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To propose an improved algorithm for the state estimation of distribution networks based on the unscented Kalman filter (IUKF). The performance comparison of unscented Kalman filter (UKF) and newly developed algorithm, termed Improved unscented Kalman Filter (IUKF) for IEEE-30, 33 and 69-bus radial distribution networks for load variations and bad data for two measurement noise scenarios, i.e. 30 and 50 per cent are shown.

State estimation (SE) plays an instrumental role in realizing smart grid features like distribution automation (DA), enhanced distribution generation (DG) penetration and demand response (DR). Implementation of DA requires robust, accurate and computationally efficient dynamic SE techniques that can capture the fast changing dynamics of distribution systems more effectively. In this paper, the UKF is improved by changing the way the state covariance matrix is calculated, to enhance its robustness and accuracy under noisy measurement conditions. UKF and proposed IUKF are compared under the cummulative effect of load variations and bad data based on various statistical metrics such as Maximum Absolute Deviation (MAD), Maximum Absolute Per cent Error (MAPE), Root Mean Square Error (RMSE) and Overall Performance Index (J) for three radial distribution networks. All the simulations are performed in MATLAB 2014b environment running on an hp core i5 laptop with 4GB memory and 2.6 GHz processor.

An Improved Unscented Kalman Filter Algorithm (IUKF) is developed for distribution network state estimation. The developed IUKF is used to predict network states (voltage magnitude and angle at all buses) and measurements (source voltage magnitude, line power flows and bus injections) in the presence of load variations and bad data. The statistical performance of the coventional UKF and the proposed IUKF is carried out for a variety of simulation scenarios for IEEE-30, 33 and 69 bus radial distribution systems. The IUKF demonstrated superiority in terms of: RMSE; MAD; MAPE; and overall performance index J for two measurement noise scenarios (30 and 50 per cent). Moreover, it is shown that for a measurement noise of 50 per cent and above, UKF fails while IUKF performs.

UKF shows degraded performance under high measurement noise and fails in some cases. The proposed IUKF is shown to outperform the UKF in all the simulated scenarios. Moreover, this work is novel and has justified improvement in the robustness of the conventional UKF algorithm.

The purpose of this paper is to propose an efficient method for detection and modification of erroneous branch parameters in real time power system simulators. The aim of the proposed method is to minimize the sum of squared errors (SSE) due to mismatches between simulation results and corresponding field measurements. Assuming that the network configuration is known, a limited number of erroneous branch parameters will be detected and corrected in an optimization procedure.

Proposing a novel formulation that utilizes network voltages and last modified admittance matrix of the simulation model, suspected branch parameters are identified. These parameters are more likely to be responsible for large values of SSE. Utilizing a Gauss-Newton (GN) optimization method, detected parameters will be modified in order to minimize the value of SSE. Required sensitivities in optimization procedure will be calculated numerically by the real time simulator. In addition, by implementing an efficient orthogonalization method, the more effective parameter will be selected among a set of correlated parameters to avoid singularity problems.

Unlike state estimation-based methods, the proposed method does not need the mathematical functions of measurements to simulation model parameters. The method can enhance other parameter estimation methods that are based on state estimation. Simulation results demonstrate the high efficiency of the proposed optimization method.

Incorrect branch parameter detection and correction procedures are investigated in real time simulators.

The purpose of this paper is to propose successive-over-relaxation (SOR) based recursive Bayesian approach (RBA) for the configuration identification of a Power System. Moreover, to present a comparison between the proposed method and existing RBA approaches regarding convergence speed and robustness.

Swift power network configuration identification is important for adopting the smart grid features like power system automation. In this work, a new SOR-based numerical approach is adopted to increase the convergence speed of the existing RBA algorithm and at the same time maintaining robustness against noise. Existing RBA and SOR-RBA are tested on IEEE 6 bus, IEEE 14 bus networks and 48 bus Danish Medium Voltage distribution network in the MATLAB R2014b environment and a comparative analysis is presented.

The comparison of existing RBA and proposed SOR-RBA is performed, which reveals that the latter has good convergence speed compared to the former RBA algorithms. Moreover, it is robust against bad data and noise.

Existing RBA techniques have slow convergence and are also prone to measurement noise. Their convergence speed is effected by noisy measurements. In this paper, an attempt has been made to enhance convergence speed of the new identification algorithm while keeping its numerical stability and robustness during noisy measurement conditions. This work is novel and has drastic improvement in the convergence speed and robustness of the former RBA algorithms.

Heuristic algorithms have been widely used in different types of optimization problems. Their unique features in terms of running time and flexibility have made them superior to deterministic algorithms. To accurately compare different heuristic algorithms in solving optimization problems, the final optimal solution needs to be known. Existing deterministic methods such as Exhaustive Search and Integer Linear Programming can provide the final global optimal solution for small-scale optimization problems. However, as the system grows the number of calculations and required memory size incredibly increases, so applying existing deterministic methods is no longer possible for medium and large-scale systems. The purpose of this paper is to introduce a novel deterministic method with short running time and small memory size requirement for optimal placement of Micro Phasor Measurement Units (µPMUs) in radial electricity distribution systems to make the system completely observable.

First, the principle of the method is explained and the observability of the system is analyzed. Then, the algorithm’s running time and memory usage when applying on some of the modified versions of the Institute of Electrical and Electronics Engineers 123-node test feeder are obtained and compared with those of its deterministic counterparts.

Because of the innovative method of step-by-step placement of µPMUs, a unique method is developed. Simulation results elucidate that the proposed method has unique features of short running time and small memory size requirements.

While the mathematical background of the observability study of electricity distribution systems is very well-presented in the referenced papers, the proposed step-by-step placement method of µPMUs, which shrinks unobservable parts of the system in each step, is not discussed yet. The presented paper is directly applicable to typical problems in the field of power systems.

In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

This paper aims to propose a method to solve the problem of localization and mapping of a two-wheeled inverted pendulum (TWIP) robot on the ground using the Stereo–inertial measurement unit (IMU) system. This method reparametrizes the pose according to the motion characteristics of TWIP and considers the impact of uneven ground on vision and IMU, which is more adaptable to the real environment.

When TWIP moves, it is constrained by the ground and swings back and forth to maintain balance. Therefore, the authors parameterize the robot pose as SE(2) pose plus pitch according to the motion characteristics of TWIP. However, the authors do not omit disturbances in other directions but perform error modeling, which is integrated into the visual constraints and IMU pre-integration constraints as an error term. Finally, the authors analyze the influence of the error term on the vision and IMU constraints during the optimization process. Compared to traditional algorithms, the algorithm is simpler and better adapt to the real environment.

The results of indoor and outdoor experiments show that, for the TWIP robot, the method has better positioning accuracy and robustness compared with the state-of-the-art.

The algorithm in this paper is proposed for the localization and mapping of a TWIP robot. Different from the traditional positioning method on SE(3), this paper parameterizes the robot pose as SE(2) pose plus pitch according to the motion of TWIP and the motion disturbances in other directions are integrated into visual constraints and IMU pre-integration constraints as error terms, which simplifies the optimization parameters, better adapts to the real environment and improves the accuracy of positioning.

The study aims at estimating the shadow economy (SE) using the method of demand for currency in Palestine for the period 2008–2018 by studying the relationship between a group of variables that affect the ratio of money traded outside the banking system to the money supply in the broad sense.

The study has adopted analytical and descriptive research methods to estimate SE in Palestinian territories. The data has been obtained from the inflation reports issued by the Palestinian Monetary Authority for ten years, from 2008 to 2018. A standard model was constructed using EViews version 8 for statistical data processing after converting the annual data to quarterly data.

The authors demonstrated that the size of the SE in Palestinian territories has varied over time, and the annual average of its size during the study period reached about $1764.893 (in millions). This amount constitutes about 15.5% of the gross domestic product. The study provides recommendations for reducing the size of the SE in Palestinian territories.

The current study shows that shadow economics could significantly matter for economic policy design by policymakers.

This study deals directly with Tanzi’s “estimation of shadow economy in Palestinian territories” concept and its impact on economic policies.

The purpose of this paper is to estimate the Tanzanian shadow economy (SE) from 2003 to 2015 and test the statistical relationships between the SE and its potential causes and indicators.

The econometric analysis is based on a multiple indicators multiple causes (MIMIC) model. To calibrate the SE from the estimates, the authors adopt the value of 55.4 percentage of the SE to official GDP from the literature for the base year 2005.

The SE ranges from 52 to 61 per cent of official GDP and slightly decreases from 2013 to 2015. Increase in inflation, unemployment and government spending were the main drivers of the SE dynamics.

Given the challenges facing estimation of the SE (e.g. small sample size, exogenous estimate to calibrate the model, meaning of the latent variable), quantification of SE should be considered to be rough measures.

To lower the size of the SE, the government needs to keep inflation and unemployment stable over time, to reduce government spending because it creates pressure on tax collection due to the limited tax base.

This is the first study specifically focused on Tanzanian SE based on the MIMIC approach. Existing estimates of Tanzanian SE are calculated by monetary models or apply a common MIMIC specification to the worldwide context.

The purpose of this article is to present an empirical analysis of complex sample data with regard to the biasing effect of non‐independence of observations on standard error parameter estimates. Using field data structured in the form of repeated measurements it is to be shown, in a two‐factor confirmatory factor analysis model, how the bias in SE can be derived when the non‐independence is ignored.

Three estimation procedures are compared: normal asymptotic theory (maximum likelihood); non‐parametric standard error estimation (naïve bootstrap); and sandwich (robust covariance matrix) estimation (pseudo‐maximum likelihood).

The study reveals that, when using either normal asymptotic theory or non‐parametric standard error estimation, the SE bias produced by the non‐independence of observations can be noteworthy.

Considering the methodological constraints in employing field data, the three analyses examined must be interpreted independently and as a result taxonomic generalisations are limited. However, the study still provides “case study” evidence suggesting the existence of the relationship between non‐independence of observations and standard error bias estimates.

Given the increasing popularity of structural equation models in the social sciences and in particular in the marketing discipline, the paper provides a theoretical and practical insight into how to treat repeated measures and clustered data in general, adding to previous methodological research. Some conclusions and suggestions for researchers who make use of partial least squares modelling are also drawn.

The purpose of this study is to analyze how occupational licensing costs within a state affect the performance of self-employed firms, as measured through annual sales.

This study utilizes an empirical approach to determine if there are additional effects on the annual sales for firms that are self-employed in high-cost states that are not explained through the individual estimations. Since the choice of self-employment is plausibly nonrandom, this study also uses a propensity score matching method to develop a matched subsample of self-employed and employee-maintaining firms. This selection methodology ensures that the set of self-employed and employee-maintaining firm observations are similar in all measurable attributes besides their regulatory environment and firm structure. Using this representative subsample, the empirical framework is repeated to reevaluate the effects of high occupational licensing fees on the sales of self-employed firms.

In both the unmatched and matched samples, there are significant, large, negative interactions representing a reduction in annual sales per employee within self-employed firms relative to employee-maintaining firms when located in states with above-average occupational licensing costs. The results using the matched subsamples are noticeably smaller in magnitude, which indicates that future policy assessments would benefit from ensuring that the sample pool, when dealing with self-employment, is limited only to firms under a common convex hull in order to not skew the size of results.

This study contributes new understanding of the financial relationship of self-employed firms and occupational licensing costs using firm-level observations of sales and firm structure. This has important policy implications for the development and evaluation of occupational licensing policies when considering effects on the self-employed.