utilizing conventional CPSS system advantages.

The small signal stability analysis has got much importance for a single machine infinite bus system (SMIB). Conventional controllers are not smart to give the faster response. Hence advanced and intelligent controllers like Fractional order PID (FOPID) and State space analysis methods are used to obtain the better responses. In this article, corresponding SMIB system is analyzed using simulation studies and are carried out in MATLAB 2017a.

Der weltweite Wettbewerb in der Fertigungsindustrie zwingt heutzutage immer mehr Unternehmen dazu, moderne, innovative Technologien in ihre Fabriken zu implementieren. Technologien wie Augmentet Reality (AR) beispielsweise werden immer präsenter, weil sie die menschliche Arbeitskraft bei ihren täglichen Aufgaben unterstützen können. Da sich der Arbeitsablauf eines Mitarbeiters in den letzten Jahren in Richtung kürzerer Arbeitszyklen und flexibleren Tätigkeitsbereichen verändert hat, sind auch deren Leistungsanforderungen deutlich gestiegen. Mitarbeiter*innen sind nun für einen größeren Arbeitsbereich verantwortlich und gezwungen, zum Teil gleichzeitig, an mehreren Arbeitsplätzen tätig zu sein. Dieser sogenannte Mehrmaschinenbetrieb kann aufgrund der wechselnden Arbeitsumgebung mithilfe von AR noch nicht in idealer Weise unterstützt werden, da die derzeitigen Technologien noch nicht vollständig ausgereift sind. Eine bessere Unterstützung wäre dann gewährleistet, wenn automatisch erkannt würde, um welche Maschine es sich handelt. Eingesetzt in Mehrmaschinenbetrieben könnte AR in diesem Fall arbeitsplatzspezifische Anweisungen bereitstellen. Deep Learning (DL) ermöglicht dabei eine Kontextsensitivität – also ein automatisches Erkennen – für mit AR ausgestattete Geräte. Ein auf AR und DL basierendes Assistenzssystem wäre daher in der Lage, die menschliche Entscheidungsfindung und den Arbeitsalltag zu erleichtern. Diese Arbeit bietet einen ersten Entwurf am Weg zu einem vollständig entwickelten Assistenzsystem zur Entlastung von Mitarbeiter*innen in Mehrmaschinenbetrieben durch die Fähigkeit des Systems zur Kontextwahrnehmung. Die beiden Hauptbestandteile dieser Arbeit sind zum Einen die Erstellung eines neuen Datensatz mit der Bezeichnung „Assembly Factory Workplaces “ – bestehend aus 4695 Bilder, 6 Arbeitsplatzkategorien und 4 speziellen Kategorien für Übergänge – und zum Anderen die Entwicklung eines neuen neuronalen Netzwerkes, basierend auf einer modifizierten ResNet-50 Architektur, welches zunächst auf den Places365 Datensatz vortrainiert und schließlich mit dem selbstentwickelten Datensatz abgestimmt wurde. Dieses final trainierte neuronale Netzwerk weist eine Validität von 77% in Bezug auf den selbst erstellten Datensatz auf. Der im Zuge dieser Arbeit in der TU-Wien Pilot-Fabrik durchgeführte Testlauf konnte zeigen, dass es möglich ist, ein neuronales Netzwerk auf einem HMD-Gerät, beispielsweise der Microsoft HoloLens 1, zu installieren, um dadurch kontextsensible AR-Geräte zu schaffen. Des Weiteren konnte im Zuge des Testlaufes gezeigt werden, dass die Klassifizierung der Haupt- und Spezialkategorien unter Testbedingungen sowohl genau als auch ausreichend schnell funktioniert. Auf dem Konzept dieser Arbeit könnte in Zukunft aufgebaut werden, indem der erstellte Datensatz vergrößert, das System auf anderen Augmentet-Reality Headsets oder Smartglasses eingesetzt wird oder neue neuronale Netzwerkstrukturen ausprobiert werden., Global competition within the manufacturing sector nowadays is forcing more and more companies to implement modern, innovative technologies into their factories. Technologies such as Augmentet Reality (AR), are becoming more and more present since they can support human workers in their daily tasks. As an employee’s workflow has changed in recent years towards shorter work cycles and more flexible job roles, their performance requirements have also increased significantly. Employees are now responsible for a larger work area and forced to work at several workstations, sometimes simultaneously. Due to the changing work environment, this so-called multi-machine operation cannot yet be accomplished in an ideal way with the help of AR, as the current technologies are limited to a single workstation. Better support would be ensured if it was possible to automatically recognize which machine is in usage. Used in multi-machine operations, in this case AR could provide workstation-specific instructions. Furthermore, Deep Learning (DL) enables context sensitivity - automated recognition - for AR-equipped devices. An assistance system based on AR and DL would therefore be able to support human decision-making in everyday work. This thesis is a proof of concept on the way to a fully developed assistance system to ease the workload of employees in multimachine operations by giving the system the ability of context-awareness. The main contributions of this thesis are twofold - the creation of a new dataset called “Assembly Factory Workplaces”, consisting of 4695 images, 6 workplace categories and 4 special categories for transitions – and the development of a new neural network, based on a modified ResNet-50 architecture, which was first pre-trained on the Places365 dataset and finally tuned with the self-developed dataset. This neural network shows an overall validation accuracy of 77% on the Assembly Factory Workplaces dataset. The performed use case, conducted at the TU Wien pilot factory, demonstrates that it is possible to install a neural network on a head mounted display (HMD) device (i.e., Microsoft HoloLens 1 ) and enable context awareness for AR devices. Furthermore, the observations within the test run have shown that the classification of main and special categories works both accurately and sufficiently fast under test conditions. This thesis provides a proof of concept that can be extended in the future by increasing the Assembly Factory Workplaces dataset to be more diverse, deploying the system on other augmented reality headsets or smartglasses, or try other neural network architectures.

This paper suggests a nonlinear controller of a Static Synchronous Series Compensator (SSSC) and a doubly-fed induction generator (DFIG) for transient stability enhancement by transient energy function (TEF) technique and super-twisting differentiator in the multi-machine power system. Initially, the TEF approach is employed to damping control and stability enhancement in a multi-machine power system including an SSSC, DFIG, and synchronous generator (SG). Then, the signals of time-derivative in the controller with a super-twisting differentiator are estimated. The significance and novelty of this work are the employs of the TEF method obtained for the multi-machine power system containing DFIG and SG. Another novelty is the use of the DFIG and SSSC simultaneous control whose uncertain parameters are estimated with a super-twisting differentiator. The feature of the nonlinear control approach is robust versus modifications in the topology of the system. Simulation is performed on the IEEE 9-bus system to appraise the operation of the nonlinear controller approach compared to back-stepping and sliding mode control. The simulation results demonstrate that the nonlinear controller approach satisfactorily reduces the first swing of oscillations.

This paper presents a dynamic state estimation method based on the least mean square algorithm to increase adaptability to measurement data and reduce runtime. The proposed method can be used to estimate the rotor angle and speed of synchronous generators in real time from the terminal voltage and phase data measured using the phase measurement unit. The proposed method is compared with the extended Kalman filter based method in a WSCC 9-bus system for three cases. The simulation results show that the RMSE of the estimation result in the proposed method decreased by at least 30% compared to the existing method, and the runtime also decreased from 0.6536 ms to 0.2439 ms. Therefore, the proposed method can be applied to power systems that are larger and contain many components. To the best of our knowledge, the proposed method is the first attempt to apply the LMS algorithm to power system dynamic state estimation., "This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."

This paper deals with the application of Thyristor Controlled Series Reactor (TCSR) FACTS controller for mitigation of congestion in one or more lines of a power system. When generation is close to thermal limit then one or more healthy lines may face congestion, if some line is out of operation due to fault or maintenance. For a given voltage difference between two end buses of a congested line, the line current can be reduced to normal value or congestion can be mitigated by increasing the reactance of that line. The reactance of the line can be suitably increased with the help of series TCSR FACTS Controller. The reactance of the Controller can be suitably increased by firing the SCRs at the appropriate firing angles. TCSR FACTS Controller has been implemented to mitigate the congestion in IEEE 5-Bus and Modified IEEE 14-Bus Test systems. The TCSR FACTS Controller is successful in mitigation of congestion of any level.

In this paper, the decentralized dynamic state estimation (DSE) problem is investigated for a class of multi-machine power systems with non-Gaussian noises and measurement outliers. A model decoupling approach is adopted to facilitate the decentralized DSE for large-scale power systems. The particle filtering technique plays a key role in the developed DSE scheme with aim to tackle the nonlinearities and the non-Gaussian noises. To mitigate the negative impact from the measurement outliers on the DSE performance, a novel sliding-window-based online algorithm is proposed to detect and further locate the possible outliers based on the historical measurement data. Specifically, some criteria are constructed to (i) determine whether a newly arriving measurement vector is contaminated by measurement outliers and (ii) locate the abnormal components of such a vector. A conditional posterior Cramér–Rao lower bound is derived to evaluate the estimation performance of the proposed DSE algorithm. Finally, simulation experiments are carried out on the IEEE-39 bus system to verify the effectiveness of the proposed DSE algorithm under the non-Gaussian noises and the measurement outliers. This work was supported in part by the National Natural Science Foundation of China under Grants 61933007, U21A2019 and 62273088, the Program of Shanghai Academic/Technology Research Leader of China under Grant 20XD1420100, the Hainan Province Science and Technology Special Fund of China under Grant ZDYF2022SHFZ105, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany .

This study contrives a discrete-time adaptive decentralized control algorithm with input quantization for interconnected multi-machine power systems with SVC. First, a dynamic surface scheme is applied to the excitation controller design, in which first-order digital low-pass filters are used to predict the next virtual control law, which overcomes the model conversion problem in backstepping. Therefore, the controller design and structure are simplified. Further, an improved hysteresis quantizer is utilized for amplitude quantization of control input signals; along with the discretization of time, this achieves digital decentralized control. Finally, semi-global uniformly ultimately boundedness (SGUUB) of the whole control system is demonstrated based on the Lyapunov stability theory, and the effectiveness of the proposed control algorithm is verified on the ModelingTech real-time simulation experimental platform for power electronics.

The impact on the stability of power systems is rising as the penetration level of renewable energy with sporadic natures rises rapidly on the grid. However, the impact of different types of renewable energy sources (wind, solar) and their combination on system stability varies even with the same penetration level. This paper concentrates mainly on the stability analysis of multi-machine systems connected to various types of renewable energy sources. The study presents a simple and novel control technique named automatic reactive power support (ARS) for both single and combinations of renewable sources by injecting the available reactive power into the system during fault through converters to enhance system stability. The permanent magnet synchronous generator (PMSG) and doubly fed induction generator (DFIG) are both considered as wind generators in this paper for comparison. In addition, transient stability enhancement is carried out by improving critical clearing time of a three-phase fault in the power system. With the creation of a 3-phase fault at various buses, stability analysis is carried out on the 9-bus WSCC test bus system and also on the 68-bus IEEE test system. Comparative analysis of six test case conditions is provided and the considered cases are without renewable source, with DFIG as a wind generator, PMSG as a wind generator, solar PV farm, wind farm with DFIG and solar PV in combination and the combination of wind farm with PMSG and solar PV. Moreover, the improvement in critical clearing time of the system is compared using conventional and proposed controls with all the aforementioned renewable sources. Comparative results show that the proposed control technique improves system stability and also that the combination of renewable energy sources ought to enhance the critical clearing time of system.

This paper concerns the development of a sensitivity method for the optimization of multi-machine system performance by which the estimated values of the parameter settings are optimal. This method does not renujre a mathematical description of the system under control except for the controllers which need to be identified. The method is suitable for implementation on model system and on real control systems in service. Also it is capable of producing a control law which is derived only from the machine signals independent on other machine signals.

In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems. CC BY 4.0Corresponding author: Alexander Karlsson (alexander.karlsson@his.se)This work was supported by grant 2019-00789 at Vinnova, Project: Predictive Maintenance using Advanced Cluster Analysis (PACA). Predictive Maintenance using Advanced Cluster Analysis (PACA)

This paper addresses the problem of scheduling $ n $ equal-processing-time jobs with release dates non-preemptively on identical machines to optimize two criteria simultaneously or hierarchically. For simultaneous optimization of total completion time (and makespan) and maximum cost, an algorithm is presented which can produce all Pareto-optimal points together with the corresponding schedules. The algorithm is then adapted to solve the hierarchical optimization of two min-max criteria, and the final schedule has a minimum total completion time and minimum makespan among the hierarchical optimal schedules. The two algorithms provided in this paper run in $ O(n^3) $ time.

This paper addresses machines, automated guided vehicles (AGVs), tool transporter (TT) and tools simultaneous scheduling in a multi-machine flexible manufacturing system (FMS) to minimize makespan ...

The increasing number of low-frequency oscillation events has posed significant challenges to power system stability. It is imperative to accurately identify and properly control generators with poor or negative damping contribution for better oscillation damping. This paper proposes a hybrid energy and mode decomposition-based method for evaluating generators damping in multi-machine power systems. It is derived that the aggregate electric torque can be modeled as the superposition of electric torques in different modes. In each oscillation mode, the electric torque can be decomposed into the damping torque and the synchronizing torque. Moreover, a novel oscillation energy function is developed to evaluate generators’ damping effect in each oscillation mode. The result proves that in each oscillation mode, generators with attenuated oscillation energies have negative damping effects. Simulation results in single and multinegative damping generators cases, as well as the comparison with other methods, demonstrate the effectiveness and robustness of the proposed method for damping evaluation.

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

Internal short-circuit (ISC) faults are a common cause of thermal runaway in lithium-ion batteries (LIBs), which greatly endangers the safety of LIBs. Different LIBs have common features related to ISC faults. Due to the insufficient volume of acquired ISC fault data, conventional machine learning models could not effectively identify ISC faults. To compensate for the above deficiencies, this paper proposes a multi-machine learning fusion method to predict ISC faults and to perform faults warning classification under multiple operating conditions using the input of voltage normalization. Firstly, learning data acquisition is captured by experiments and simulation. Secondly, the simulation data are inputted into the ResNet-convolutional neural network (CNN) for pretraining, followed by the transfer learning method to freeze parts of the model layers in the CNN, and part of the experimental data are also inputted into the CNN model for parameter fine-tuning to build a multi-machine learning model. Finally, the degree of ISC faults within the laboratory battery is predicted based on the multi-machine learning model. The results show that the CNN model had a 99.9% prediction accuracy on the simulated dataset, and the multi-machine learning fusion model after transfer learning had a 96.67% prediction accuracy on the laboratory battery dataset, which can accurately identify different levels of ISC faults in batteries and realize the graded warning of ISC faults.

This paper proposes a new controller for stability and voltage improvement of power networks equipped by wind turbine which optimize the dynamical response of power systems performances (voltage and transient stability) after fault. The proposed control algorithm based on new Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to enhance the mechanical power of the synchronous machine into power system. The efficiency of developed control strategy has been tested using IEEE 9 Bus. Simulation results have showed that the proposed method perform better performances over wide range of disturbances for three considered scenarios studied.

This paper proposes a novel nonlinear decentralized control method for a class of multi‐machine power systems. The aim is to construct a suitable decentralized feedback control law so as t...

The execution of production processes in real manufacturing systems is associated with the occurrence of numerous disruptions, which predominantly revolve around technological machine failure. Therefore, various maintenance strategies are being developed, many of which tend to emphasise effective preventive measures, such as the Time-Based Maintenance (TBM) discussed in this paper. Specifically, this publication presents the time-based machine failure prediction algorithm for the multi-machine manufacturing environment. The Introduction section outlines the body of knowledge related to typical strategies applied in maintenance. The next part describes an approach to failure prediction that treats processing times as makespan and is followed by highlighting the key role of historical data in machine failure management, in the subsequent section. Finally, the proposed time-based machine failure prediction algorithm is presented and tested by means of a two-step verification, which confirms its effectiveness and further practical implementation

This paper describes an application of nonlinear adaptive Hamilton minimax control to multi-machine power systems with steam valve controllers. The controllers are designed explicitly to maintain transient stability of systems. First, the feedback link is introduced to transform the traditional power system model into Hamilton structure. It is worth mentioning that in the process of building the system model and designing the controllers, this paper considers the problem of interconnection interference. The proposed controllers enable the system to have L2 interference suppression characteristics in the case of maximum disturbance. Finally, the simulation results show that the adaptive Hamilton minimax method has better transient performance.

This paper addresses machines and automated guided vehicles (AGVs) simultaneous scheduling with alternative machines in a multi-machine flexible manufacturing system (FMS) to produce the best optimal sequences for the minimization of makespan (MKSN). This problem is highly complex to solve because it involves the selection of machines for job operations (jb-ons), the sequencing of jb-ons on the machines, the assignment of AGVs, and associated trips such as AGVs’ deadheaded trip and loaded trip times to jb-ons. This paper offers a nonlinear mixed integer programming (MIP) formulation for modeling the problem and the symbiotic organisms search algorithm (SOSA) for solving the problem. For verification, a manufacturing company’s industrial problem is employed. The results show that SOSA outperforms the existing methods and the Jaya algorithm, and using alternate machines for the operations can reduce the MKSN.

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in the underutilization of capital-intensive machines and a high machine idle time. Tools are transferred by a tool transporter (TT) between machines. Therefore, efficient scheduling of jobs, TT, and the tools with alternate machines enables a manufacturing system to increase machines’ utilization and decrease their idle times. This paper addresses machines, TT, and tools concurrent scheduling with alternate machines and only one copy of every tool variety is made available where the tools are costly, in a multi-machine flexible manufacturing system, taking into account tool transfer times for makespan (MS) minimization. The tools are placed in a central tool magazine, which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. The problem is to assign machines from alternate machines, tools, and corresponding TT trips, including the deadheading trip and loaded trip, to job operations for MS minimization. This paper uses a nonlinear mixed-integer programming framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. The results show that FPA outperforms the Jaya algorithm, and the usage of alternate machines for the operations reduces the MS. Reduction in MS indicates an improvement in utilization of resources.

The coordinated motion between the press and the feeding mechanism directly determines the production efficiency of the high-speed stamping line. In order to generate the high-performance trajectory of the feeding mechanism, this paper investigates the optimization of the trajectory with the local variable period. Based on the quintic B-spline curve and normal distribution, the smooth interpolation method of variable-time interval is proposed to generate the collision-free and energy-jerk-minimal trajectory with variable-time intervals. The advantage of the proposed method is that it can make the feeding mechanism transition smoothly between districts of variable-time and fixed-time intervals. It is beneficial to avoid re-performing the entire process of trajectory planning. ADAMS and actual experiments are used to validate the effectiveness of the proposed method. Results show that the proposed method can maintain the high performance of the initial trajectory, and there is no sharp point in the displacement-time and velocity-time curves. The investigation provides a new direction for the direct generation of local variable-period trajectories in the multi-machine coordination of the stamping line.

In this paper, transient stability analysis of a detailed model multi-machine power system was carried out to focus on the impact of HVDC link on system stability by comparing the critical fault clearing times of two operational scenarios, namely an AC transmission system with and without parallel HVDC transmission. The advantage and performance of AC-DC transmission for the improvement of response to transient instability caused by phase to ground fault was researched by detail simulation carried out in MATLAB/SIMULINK.

Turbine generator shaft torsional oscillations is an interdisciplinary power system dynamic problem as it involves mechanical and electrical engineering. Torsional oscillations occur in the mechanical for electrical reasons. Torsional oscillations cause fatigue life expenditure of the mechanical shaft system. There have been great motivations to mitigate the shaft torsional oscillations especially when unrestricted high speed reclosure (HSR) is utilized on the overhead transmission lines emanating from a generation station. Mitigation of torsional oscillation compromises between the use of HSR and preserving the mechanical integrity of the involved turbine generator set. Therefore, braking resistor (BR) controlled by fuzzy logic controller is presented in this paper as a low cost, reliable mean for torsional oscillations mitigation. BR was first utilized for the system transient stability enhancement. It serves as an extra load capable of dissipating extra generated power in case of system severe faults close to a generation station consequently prevents generator pole slipping conditions. IEEE 3 machine 9 bus system is adopted in this paper to test the effects of BR on shaft torsional oscillations mitigation in interconnected power system. Comparative simulation studies between the unsuccessful reclosure with and without fuzzy controlled BR prove the effectiveness of the scheme for mitigation of torsional oscillations significantly.

Simultaneous optimization of controllers in power systems is a challenging research due to the inherent nonlinearity of such a system. Multi-objective optimization is a useful tool for tuning excitation controllers and minimizing oscillations that are described through definition of transient and small-signal stability in power systems. In this paper, a Two-Area-Four-Machine (TAFM) power system model is tested on multiple short circuit and load disturbances. A multi-objective performance analysis is investigated by observing the system's behaviour in different cases involving the no-preference method and a priori method called weighted sum objective. The analysis is done through observation of two different objective functions. First objective function includes the sum of the integral of time-weighted absolute errors of rotor speed differences, generator voltage, and tie-line power transfer. Second objective function observes time domain elements: overshoot, undershoot, and settling time of machines' rotor speeds. Results are compared for two methods combined with four different algorithms to provide better insight into the computational performance of each algorithm and objective search method. Algorithms used for controllers' parametrization include two novel algorithms: multi-objective ant lion optimizer (MOALO) and salp swarm algorithm (MOSSA), and two classic algorithms: multi-objective particle swarm optimization with velocity relaxation (MOVRPSO) and simulated annealing (MOSA).

As part of ITPA-Integrated Operational Scenario activities, this contribution reviews recent experimental characterizations of radio-frequency (RF)-induced scrape-off layer (SOL) modifications of various tokamaks worldwide and of the LArge Plasma Device at UCLA. The phenomenology, as observed using a large variety of measurement techniques, is consistent with expectations from RF sheath rectification. Emphasis is then put on the complex three-dimensional (3D) spatial patterns of the RF–SOL interaction, in relation to the magnetic topology and the spatial distribution of RF currents over the metallic structures surrounding the RF wave launchers. Dependence on the local plasma parameters in the antenna vicinity is also briefly addressed. The final part discusses implications for future devices.

This paper proposes an adaptive fault diagnosis algorithm based on vibration signals for fault diagnosis of bearings and diesel engines. First, the improved nonlinear gray wolf optimization algorithm (NGWO) is adopted to optimize the key parameter for variational mode decomposition (VMD) with the power spectral entropy as the fitness value. Meanwhile, adaptive noise reduction of the signal is realized. Then, sensitive fault features of bearings and diesel engines are selected through a feature sensitivity analysis on the vibration signals. Also, a single-layer sparse autoencoder is used to align the feature dimensions of each type of data to construct feature matrix samples. Subsequently, a deep neural network (DNN) consisting of a two-layer stacked sparse autoencoder (SSAE) and a Softmax classification layer is constructed to realize failure mode recognition. During the training process of DNN, a surrogate model formed by NGWO and a back propagation neural network is employed to optimize the hyperparameters of SSAE. Finally, to verify the effectiveness of the proposed fault diagnosis algorithm, fault diagnosis experiments are conducted on the fault data set of bearings and diesel engines. The diagnosis results show that the proposed method achieves high-precision fault diagnosis for bearings and diesel engines and performs stably for small samples.

This paper deals with simultaneous scheduling of machines and tools with alternate machines in a multi machine flexible manufacturing system (FMS) to minimize makespan (MS). Only one copy of each type of tools is made available due to economic restrictions and the tools are stored in a central tool magazine (CTM) that shares with and serves for several machines. The problem is to select machines from alternate machines for job-operations, allocation of tools to job-operations and job-operations’ sequencing on machines for MS minimization. This paper presents a nonlinear mixed integer programming (MIP) formulation to model the combined scheduling of machines and tools with alternate machines and a symbiotic organisms search algorithm (SOSA) built on the symbiotic interaction strategies that organisms employ to continue to exist in the ecosystem for solving the scheduling of machines and tools with alternate machines. The results have been tabulated, analyzed. It is observed that there is a reduction in MS when the alternate machines are considered for job-operation.

In this paper, a novel energy flow analysis (EFA) is proposed based on the signal reconstruction and decomposition to investigate the electromechanical oscillations. In contrast to the conventional EFA, the connection between the proposed EFA and modal analysis (MA) can be quantitatively revealed for arbitrary models of synchronous generators in multi-machine power systems. Firstly, the time-domain implementation (TDI) of the proposed EFA is designed. Specifically, the measurements at the terminal of a local generator are reconstructed through an exponential operator and then decomposed with respect to an angular frequency. Then, the mode-screened damping torque coefficient is defined to extract the damping feature with respect to an electromechanical oscillation mode. After that, the frequency-domain implementation (FDI) is derived. Specifically, the Parsevals Theorem is applied to transform the proposed EFA from the time domain to frequency domain. On this basis, the consistency between the proposed EFA and MA is strictly proved, which is applicable for arbitrary models of synchronous generators in multi-machine power systems. Additionally, the application procedure of the proposed EFA in investigating electromechanical oscillations is given. Finally, the proposed EFA is substantially demonstrated in multiple case studies.

This study performed a comparative analysis of five new meta-heuristic algorithms specifically adopted based on two general classifications; namely, nature-inspired, which includes artificial eco-system optimization (AEO), African vulture optimization algorithm (AVOA), gorilla troop optimization (GTO), and non-nature-inspired or based on mathematical and physics concepts, which includes gradient-based optimization (GBO) and Runge Kutta optimization (RUN) for optimal tuning of multi-machine power system stabilizers (PSSs). To achieve this aim, the algorithms were applied in the PSS design for a multi-machine smart power system. The PSS design was formulated as an optimization problem, and the eigenvalue-based objective function was adopted to improve the damping of electromechanical modes. The expressed objective function helped to determine the stabilizer parameters and enhanced the dynamic performance of the multi-machine power system. The performance of the algorithms in the PSS’s design was evaluated using the Western System Coordinating Council (WSCC) multi-machine power test system. The results obtained were compared with each other. When compared to nature-inspired algorithms (AEO, AVOA, and GTO), non-nature-inspired algorithms (GBO and RUN) reduced low-frequency oscillations faster by improving the damping of electromechanical modes and providing a better convergence ratio and statistical performance.