Single-machine System Research Articles

Joint Prescriptive Maintenance and Production Planning and Control Process Simulation for Extrusion System

Production planning and control (PPC) is the mainstay of every manufacturer and ensures flawless production processes. However, PPC is jeopardized by breakdowns that can only be tackled with appropriate maintenance. In the past, static strategies, such as reactive and scheduled maintenance, have been used. Yet, with growing system complexity, Industry 4.0, and abundant sensor data, dynamic strategies through PHM have emerged. The most advanced maintenance strategy is prescriptive maintenance (PxM), which allows manufacturers not only to predict failures but also to establish condition-based production plans and controls. To this end, our study explores the integration of PxM with PPC. First, we propose a fault prediction model based on health indicators and future loads of a single-machine system. The proposed fault prediction is integrated into a joint PxM and PPC simulation model that compares the make­span of three joint PxM and PPC strategies inter se and versus reactive and scheduled maintenance. A simulation study using industrial data from an extrusion process evaluates the different strategies across different time horizons (one month to a year). The findings indicate that joint PxM and PPC outperform other strategies, providing significant time savings over traditional methods. Further, a sensitivity analysis is conducted to assess the robustness of the PxM strategies under varying levels of measurement noise, revealing potential challenges under high noise conditions. The study contributes to the field of PHM by providing insights into the effectiveness of joint PxM and PPC strategies and offering a comprehensive analysis of their performance under different conditions.

Investigations on application of curve fitting in power system inertia estimation

ABSTRACT As the share of renewable energy sources in the power system keeps growing, a new issue with frequency stability – diminishing and changing inertia – has emerged. Estimating power system inertia has received a lot of attention lately in order to market design of synthetic inertia as an additional service and implement timely corrective steps for robust frequency control. Curve fitting technique has emerged as one of the promising techniques for estimation of power system inertia, however, many challenging aspects need to be addressed for its practical applications. This study aims to systematically assess the effectiveness of the curve fitting method under a range of operational parameters and conditions, such as the curve fitting order, the length of data to be used for curve fitting after an event begins, and the length of the moving average filter applied to the frequency data to remove oscillatory and noise from the frequency transient. The simulation studies have been performed on DIgSILENT and MATLAB platforms. The results show that very high and very low order curve fitting results are high errors and the 5th order model with mean error in estimated value of inertia less than 1% has outperformed others. The analysis on various window width of moving average filter found that increasing the window width results in more error. For IEEE-9 bus system, a filter up to 850 ms width has a mean percentage error below 1%. The analysis also revealed that the duration of the data used for curve fitting is not significant. For variation of the duration of the data used for the curve fitting from 50 ms to 500 ms, the estimated inertia values are found to be in a close range of 2.4881 s to 2.5262 s, for the single machine system with H = 2.5 s. The analysis presented in this study will be useful for effective application of curve fitting method in inertia estimation.

OPC UA DRIVEN AUTONOMOU DECISION MAKING IN INDUSTRIAL IOT

The creation of autonomous and intelligent devices for manufacturing firms has been made possible by the Industrial Internet of Things and the progress made in machine-to-machine communication. These autonomous machines are able to collect data from various sensing devices and other related machine systems by using communication protocols such as OPC UA. Here we perform machine to machine communication through an Arduino microcontroller. Any board or computer with a full-size USB A host connector can be connected to the Arduino with the help of a USB-A to USB-B cable. The communication takes place over the serial port (USART/UART). Machines can currently send and receive enough data to enable independent decision-making. Existing low-level machine-to-machine communication only involves data processing, and higher-level decision-making processes are limited to a single machine system rather than communications amongst networked machines. The goal of this effort is to employ OPC UA protocols to improve these limitations in machine-to-machine communication. This could mean deciding how to respond to receiving information and directing both horizontally (same level machines) and vertically (higher level machines) connected equipment. Proactive machine-to-machine communication improves factory intelligence in this way. Keywords: M2M,OPC UA,IIOT

Modeling of Linearized Generator Inertia Constraints for Unit Commitment

This study presents a novel approach to modeling linearized inertia constraints of generators, considering frequency stability, and applies it to the unit commitment (UC). Specifically, we modeled the average rate of change of frequency (RoCoF) constraint and the minimum frequency constraint using the analytical expression derived from the reduced frequency response (RFR) model. We also considered the load-damping constant as a variable. As the power system has different nonlinear characteristics according to its operating status, the system can be expressed as several different systems. Each subsystem, with its own properties at a given operating point, is modeled as a single-machine system, categorized by pumped storage hydropower (PSH) status. The minimum frequency of each subsystem is determined by its individual machine time constant. We incorporated an additional constraint to ensure the quasi steady-state performance of frequency. This constraint can be omitted when it is not necessary. The proposed concepts have been validated on the Korean Power System. The UC, with the proposed inertia constraints, can secure system inertia and primary frequency response (PFR) that satisfies frequency stability. Our proposed method is more efficient in securing inertia and PFRs and more economical in terms of generation cost compared to existing methods.

A novel modified dandelion optimizer with application in power system stabilizer

<p>This article presents a newly developed modification of the dandelion optimizer (DO). The proposed method is a chaotic algorithmic integrity and modification of the original dandelion optimizer. Dandelion is one of the plants that rely on wind for seed propagation. This article presents the tuning of the power system stabilizer with the method proposed in a case study of a single machine system. The validation of the proposed method uses the benchmark function and performance on a single engine system against transient response. The method used as a comparison in this article is the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA) and the original dandelion optimizer (DO). The simulation results show that the proposed method, which is a modified dandelion optimizer, provides promising performance.</p>

Study on stability margin of ultra-low frequency oscillation in hydropower system

In recent years, the ultra-low frequency oscillation of power system caused by hydropower units has occurred frequently. It is very important to study the ultra-low frequency oscillation characteristics of hydropower stations for the stable operation of power system. It is worth further research on how to determine the system stability margin in engineering practice. Based on the established model of single machine system with load, the analysis of the characteristic equation of closed-loop transfer function of single machine system shows that the ultra-low frequency oscillation will occur when the ratio of proportional parameter to integral parameter is too small. Based on the single machine with load system model and the equivalent single machine with load model of multi machine system, the ultra-low frequency oscillation mode of the system is analyzed by eigenvalue analysis method. According to the requirements of the damping ratio index, the security domain of the system ultra-low frequency oscillation is constructed, and the risk and stability margin of the system ultra-low frequency oscillation are evaluated by the characteristic values of the system ultra-low frequency oscillation mode.

Design of Multivariate PID Controller for Power Networks Using GEA and PSO

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used. One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller.

Post-prognostics demand management, production, spare parts and maintenance planning for a single-machine system using Reinforcement Learning

Production Planning and Control (PPC) is crucial for any manufacturer and comprises steps such as demand management, production, or source planning. Manufacturers achieve competitive advantage by sustaining continuous production, which can be realised through Condition-based Maintenance and Prognostics and Health Management. Hereby, the machine’s health can be predicted, and post-prognostics decision-making allows to optimise PPC to meet customer demands and minimise costs. Unfortunately, the complex dynamic, stochastic and intransparent nature of post-prognostics PPC makes it intractable to use ‘traditional’ static or deterministic optimisation techniques or approaches that require an exact mathematical model or objective function. To tackle this, a data-driven post-prognostics Reinforcement Learning model is developed to plan and control the sourcing of spare parts, production, and maintenance of a single-machine production system to maximise production revenue by meeting customer demands and minimising costs. In a case study, Proximal Policy Optimisation, which is well-known from OpenAI’s ChatGPT, is applied to a post-prognostics PPC decision-making problem. The Proximal Policy Optimisation is compared to other state-of-the-art learners, and the performance and robustness are evaluated. Analyses show that our model outperforms other learners, as well as reactive and scheduled preventive maintenance strategies and is robust to noise and cost changes.

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

Power system stabilizer (PSS) is applied to dampen system oscillations so that the frequency does not deviate beyond tolerance. PSS parameter tuning is increasingly difficult when dealing with complex and nonlinear systems. This paper presents a novel hybrid algorithm developed from incorporating chaotic maps into the sea-horse optimizer. The algorithm developed is called the chaotic sea-horse optimizer (CSHO). The proposed method is adopted from the metaheuristic method, namely the sea-horse optimizer (SHO). The SHO is a method that duplicates the life of a sea-horse in the ocean when it moves, looks for prey and breeds. In This paper, The CSHO method is used to tune the power system stabilizer parameters on a single machine system. The proposed method validates the benchmark function and performance on a single machine system against transient response. Several metaheuristic methods are used as a comparison to determine the effectiveness and efficiency of the proposed method. From the research, it was found that the application of the logistics Tent map from the chaotic map showed optimal performance. In addition, the application of the PSS shows effective and efficient performance in reducing overshoot in transient conditions.

Energy-conscious maintenance and production scheduling for single machine systems under time-of-use tariffs

In view of the joint optimization problem of preventive maintenance and production scheduling for modern production systems under time-of-use tariffs, a two-stage joint decision-making policy is proposed to achieve the peak-shifting reduction of production power. In the first stage, a dynamic preventive maintenance schedule is sequentially obtained based on the availability of machine. In the second stage, the production scheduling optimization of multi-workpiece processing is further carried out. The power consumption cost and the delay penalty cost under the time-of-use electricity tariff are considered, and the mixed integer programming model is established to achieve the balance of energy consumption and production delay. Numerical experiments have shown that by reasonably planning the idle time at the time of production batch conversion, the proposed model can effectively shift the on-peak power demand to off-peak, meet the stable electricity demand of enterprises, and improve the sustainable utilization level of power.

Battery storage to improve the cutter negative effect transient stability control strategy

To improve the transient stability of power grid, this paper proposes an emergency control strategy considering battery storage to participate in the transient stability of power grid. The phase plane trajectory convexity is used to identify the transient stability of the power grid. After the system is unstable, the dual-machine equivalence is carried out for the multi-machine power system regulated by the energy storage power supply, and the control quantity required by the energy storage power supply to restore the system stability in the single-machine system is calculated. The effectiveness of the control strategy was verified in the EPRI-7 system. The simulation results show that the transient stability emergency control strategy with energy storage power participating in the regulation can effectively improve the transient stability, reduce the emergency control failure, and inhibit the frequency and voltage reduction.

Technician Collaboration and Routing Optimization in Global Maintenance Scheduling for Multi-Center Service Networks

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

Adaptive Tuning of PV Generator Control to Improve Stability Constrained Power Transfer Capability Limit

The stability of power systems can largely limit the power transfer capability limit of transmission lines. The PV generator control is different from synchronous generator control. The impact of PV generator dynamics on the power transfer limit constrained by stability is not well explored in the literature. This paper focuses on improving the stability constrained power transfer capability limit of transmission lines emanating from PV generators. The PV generator is provided with dc link voltage and reactive power control and is equipped with low voltage ride through capability, voltage and frequency support functionalities. In this paper, Lyapunov function analysis based adaptive tuning laws are proposed for PV generator control parameters to improve the power transfer capability limit (constrained by stability) of transmission lines connecting PV generators to grid. The tuning laws are proposed for Phase Locked Loop (PLL), outer and inner control loop parameters. The effectiveness of the proposed method is validated on a Single PV-Synchronous Machine system, modified IEEE-39 and IEEE-118 bus system. Comparison with existing method shows that the proposed tuning method can achieve higher power transfer capability limit considering stability.

A Cloud Approach for Melanoma Detection Based on Deep Learning Networks.

In the era of digitized images, the goal is to extract information from them and create new knowledge thanks to Computer Vision techniques, Machine Learning and Deep Learning. This enables the use of images for early diagnosis and subsequent treatment of a wide range of diseases. In the dermatological field, deep neural networks are used to distinguish between melanoma and non-melanoma images. In this paper, we have underlined two essential points of melanoma detection research. The first aspect considered is how even a simple modification of the parameters in the dataset determines a change of the accuracy of classifiers. In this case, we investigated the Transfer Learning issues. Following the results of this first analysis, we suggest that continuous training-test iterations are needed to provide robust prediction models. The second point is the need to have a more flexible system architecture that can handle changes in the training datasets. In this context, we proposed the development and implementation of a hybrid architecture based on Cloud, Fog and Edge Computing to provide a Melanoma Detection service based on clinical and dermoscopic images. At the same time, this architecture must deal with the amount of data to be analyzed by reducing the running time of the continuous retrain. This fact has been highlighted with experiments carried out on a single machine and different distribution systems, showing how a distributed approach guarantees output achievement in a much more sufficient time.

Use of reinforcement learning algorithms to optimize control strategies for single machine systems

The stability of power systems is critical to ensuring reliable and efficient operation of electrical grids. In recent years, there has been a growing interest in the use of artificial intelligence techniques, such as reinforcement learning, to improve the stability of single machine systems. Reinforcement learning is a machine learning approach that enables agents to learn optimal control policies through trial and error. In this paper, we explore the use of reinforcement learning algorithms to optimize control strategies for single machine systems. We demonstrate how these algorithms can be used to identify the best control actions to take in real-time to prevent system instability. The challenges and limitations of using reinforcement learning in power system applications are discussed and recommendations are provided for future research in this area. Our results show that reinforcement learning has great potential for improving the stability of single machine systems and can be a valuable tool for power system operators and engineers.

Parallel Cleaning Algorithm for Similar Duplicate Chinese Data Based on BERT

Data is an important source of knowledge discovery, but the existence of similar duplicate data not only increases the redundancy of the database but also affects the subsequent data mining work. Cleaning similar duplicate data is helpful to improve work efficiency. Based on the complexity of the Chinese language and the bottleneck of the single machine system to large-scale data computing performance, this paper proposes a Chinese data cleaning method that combines the BERT model and a k-means clustering algorithm and gives a parallel implementation scheme of the algorithm. In the process of text to vector, the position vector is introduced to obtain the context features of words, and the vector is dynamically adjusted according to the semantics so that the polysemous words can obtain different vector representations in different contexts. At the same time, the parallel implementation of the process is designed based on Hadoop. After that, k-means clustering algorithm is used to cluster similar duplicate data to achieve the purpose of cleaning. Experimental results on a variety of data sets show that the parallel cleaning algorithm proposed in this paper not only has good speedup and scalability but also improves the precision and recall of similar duplicate data cleaning, which will be of great significance for subsequent data mining.

An Open Source LoRa Based, Low-Cost IoT Platform for Renewable Energy Generation Unit Monitoring and Supervisory Control

SCADA provides real-time system monitoring by constant communication and data exchange between various system devices to achieve data visualization and logging. Presently, in industrial systems, commercial SCADA systems are being used for data monitoring and control. These systems can be expensive, and as such can only be afforded by select industries. Even at these costs, the commercial SCADA systems face some challenges, which include interoperability and scalability issues. Research has shown that these problems can be solved by the introduction of low-cost materials and open-source software to achieve data monitoring for all levels of processes. This paper proposes an open source, low-cost Internet of Things (IoT)-based SCADA system that employs the IoT architecture for SCADA functions. The proposed system is an improvement to the existing IoT solutions by eliminating cloud based IoT platforms and introducing a single machine system. This solution increases the robustness of the system while reducing costs. The proposed system prototype consists of voltage and current sensors, Arduino Uno microcontroller and Raspberry Pi. The sensors acquire data from the monitored unit. The Arduino Uno receives the data and processes them for transmission to the Raspberry Pi using the LoRa communication technology. At the Raspberry Pi, the local Chirpstack platform processes the data and displays the measured data using the Grafana dashboard for real-time data monitoring, and the data is stored in an InfluxDB database. For system validation purposes, the prototype is designed, developed, and set up to monitor the panel voltage, current and battery voltage of a solar photovoltaic system. The results obtained from the experimental set-up are compared with the test data from physical digital multimeters. The system presented in this paper is a low-cost, open source, scalable and interoperable system. This, therefore, makes the proposed SCADA system an alternative for commercial SCADA systems, especially for select applications. The system proposed in this paper can be deployed to large industrial systems with appropriate upgrades and customization. The main contribution of this research is the design and development of a SCADA system that performs all the functions of a proprietary SCADA system at a very low-cost with scalable and interoperability features which are the main limitations of the traditional SCADA systems.

Research on Governor Parameter Optimization to Suppress Ultra-Low Frequency Oscillation of Power System Caused by Hydropower Unit

In the actual power system with hydropower, long-time and ultra-low frequency oscillation events occur many times. It is found that the unreasonable setting of governor parameters is an important reason for the oscillation. Firstly, the single machine on load system model is used to analyse the relationship between the PID parameters of the governor and the system stability, then the relationship between oscillation mode and PID parameters of governor is analyzed by eigenvalue analysis method, and the negative damping provided by speed regulation system is analyzed by damping torque method, and then the particle swarm optimization algorithm is used to optimize the PID parameters. Through the analysis of the step response of the single machine system before and after the optimization and the damping torque coefficient provided by the speed regulation system, it shows the effectiveness of the optimization algorithm. Finally, in the simulation platform MATLAB/SIMULINK, a single machine load system model which is closer to the actual power grid is built. The governor parameters of the generator are simulated and verified, and the PID parameters are adjusted by using the parameters obtained by the optimization algorithm. The results show that the optimized parameters have a good suppression for the ultra-low frequency oscillation.

Task Scheduling Method for Embedded System Composed of All Linear Tasks

There are still a large number of small and medium-sized embedded software applications that do not use operating system due to various conditions, for which a lot of time and effort are spent to build appropriate software structure in doing development. This paper presents a concept of linear task and a scheduling method based on the linear task, which can all use C language to realize the fully distributed dynamic multi-task scheduling of embedded single machine system based on the basic foreground-background system structure. On this basis, the system structure of the embedded software application without operating system can be quickly built and the subsequent application function development can be completed. Through the example implementation and simulation verification, it is proved that this method has good hardware independence, low CPU occupancy, small data and code space, flexible application support, comprehensive function, easy time coordination and simple real-time control. The application of this method to build an embedded system composed of all linear tasks can provide an effective foundation for dealing with the increasingly complex application requirements and limited development cycle.

An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator

The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation neural network (CFBNN), Elman recurrent neural network (E-RNN), focused time-delay neural network (FTDNN), and distributed time-delay neural network (DTDNN). The results show that the proposed method exhibits the best speed and rotor angle performance. The PPA-CFBNN method has the ability to reduce the overshoot of the speed by 1.569% and the rotor angle by 0.724%.