Multi Machine Research Articles

Research and Analysis of On-Line Optimization Algorithm of Nonlinear Model Predictive Control Based on Hydropower Installation Field

Because of the strong nonlinearity of the controlled system, it cannot be expressed in the form of state space, and the calculation of nonlinear programming is large, so it is difficult to get the analytical solution. At present, it is generally solved by numerical method. The GPC stability theory based on state space analysis and internal model control is difficult to be applied in this case. In this paper, a fast algorithm of online optimization of nonlinear model predictive control based on the field of hydropower installation is presented. A fast algorithm control system of online optimization of nonlinear model predictive control is established and simulated by MATLAB. The simulation results show that the dimension of nonlinear programming solution in rolling optimization is effectively reduced and the feasibility of MPC is improved. Compared with single machine and multi machine systems, the model predictive control method is proved to have strong ability of generator terminal voltage maintenance and electromechanical oscillation damping.

Research and Analysis of Step Intelligent Model Predictive Control of Generator Excitation System Based on the Field of Building Installation

The traditional hydroelectric unit control system mostly adopts proportional integral derivative (PID) control strategy, which has simple control algorithm structure and is easy to understand and realize by the operation and maintenance personnel of the power station. However, its control objective is single, and the dynamic process and internal state information of the system water machine electricity coupling are less considered, especially the lack of the ability to constrain the system state variables In order to meet the increasingly complex operating environment in the regulation system of hydropower units high-quality control needs. Therefore, a step intelligent model predictive control method for synchronous generator excitation system based on tree algorithm is proposed. The step control and intelligent optimization algorithm are introduced into the rolling optimization of model predictive control, which effectively reduces the dimension of nonlinear programming solution in the rolling optimization and improves the feasibility of the control algorithm. In order to ensure the closed-loop stability of the control system, the concept of "quasi infinite time domain optimization" is introduced into the design process of the model predictive control of the excitation system of synchronous generator. The terminal penalty term and the terminal inequality domain are added in the rolling optimization link, and the "virtual" local linear feedback controller outside the prediction domain is designed. Compared with the traditional excitation control method in single machine and multi machine systems, it is proved that the predictive control method has stronger ability of generator terminal voltage maintenance and electromechanical oscillation damping than the conventional excitation control method.

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms’ performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet’s productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm’s most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.

Invasive Weed Optimized Area Centralized 2 Degree of Freedom Combined PID Controller Scheme for Automatic Generation Control

Frequency disturbances caused by load perturbations of multi area interconnected power system are reduced by using proper control schemes provided via secondary control loop along with primary control of generation plants. These secondary controllers provide simultaneous control signal to individual machines of a particular area when the control scheme is area centralized. This area centralized mechanism provides quick control action with less computational burden since control problem dimensions gets reduced even the area consists multiple plants/machines. Such centralized new cascade control scheme is proposed in this paper to supervise the secondary control mechanism and it is implemented with parallel connection of 2-Degree of Freedom Proportional-Integral-Derivative (2-DOF PID) controller combined with regular PID controller. The performance of this new controller is studied on multi area multi machine interconnected power system with participation factor concept and later its relative performance in terms of dynamic and steady state specifications are compared with conventional PID and 2-DOF PID controllers. These control parameters are tuned by Invasive Weed Optimization (IWO) algorithm to achieve better system outputs. Case studies presented in this paper show the advantages of proposed control scheme.

Efek Penambahan SCES Pada Sistem Multimesin dengan Damping dan Kontroler Berdasarkan Metode Critical Trajectory

The system can maintain its synchronization under a transient condition. The transient stability system can be evaluated using the critical trajectory method by calculating the Critical Clearing Time (CCT). The advantage of using a critical trajectory method is that the CCT can be found directly and more accurate than the time domain simulation method. This paper proposed the addition of Supercapacitor Energy Storage (SCES) and damping to enhance the transient stability on multi machine system. SCES is an electrical energy storage device that quickly stores and supplies large amounts of electricity, while damper winding damp the oscillations during unstable steady state conditions. Within the addition of SCES and damping in the system, the stability will last longer than before. The stability system is seen by the extended CCT. Furthermore, multi-machine to infinite bus used to validate the proposed method. The test system also includes the Automatic Voltage Regulator (AVR) and the governor

RETRACTED ARTICLE: Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images

From the classifications, an effective brain tumor classification and segmentation is the curious part for identifying the tumor and non-tumor cells in brain and the cell levels are evaluated. The brain tumor segmentation and classification is established on their experiences. The accuracy of tumor segmentation is very crucial to diagnosis accuracy. So, in our work we are align and improve an approach for tumor identification applying brain MR image segmentation. With an efficient, accurate and reproducible manner, the aim of our suggested method is to evaluate the tumor. Then the brain tumor is separated by using the effective techniques. For segmentation process, first the MRI image must be preprocessed. Next, the process of feature extraction is done by using preprocessed images. In feature extraction process, a raised Gabor wavelet transform (IGWT) is applied. In this research, the means of optimization technique is changed from the traditional Gabor wavelet transform. And the effectiveness of that optimization technique is aligned by using an oppositional fruit fly algorithm. At the end of the process, feature values are transferred in to the clustering process for segmentation. In this article we are introduced an algorithm called as rough k means clustering algorithm for segmentation. Here, we are applying an oppositional fruit fly algorithm to develop an effectiveness of the Gabor filter. Further to raise the classification accuracy of brain tumor we are introduced a multi kernel support vector machine algorithm.

Stability Analysis and Fault Changes on Wind Turbine Effect in Multi Machine Power System

This study presents stability analysis using a new approach to model the disturbances that occur in multi-machine systems connected with wind turbines. Consideration of stability and operability of conventional electric power systems planning and developing facing problems with the integration of installations in wind power plants (WPP) is needed because the current system is connected with renewable energy. This study highlighting the effects of WPP connected to a multi-engine 9 bus system from the effect of changes in disturbances occurring on several buses when operating. Disruption in multi-machine systems connected with wind turbines can affect system stability. The noise changes from bus to bus in the voltage drop effect on the system bus. The effect of wind turbine when a fault has a small moment of inertia can make the system unstable on the bus. The results of simulations carried out using ETAP to observe system stability have been carried out. The simulation results show that a system disruption that occurs in a multi engine system connected to the wind turbine does not result in a decrease in the bus near the fault then the stability decreases.

Application of phase sequence exchange in emergency control of a multi-machine system

Phase sequence exchange (PSE) is a recently developed emergency control technology. The effectiveness of PSE in a multi machine system is demonstrated in this paper. Two PSE device installation positions in a multi-machine system are first proposed. The first is at the outlet of each generator, and the second is at the connection lines between two large systems with weak connections. Based on extended equal area criterion (EEAC), a control method is then proposed. After the system out of step, the generator that need PSE and the power angle threshold can be determined quickly. When the threshold value of power angle is reached, the generator in the critical cluster exchanges phase sequence at the same time, which reduces the power angle of the one-machine to infinite bus (OMIB) image by 120° and increases the deceleration area of OMIB image. This prevents out-of-step conditions while maintaining the integrity of the system. The effectiveness of the PSE is verified using Kundur's two-area four-machine power system and the IEEE 39-bus system, and compared with other emergency control methods.

A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings

Rolling-element bearing is one of the main parts of rotating equipment. In order to avoid the mechanical equipment damage caused by the sudden failure of rolling-element bearings, it is necessary to monitor the condition of bearing and predict its life. Therefore, a two-stage prediction method based on extreme learning machine is proposed to predict the remaining useful life of rolling-element bearings quickly and accurately. This method uses the relative root mean square value (RRMS) to divide the operation stage of the bearing into two stages: normal operation and degradation. Starting from the normal operation stage, according to the principle of univariate prediction, a feedback extreme learning machine model is constructed for real-time short-term prediction of bearing degradation trend. Once the predicted value shows that the bearing has entered the degradation stage, the sensitive features are selected as the input by correlation analysis, and the multi variable feedback extreme learning machine model, which takes into account the dual advantages of multivariable regression and small sample prediction, is constructed to predict the remaining useful life. The experimental results show that the proposed method has higher short-term prediction accuracy and faster operation speed in the case of limited learning sample size.

Correlation of intraoperative and HRCT of temporal bone findings in CSOM

Introduction: The aim of the study was to assess diagnostic role of High Resolution Multi-detector Computed Tomography (HRMDCT) of temporal bone in the evaluation of Chronic Suppurative Otitis Media (CSOM) and to correlate intraoperative and HRCT findings in CSOM in selective cases. Material and Methods: 57 patients presented with CSOM in ENT department, referred to Radio diagnosis department were included in this study. The suspected patients were subjected to HRCT of Temporal bone using GE Bright speed Elite16 multi slice CT machine with 0.6mm plain axial and coronal slices with multi planar reformations. Results: The reported findings were correlated with clinical and surgical findings in subset of cases. Sensitivity, Specificity of HRMDCT with respect to clinical and surgical correlation in selective cases was evaluated. Among 30 surgical cases, most of them were atticoantral (53.4%) followed by tubotympanic CSOM (46.6%). On surgical correlation, HRCT has a high sensitivity (84.36%), specificity (80%), PPV of 90.4% and accuracy of 90% in the diagnosis of type of CSOM. A significant association with p value Conclusion: a good sensitivity, specificity of HRCT of temporal bone has a major role in the diagnosis of type of CSOM with respect to surgical correlation. HRCT of Temporal bone affords essential information regarding the disease extent, associated complications, aiding clinicians in deciding the appropriate surgical planning and management, thereby making HRMDCT an essential pre workup diagnostic tool. Keywords: Temporal bone, Cholesteatoma, HRCT, Facial canal dehiscence.

Power System Stability Study on Multi Machine Systems having DFIG Based Wind Generation System

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.

Optimal Localization of Distributed Generator (Dg) in Multi Machine Systems

Evolution to a sustainable power environment consequences in aggregated originator and load dynamics in the distribution network. State estimation is a key characteristic in construct ok community fashions for online monitoring and analyzes. The requirements of distribution device kingdom estimation (DSSE) is becoming rigorous because of the wishes of recent gadget modeling and function practices associated with integration of allotted electricity sources and the adoption of better technologies in distribution network. This paper summarizes the most recent technology, predominant hurdles, and challenges in DSSE expansion. The possibilities, paradigm shift, and future study directions that might facilitate the want of DSSE are discussed.

Comprehensive Power System Stability Improvement with ROCOF Controlled SMES

In order to mitigate most power quality issues in renewable power penetrated systems, load frequency control in automatic generation control based power systems and transient stability concerns in any power system, the deviation in frequency at the point of common coupling is chosen as the driving input for SMES active power control (Δf-control). We report a rate of change of frequency (ROCOF) controlled SMES as a resilient damping device to counter exogenous uncertainties in a wind based multi machine system. Primarily, the ROCOF is contingent on the active power mismatch. SMES reference active power command is issued using a genetic algorithm tuned scheme, participating through ROCOF measurement. The non linear action of SMES is realized by controlling the converter firing angles by a smooth four-zone operation, thus determining the voltage applied across the superconducting inductor. While preserving the dynamics of the DC link and supervising the state of charge (SoC) of the inductor, the effectiveness of the aforementioned control strategy is authenticated by performing non-linear simulations in MATLAB/Simulink. In addition, the dominant modes are also tracked and it is found that the percentage increase in aggregate damping ratio is around 133% using the proposed ROCOF-SMES control.

RETRACTED ARTICLE: Electrical power system transmission quality and power supplier micro grid control functional reliability

Electrical pulse generator or a transformer transports energy from various sources such as wind farm, electromagnetic field through a gas propelled air turbine, transmits currents through the high sky transmission lines. The consumer industry and domestic user multi machines’ power system micro and macro grids control functional has facilities for measuring both synchronous and asynchronous transmittance phased flow current pulses. The petroleum refinery located at islands uses powers from both the wind farm energy and gas fueled hydro-air mixture operated turbine’s mechanically air energy. Electric generator holding columns stability, wind farms air pressure regularity are some of the power grid supporting analytics helps measuring a grid potential, transmitter capacity, transmitting fault-line determination, fault type recognition, fault prohibition in the DC and AC system. Mathematical graph models with the differential equation solutions stability graphical lines helps a transmission quality and micro grid control functions reliability measure and requisite transmission path activeness or passiveness decision making process. Decisive outcome motivates system passive components repair and be fit for active up to the active components rest or temporary halt periodic time forecast. Supporting vending machine tasks schedule are automatically adjusted for readily actions at any time from the repairing time. In the present paper writer prefers to review the article of reliability decision making process optimization topics for helping the power plant maintenance servers cum reinforcement machine learners.

EEG-based epileptic seizure detection using GPLV model and multi support vector machine

Epilepsy is chronic neurological disorder that is clinically detected by continuous monitoring of Electroencephalogram (EEG) signals by experienced clinicians. Epilepsy is detected by clinicians based on the visual observation of EEG records that normally consumes more time and sensitive to noise. To address these issues, a new system is proposed for automatic epileptic seizure recognition. Gaussian Process Latent Variable Model (GPLVM) is used to lessen the number of features by achieving a set of principal features that significantly rejects the “curse of dimensionality” concern. Then, a supervised classifier (Multi Support Vector Machine (MSVM)) is used to classify the epileptic seizure classes such as normal, ictal, and interictal. Experimental result exemplifies that the proposed work effectively classifies the epileptic seizure classes in light of sensitivity, specificity, False Positive Rate (FPR), False Negative Rate (FNR), and accuracy. The proposed work improves the classification accuracy upto 2.5-12% related to the existing works. The proposed work delivers a new avenue for assisting clinicians in diagnosing epileptic seizure.

Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images

Infrared thermography (IRT) has played an essential part in observing and examining thermal defects of electrical equipment without ending, which has vital enormity for the dependability of electrical recorded. This paper dissected the electrical parts are faulted or non-faulted with the help of segmentation and classification model. The features are calculated from the input thermal images and regions of interest (ROI) is segmented by utilising optimal region growing (ORG) technique and faults are classified using multi kernel support vector machine (MKSVM). In the tests, the classification performances from different input features are assessed. For enhancing the performance of the segmentation investigation optimisation procedure that is whale optimisation (WO) is used. Before classifying, the extracted electrical components are fused by using feature level fusion (FLF) procedure to fused vector in all images. These multi kernel classification performance indices, including sensitivity, specificity and accuracy are utilised to recognise the most appropriate input feature and the best arrangement of classifiers. The performance of SVM is contrasted with a neural network. The correlation comes about demonstrating that our technique can accomplish a superior performance with accuracy at 98.21%.

Multi Modal RGB D Action Recognition with CNN LSTM Ensemble Deep Network

Human action recognition has transformed from a video processing problem into multi modal machine learning problem. The objective of this work is to perform multi modal human action recognition on an ensemble hybrid network of CNN and LSTM layers. The proposed CNN - LSTM ensemble network is a 2 - stream framework with one ensemble stream learning RGB sequences and the other depth. This proposed framework can learn both temporal and spatial dynamics in both RGB and depth modal action data. The hybrid network is found to be receptive towards both spatial and temporal fields because of the hierarchical structure of CNNs and LSTMs. Finally, to test our proposed model, we used our own BVCAction3D and three RGB D benchmark action datasets. The experiments were conducted on all the datasets using the proposed framework and was found to be effective when compared to similar deep learning architectures.

Implementation of Programmable Logic Controller in multi machine operations with product sorting and packaging based on colour detection

Automation is widely used in various industries to increase the speed, accuracy and effectiveness of production and reduce the risk of hazards. In general, the manufacturing industry has a product packaging and sorting process based on size, weight, shape, height and color. Currently, the sorting process in the industry is still mostly done manually which causes human error problems, so the development of industrial automation that is low cost, easy to maintain, efficient, user friendly and more accurate needs to be done. Programmable Logic Controller (PLC) is one of the most widely used for automation equipment in the industry and can be programmed to control machine operations. In this paper, the Mitsubishi Melsec PLC with ladder diagram programming using GX work2 has been developed to control multi-machine operation by product sorting and packaging based on product color. Multi-machine operation consists of three DC motors used for conveyors, two single solenoids, proximity sensors, inductive sensors and limit switches. The proximity sensors are used to detect the products based on color. The system is equipped with an emergency button and alarm system to facilitate the implementation of troubleshooting. To facilitate communication with operators and user-friendly systems, the human machine interface (HMI) is used as an interface for system monitoring and controlling. HMI uses Pro-face from Schneider which is programmed using GP-Pro EX4.0. The ladder diagram has been implemented in the PLC and tested on hardware to simulate the system. Based on test results, MELSEC PLC can communicate with HMI and control multi-operation machines with product sorting and packaging. The machine can separate and package metal products by colour and the packaging system can be monitored and controlled remotely automatically with HMI.

RETRACTED: Application of Machine Learning and Big Data in Doubly Fed Induction Generator based Stability Analysis of Multi Machine System using Substantial Transformative Optimization Algorithm

With the increase in the amount of data captured during the manufacturing process, surveillance systems are the most important decision making decisions. Current technologies such as Internet of Things (IoT) can be considered a solution to provide efficient monitoring of productivity. In this study, it has suggested a real-time monitoring system that uses an IoT, big data processing and an Offshore Wind Farm (OWF) model is proposed. The Offshore Wind Farm (OWF) is an extended level invasion in modern power electronics systems, in this proposed work Doubly Fed Induction Generator (DFIG) based multi machined OWF was designed, and power stability was analyzed using Substantial Transformative Optimization Algorithm (STOA). The Voltage Source Converter (VSC) and High Voltage Direct Current (HVDC) system was combined with onshore network. The terminal voltage of onshore network was controlled through Onshore Side Converter (OSC), active and reactive power was regulated separately using VSC. The performance of the onshore network was evaluated under renewable network errors (Total Harmonics distortion and steady state error) beside with OWF. The OWF - DFIG active and reactive power was controlled smoothly with in the limit of HVDC, and the power framework security can be updated by controlling the active power of the OSC to help its terminal voltage using STOA methodology. From the voltage control mode, the electrical faults are recovered rapidly with minimum fluctuation. The dynamic simulation comes about additionally demonstrate that onshore network fault can't impact OWF behind HVDC transmission system. Because of the specialized favorable circumstance, VSC-HVDC innovation, the constancy in OWF is very much ensured against the onshore grid faults. The proposed STOA based system has validated through simulation in Matlab Simulink environment. General, 97% effectiveness, accomplished at full load condition in light of the proposed system. The results showed that the IoT system and the proposed large data processing system were sufficiently competent to monitor the manufacturing process.

Coordination of Generation of Multi Machine Power Systems

Coordination of Generation of Multi Machine Power Systems