Dynamic State Estimation of Power System Based on Improved Adaptive Unscented Particle Filter
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In this paper, an improved adaptive unscented particle filter (IAUPF) algorithm is proposed to address the shortcomings of the unscented particle filter (UPF) algorithm on the state estimation of distribution network, such as vulnerability to process noise and low quality of the importance density function, in order to obtain a more accurate state estimation result and reduce the effect of unknown system noise in the dynamic state estimation. The IAUPF can estimate the mean and variance of the system noise and so increase the filtering accuracy of the system with unknown noise by employing a novel statistical estimator for the noise parameter and modifying the scale correction factor in real-time. The simulation results on the IEEE 33-node system show that, as opposed to the conventional UPF algorithm, the proposed IAUPF can address the issue of decreasing estimation accuracy due to unknown system noise in the filtering process and ensure high precision of state estimation when the system experiences abrupt changes.State-space representation
Auxiliary particle filter
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Alpha beta filter
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Fault detection and diagnosis (FDD) is increasingly important for wheeled mobile robots (WMRs). One of the most promising approaches is the so-called particle filter (also known as sequential Monte Carlo) method. In this paper, rule based inference and multiple particle filters are integrated to diagnose hard faults of WMR's dead reckoning system. The rule based inference method is employed to determine the states of the movement of the robot in plane and each state of movement is monitored with a particle filter. This approach presents a general framework to combine domain knowledge with particle filters. The key advantage of the proposed method is that it decreases the size of the state space for each particle filter. As a result, it decreases particle number and increases efficiency and accuracy for each particle filter. Experiment performed on a mobile robot shows the improvement in accuracy and efficiency.
Monte Carlo localization
Dead reckoning
Particle (ecology)
Auxiliary particle filter
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State-space representation
Auxiliary particle filter
Particle (ecology)
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For many years, one of the difficult components of sampling theory has been the estimation of population characteristics, especially variance. The estimation of variability is very essential in many fields (Chemistry, Biology, Mathematics, and so on) to know how one quantity varies with respect to another quantity. This paper proposes arithmetic estimators of a group of ratio estimators for populations with finite variance. Using a Taylor series technique, the bias and MSE of the proposed estimators are determined up to the first order of approximation together with the efficiency conditions over existing estimators. The effectiveness of the proposed estimators in comparison to the current estimators is evaluated using a real-world data set. The empirical findings demonstrate that the suggested estimators outperform the current estimators taken into account in the study. Hence, these suggested estimators are recommended for use in real life scenario.
Extremum estimator
Population variance
Bootstrapping (finance)
Ratio estimator
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<abstract><p>Estimation of population characteristics has been an area of interest for many years. Various estimators of the population mean and the population variance have been proposed from time-to-time with a view to improve efficiency of the estimates. In this paper, we have proposed some estimators for estimation of the general population parameters. The estimators have been proposed for single-phase and two-phase sampling using information of single and multiple auxiliary variables. The bias and mean square errors of the proposed estimators have been obtained. Some comparison of the proposed estimators has been done with some existing estimators of mean and variance. Some specific cases of the proposed estimators have been discussed. Simulation and numerical study have also been conducted to see the performance of the proposed estimators.</p></abstract>
Extremum estimator
Population variance
Population mean
Bootstrapping (finance)
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Kernel density estimation
Extremum estimator
Bootstrapping (finance)
Square (algebra)
Kernel (algebra)
Density estimation
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Some existing estimators based on auxiliary attribute have been proposed by many authors. In this paper, we use the concept of power transformation to modify some existing estimators in order to obtain estimators that are applicable when there is positive or negative correlation between the study and auxiliary variable. The properties (Biases and MSEs) of the proposed estimators were derived up to the first order of approximation using Taylor series approach. The efficiency comparison of the proposed estimators over some existing estimators considered in the study were established. The empirical studies were conducted using existing population parameters to investigate the proficiency of the proposed estimators over some existing estimators. The results revealed that the proposed estimators have minimum Mean Square Errors and higher Percentage Relative Efficiencies than the conventional and other competing estimators in the study. These implies that the proposed estimators are more efficient and can produce better estimates of the population mean compared to the existing estimators considered in the study.
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<abstract> <p>The estimation of a certain population characteristics is required for several situations. The estimates are built so that the error of estimation is minimized. In several situations estimation of the population mean is required. Different estimators for the mean are available but, there is still room for improvement. In this paper, a new class of ratio-type estimators is proposed for the estimation of the population mean. The estimators are proposed for single- and two-phase sampling schemes. The expressions for bias and mean square error are obtained for single-phase and two-phase sampling estimators. Mathematical comparison of the proposed estimators has been achieved by using some existing single-phase and two-phase sampling estimators. Extensive simulations have been conducted to compare the proposed estimators with some available single- and two-phase sampling estimators. It has been observed that the proposed estimators are better than the existing estimators. Consequently, the proposed ratio estimators are recommended for use by the practitioners in various fields of industry, engineering and medical and physical sciences.</p> </abstract>
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Population mean
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The problem of non-response generally occurs due to the lack of interest, not at home, refusal etc. Many authors developed estimators for estimation of population mean in the presence of nonresponse. Sometimes estimation of product of two population means in the presence of non-response is also needed. Keeping this fact in the view, some estimators for the product of two population means using auxiliary attribute in the presence of non-response have been suggested. The properties of the suggested estimators are also studied. A comparative study of the suggested estimators with the relevant estimators is given. Using a real data set, a numerical study is also given to check the efficiency of the proposed estimators in comparison to the relevant estimators. From numerical study it has been found that the proposed estimators work better that the other exiting estimators in some given conditions.
Extremum estimator
Population mean
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