Industrial robots are increasingly used for five-axis machining operations, where the rotation of the end effector along the toolaxis direction is functionally redundant. This functional redundancy should be carefully...
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Industrial robots are increasingly used for five-axis machining operations, where the rotation of the end effector along the toolaxis direction is functionally redundant. This functional redundancy should be carefully resolved when planning the robot path according to the tool path generated by a computer-aided manufacturing(CAM) system. Improper planning of the redundancy may cause drastic variations of the joint motions, which could significantly decrease the machining efficiency as well as the machining accuracy. To tackle this problem, this paper presents a new optimization-based methodology to globally resolve the functional redundancy for the robotic milling process. Firstly, a global performance index concerning the smoothness of the robot path at the joint acceleration level is proposed. By minimizing the smoothness performance index while considering the avoidance of joint limits and the singularity and the constraint of the stiffness performance, the resolution of the redundancy is formulated as a constrained optimization problem. To efficiently solve the problem, the sequentiallinearization programming method is employed to improve the initial solution provided by the conventional graph-based method. Then, simulations for a given tool path are presented. Compared with the graph-based method, the proposed method can generate a smoother robot path in which a significant reduction of the magnitude of the maximum joint acceleration is obtained, resulting in a smoother tool-tip feedrate profile. Finally, the experiment on the robotic milling system is also presented. The results show that the optimized robot path of the proposed method obtains better surface quality and higher machining efficiency, which verifies the effectiveness of the proposed method.
This paper addresses the problem of coordinating the operation of electricity and natural gas (NG) transmission systems with green hydrogen (H2) production and injection into existing NG networks. In particular, the o...
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This paper addresses the problem of coordinating the operation of electricity and natural gas (NG) transmission systems with green hydrogen (H2) production and injection into existing NG networks. In particular, the operation of the two systems consists of a two-stage optimization framework that solves a network-constrained unit commitment (UC) problem with transmission power losses to obtain the profiles of gas energy demands from gas-powered generators and the maximum allowable H2 injection flow rates from power-to-gas (PtG), which are then used as inputs to an optimal transient NG-H2 flow problem with H2 concentration tracking. The nonlinearities introduced by the discretization of the H2 concentration tracking equations are particularly challenging to solve using second-order nonlinearprogramming (NLP) methods. Moreover, the nonlinear constraints capturing transmission line losses make the electricity operational problem intractable if solved with mixed-integer NLP methods. Therefore, this work leverages the reliability and scalability of linearprogramming (LP) by designing two novel and distinct sequential LP (SLP) methods that exploit the particular structures of the two problems to find feasible, possibly optimal solutions, using only first-order information. The algorithmic framework is demonstrated on the IEEE 24-bus RTS connected to the 22-node Belgian gas network. This paper is the first to demonstrate H2 concentration tracking under transient gas flow in a multi-energy optimization framework on a realistic gas transmission network with multiple H2 injection locations.
The reliability of transmission lines (TLs) has great influences on the safe operation of power systems. Random failures from TLs can be caused by multiple uncertainties such as the high load current. In this study, a...
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The reliability of transmission lines (TLs) has great influences on the safe operation of power systems. Random failures from TLs can be caused by multiple uncertainties such as the high load current. In this study, a multi-stage risk-based dispatch model is proposed to minimise the sum of generation cost and risk cost. Impacts of natural ageing, load current, health condition and disaster weather on the short-term reliability of TLs are studied. For ensuring the operation security of power systems in the post-contingency stage, the dynamic thermal rating (DTR) technology is implemented to improve capacities of TLs for coping with the potential overload. In order to solve this non-linear optimiation problem caused by load current-dependent failure probabilities and thermal rating constraints, a double-iteration solving strategy is proposed. In exterior iterations, the sequential linear programming is applied to decouple bilinear terms of the risk cost and locally linearise failure probabilities. Meanwhile, in interior iterations, the Benders decomposition is utilised to further divide the model into the main problem and the sub-problem for coordinating the preventive and corrective control and checking the feasibility of DTR, respectively. Proposed approaches are validated on a modified IEEE 24-bus test system.
Due to concrete being consistently used in the filling of prefabricated linear steel structural floor slabs, the practice of constructing steel-concrete composite structures is becoming more and more popular. The join...
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Due to concrete being consistently used in the filling of prefabricated linear steel structural floor slabs, the practice of constructing steel-concrete composite structures is becoming more and more popular. The joint action of the two materials is generally ensured by mechanical connectors that considerably increase the performance of the composite element structure. For a majority of practical cases, these elements are formed by a concrete slab connected to I-shaped steel beams. In this study, models of finite elements for the steel-concrete composite beams with partial interaction are optimized using the sequential linear programming algorithm. The design variables are considered with two approaches: in the first, only the parameters that define the cross section of the steel "I" profile vary, while in the second, besides the aforementioned parameters that define the cross section of the "I" profile, also considered are those that define the concrete section. In addition, the optimum distribution of the shear connectors along the composite beam are verified;in other words, the longitudinal rigidity of the deformable connection is considered to be a design variable. The design constraints are those defined in standard specifications referring to the dimensioning of concrete, steel and composite steel-concrete structures, as well as the side constraints with respect to the parameters defining the cross section and the step-size for the non-linear optimization algorithm. The results for the composite beam optimization problems are presented taking into consideration different boundary conditions. For a given optimized project, the analysis of the results is done regarding the influence of the constraints on the optimization process, the graph of the load-slip curve along the composite beam, and the values obtained for the design variables.
In this paper, a possibility is investigated to reduce the monthly electricity bills for a production plant, mostly through peak power costs reduction, by using a diesel-generator of appropriate size. The research rel...
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ISBN:
(纸本)9781728169903
In this paper, a possibility is investigated to reduce the monthly electricity bills for a production plant, mostly through peak power costs reduction, by using a diesel-generator of appropriate size. The research relies on a dataset of mean powers drawn by a production plant from the electrical distribution grid in 15-minute intervals. A sequentiallinear program (SLP) based algorithm is created to account for the non-linearity of diesel consumption in a diesel-generator and the problem of optimal diesel-generator operation is solved for its various sizes. Diesel-generator maintenance is also considered and the cost suitably added to the overall value function.
Increased penetration of distributed energy resources (DERs) creates challenges in formulating the security constrained optimal power flow (SCOPF) problem as the number of models for these resources proliferate. Speci...
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ISBN:
(纸本)9781728155081
Increased penetration of distributed energy resources (DERs) creates challenges in formulating the security constrained optimal power flow (SCOPF) problem as the number of models for these resources proliferate. Specifically, the number of devices with different mathematical models is large and their integration into the SCOPF becomes tedious. Henceforth, a process that seamlessly models and integrates such new devices into the SCOPF problem is needed. We propose an object-oriented modeling approach that leads to the autonomous formation of the SCOPF problem. All device models in the system are cast into a universal syntax. We have also introduced a quadratization method which makes the models consisting of linear and quadratic equations, if nonlinear. We refer to this model as the State and Control Quadratized Device Model (SCQDM). The SCQDM includes a number of equations and a number of inequalities expressing the operating limits of the device. The SCOPF problem is then formed in a seamless manner by operating only on the SCQDM device objects. The SCOPF problem, formed this way, is also quadratic (i.e. consists of linear and quadratic equations), and of the same form and syntax as the SCQDM for an individual device. For this reason, we named it security constrained quadratic optimal power flow (SCQOPF). We solve the SCQOPF problem using a sequential linear programming (SLP) algorithm and compare the results with those obtained from the commercial solver Knitro on the IEEE 57 bus system.
Parameter identification of quantum systems is a fundamental task in developing practical quantum technology. In this article, we study the identification of time-varying decoherence rates for open quantum systems. Gi...
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Parameter identification of quantum systems is a fundamental task in developing practical quantum technology. In this article, we study the identification of time-varying decoherence rates for open quantum systems. Given the measurement data of local observables, this can be formulated as an optimization problem. We expand the unknown decoherence rates into Fourier series and take the expansion coefficients as optimization variables. We then convert it into a minimax problem and apply a sequential linear programming technique to solve it. Numerical study on a two-qubit quantum system with a time-varying decoherence rate demonstrates the effectiveness of our algorithm.
This paper presents a modification of classic SLP algorithms for the resolution of NLP and MINLP problems, and does it with a clear application in mind: optimization of gas transmission networks. The SLP-NTR and 2-ste...
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This paper presents a modification of classic SLP algorithms for the resolution of NLP and MINLP problems, and does it with a clear application in mind: optimization of gas transmission networks. The SLP-NTR and 2-step SLP algorithms we present have been developed within the collaboration with a company of the gas industry and thoroughly tested with real problems in this field. Here we present a comparison of their performance with that of classic SLP algorithms and state of the art solvers. Importantly, to provide some foundations for the potential applicability of these new algorithms to general NLP and MINLP problems, we present a theoretical analysis of their properties.
Gradient and nongradient optimization algorithms are currently available for structural design in structural optimization course. Despite the successful application of gradient algorithm in structural optimization, no...
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Gradient and nongradient optimization algorithms are currently available for structural design in structural optimization course. Despite the successful application of gradient algorithm in structural optimization, nongradient algorithm is also extensively adopted to solve the structural optimization problem. However, the efficiency of nongradient algorithm has caused a heated debate recently. To clarify this issue for the graduate students, sequential linear programming and genetic algorithm are, respectively, chosen as the representatives of gradient and nongradient algorithms to solve truss size optimization problem. Firstly, the size optimization formulations of truss structure for sequential linear programming and genetic algorithm are summarized, respectively. Secondly, an educational finite element software for truss structure is developed by using the object-oriented programming to create the software framework. This study aims to provide an open-source, extensible, and benchmarking software, which do assist the students to understand the structural optimization process in engineering education. Finally, two benchmarking examples are introduced to compare the efficiency and accuracy of sequential linear programming and genetic algorithm.
With the increasement of gas-fired generators, the coupling of power system and natural gas network has been strengthened. To guarantee the security and efficiency of energy supply, a joint day-ahead market clearing f...
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ISBN:
(纸本)9781728119816
With the increasement of gas-fired generators, the coupling of power system and natural gas network has been strengthened. To guarantee the security and efficiency of energy supply, a joint day-ahead market clearing framework for natural gas, electricity energy and reserve is proposed considering linepack. Security-constrained unit commitment is first modelled to determine generator status and gas flow direction which is solved by a second-order cone relaxation. Second, economic dispatch model is presented to decide market clearing results. A special sequential linear programming (SLP) is proposed to solve the nonlinear market clearing problem and automatic price transmission from natural gas to electricity based on Lagrangian multipliers of coupling constraints is proved. Numerical cases are tested to demonstrate the effectiveness of proposed model and algorithm and the effect of linepack on dispatching and pricing results is analyzed.
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