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According to recent studies, the importance of additive manufacturing technologies will be growing tremendously over the next years. It is estimated that the number of additive manufacturing machines used for commercial applications will rise further. The general concept of additive manufacturing is neither new nor completely undiscovered in any way. The field has been constantly developed by researchers over the last two decades. However, as additive manufacturing operations becoming a substantial part of efficient supply chains, the problem of job allocation and scheduling is likely to arise. Quite in contrast to the large amount of available research with respect to the technology itself, only little work has been conducted on surrounding factors such as production planning and scheduling. As much as additive manufacturing is a widely definable term, most technologies summarized by the term, have in common that they have inherent characteristics that make them quite different to traditional manufacturing operations. The question inevitably arises, what are those differences with respect to production planning and scheduling and how can these differences be addressed in a formal way. This research answers this question. Furthermore, it presents a scheduling and job allocation approach specifically designed for the needs of most additive manufacturing techniques. As part of this work, a scheduling process based on candidate orientations is newly developed and applied with a genetic algorithm that improves relevant performance parameters. The effectiveness is proven through a simulation study.

Multi-stage Adomian decomposition method (MADM) is a proven semi-analytical approximation solution technique for ordinary differential equations (ODEs), which provides a rapidly convergent series by integrating over multiple time intervals. Applicability of MADM for large nonlinear differential algebraic systems (DAEs) is established for the first time in this paper using the partitioned solution approach. Detailed models of power system components are approximated using MADM models. MADM applicability is verified on 7 widely used test systems ranging from 10 generators, 39 buses to 4092 generators, 13659 buses. Impact of the step size and the number of terms is investigated on the stability and accuracy of the method. An average speed up of 42% and 26% is observed in the solution time of ODEs alone using the MADM when compared to the midpoint-trapezoidal (TrapZ) method and the modified-Euler (ME) method, respectively. MADM accuracy is found to be similar to the ME and comparable to the TrapZ method. MADM stability properties are found to be better than the ME and weaker than the TrapZ method. An average speed up of 13% and 5.85% is observed in the overall solution time using MADM w.r.t. TrapZ and ME methods, respectively.

Power-system stabilizers (PSSs) are added to excitation systems to enhance the damping during low-frequency oscillations. In this paper, the design of robust decentralized PSS for four machines with a 10-bus system using fast-output sampling feedback is proposed. The nonlinear model of a multimachine system is linearized at different operating points, and 16 linear state space models are obtained. For all of these plants, a common stabilizing state feedback gain, F, is obtained. A robust decentralized fast-output sampling feedback gain which realizes this state feedback gain is obtained using LMI approach. This method does not require all the states of the system for feedback and is easily implementable. This robust decentralized fast-output sampling control is applied to a nonlinear plant model of several machines at different operating ( equilibrium) points. This method yields encouraging results for the design of power-system stabilizers.

The ever increasing demand for electrical energy has meant that electrical power systems have evolved to be the highly complex networks that are in existence today, where the transient interactions that take place between large synchronous generators have been the focus of advanced research for many decades. More recently, the proliferation of synchronous and asynchronous generators of comparatively small rating, which are not centrally planned, has added new impetus to this area of research. At a time when not only technical but also economic and environmental concerns must be carefully assessed, more realistic models of the individual power plant transient response and its interaction with the rest of the network become mandatory to achieve more accurate analysis of the network operation and prevent wastage of technical and economical resources. The main thrust of this research project is to develop comprehensive models of rotating machinery with which to assess the transient response of electrical power networks that undergo severe unbalanced operation due to faulty conditions developing anywhere in the power network, including the rotating machinery’s windings. To achieve an unrivalled speed of response, a nodal-based model of the electrical power network has been developed, enabling the study of power networks of any size and topological formation with the utmost flexibility and efficiency. The rotating machinery addressed in this research work is the three-phase synchronous generator and the three-phase asynchronous (induction) machine. In multi-machine power systems transient studies, it is common to look at all the rotating machinery in the network as being healthy units having an idealized sinusoidally distributed stator winding – the possibility of a severe unbalanced condition internal to the machine due to internal fault or the effect of space harmonics, has, so far, not been addressed in a multi-machine environment. In this research work, new EMTP type models, with enhanced numerical properties, are constructed for the simulation of internal and external machine fault phenomena with different levels of details including the effects of saturation and space harmonics. Advanced numerical methods are employed to improve on the numerical stability of the network model allowing the use of larger time steps than what otherwise is possible with traditional numerical methods, enabling the new model to execute faster than existing models and yet to exhibit the same degree of numerical accuracy with an unrivalled degree of modelling flexibility. A case in point is an application made to model the transient response of a Variable Frequency Transformer (VFT) undergoing internal faults on its stator side. The rotating machinery models with internal fault representation developed in this research lend themselves to an application in the area of condition monitoring, where a new method is developed to detect internal faults in the stator side of three phase rotating machinery. The method is shown to be highly effective when applied to machines operating in an isolated environment as well as in a multi-machine power system, performing very well when applied to different types of equipment and various kinds of internal faults.

This paper illustrates the application of a new technique, based on Support Vector Clustering (SVC) for the direct identification of coherent synchronous generators in a large interconnected Multi-Machine Power Systems. The clustering is based on coherency measures, obtained from the time domain responses of the generators following system disturbances. The proposed clustering algorithm could be integrated into a wide-area measurement system that enables fast identification of coherent clusters of generators for the construction of dynamic equivalent models. An application of the proposed method is demonstrated on a practical 15 generators 72-bus system, an equivalent of Indian Southern grid in an attempt to show the effectiveness of this clustering approach. The effects of short circuit fault locations on coherency are also investigated.

This paper discusses the effects of thyristor controlled series compensator (TCSC), a series FACTS controller, on the transient stability of a power system. Trajectory sensitivity analysis (TSA) has been used to measure the transient stability condition of the system. The TCSC is modeled by a variable capacitor, the value of which changes with the firing angle. It is shown that TSA can be used in the design of the controller. The optimal locations of the TCSC-controller for different fault conditions can also be identified with the help of TSA. The paper depicts the advantage of the use of TCSC with a suitable controller over fixed capacitor operation.

A generating unit in an interconnected power system often participates in both local and inter-area modes of oscillations. The spectral and temporal distributions of these modes are largely determined by the dynamics of the rest of the system. An adaptive stabilizer, capable of damping all modes of oscillation would require estimations of the dominant frequency of oscillation and the external equivalent line impedance as seen from the terminal of the machine concerned. The authors describe two new methods, one for identification of the external impedance and the other to obtain the power spectral density during hunting. The identified parameters are used in an adaptive PSS to evaluate it's performance in a multi-machine system.

It is well-known that the existing power system stabilizers (PSS) cannot properly deal with composite mode oscillations of machines. Because it is difficult to design the PSS which can be effective for both local and inter-area modes. In this paper, a self-tuning power system stabilizer using speed and active power input signals without a lead-lag network is proposed for damping multi-modal oscillations in a power system. The dominant frequency is identified by introducing a lowpass filter and a highpass filter. Based on the value of the dominant frequency, the optimal gains for both speed and active power signals are obtained. The proposed PSS is implemented on a digital computer. Fourth order Chebyshev filters have been used. The proposed PSS has been tested on multi-machine systems for various disturbances and found to provide good damping under any disturbance with arbitrary initial conditions

Multi-machine single-inverter induction motor drives are attractive in situations in which all machines are of similar ratings and operate at approximately the same load torques. The advantages include their small size compared to multi-machine multi-inverter systems, lower weight, and overall cost. This article presents a consolidated study and comparison of vector control methods applied to multi-machine drives along with relevant simulation and experimental results. The mean control method and its modifications prove to be superior to the other control approaches.

Multi-machine, single-inverter induction motor drives are attractive in situations in which all machines are of similar ratings, and operate at approximately the same load torques. The advantages include small size compared to multi-inverter system, lower weight and overall cost. However, field oriented control of such drives is a challenge since no two motors will have exactly the same operating conditions at any time. In general, at least some motors in the system will operate away from perfect field orientation. It is therefore important to analyse their torque dynamics carefully. This paper discusses existing multi-machine field-oriented control methods, and analyses and compares them from the viewpoint of dynamic performance. For this, an analytical framework based on small-signal analysis is developed to compare multi-machine control methods. This analysis is verified by simulations and experiments.

In this paper we consider the problem of scheduling n jobs on two identical parallel machines in order to minimise the mean squared deviation (MSD) of job completion times about a given common due date. When due dates are small and large deviations of job completion times from the due dates are undesirable, it becomes necessary to consider parallel machines. MSD comes under the category of non-regular performance measures, which penalises jobs that are early as well as late. In this paper we develop a lower bound on MSD for a given partial schedule and present a branch and bound algorithm to solve the problem. Optimal solutions for problem instances up to 35 jobs have been obtained for different values of due dates and the results of computational testing are presented. Based on our experiments we observe that when the due date exceeds a certain value the second machine becomes undesirable. We also propose a heuristic to provide quick solutions for problems of larger size. Copyright � 2011 Inderscience Enterprises Ltd.

Global demand for electrical energy is at an all time high. In industrialised societies consumers have come to expect an interruption-free, high-quality electricity supply and environmentally aware consumers and pressure groups have been very successful in encouraging the electricity supply industry towards incorporating, as part of the generation mix, sources of electricity that are benign to the environment. In some European countries great progress has been made in the integration of wind generation and photo-voltaics. Moreover, the industry has gone through major privatisation and deregulation programmes worldwide; and there is the notion in some quarters that deregulation and widespread cross-border interconnections may have exacerbated the incidence of wide-area break-downs in electricity supply. The challenges facing today’s electricity supply industry are many, and the technology to deliver the necessary grid control is still underdeveloped. A major research thrust is required to make a power network flexible, resilient and responsive to the consumer’s wishes of being supplied with environmentally sound electricity. Renewable generation such as wind and marine turbines and photo-voltaic cells need power electronics and effective controllers if they are to be successfully integrated into the electricity grid without reduction of supply quality. The dynamical interaction of multi-machine networks, power electronics and large penetration of intermittent generation are highly complex phenomena and a better understanding of their dynamical behaviour is mandatory before larger increases of intermittent generation are added to it, to avoid widespread black-outs and thwarted energy transactions. The impact of successful integration of FACTS equipment into power systems networks worldwide is affecting all sectors of the market: power generation, transmission, distribution, utilisation and equipment manufacturers. However, further progress requires investigating further the dynamic performance of the FACTS technology in order to continue acquiring leading-edge, relevant knowledge. Devices used to enhance the stability of power systems such as the Static VAr Compensator (SVC) and the Thyristor-Controlled Series Compensator (TCSC) are prime candidates for investigation owing to their popularity. Both FACTS controllers are comprehensively investigated in this research. The main aims of this research project are to develop and evaluate dynamic high-order multi-machine models, dynamic models of FACTS devices, such as the SVC and the TCSC, with particular emphasis in their electromechanical oscillations damping capabilities; to carry out fundamental analyses and control system designs of synchronous generators and FACTS controllers; and to investigate their dynamic effects and interactions with the power network. Individual Channel Analysis and Design (ICAD), a classical oriented multivariable control systems framework is used in this research project. ICAD has shown its suitability for carrying out small-signal stability assessments, with which it has been possible to evaluate the potential robustness and performance of the control system design, affording physical insight.

This paper presents a novel approach for designing of generator excitation controllers using Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) technique for a Single Machine Infinite Bus (SMIB) system that can also be directly used in a multi-machine environment. The generator system equations are modified by referencing the rotor angle with respect to the secondary of the transformer bus instead of the infinite bus. For the modified system equations, IDA-PBC is applied to stabilize the system around an operating condition. The IDA-PBC design results in a Lyapunov function for the modified system. The new control law is practically feasible and can be applied directly to multi-machine system without referring to external system parameters. The effectiveness of the proposed controller is tested on a SMIB and a 10 generator 39 bus test system for a range of operating conditions. The Proposed excitation controller has shown good performance for both small and large disturbances when compared to the performance of a conventional static exciter with power system stabilizer.

We address the problem of jointly determining production planning and scheduling decisions in a complex multi-stage, multi-product, multi-machine, and batch-production environment. Large numbers of process and discrete parts manufacturing industries are characterized by increasing product variety, low product volumes, demand variability and reduced strategic planning cycle. Multi-stage batch-processing industries like chemicals, food, glass, pharmaceuticals, tire, etc. are some examples that face this environment. Lack of efficient production planning and scheduling decisions in this environment often results in high inventory costs and low capacity utilization. In this research, we consider the production environment that produces intermediate products, by-products and finished goods at a production stage. By-products are recycled to recover reusable raw materials. Inputs to a production stage are raw materials, intermediate products and raw materials. Complexities in the production process arise due to the desired coordination of various production stages and the recycling process. We consider flexible production resources where equipments are shared amongst products. This often leads to conflict in the capacity requirements at an aggregate level and at the detailed scheduling level. The environment is characterized by dynamic and deterministic demands of finished goods over a finite planning horizon, high set-up times, transfer lot sizes and perishability of products. The decisions in the problem are to determine the production quantities and inventory levels of products, aggregate capacity of the resources required and to derive detailed schedules at minimum cost. We determine production planning and scheduling decisions through a sequence of mathematical models. First, we develop a mixed-integer programming (MIP) model to determine production quantities of products in each time period of the planning horizon. The objective of the model is to minimize inventory and set-up costs of intermediate products and finished goods, inventory costs of by-products and reusable raw materials, and cost of fresh raw materials. This model also determines the aggregate capacity of the resources required to implement the production plan. We develop a variant of the planning model for jointly planning sales and production. This model has additional market constraints of lower and upper bounds on the demand. Next, we develop an MIP scheduling model to execute the aggregate sales and productions plans obtained from the planning model. The scheduling model derives detailed equipment wise schedules of products. The objective of the scheduling model is to minimize earliness and tardiness (E/T) penalties. We use branch and bound procedure to solve the production-planning problem. Demand of finished goods for each period over the planning horizon is an input to the model. The planning model is implemented on a rolling horizon basis. We consider flowshop setting for the finished goods in the production environment. The due dates of finished goods are based on the customers orders. We report some new results for scheduling decisions in a permutation flowshop with E/T penalties about a common due date. This class of problems can be sub-divided into three groups-one, where the common due date is such that all jobs are necessarily tardy; the second, where the due date is such that the problem is unrestricted; and third is a group of problems where the due date is between the above two. The models developed are tested on data for a chemical company in India. The results of cost minimization model in a particular instance indicated savings of 61.20 percent in inventory costs of intermediate products, 38.46 percent in set-up costs, 8.58 percent in inventory cost by-products and reusable raw materials, and 20.50 percent in fresh raw material costs over the actual production plan followed by the company. The results of the contribution maximization model indicate 42.54 percent increase in contribution. We also perform sensitivity analysis on results of the production planning and scheduling problem. The contribution flowshop setting for the finished goods in the production planning and scheduling problem. Traditional models on multi-stage production planning and scheduling are primarily based on assembly and fabrication types of product structures and do not consider the issues involved in recycling process. Scheduling theory with E/T penalties is largely limited to single machine environment. We expect that models developed in this research would from basis for production planning and scheduling decisions in multi-stage, multi-machine batch processing systems. The sensitivity analysis of the models would provide an opportunity to the managers to evaluate the alternate production plans and to respond to the problem complexities in a better way.

The integration of stochastic wind power has accentuated a challenge for power system stability assessment. Since the power system is a time-variant system under wind generation fluctuations, pure time-domain simulations are difficult to provide real-time stability assessment. As a result, the worst-case scenario is simulated to give a very conservative assessment of system transient stability. In this study, a probabilistic contingency analysis through a stability measure method is proposed to provide a less conservative contingency analysis which covers 5-min wind fluctuations and a successive fault. This probabilistic approach would estimate the transfer limit of a critical line for a given fault with stochastic wind generation and active control devices in a multi-machine system. This approach achieves a lower computation cost and improved accuracy using a new stability measure and polynomial interpolation, and is feasible for online contingency analysis.

In this paper a new approach is proposed for interpreting of regional frequencies in multi machine power systems. The method uses generator aggregation and system reduction based on coherent generators in each area. The reduced system structure is able to be identified and a kalman estimator is designed for the reduced system to estimate the inter-area modes using the synchronized phasor measurement data. The proposed method is tested on a six machine, three area test system and the obtained results show the estimation of inter-area oscillations in the system with a high accuracy.

A typical irrigation scheduling problem is one of preparing a schedule to service a group of outlets. These outlets may either be serviced sequentially or simultaneously. This problem has an analogy with the classical earliness/tardiness machine scheduling problems in operations research (OR). In previous published work integer programme were used to solve such problems; however, such scheduling problems belong to a class of combinatorial problems known to be computationally demanding (NP-hard). This is widely reported in OR. Hence integer programme can only be used to solve relatively small problems usually in a research environment where considerable computational resources and time can be allocated to solve a single schedule. For practical applications meta-heuristics such as genetic algorithms, simulated annealing or tabu search methods need to be used. However as reported in the literature, these need to be formulated carefully and tested thoroughly.This thesis demonstrates how arranged-demand irrigation scheduling problems can be correctly formulated and solved using genetic algorithms (GA). By interpreting arrangeddemand irrigation scheduling problems as single or multi-machine scheduling problems, the wealth of information accumulated over decades in OR is capitalized on. The objective is to schedule irrigation supplies as close as possible to the requested supply time of the farmers to provide a better level of service. This is in line with the concept of Service Oriented Management (SOM), described as the central goal of irrigation modernization in recent literature. This thesis also emphasizes the importance of rigorous evaluation of heuristics such as GA.First, a series of single machine models is presented that models the warabandi (rotation) type of irrigation distribution systems, where farmers are supplied water sequentially. Next, the multimachine models are presented which model the irrigation water distribution systems where several farmers may be supplied water simultaneously. Two types of multimachine models are defined. The simple multimachine models where all the farmers are supplied with identical discharges and the complex multimachine models where the farmers are allowed to demand different discharges. Two different approaches i.e. the stream tube approach and the time block approach are used to develop the multimachine models. These approaches are evaluated and compared to determine the suitability of either for the irrigation scheduling problems, which is one of the significant contributions of this thesis. The multimachine models are further enhanced by incorporating travel times which is an important part of the surface irrigation canal system and need to be taken into account when determining irrigation schedules. The models presented in this thesis are unique in many aspects. The potential of GA for a wide range of irrigation scheduling problems under arranged demand irrigation system is fully explored through a series of computational experiments.

In this paper a modified Heffron-Phillip's (K-constant) model is derived for the design of power system stabilizers. A knowledge of external system parameters, such as equivalent infinite bus voltage and external impedances or their equivalent estimated values is required for designing a conventional power system stabilizer. In the proposed method, information available at the secondary bus of the step-up transformer is used to set up a modified Heffron-Phillip's (ModHP) model. The PSS design based on this model utilizes signals available within the generating station. The efficacy of the proposed design technique and the performance of the stabilizer has been evaluated over a range of operating and system conditions. The simulation results have shown that the performance of the proposed stabilizer is comparable to that could be obtained by conventional design but without the need for the estimation and computation of external system parameters. The proposed design is thus well suited for practical applications to power system stabilization, including possibly the multi-machine applications where accurate system information is not readily available.

This paper describes the design of a fuzzy logic based controller to counter the small signal oscillatory instability in power systems. The stabilizing signal is computed in real time using suitable fuzzy membership functions depending upon the state of the generator on the speed-acceleration phase plane. The use of output membership function permits further fine tuning of the controller parameters for varied system configurations specially in multimachine environment. The efficacy of the proposed stabilizing technique has been demonstrated using multi-machine computer simulation model of power system under a wide range of system and test conditions.

This paper presents a methodology for dynamic analysis of short term small signal voltage instability in a multi-machine power system. The formulation of the problem is done by decoupling the angle instability from the voltage instability. The method is based on the incremental reactive current flow network (IRCFN), where the incremental reactive current injection at each bus is related to the incremental voltage magnitude at all the buses. Small signal stability using the eigenvalue analysis is illustrated utilizing a single-machine load bus (SMLB) and three-machine system examples. The role of a static var compensator (SVC) at the load bus is also examined.