Multiple Machine System Research Articles

Problems and challenges of power-electronic-based power system stability: A case study of transient stability comparison

With the development of power electronic technology and requirement for clean energy, the traditional power systems which are dominated by synchronous generators are gradually changing into the power-electronic-based power systems with diversified power electronic equipment. The power systems are facing a great revolution in their primary equipment, and this has not happened in the past one hundred years. In recent years, with great increasing penetration of power electronic devices into power grids, the large-scale blackouts caused by power electronic devices have been reported, which seriously threatens the safe and stable operation of power systems. Under the above background, in this paper we first introduce several methods of analyzing the traditional power system transient stability from the equal area criterion for the single machine infinite bus system to several Lyapunov function based direct methods for multi-machine systems. Then we introduce some of our recent work on the nonlinear modeling and analysis of a key component of power-electronic-based power systems, voltage source converter (VSC), and propose a multiple machine system model including power electronic equipment and traditional synchronous machines. Finally, we illustrate the transient characteristics of the power electronic devices, and summarize the basic problems and challenges for the transient stability of power-electronic-based power systems. We hope that these basic problems in power-electronic-based power system dynamics including nonlinearity, multi-time-scale, and complexity could arouse the general interest of researchers in the fields of complex systems and statistical mechanics.

Development of PSS tuning rules using multi-objective optimization

This paper explores the process of tuning controllers for conditions that better reflect realistic operating conditions, such as input signals of different shapes. In this study, controllers are tuned simultaneously for input signals of different shapes, using the tools of double objective optimization. For this purpose, a novel performance index is proposed to evaluate a system’s ramp response. At the same time, a novel method is proposed for faster and more efficient generation of Pareto plots and Pareto frontiers. The proposed method retrieves the dominant Pareto solutions only, and produces the Pareto frontier directly. The study is applied on the tuning of a Power System Stabilizer (PSS) of a simple power system, at several operating points of the synchronous machine. Finally, a relation between the optimum PSS parameters and the operating points is sought. A linear regression model is used for this purpose, and the tuning rules are tested in an application study. Finally, the optimization technique is applied on a multiple machine system. The results show significant improvement, while maintaining fault resilience.

An Evolutionary Clustering-Based Optimization to Minimize Total Weighted Completion Time Variance in a Multiple Machine Manufacturing System

This paper discusses clustering as a new paradigm of optimization and devises an integration of clustering and an evolutionary algorithm, neighborhood search algorithm (NSA), for a multiple machine system with the case of reducible processing times (RPT). After the problem is formulated mathematically, evolutionary clustering search (ECS) is devised to reach the near-optimal solutions. It is a way of detecting interesting search areas based on clustering. In this approach, an iterative clustering is carried out which is integrated to evolutionary mechanism NSA to identify which subspace is promising, and then the search strategy becomes more aggressive in detected areas. It is interesting to find out such subspaces as soon as possible to increase the algorithm's efficiency by changing the search strategy over possible promising regions. Once relevant search regions are discovered by clustering they can be treated with special intensification by the NSA algorithm. Furthermore, different neighborhood mechanisms are designed to be embedded within the main NSA algorithm so as to enhance its performance. The applicability of the proposed model and the performance of the NSA approach are demonstrated via computational experiments.

Parallel-machine scheduling to minimize tardiness penalty and power cost

Traditional research on machine scheduling focuses on job allocation and sequencing to optimize certain objective functions that are defined in terms of job completion times. With regard to environmental concerns, energy consumption becomes another critical issue in high-performance systems. This paper addresses a scheduling problem in a multiple-machine system where the computing speeds of the machines are allowed to be adjusted during the course of execution. The CPU adjustment capability enables the flexibility for minimizing electricity cost from the energy saving aspect by sacrificing job completion times. The decision of the studied problem is to dispatch the jobs to the machines as well as to determine the job sequence and processing speed of each machine with the objective function comprising of the total weighted job tardiness and the power cost. We give a formal formulation, propose two heuristic algorithms, and develop a particle swarm optimization (PSO) algorithm to effectively tackle the problem. Since the existing solution representations do not befittingly encode the decisions involved in the studied problem into the PSO algorithm, we design a tailored encoding scheme which can embed all decisional information in a particle. A computational study is conducted to investigate the performances of the proposed heuristics and the PSO algorithm.

Condition-Based Maintenance Decision-Making for Multiple Machine Systems

Maintenance decision-making has emerged as an important area of industrial research. Over the past two decades, maintenance policies have evolved from simple reactive maintenance to complex versions of condition-based maintenance (CBM). A quantitative description of a machine’s health, as found in CBM, is essential to plan maintenance effectively as it helps avoid excessive or insufficient maintenance. In spite of several advancements in the degradation monitoring techniques, most CBM decision-making methods still focus on a single machine system. Maintenance analysis of a single machine provides good insights, but lacks practical applications. In this paper, we develop a continuous time Markov chain degradation model and a cost model to quantify the effects of maintenance on a multiple machine system. An optimal maintenance policy for a multiple machine system in the absence of resource constraints is obtained. In the presence of resource constraints, two prioritization methods are proposed to obtain effective maintenance policies for a multiple machine system. A case study focusing on a section of an automotive assembly line is used to illustrate the effectiveness of the proposed method.

Employing reprogrammable analog VLSI in real-time analysis of transient stability

By using the scaled-down analog model built by integrated circuits, the transient stability analysis of power systems can be performed in real-time. However, neither the parameters nor the topology of the analog VLSI chip can be changed after fabrication. Consequently, it requires one to design and manufacture dedicated chips to perform the transient stability analysis for different power systems, which results in prohibitively high fabrication cost. To overcome this shortcoming, this paper presents a new method to employ reconfigurable analog model. A novel concept for reconfiguring system parameters of multiple machine systems has been developed and tested by a 30-bus 6-generator system.

Approach to measure the mix response flexibility of manufacturing systems

The mix response flexibility of a manufacturing system is the ability to change between product types quickly and economically. This paper proposes a new approach to measure this flexibility in terms of both capability and capacity. While the processing capability is represented as the number of operations that the machines can perform, the manufacturing capacity is modelled as the efficiency of different machines. The proposed model is applied to measure the mix response flexibility for both single and multiple machine systems.

Measuring the mix response flexibility of manufacturing systems

This paper is concerned with a particular flexibility parameter, referred to as mix response flexibility. This is a measure of the difficulty of processing different products on the same equipment, where a different set-up is required for each product type. A mathematical model for measuring mix response flexibility is described, and its results are compared with a simulation. The model is found to give meaningful results for both single machines and multiple machine systems.