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Register a new userOptimal Excitation Matching Strategy for Sub-Arrayed Phased Linear Arrays Generating Arbitrary-Shaped Beams
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The design of phased arrays able to generate arbitrary-shaped beams through a sub-arrayed architecture is addressed here. The synthesis problem is cast in the excitation matching framework, so as to yield clustered phased arrays providing optimal trade-offs between the complexity of the array architecture (i.e., the minimum number of control points at the sub-array level) and the matching of a reference pattern. A synthesis tool based on the k-means algorithm is proposed for jointly optimizing the sub-array configuration and the complex sub-array coefficients. Selected numerical results, including pencil beams with sidelobe notches and asymmetric lobes as well as shaped main lobes, are reported and discussed to highlight the peculiarities of the proposed approach also in comparison with some extensions to complex excitations of state-of-the-art sub-array design methods.
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The modular design of planar phased array antennas with hexagonal apertures is addressed by means of innovative diamond-shaped tiling techniques. Both tiling configuration and subarray coefficients are optimized to fit user-defined power-mask constraints on the radiation pattern. Toward this end, suitable surface-tiling mathematical theorems are customized to the problem at hand to guarantee optimal performance in case of low/medium-size arrays, while the computationally hard tiling of large arrays is yielded thanks to an effective integer-coded GA-based exploration of the arising high-cardinality solution spaces. By considering ideal as well as real array models, a set of representative benchmark problems is dealt with to assess the effectiveness of the proposed architectures and tiling strategies. Moreover, comparisons with alternative tiling architectures are also performed to show to the interested readers the advantages and the potentialities of the diamond subarraying of hexagonal apertures.
The paper provides a comprehensive battery storage modelling approach, which accounts for operation- and degradation-aware characteristics, i.e., variable efficiency, internal resistance growth, and capacity fade. Based on the available experimental data from the literature, we build mixed-integer linear programming compatible lithium iron phosphate (LiFePO$_4$) battery model that can be used in problems related to various applications, i.e., power system, smart grid, and vehicular applications. Such formulation allows finding the globally optimal solution using off-the-shelf academic and commercial solvers. In the numerical study, the proposed modelling approach has been applied to realistic scenarios of peak-shaving, where the importance of considering the developed models is explicitly demonstrated. For instance, a time-varying operation strategy is required to obtain the optimal utilization of the LiFePO$_4$ battery storage. Particularly, during the battery operational lifetime its optimal average SoC may change by up to $20%$, while the duration of charging process may increase by $75%$. Finally, using the same LiFePO$_4$ benchmark model from the literature, we compare the results of using the proposed approach to the state-of-the-art in the optimal sizing and scheduling problems. The proposed approach led to a $12.1%$ reduction of battery investment and operating costs compared to the state-of-the-art method.
Given an array with defective elements, failure correction (FC) aims at finding a new set of weights for the working elements so that the properties of the original pattern can be recovered. Unlike several FC techniques available in the literature, which update all the working excitations, the Minimum-Complexity Failure Correction (MCFC) problem is addressed in this paper. By properly reformulating the FC problem, the minimum number of corrections of the whole excitations of the array is determined by means of an innovative Compressive Processing (CP) technique in order to afford a pattern as close as possible to the original one (i.e., the array without failures). Selected examples, from a wide set of numerical test cases, are discussed to assess the effectiveness of the proposed approach as well as to compare its performance with other competitive state-of-the-art techniques in terms of both pattern features and number of corrections.
We formulate the optimal energy arbitrage problem for a piecewise linear cost function for energy storage devices using linear programming (LP). The LP formulation is based on the equivalent minimization of the epigraph. This formulation considers ramping and capacity constraints, charging and discharging efficiency losses of the storage, inelastic consumer load and local renewable generation in presence of net-metering which facilitates selling of energy to the grid and incentivizes consumers to install renewable generation and energy storage. We consider the case where the consumer loads, electricity prices, and renewable generations at different instances are uncertain. These uncertain quantities are predicted using an Auto-Regressive Moving Average (ARMA) model and used in a model predictive control (MPC) framework to obtain the arbitrage decision at each instance. In numerical results we present the sensitivity analysis of storage performing arbitrage with varying ramping batteries and different ratio of selling and buying price of electricity.
Identifiability of a single module in a network of transfer functions is determined by the question whether a particular transfer function in the network can be uniquely distinguished within a network model set, on the basis of data. Whereas previous research has focused on the situations that all network signals are either excited or measured, we develop generalized analysis results for the situation of partial measurement and partial excitation. As identifiability conditions typically require a sufficient number of external excitation signals, this work introduces a novel network model structure such that excitation from unmeasured noise signals is included, which leads to less conservative identifiability conditions than relying on measured excitation signals only. More importantly, graphical conditions are developed to verify global and generic identifiability of a single module based on the topology of the dynamic network. Depending on whether the input or the output of the module can be measured, we present four identifiability conditions which cover all possible situations in single module identification. These conditions further lead to synthesis approaches for allocating excitation signals and selecting measured signals, to warrant single module identifiability. In addition, if the identifiability conditions are satisfied, indirect identification methods are developed to provide a consistent estimate of the module. All the obtained results are also extended to identifiability of multiple modules in the network.
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