Power system state estimation using a direct non-iterative method

This paper describes a new method for state estimation of a non-linear AC power system in a non-iterative manner. This method is based on the Kipnis–Shamir relinearization technique that is used to solve over-defined sets of polynomial equations. The technique transforms the equations to a higher dimensional linear space which allows the states to be solved in a non-iterative manner. Given accurate measurements, this new state estimation method provides the same results as traditional iterative state estimation methods, and the proposed method does not require an initial guess of system states nor does it have issues with convergence.     

Probabilistic measurement evaluation for the implementation of the Measuring Instrument Directive

The approval of the European Measuring Instrument Directive (2004) constitutes a noteworthy novelty in legal metrology, since this Directive implements the so called “new approach” to technical normalization and harmonization and moves toward a “global approach” to evaluation and conformity. According to the principles of the new approach regulatory model, the Directive establishes some essential requirements for a wide class of measuring devices, subject to legal control, whilst leaving manufacturer free to develop proper technical solutions for meeting them. Moreover, the conformity of instruments to prescribed requirement may now be ensured through quality assurance procedures, in the context of a quality system. This increase in technical and metrological freedom on the manufactures side may promote innovation, social benefit and costumers’ satisfaction, provided that some recent advances in measurement theory are properly employed.In this paper, after a brief discussion regarding the philosophy of the Directive, a probabilistic approach to conformity assessment is presented. The proposed approach concerns some currently open issues such as measurement uncertainty due to the influence of operating conditions, the assessment and reduction of the risk related to measurement uncertainty in conformity decision and the costs of instrument uncertainty and of non-conformity, for both the manufacturer and the user. The approach is supported by a package of software codes that assists the application of verification procedures, with a user-friendly approach.All these aspects are discussed with reference to the case of water meters, considering both end-of-production acceptance tests and the performance of meters under operation.     

A MULTI-RESOLUTION SPECTRUM SENSING （MRSS） SCHEME UNDER MEASUREMENT-BASED CHANNEL MODELS IN COGNITIVE RADIO

Spectrum sensing is one of the key technologies in Cognitive Radios (CRs). Previous works are accomplished under simple channel models, which may lead to unreliable results when it applied to the over-the-air systems. In this paper, we investigate the performance of a Multi-Resolution Spectrum Sensing (MRSS) algorithm under measurement-based channel models in China. MRSS is a wavelet based algorithm which is suitable for non-stationary, wideband signal analysis. Using statistical modeling, measurement-based channel models are presented under typical urban and suburban scenarios in Shanghai, China. Then, the performance of the MRSS algorithm is evaluated under the measurement-based channel models. Simulation results show that, using MRSS, the performance is always better in the scenarios where Line-Of-Sight (LOS) path exist; also, in LOS scenarios, rich scattering effect helps to increase the performance.     

A nonparametric approach to design robust controllers for uncertain systems: Application to an air flow heating system

This paper presents an approach to design robust fixed structure controllers for uncertain systems using a finite set of measurements in the frequency domain. In traditional control system design, usually, based on measurements, a model of the plant, which is only an approximation of the physical system, is first built, and then control approaches are used to design a controller based on the identified model. Errors associated with the identification process as well as the inevitable uncertainties associated with plant parameter variations, external disturbances, measurement noise, etc. are expected to all contribute to the degradation of the performance of such a scheme. In this paper, we propose a nonparametric method that uses frequency-domain data to directly design a robust controller, for a class of uncertainties, without the need for model identification. The proposed technique, which is based on interval analysis, allows us to take into account the plant uncertainties during the controller synthesis itself. The technique relies on computing the controller parameters for which the set of all possible frequency responses of the closed-loop system are included in the envelope of a desired frequency response. Such an inclusion problem can be solved using interval techniques. The main advantages of the proposed approach are: (1) the control design does not require any mathematical model, (2) the controller is robust with respect to plant uncertainties, and (3) the controller structure can be chosen a priori, which allows us to select low-order controllers. To illustrate the proposed method and demonstrate its efficacy, an application to an air flow heating system is presented.     

Adaptation strategies for real-time optimization

Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the context of process optimization. Three approaches can be distinguished according to the quantities that are adapted: model-parameter adaptation updates the parameters of the process model and repeats the optimization, modifier adaptation modifies the constraints and gradients of the optimization problem and repeats the optimization, while direct input adaptation turns the optimization problem into a feedback control problem and implements optimality via tracking of appropriate controlled variables. This paper argues in favor of modifier adaptation, since it uses a model parameterization and an update criterion that are well tailored to meeting the KKT conditions of optimality. These considerations are illustrated with the real-time optimization of a semi-batch reactor system.     

Dynamic optimization in the presence of uncertainty: From off-line nominal solution to measurement-based implementation

The problem of optimizing a dynamic system in the presence of uncertainty is typically tackled using measurements. The method most widely documented in the literature is based on repetitive optimization of an updated process model. Recently, a computationally less expensive alternative that is based on the real-time adaptation of a solution model using measurements has been proposed. The solution model, which relates elements of the input profiles to the set of active constraints and to appropriate sensitivities, has typically been derived manually from physical insight and intuition. This paper presents a systematic and automated approach to generate a solution model based on recent results in numerical optimization of dynamic systems. This concept provides the first step toward an entirely automated procedure for constrained dynamic optimization of uncertain large-scale processes.     

Process optimization via constraints adaptation

In the framework of real-time optimization, measurement-based schemes have been developed to deal with plant-model mismatch and process variations. These schemes differ in how the feedback information from the plant is used to adapt the inputs. A recent idea therein is to use the feedback information to adapt the constraints of the optimization problem instead of updating the model parameters. These methods are based on the observation that, for many problems, most of the optimization potential arises from activating the correct set of constraints. In this paper, we provide a theoretical justification of these methods based on a variational analysis. Then, various aspects of the constraint-adaptation algorithm are discussed, including the detection of active constraints and convergence issues. Finally, the applicability and suitability of the constraint-adaptation algorithm is demonstrated with the case study of an isothermal stirred-tank reactor.     

动态负荷建模中的负荷时变性研究

负荷的时变性是指受人们生活习惯等因素的影响负荷特性随时间变化的性质。该文将负荷时变性分解为负荷组成成分的时变性和负荷幅值大小的时变性分别进行研究，以降低问题的复杂程度，并探讨了实施的方法。提出了基于改进综合负荷模型结构及分组算法和多曲线拟合参数辨识的建模方法。该文根据负荷的时变规律，在不同时间选择不同的负荷模型参数来表征负荷，从而提高了负荷模型的精度，并以在实际工程中的一个应用实例说明了该方法的有效性。     

Parallelizing quantum circuits

We present a novel automated technique for parallelizing quantum circuits via the forward and backward translation to measurement-based quantum computing patterns, and analyze the trade off in terms of depth and space complexity. As a result we distinguish a class of polynomial depth circuits that can be parallelized to logarithmic depth while adding only a polynomial number of auxiliary qubits. In particular, we provide for the first time a full characterization of patterns with flow of arbitrary depth, based on the notion of influencing walks and a simple rewriting system on the angles of the measurement. Our method provides new insight for constructing parallel circuits and as applications, we demonstrate several classes of circuits that can be parallelized to constant or logarithmic depth. Furthermore, we prove a logarithmic separation in terms of quantum depth between the quantum circuit model and the measurement-based model.     

电力系统负荷模型的建立

本文提出了电力系统负荷特性是一个具有零均值白噪声的随机系统。并用三组现场实测数据辨识出模型的良好重现性,表明了总体测辨法是可行的。 本文还提出了参数辨识不稳定问题及其解决方法。比较了在东北网分析计算中采用实测负荷模型和常规负荷模型的结果。