A Mathematical Approach to Simultaneously Plan Generation and Transmission Expansion Based on Fault Current Limiters and Reliability Constraints

Today, generation and transmission expansion planning (G&TEP) to meet potential load growth is restricted by reliability constraints and the presence of uncertainties. This study proposes the reliability constrained planning method for integrated renewable energy sources and transmission expansion considering fault current limiter (FCL) placement and sizing and N-1 security. Moreover, an approach for dealing with uncertain events is adopted. The proposed planning model translates into a mixed-integer non-linear programming model, which is complex and not easy to solve. The problem was formulated as a tri-level problem, and a hybridization framework between meta-heuristic and mathematical optimization algorithms was introduced to avoid linearization errors and simplify the solution. For this reason, three meta-heuristic techniques were tested. The proposed methodology was conducted on the Egyptian West Delta system. The numerical results demonstrated the efficiency of integrating G&TEP and FCL allocation issues in improving power system reliability. Furthermore, the effectiveness of the hybridization algorithm in solving the suggested problem was validated by comparison with other optimization algorithms.

Lane Detection Aided Online Dead Reckoning for GNSS Denied Environments

With the emerging interest of autonomous vehicles (AV), the performance and reliability of the land vehicle navigation are also becoming important. Generally, the navigation system for passenger car has been heavily relied on the existing Global Navigation Satellite System (GNSS) in recent decades. However, there are many cases in real world driving where the satellite signals are challenged; for example, urban streets with buildings, tunnels, or even underpasses. In this paper, we propose a novel method for simultaneous vehicle dead reckoning, based on the lane detection model in GNSS-denied situations. The proposed method fuses the Inertial Navigation System (INS) with learning-based lane detection model to estimate the global position of vehicle, and effectively bounds the error drift compared to standalone INS. The integration of INS and lane model is accomplished by UKF to minimize linearization errors and computing time. The proposed method is evaluated through the real-vehicle experiments on highway driving, and the comparative discussions for other dead-reckoning algorithms with the same system configuration are presented.

A Redistributed Bundle Algorithm Based on Local Convexification Models for Nonlinear Nonsmooth DC Programming

For nonlinear nonsmooth DC programming (difference of convex functions), we introduce a new redistributed proximal bundle method. The subgradient information of both the DC components is gathered from some neighbourhood of the current stability center and it is used to build separately an approximation for each component in the DC representation. Especially we employ the nonlinear redistributed technique to model the second component of DC function by constructing a local convexification cutting plane. The corresponding convexification parameter is adjusted dynamically and is taken sufficiently large to make the ”augmented” linearization errors nonnegative. Based on above techniques we obtain a new convex cutting plane model of the original objective function. Based on this new approximation the redistributed proximal bundle method is designed and the convergence of the proposed algorithm to a Clarke stationary point is proved. A simple numerical experiment is given to show the validity of the presented algorithm.

An Adaptive Proximal Bundle Method with Inexact Oracles for a Class of Nonconvex and Nonsmooth Composite Optimization

In this paper, an adaptive proximal bundle method is proposed for a class of nonconvex and nonsmooth composite problems with inexact information. The composite problems are the sum of a finite convex function with inexact information and a nonconvex function. For the nonconvex function, we design the convexification technique and ensure the linearization errors of its augment function to be nonnegative. Then, the sum of the convex function and the augment function is regarded as an approximate function to the primal problem. For the approximate function, we adopt a disaggregate strategy and regard the sum of cutting plane models of the convex function and the augment function as a cutting plane model for the approximate function. Then, we give the adaptive nonconvex proximal bundle method. Meanwhile, for the convex function with inexact information, we utilize the noise management strategy and update the proximal parameter to reduce the influence of inexact information. The method can obtain an approximate solution. Two polynomial functions and six DC problems are referred to in the numerical experiment. The preliminary numerical results show that our algorithm is effective and reliable.

Resilient set-membership state estimation for nonlinear complex networks with time-delay and incomplete measurements

Taking the incomplete measurements and the weighted try-once-discard (WTOD) protocol into account, this paper develops a novel resilient set-membership state estimation (RSMSE) method for time-varying nonlinear complex networks with time-invariant delay. A classic interval matrix technique is utilized to describe incomplete measurements. The Taylor series expansion is applied to dispose the nonlinearities, where the high-order terms of the linearization errors are described by norm-bounded uncertainties. To mitigate the communication burden, the WTOD protocol is introduced, where only one node can send updated data through a shared communication network at each certain transmission step. Using the recursive linear matrix inequalities (RLMIs), a series of ellipsoidal sets including the state vector can be determined. The desirable estimator gain and a smallest possible estimation ellipsoid can be calculated via solving the convex optimization problem. Lastly, we use an illustrative example to show the feasibility of the introduced RSMSE technique.

Analysis of nonlinear gas turbine models using influence coefficients

The limited availability of gas turbine data, especially faults data and the high costs and risks of using test benches to obtain it,causes that rarely have enough data for form a fault classification. These circumstances have created the need to develop models that can provide simulated data. The quality of the data generated depends on the complexity of the thermodynamic model and the mathematical solution. A method to evaluate the accuracy of the models and their linearization capacity is presented. The method is applied to the models of a turbo shaft and a turbo fan of the commercial software GasTurb 12, as an example. It was simulated a wide database with influence of fault parameters and condition operation, then it calculed the influence matrix ""H"" and ""G"" for prove the influence theirs on behavior of the models. The results show that if the model is sufficiently accuracy, it is possible to find an adequate interval where the linearization errors are not very large and it is just possible the linearization.

A Redistributed Bundle Algorithm for Generalized Variational Inequality Problems in Hilbert Spaces

Based on the redistributed technique of bundle methods and the auxiliary problem principle, we present a redistributed bundle method for solving a generalized variational inequality problem which consists of finding a zero point of the sum of two multivalued operators. The considered problem involves a nonsmooth nonconvex function which is difficult to approximate by workable functions. By imitating the properties of lower-[Formula: see text] functions, we consider approximating the local convexification of the nonconvex function, and the local convexification parameter is modified dynamically in order to make the augmented function produce nonnegative linearization errors. The convergence of the proposed algorithm is discussed when the sequence of stepsizes converges to zero, any weak limit point of the sequence of serious steps [Formula: see text] is a solution of problem (P) under some conditions. The presented method is the generalization of the convex bundle method [Salmon, G, JJ Strodiot and VH Nguyen (2004). A bundle method for solving variational inequalities. SIAM Journal on Optimization, 14(3), 869–893].

Extended State Observer Based Ascent Trajectory Tracking Method

The ascent trajectory tracking problem of a launch vehicle is investigated in this paper. To improve the conventional trajectory linearization method which usually omits the linearization errors, the extended state observer (ESO) is employed in this paper to timely estimate the total disturbance which consists of the external disturbances and the modeling uncertainties resulting from linearization error. It is proven that the proposed trajectory tracking controller can guarantee the desired performance despite both external disturbances and the modeling uncertainties. Moreover, compared with the conventional linearization control method, the proposed controller is shown to have much better performance of uncertainty rejection. Finally, the feasibility and performance of this controller are illuminated via simulation studies.

A Sampling Method for Quantifying the Information Content of IASI Channels

There is a vast amount of information about the atmosphere available from instruments on board satellites. One example is the Infrared Atmospheric Sounding Interferometer (IASI) instrument, which measures radiances emitted from Earth’s atmosphere and surface in 8461 channels. It is difficult to transmit, store, and assimilate such a large amount of data. A practical solution to this has been to select a subset of a few hundred channels based on those that contain the most useful information. Different measures of information content for objective channel selection have been suggested for application to variational data assimilation. These include mutual information and the degrees of freedom for signal. To date, the calculation of these measures of information content has been based on the linear theory that is at the heart of operational variational data assimilation. However, the retrieval of information about the atmosphere from the satellite radiances can be highly nonlinear. Here, a sampling method for calculating the mutual information that is free from assumptions about the linearity of the relationship between the observed radiances and the state variables is examined. It is found that large linearization errors can indeed lead to large discrepancies in the value of mutual information. How this new estimate of information content can be used in channel selection is addressed, with particular attention given to the efficiency of the new method. It is anticipated that accounting for the nonlinearity in the channel selection will be beneficial when using nonlinear data assimilation methods currently in development.