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Aiming at the problems of asymmetric information and unreasonable emergency allocation schemes in the current cross-regional emergency operation, the emergency deployment process of multi-machine and multi-task is analyzed, and the emergency allocation model with the goal of minimizing the allocation cost and loss is established in the paper. Emergency allocation algorithm based on rule of nearest-distance-first, which allocate machinery for the nearest farmland firstly, and emergency allocation algorithm based on rule of max-ability-first, by which machinery with maximum ability to farmland is allocated firstly, are proposed. The operational data of farmland and agricultural machinery generated randomly are calculated and analyzed. The results show that when the amount of agricultural machinery is sufficient, the algorithm based on the maximum contribution capacity priority is better. When the agricultural machinery is insufficient, the calculation results of the emergency allocation algorithm based on the nearest distance priority are better. When the number of farmland is not more than 30, the average operation time of the two algorithms in this paper is not more than 3.8 seconds, and both two algorithm have good performance.

This study presents a framework to detect malicious windows commands by using both statistical and deep learning algorithms. Our method consists of two stages. In the first stage, malicious windows commands are identified using a benign profile with the Euclidean distance. In the second stage, we recognize the malicious part in the Windows commands by using Natural Language Processing and proposed a scoring mechanism. Finally, we used two real cases to verify our method's effectiveness in detecting malicious commands and malicious parts. We also verify that the factor affecting the precision, recall and demonstrated our approach was effective through experiments.

The multimachine system is one of the most common systems present in the power system network. System expansion is necessary due to ever-increasing load, and thus, the analysis of the response of multimachine becomes important too. UPFC is the most common FACTS device used in power system in current scenario as it can control all parameters of the power at same time. IEEE 14 bus test system with three generators has been taken for the analysis, and also voltage stability is calculated with the help of different indices to find out suitable position of UPFC to be implemented in the system. All the simulation is carried out in the PSAT software [1]. The loss with and without UPFC is calculated.

Wind energy is one of the fastest growing energy resources of alternative power generation across the globe. Maximum number of generators in wind farm is provided with Doubly Fed Induction Generator (DFIG) due to its many advantages. The effects on small signal stability of system with distributed additional load, equipped with DFIG based wind farms, is presented here. The additional load added in the existing system is made same in value as the power generated by the wind farm. Different distributions of additional load are considered to determine the small signal stability of system with varying wind power penetration. The position of the negative real part of eigenvalues of system matrix helps to determine the best location for the integration of wind power for particular load distribution of additional load among different load buses. A new sensitivity based index has been introduced here to determine the maximum penetration level in terms of the system small signal stability. WSCC 3-machine 9-bus system is used for the study which is carried out in MATLAB.

We investigate an adaptive neuro-control approach, namely goal representation heuristic dynamic programming (GrHDP), and study the nonlinear optimal control on the multi-machine power system. Compared with the conventional control approaches, the proposed controller conducts the adaptive learning control and assumes unknown of the power system mathematic model. Besides, the proposed design can provide an adaptive reward signal that guides the power system dynamic performance over time. In this paper, we integrate the novel neuro-controller into the multi-machine power system and provide adaptive supplementary control signals. For fair comparative studies, we include the control performance with the conventional heuristic dynamic programming (HDP) approach under the same conditions. The damping performances with and without the conventional power system stabilizer (PSS) are also presented for comparison. Simulation results verify that the investigated neuro-controller can achieve improved performance in terms of the transient stability and robustness under different fault conditions.

This paper combines the characteristics of windshield glass production process and compares various intelligent algorithms. It proposes to start from the windshield parallel multi-machine manufacturing process and establish the simulation unit of windshield manufacturing process. The method considers the windshield manufacturing process. The sequence of the process and the factors affecting the tempo of each process are transformed into genomic information. This paper establishes a simulation model of the two-chrome parallel multi-machine production line to optimize the scheduling of the windshield. This method increases the production efficiency of the windshield manufacturing process while achieving line balance with minimal processing time. Based on the Plant Simulation simulation platform, the effectiveness of the proposed method is verified by optimizing the production line of parallel multi-machine.

This paper propose, a robust excitation controller designed by a coordination of the optimal H∞ tracking control and the proportional integral derivative (PID) controller optimized by the hybrid differential evolution and teaching–learning based optimization algorithm (DE–TLBO). These two controllers are used in order to guarantee the transient stability during a change in the operating conditions and the uncertainties in parameters. We have applied a method based on the modified tracking error by using the optimized exponential function, to avoid the compromise between the high gain in the control input and the H∞ tracking performance with the variation in the system parameter. A new hybrid algorithm (DE–TLBO) is employed in this study to adjust optimally the parameters of the (PID–PSS) controller and the exponential form of the tracking error modified. The purpose of the suggested approach is to ensure a good tracking accuracy and to enhance the level of the oscillations damping in the multi-machine power system with an optimal choice of the parameters of all proposed controllers. The results of simulation demonstrate the efficient, and the robustness of the proposed approach (H∞ and DE–TLBO–PID–PSS) under the different operation conditions.

The direction of modernization of the wind power plant is substantiated, which is aimed at a comprehensive increase in energy efficiency and resource conservation. Taking into account the reasons for the destruction of the teeth of the gear, a constructive scheme of a single-stage gearbox with several output shafts is proposed. This scheme allows you to distribute the output power to several electric machines, which, in turn, reduces the mechanical stress in the area of contact of the teeth of the gears and gears of the output shafts. Due to the division of the output power into several machines, the mass and dimensions of the gearbox are also reduced. The uniformity of power distribution on the shafts can be violated by the discrepancy between the parameters of gears, electromagnetic and mechanical parameters of electric machines. In the proposed design scheme, the uniformity of energy flows along the shafts is ensured by a sequential connection of the phases of the stator windings of electric generators., Обґрунтовано напрямок модернізації вітроенергетичної установки, який спрямовано на комплексне підвищення енергетичної ефективності та ресурсозбереження. З урахуванням причин руйнування зубців зубчастої передачі запропонована конструктивна схема одноступеневого редуктора з кількома вихідними валами. Така схема дає змогу розподілити вихідну потужність на декілька електричних машин, що, в свою чергу, зменшує механічне напруження в зоні контакту зубців зубчастого колеса і шестерень вихідних валів. За рахунок розділення вихідної потужності на декілька машин також зменшуються масогабаритні показники редуктора. Рівномірність розподілу потужності на вали може порушуватись розбіжністю параметрів зубцевих передач, електромагнітних і механічних параметрів електричних машин. В запропонованій конструктивній схемі рівномірність потоків енергії за валами забезпечується послідовною схемою з’єднання фаз обмоток статорів електричних генераторів.

Power system stability is related to principles of rotational motion and the swing equation governing the electromechanical dynamic behavior. In the special case of two finite machines the equal area criterion of stability can be used to calculate the critical clearing angle on the power system, it is necessary to maintain synchronism, otherwise a standard of service to the consumers will not be achieved. With the increasing penetration of doubly fed induction generators (DFIGs), the impact of the DFIG on transient stability attracts great attention. Transient stability is largely dominated by generator types in the power system, and the dynamic characteristics of DFIG wind turbines are different from that of the synchronous generators in the conventional power plants. The analysis of the transient stability on DFIG integrated power systems has become a very important issue. This paper is a review of three types of stability condition. The first type of stability, steady state stability explains the maximum steady state power and the power angle diagram. There are several methods to improve system stability in which some methods are explained.

Aiming at the uncertainties of wind turbines clustering results and equivalent model caused by the changes of operating scenarios in wind farms, an aggregate modeling method for wind farms based on multi-machine representation is proposed. Firstly, based on the Markov property of wind speed, the probability transfer matrix is established to characterize the seasonal and daily characteristics of wind speed. Secondly, the two-step method is used to aggregate and classify wind turbines according to wind speed and operation characteristics of front-end speed-regulating wind turbines. According to Fisher criterion, the clustering results were analyzed by probability analysis and regression test in order to merge the groups with higher correlation. Finally, the highest probability of each quarter as the final clustering results, so as to get the wind farm aggregation model. Compared with the output characteristics of the detailed model, the proposed modeling method and the aggregation model can more comprehensively characterize the operation characteristics of wind farm. Thus, the adaptability of the equivalent model is improved effectively, and the uncertainty of the clustering results of the equivalent model is solved.

Harvest operations carried out by a single logger operating more than one machine are common, yet little is understood of their productivity and costs. A case study of three single-logger operations (SLO) located in New York, USA was conducted to assess their harvest costs and productivity. Daily activity and production data were collected from each harvest operation and analyzed in conjunction with machine rate calculations to estimate system productivity and costs for each SLO. Based on these data, deterministic simulations were conducted to assess how the addition of second and third loggers affected harvest system efficiency. Operator and equipment utilization rates were calculated at the systems level and compared among the observed SLOs and simulated two- and three-logger simulations. In most cases, the introduction of additional loggers reduced operator utilization due to the introduction of system delays, and therefore, increased labor costs. However, this investment in labor often led to ...

This paper proposes a coordinated switching power system stabilizer (SPSS) to enhance the stability of multimachine power systems. The SPSS switches between a bang-bang power system stabilizer (BPSS) and a conventional power system stabilizer (CPSS) based on a state-dependent switching strategy. The BPSS is designed as a bang-bang constant funnel controller (BCFC). It is able to provide fast damping of rotor speed oscillations in a bang-bang manner. The closed-loop stability of the power system controlled by the SPSSs and the CPSSs is analyzed. To verify the control performance of the SPSS, simulation studies are carried out in a 4-generator 11-bus power system and the IEEE 16-generator 68-bus power system. The damping ability of the SPSS is evaluated in aspects of small-signal oscillation damping and transient stability enhancement, respectively. Meanwhile, the coordination between different SPSSs and the coordination between the SPSS and the CPSS are investigated therein.

This article proposes a volumetric error generalized model and a distributed error compensation technique for multi-machine tools. A detailed generalized kinematic model for three- and five-axis machine tools is built. It can be applied to 4 types of three-axis machine tools and 12 types of five-axis machine tools. By simply inputting the translational and rotary type numbers of the specific machine tool, the final kinematic model can be obtained without further human interventions. A new compensation technique based on an Ethernet distributed numerical control system is proposed. Ethernet distributed numerical control software integrated with the generalized kinematic model is developed for machine tools with Fanuc and Siemens systems. Based on the Ethernet distributed numerical control system, the real-time volumetric errors of multi-machine tools are simultaneously compensated for. The proposed generalized kinematic model and Ethernet distributed numerical control systems are experimentally verified on three machine tools, whose volumetric errors are significantly reduced after compensation.

This paper proposes a new nonlinear control approach to coordinate doubly fed induction generator (DFIG)-based wind turbines and static synchronous compensator (STATCOM) controllers in multi-machine power systems. Its main objectives are to improve the transient and voltage stability of the inter-connected multi-machine power system via simultaneous design of the voltage of the DFIGs’ rotors and reference current of STATCOM. The nonlinear approach is based on a multi input-multi output (MIMO) zero dynamics (ZD) technique augmented by a super twisting sliding mode control (STSMC) to reduce the effects of the linearization error. The proposed approach was implemented to the Kundur’s four-machine two-area power system and the IEEE 39-bus power system. Its performance was assessed in the presence of various faulty scenarios and disturbances. The effectiveness of the proposed approach (ZD-STSMC) was evaluated via eigenvalue analysis, transient simulations and transient stability margin analysis (by critical clearing time (CCT)). The merits of the proposed design are further compared to those of the exact feedback linearization (EFL) approach. Enhancing the power and voltage stability of large scale power systems, damping power oscillations and providing faster post-fault recovery time are among the positive features of the proposed approach.

Traditional dispatcher training simulator (DTS) is commonly used for individual practice or joint anti-accident exercises that all people in single scenario. Problems includes the lack of sharing mechanism, fixed process and single machine control, which cannot support multiple people to train or do competitive exam in the same scenario simultaneously. In order to expand the use scenarios of DTS and solve the above problems, this paper suggests that applying the multi-machine technology to traditional DTS, including cloud storage technology through WEB to realize the sharing of exam cases, and establish an event-aware triggering mechanism and multi-machine centralized control technology of simulation virtual machines to achieve the individual process leading and multi-people competitive exam centralized control and interaction. Finally, by an simulation application instance of DTS in the multi-people competition and comparing with the traditional DTS functions and performance, the feasibility and practicability of DTS system in multi-person competitive exam scenario after implementing these new technologies are verified.

This paper provides a general dynamic mathematical model of Multiple Unified Power Flow Controllers (UPFC) development; those installed into any selected lines of a Multi-Machine power system network. The development will be based on two UPFCs installed into the New England Power System (NEPS) grid. This mathematical development applicable on any Multi-Machine network based on the required number of UPFCs to achieve power system control enhancement.

We provide a remote online program update method for multi-machine control system in distributed industrial control field based on WiFi Wireless LAN and CAN bus in this paper. On this basis to achieve the accurate and efficient update for remote multi-machine.

A new optimization technique known as the flower pollination algorithm is suggested in this article for robust tuning of a static VAR compensator to mitigate power system oscillations. The flower pollination algorithm is based on the properties of flowering plants. The tuning of a static VAR compensator controller is established as an optimization process. The flower pollination algorithm is applied to find out the optimal parameters of a static VAR compensator by diminishing certain performance index. The behavior of the suggested algorithm has been appreciated with the behavior of the genetic algorithm and bacteria foraging to certify the notability of the suggested flower pollination algorithm in designing a static VAR compensator. Simulation results of the flower pollination algorithm based static VAR compensator provide greater attenuation for various loading conditions than the optimized static VAR compensator by genetic algorithm and the optimized static VAR compensator by bacteria foraging...

This paper proposes a new decentralized output feedback control scheme for three-machine power system. The decentralized controller synthesis problem is formulated as a scaled H∞ control problem, and an LMI-based algorithm is developed to synthesize the decentralized controller. The proposed controller provides robustness with regard to parametric uncertainties and also attenuates bounded exogenous disturbances in the sense of L2-gain. Simulation results clearly show the effectiveness of the developed decentralized output feedback control scheme.

This work presents a comprehensive methodology for location and tuning of Power System Stabilizers (PSSs) based on multi-machine criteria. This goal is achieved by means of exploiting the benefits of the DIgSILENT PowerFactory software for management of large and complex electric power systems, and the potential of DIgSILENT Programming Language (DPL) that allows combining different application routines, such as: Power Flow, Small Signal Stability, Optimization and tuning of variables in control systems. Thus, a multistage analysis is proposed in order to: i) define several operating scenarios, ii) determine the proper location of the PSS via analysis of participation factors, and iii) estimating the optimal parameters of the selected PSS for reaching a specific system damping. The complete method has been applied to the Ecuadorian Electric Power System, and the results show a satisfactory PSS tuning for achieving the specified system damping. This method can easily be applied to any other power system.

For the parameter optimization of multi-machine Power System Stabilizer (PSS), a new method is proposed in this paper, which uses Improved Artificial Bee Colony (IABC) algorithm to coordinate the parameters of PSS. IABC aims to model the behavior of foragers of artificial bee colony more accurately and improve the performance of ABC algorithm in terms of local search ability. Simulation of IEEE four-machine two-area system verifies that, the PSS optimized by IABC has better performance than other algorithm in two typical modes.

In recent years, grid-connected photovoltaic (PV) power generations have become the most extensively used energy resource among other types of renewable energies. Increasing integration of PV sources into the power network and their dynamic performances under fault conditions is an important issue for grid code requirements. In this paper, a PV source as a unified interphase power controller (UIPC) is used to enhance the low voltage ride through (LVRT) and transient stability of a multi-machine power system. The suggested PV-based UIPC consists of two series voltage inverters and a parallel inverter. The UIPC injects the required active and reactive power to prevent voltage drop under grid fault conditions. Accordingly, a dynamic control system is designed based on proportional-integral (PI) controllers for the PV-based UIPC to operate in both normal and fault conditions. Simulations are done using Matlab/Simulink software, and the performance of the PV-based UIPC is compared with the conventional unified power flow controller (UPFC). The results of this study indicate the more favorable impact of the PV-based UIPC on the system compared to UPFC in improving LVRT capabilities and transient stability.

Fault detection, diagnosis, identification and location are crucial to improve the sensitivity and reliability of system protection. This maintains power systems continuous proper operation; however, it is challenging in large-scale multi-machine power systems. This paper introduces three novel Deep Learning (DL) classification and regression models based on Deep Recurrent Neural Networks (DRNN) for Fault Region Identification (FRI), Fault Type Classification (FTC), and Fault Location Prediction (FLP). These novel models explore full transient data from pre- and post-fault cycles to make reliable decisions; whereas current and voltage signals are measured through Phasor Measurement Units (PMUs) at different terminals and used as input features to the DRNN models. Sequential Deep Learning (SDL) is employed herein through Long Short-Term Memory (LSTM) to model spatiotemporal sequences of high-dimensional multivariate features to achieve accurate classification and prediction results. The proposed algorithms were tested in a Two-Area Four-Machine Power System. Training and testing data are collected during transmission lines faults of different types introduced at various locations in different regions. The presented algorithms achieved superior detection, classification and location performance with high accuracy and robustness compared to contemporary techniques.

In recent years, with the development of the structure and function of computers, control equipment and machinery, the application of flexible manufacturing systems in the stamping processing industry has been increased, and the automation of multi-variety medium and small batch production has been realized, which greatly improves the flexibility of production. As an industry, stamping processing plays an important role in the processing industry of the national economy, but research on this aspect is still lacking. The purpose of this paper is to explore the development of robotic and multi-machine automatic stamping production combination, to improve production efficiency and enhance enterprise competitiveness for the use of robots. This paper mainly uses an industrial robot with three presses to discuss the situation of the beat improvement. Finally, the equipment model and the virtual model of the collaborative production unit are established by the virtual prototype technology to analyze the trajectory planning, workspace and free vibration of the virtual prototype of the industrial robot. Finally, the virtual production unit is simulated and analyzed.

This paper proposes to optimally coordinate multiple Energy Storage System (ESS) based stabilizers and Power System Stabilizers (PSS) to increase damping in a multi-machine power system using an improved particle swarm optimization (IPSO). The IPSO has the strong and global searching ability and high efficiency by applying chaos initialization and simulated annealing (SA) algorithm in iteration. All eigenvalues, including both electromechanical and non-electromechanical modes, that meet the stability requirements in a wide range of operating conditions have been included in the objective function. In the 4-machine power system, the power system oscillations are effectively suppressed at different operating conditions by multiple ESS-based stabilizers and PSSs coordinated by IPSO, where the damping effect is better with the stabilizers designed by the IPSO than with the stabilizers tuned individually by the conventional compensation method. The results show the validity, robustness and superiority of the IPSO design.

An IoT (Internet of Things) system is a multi-machine system composed of a master controller and multiple slave controllers. In some automatic control systems such as petroleum and electric power, the main control uses UART (Universal Asynchronous Receiver/Transmitter) to communicate with the slave controller. However, the communication rate of the UART interface is limited and the delay time is long, which affects the system’s response to external events. In order to improve the response speed of the system to external events, this paper designs a multi-machine system based on SPI (Serial Peripheral Interface) interface. First, we use the SPI interface of the microcontroller to build a communication network. The circuit structure of the system is simple, and the hardware resource consumption is low. Then, for the problem that the SPI interface cannot detect communication errors and there is no response mechanism, we designed a communication protocol to make up for these defects from the software level. Finally, we use STM32F429IGT6 as the master controller and STM32F103C8T6 as the slave controller to build the experimental platform. The experimental results show that the system is safe and reliable, and can meet the requirements of real-time applications.

WSCC and IEEE have suggested that PSS should be widely used. In China, many PSS have already been used as the inter-connected grid is getting larger. However, the feasibility of Widely Usage of PSS hasn’t been proved, as PSS may introduce some negative damping. This paper indicates that PSS mainly transfers the damping from non-electromechanical mode to the electromechanical mode. And one generator’s PSS mainly enhances the electromechanical mode’s damping associated tightly with the generator, while introducing very little positive or negative damping to the electromechanical mode associated weakly with the generator. Widely Usage PSS’s positive effect is much more than the negative effect. As a result, Widely Usage of PSS is feasible.

This paper introduces a new approach to obtain the generator rotor angle difference in the multi-machine power system for transient stability studies. It aims to replace the center of inertia (COI) for on-line transient stability assessment. Compared with COI, the proposed method can give more reasonable estimations of rotor angle difference between the individual generator and the multi-machine power system. In addition, it allows generators to have their own references for evaluating transient stability, which is more reasonable and accurate than using COI. Moreover, the proposed technique calculates the rotor angle difference by using the local generator's active and reactive power output. This does not add any extra burden to the communication channel.

Small-signal oscillations is one of the important problems in power system operation that caused by insufficient natural damping in the system. This paper uses the Quantitative Feedback Theory (QFT) to design a new robust PSS for multi-machine power systems able to provide acceptable damping over a wide range of operating points. In the design procedure the main purpose is to reject the load fluctuations and, therefore, a particular transfer function is used as the nominal plant. The parametric uncertainty in power system is readily handled using QFT. The decentralized design with a simple structure is easily applied to multi-machine power systems. The nonlinear time-domain simulations are carried out to validate the effectiveness of the proposed controller. Results clearly show the benefits of the proposed controller for stability enhancement of power systems.

The Iraqi national super grid is integrating more Renewable Energy Sources (RESs) and planning for extra additional capacity. However, increasing the level of RESs leads to reduce the system inertia. Inertia reduction will decrease the frequency stability especially with a system like the Iraqi super grid due to many infrastructure and stability issues. This work is presented to the system operator of the Iraqi Super Grid to investigates the impact of using a modern type of Power System Stabilizer (PSS) on the stability of the frequency with the presence of different types of disturbances. The main aim is to focus on the impact of those types of PSS on small and large stability of frequency by using different dynamic disturbances in a large multi-machine interconnected power system. The integrated modern type of PSS has a noticeable impact on frequency deviation and RoCoF following disturbances.

Proposed method of coordinated design in the paper is in two steps. Firstly by taking the damping torque contributions of a PSS to all synchronous generators in a multi-machine power system into consideration, two parameters of each of multiple PSSs are set for the PSS to supply the most damping to the electromechanical oscillation mode of interests with a unity gain. Secondly, the gain of all PSSs is set in coordination by parameter optimization. The optimal searching is in the M dimensional parameter space of M PSSs. An example multi-machine power system with multiple PSSs to be installed is presented in the paper to demonstrate and validate the method proposed in the paper. In the demonstration, various forms of commonly-used objective functions are tested and compared. Detailed examination of optimum for each form of objective functions is carried out to provide an insight into the connections of optimum with the objective functions in the coordinated design of multiple PSSs.

Figure 5.1 shows the configuration of a two-machine power system where two synchronous generators are connected by a transmission line. This simple system is used in this section to demonstrate the procedure of establishing the mathematical model of a multi-machine power system . Later, it will be used for introducing basic concepts and methods in the analysis of power system oscillations and design of PSSs in the multi-machine power system. For the purpose of demonstration, a load is connected at the terminal of two generators and a node along the transmission line, respectively.

This paper reports the study carried out to asses the dynamic behaviour of a real wind farm during fault conditions in the distribution network. The objective is to obtain a transient model valuable for all kinds of operational conditions. The paper begins with a detailed modelling of the distribution network between the wind farm common busbar and the point of common coupling with the HV transmission system, for all types of short-circuits. The state-space network model, together with the electromagnetic transient model of each wind generator, configure a multi-machine wind farm model, with 572 state variables. Simulation results prove that multi-machine modelling is mandatory in order to tackle the wind farm contribution to symmetric and asymmetric short-circuits.

Disconnection of large wind turbine generators (WTGs) in renewable-integrated power systems has been well-recognized as one of the main origins of system instability. The ongoing study aims at establishing a three-stage fuzzy coordinator (TSFC) intended to enhance the power system stability encountering with major outages of large WTGs. The new TSFC employs wide area measurement system (WAMS) for a global coordination in multi-machine power system. The proposed TSFC founds a fuzzy logic coordination basis for tuning the interactions of multiple control devices. To this end, the WAMS-based TSFC is devised to end in performance harmonization of multiple control devices including automatic voltage regulator (AVR), power system stabilizer (PSS), and static var compensator (SVC). The TSFC receives the normalized voltage and rotor angle deviations of all machines and also the penetration level of wind farms as the input signals. The penetration level as the input signal determines the coordination strat...

The Vector controlled doubly fed induction generator (DFIG) wind energy system in a multi-machine power system is very sensitive to faults on transmission lines and thus affects the stability of th...

Modern power networks are complex multi-component dynamic system with many operational levels made up of a wide range of renewable energy resources. The low frequency oscillations may undergo and sustain to cause system separation if adequate damping is not provided. This research work presents the mathematical model of Solid Oxide Fuel Cells (SOFC) within transmission network in order to dynamic stability analysis and control. The main contribution is to propose the new linearized Heffron-Philips model of the system under consideration the SOFC power plant to studying of low frequency oscillations. The eigenvalue-based objective function is used to search optimal multi-machine power system stabilizer (PSS) with the Particle Swarm Optimization (PSO) technique under various operating conditions. The effectiveness of the proposed model is tested on three-machine power system integrated with the single SOFC power plant. The time-domain simulation results are promising and illustrate the effectiveness of the proposed stabilizers. It can be damp the low frequency oscillations in the best possible procedure and significantly improves the stability performance of the case study system.

With the global consciousness of climate change, renewable energy systems are prioritized over the conventional energy systems. The deep injection of renewables into the power systems is creating several challenges to the grid due to wide variations in their output power depending on the time of the day, weather etc. Of these challenges, frequency change plays a vital role in maintaining the power quality. This paper presents a novel sliding mode controller with non-linear disturbance observer to effectively mitigate the wide changes in the frequency. A sliding mode surface based on estimated disturbance along with states is designed. A sliding mode control law is proposed to compensate disturbances including variations in renewables, load and parameters under mismatched uncertainties. The proposed observer based controller is tested for three area multi-machine power system in MATLAB/Simulink. The simulated results proved to alleviate the frequency variations effectively compared to the conventional controllers.

The objective of this paper is to investigate the power system damping enhancement via power system stabilizers (PSSs). However, the conventional power system stabilizers (CPSSs) have certain drawbacks. There are many techniques proposed in the literature for damping improvement of low frequency power system oscillations. In this paper, adaptive neuro-fuzzy inference system (ANFIS) technology has been proposed to coordinate the CPSSs in a multi-machine power system. The time-domain simulations are carried out in Matlab/Simulink environment to validate the effectiveness of the proposed control scheme under different operating conditions.

We address the two-stage multi-machine assembly scheduling problem. The first stage consists of m independently working machines where each machine produces its own component. The second stage consists of two independent and identical assembly machines. The objective is to come up with a schedule that minimizes total or mean completion time for all jobs. The problem has been addressed in the scheduling literature and several heuristics have been proposed. In this paper, we propose a new heuristic called artificial immune system (AIS). We conduct experimental analysis for comparing the newly proposed heuristic AIS with the best known heuristic in the literature. Experimental results show that our proposed heuristic AIS performs better than the best known existing heuristic. More specifically, our new heuristic AIS reduces the error of the best known heuristic by 60% while the computational times of both AIS and the best known heuristic are almost the same.

This paper presents a new optimization method for designing the parameters of a power system stabilizer (PSS) using a smart bacteria foraging algorithm (SBFA). The proposed technique, which is a modification of the bacteria foraging method (BFA), can direct bacteria by performing a tumble with a smart unit of length, decreasing the cost function better than the conventional BFA method. This algorithm not only considers social intelligence, but also emphasizes the individual intelligence of bacteria for finding a better nutritional path. A new cost function in the proposed SBFA has been used for specifying the direction of movement after a tumble. This approach led to a higher convergence speed and also better performance than the BFA. The effectiveness of the proposed method has been tested on a multi-machine power system while considering a frequency error-based objective function to enhance damping of the electromechanical oscillation modes. Simulation results for the proposed method are compared with conventional PSS, BFA- and fuzzy-based PSS methods. The results show the superior performance of the proposed SBFA-based PSS in comparison with other techniques for damping power system oscillations.

This paper presents the design and implementation of Power System Stabilizers in a multimachine power system based on innovative evolutionary algorithm overtly as Breeder Genetic Algorithm with Adaptive Mutation. For the analysis purpose a Conventional Power System Stabilizer was also designed and implemented in the same system. Simulation results on multimachine systems subjected to small perturbation and three phase fault radiates the effectiveness and robustness of the proposed Power System Stabilizers over a wide range of operating conditions and system configurations. The results have shown that Adaptive Mutation Breeder Genetic Algorithms are well suited for optimal tuning of Power System Stabilizers and they work better than conventional Genetic Algorithm, since they have been designed to work on continuous domain. This proposed Power System Stabilizer is demonstrated through a weakly connected three multi-machine test systems.

Conventional power system stabilizer (CPSS) has been widely used as a supplementary controller to damp out the low frequency oscillations. The tuning of CPSS parameters for nonlinear power systems in a robust way in order that the overall system stability can be improved is a major drawback of CPSS. Several meta-heuristic optimization techniques have been tried out to overcome this shortcoming. This paper presents a new technique named cultural algorithms (CAs) to tune the PSS parameters. This technique is robust and computationally efficient as compared with other meta-heuristic algorithms. Simulations are carried out on two typical multi-machine electric power systems. The results clearly verify that the proposed method improves the dynamic stability of the system, especially when the operating point changes.

PurposeAccording to the high‐reliability requirement and especial structure of triple modular redundancy (TMR) emergent trip multi‐machine system, the purpose of this paper is to introduce a highly effective invalidation detecting mechanism based on heartbeat mechanism.Design/methodology/approachThe structure of TMR system is designed to be multi‐master and multi‐save. According to the multi‐machine environment, an invalidation detecting mechanism is designed. Considering the advantage and weakness of the traditional PULL and PUSH heartbeat model, the mechanism combines PUSH model with PULL model. To further enhance real‐time ability and reliability of the invalidation detecting mechanism, hierarchy heartbeat monitor and integrating the heartbeat message into data transmission are designed in the mechanism.FindingsThe invalidation detecting mechanism combines PUSH with PULL heartbeat monitor model can enhance reliability and decrease the system misdiagnosis rate greatly. By adapting hierarchy heartbeat mechanism and integrating the heartbeat detecting message into the data transmission, the system communication cost is decreased and the input‐to‐output response time is shortened. The effectiveness and reliability of this mechanism are proved by system field commissioning and MATLAB simulation.Originality/valueUsing a new heartbeat invalidation detecting mechanism, this paper provides a new way of fault diagnosis of TMR multi‐machine system.

In railway transport, stochastic resonance (SR) and some phenomena may happen when multi-machine traction system is hauling a sequence of carriages. These phenomena may vary with the coupling coefficients among these traction machines. To work together fluently and produce the maximum output, energy changes and maximum output of this system can be worked out with the power spectrum of the coupling system. Genetic algorithm can be used to search for the best coupling matrix so as different traction machines can work fluently and efficiently as a whole.

Modeling of unified power flow control (UPFC) into multi-machine power systems by Wang, published by IET, has been widely quoted by many researchers. However, the correctness of the derivation of its equations has not been strictly scrutinized. The model cannot properly represent power system networks in its current form and need to be corrected. In this paper, the model proposed by Wang is revisited and the equations re-derived obtaining different results. The corrected model was developed and tested on three-machine and New England power systems installed with a UPFC. The DC link capacitor voltage is controlled in UPFC by modulating the phase angle of the excitation transformer voltage. The effect of the controller in damping of the low-frequency oscillations is demonstrated by using the proposed correct model and the results are compared with model presented by Wang.