Fault Sensitivity Analysis of Data Feedback Control Design Based on Multiobjective Optimization

2016 ◽  
Author(s):  
Young-Man Kim
Keyword(s):  
Feedback Control ◽  
Multiobjective Optimization ◽  
Estimation Error ◽  
Optimization Technique ◽  
Control Synthesis ◽  
Optimization Approach ◽  
Worst Case ◽  
Mean Square Stability ◽  
Fault Sensitivity ◽  
Data Feedback

This paper analyzes the fault sensitivity of data feedback control which is synthesized with H∞/H_ optimization technique. With I/O data, a closed-loop output predictor is parameterized by stochastically uncertain Markov parameters which are estimated by least squares. The estimation error due to bias and noise are rejected over infinite horizon and guarantees mean square stability in the sense of worst case. The measured I/O data is setup as state which makes the stability analysis and data feedback control synthesis possible. In order to improve fault sensitivity, the H_ index method is applied. Then, the controller design problem based on multiobjective optimization approach is solved in a numerically efficient way such as Linear Matrix Inequality (LMI). The fault sensitivity is analyzed in the full frequency range and its effect on the pre-defined performance is described with a simulation example.

Determination of Horizontal In-Situ Stress Profiles and Rock Deformation Moduli in Karamay Basin Using a Multiobjective Optimization Technique

SPE Journal ◽  
2021 ◽  
pp. 1-18
Author(s):  
Hongxue Han ◽  
Maurice B. Dusseault ◽  
Shunde Yin ◽  
Guowei Xia ◽  
Mingchao Peng
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Cost Effective ◽  
In Situ Stress ◽  
Rock Deformation ◽  
Optimization Approach ◽  
Stress Profiles ◽  
Optimum Solution ◽  
In Situ Stress Measurements

Summary We introduce a quick and cost-effective method of estimating horizontal in-situ stress profiles and rock elastic moduli vs. depth from geophysical logs taken in vertical well sections. A multiobjective optimization approach finds the optimum solution for the inversion of in-situ stresses and the rock mechanical parameters from elastic borehole deformations measured by the commonly available four-arm caliper tools. The four-arm caliper log responses also permit quality control (QC) of input and identification and classification of borehole sections that display breakouts and sloughing. The method is applied in the estimation of horizontal in-situ stress profiles and rock deformation moduli vs. depth in Karamay Basin, Northwestern China. The results have demonstrated good agreement with available field in-situ stress measurements, indicating promising broader applications of the method.

scholarly journals A Generic WebLab Control Tuning Experience Using the Ball and Beam Process and Multiobjective Optimization Approach

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 132 ◽  
Author(s):  
Ricardo Massao Kagami ◽  
Guinther Kovalski da Costa ◽  
Thiago Schaedler Uhlmann ◽  
Luciano Antônio Mendes ◽  
Roberto Zanetti Freire
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Technique ◽  
Open Loop ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Control Engineering ◽  
Virtual Laboratories ◽  
Interesting Approach ◽  
Reducing Costs ◽  
Remote Experimentation

In control engineering education, the possibility of using a real control system in the learning process motivates professors to improve both students’ knowledge and skills, thus avoiding an approach only based on control theory. While considering that control engineering laboratories are expensive, mainly because educational plants should reproduce classical problems that are found in the industry, the use of virtual laboratories appears as an interesting strategy for reducing costs and improving the diversity of experiments. In this research, remote experimentation was assumed regarding the ball and beam process as an alternative didactic methodology. While assuming a nonlinear and unstable open-loop process, this study presents how students should proceed to control the plant focusing on the topic that is associated with multiobjective optimization. Proportional-Integral-Derivative (PID) controller was tuned considering the Non-dominated Sorting Genetic Algorithm (NSGA-II) to illustrate the WebLab learning procedures described in this research. The proposed strategy was compared to the Åström’s robust loop shaping method to emphasize the performance of the multiobjective optimization technique. Analyzing the feedback provided by the students, remote experimentation can be seen as an interesting approach for the future of engineering learning, once it can be directly associated with industry demand of connected machines and real-time information analysis.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

Optimization approach to the constrained regulation problem for linear continuous-time fractional-order systems

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xindong Si ◽  
Hongli Yang
Keyword(s):  
Feedback Control ◽  
Fractional Order ◽  
State Feedback Control ◽  
Invariant Sets ◽  
Linear Feedback ◽  
Control Law ◽  
Optimization Approach ◽  
Fractional Order Systems ◽  
Linear Feedback Control ◽  
Computational Point

AbstractThis paper deals with the Constrained Regulation Problem (CRP) for linear continuous-times fractional-order systems. The aim is to find the existence conditions of linear feedback control law for CRP of fractional-order systems and to provide numerical solving method by means of positively invariant sets. Under two different types of the initial state constraints, the algebraic condition guaranteeing the existence of linear feedback control law for CRP is obtained. Necessary and sufficient conditions for the polyhedral set to be a positive invariant set of linear fractional-order systems are presented, an optimization model and corresponding algorithm for solving linear state feedback control law are proposed based on the positive invariance of polyhedral sets. The proposed model and algorithm transform the fractional-order CRP problem into a linear programming problem which can readily solved from the computational point of view. Numerical examples illustrate the proposed results and show the effectiveness of our approach.

scholarly journals A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems

2021 ◽  
Vol 13 (6) ◽  
pp. 3308
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Mohammed H. Alsharif ◽  
Mun-Kyeom Kim ◽  
Jamel Nebhen
Keyword(s):  
Multiobjective Optimization ◽  
Distributed Generation ◽  
Radial Distribution ◽  
Hybrid Method ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Metaheuristic Optimization

Distributed generation (DG) and capacitor bank (CB) allocation in distribution systems (DS) has the potential to enhance the overall system performance of radial distribution systems (RDS) using a multiobjective optimization technique. The benefits of CB and DG injection in the RDS greatly depend on selecting a suitable number of CBs/DGs and their volume along with the finest location. This work proposes applying a hybrid enhanced grey wolf optimizer and particle swarm optimization (EGWO-PSO) algorithm for optimal placement and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves. On the other hand, PSO is a swarm-based metaheuristic optimization algorithm that finds the optimal solution to a problem through the movement of the particles. The advantages of both techniques are utilized to acquire mutual benefits, i.e., the exploration ability of the EGWO and the exploitation ability of the PSO. The proposed hybrid method has a high convergence speed and is not trapped in local optimal. Using this hybrid method, technical, economic, and environmental advantages are enhanced using multiobjective functions (MOF) such as minimizing active power losses, voltage deviation index (VDI), the total cost of electrical energy, and total emissions from generation sources and enhancing the voltage stability index (VSI). Six different operational cases are considered and carried out on two standard distribution systems, namely, IEEE 33- and 69-bus RDSs, to demonstrate the proposed scheme’s effectiveness extensively. The simulated results are compared with existing optimization algorithms. From the obtained results, it is observed that the proposed EGWO-PSO gives distinguished enhancements in multiobjective optimization of different conflicting objective functions and high-level performance with global optimal values.

scholarly journals A New Method for Solving Multiobjective Bilevel Programs

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Ying Ji ◽  
Shaojian Qu ◽  
Zhensheng Yu
Keyword(s):  
Multiobjective Optimization ◽  
Demand Uncertainty ◽  
Pareto Optimum ◽  
Compact Set ◽  
Supply Chain Risk Management ◽  
Equilibrium Constraints ◽  
Supply Chain Risk ◽  
Worst Case ◽  
Bilevel Programs ◽  
Real World Problems

We study a class of multiobjective bilevel programs with the weights of objectives being uncertain and assumed to belong to convex and compact set. To the best of our knowledge, there is no study about this class of problems. We use a worst-case weighted approach to solve this class of problems. Our “worst-case weighted multiobjective bilevel programs” model supposes that each player (leader or follower) has a set of weights to their objectives and wishes to minimize their maximum weighted sum objective where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto optimum concept, which we call “robust-weighted Pareto optimum”; for the worst-case weighted multiobjective optimization with the weight set of each player given as a polytope, we show that a robust-weighted Pareto optimum can be obtained by solving mathematical programing with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multiobjective optimization to a supply chain risk management under demand uncertainty. By the comparison with the existing weighted approach, we show that our method is more robust and can be more efficiently applied to real-world problems.

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