Proposed Estimation Technique Research Articles

Estimation of Transmission Line Parameters Using Voltage-Current Measurements and Whale Optimization Algorithm

Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.

A New Three Parameter Lifetime Model: The Complementary Poisson Generalized Half Logistic Distribution

We propose a new model with flexible failure rate called complementary Poisson generalized half logistic (CPGHL). Various properties of the model are explored and examine numerically such as the explicit expressions of the moments, mean deviations, Bonferroni and Lorenz curves, Shannon and Renyi entropy. The distribution of mixture of two CPGHL and some related models based on the log-transform of CPGHL are discussed. The asymptotic of moments of residual life and asymptotic distribution of order statistics are obtained. The characterization of Poisson half logistic (PHL) by truncated moments of a certain function of a random variable is discussed. Estimation of the model parameters was approached by maximum likelihood, least square, and percentile methods. Further, the estimation by maximum likelihood for right censored data of the model were considered. The proposed estimation techniques were assessed by simulation studies. Three data applications are provided one of them is a censored data to demonstrate how the new model outperforms some other existing distribution in practice.

Nonlinear Least Absolute Value Estimator for Topology Error Detection and Robust State Estimation

Topology error, a modeling misrepresentation of the power system network configuration, can undermine the quality of state estimation. In this paper, we propose a new methodology for robust power system state estimation (PSSE) modeled by AC power flow equations when there exists a small number of topological errors. The developed technique utilizes the availability of a large number of SCADA measurements and minimizes the $\ell _{1}$ norm of nonconvex residuals augmented by a nonlinear, but convex, regularizer. Representing the power network by a graph, we first study the properties of the solution obtained from the proposed NLAV estimator and demonstrate that, under mild conditions, this solution identifies a small subgraph of the network that contains the topological errors in the model used for the state estimation problem. Then, we introduce a method that can efficiently detect the topological errors by searching over the identified subgraph. In addition, we develop a theoretical upper bound on the state estimation error to guarantee the accuracy of the proposed state estimation technique. The efficacy of the developed framework is demonstrated through numerical simulations on IEEE benchmark systems.

Deep Learning for MMSE Estimation of a Gaussian Source in the Presence of Bursty Impulsive Noise

We develop a deep learning (DL) based Gaussian source estimation technique when the source is impaired by bursty impulsive noise. This noise is correlated in time, and hence estimating the source with a low-complexity algorithm is a challenging task. To address this challenge, we train a long short term memory (LSTM) based deep neural network (DNN) model offline with different bursty noisy observations and deploy the trained model in real-time. The trained model detects the noise state online and thus applies a linear minimum mean square error (LMMSE) method to estimate the source signal. To demonstrate the effectiveness of the proposed scheme, we compare its performance with baseline schemes. Simulation results reveal the effectiveness of the proposed estimation technique in terms of mean square error and computational complexity.

Estimation of the Reliability of a Stress–Strength System from Poisson Half Logistic Distribution

This paper discussed the estimation of stress-strength reliability parameter based on complete samples when the stress-strength are two independent Poisson half logistic random variables (PHLD). We have addressed the estimation of R in the general case and when the scale parameter is common. The classical and Bayesian estimation (BE) techniques of R are studied. The maximum likelihood estimator (MLE) and its asymptotic distributions are obtained; an approximate asymptotic confidence interval of R is computed using the asymptotic distribution. The non-parametric percentile bootstrap and student’s bootstrap confidence interval of R are discussed. The Bayes estimators of R are computed using a gamma prior and discussed under various loss functions such as the square error loss function (SEL), absolute error loss function (AEL), linear exponential error loss function (LINEX), generalized entropy error loss function (GEL) and maximum a posteriori (MAP). The Metropolis–Hastings algorithm is used to estimate the posterior distributions of the estimators of R. The highest posterior density (HPD) credible interval is constructed based on the SEL. Monte Carlo simulations are used to numerically analyze the performance of the MLE and Bayes estimators, the results were quite satisfactory based on their mean square error (MSE) and confidence interval. Finally, we used two real data studies to demonstrate the performance of the proposed estimation techniques in practice and to illustrate how PHLD is a good candidate in reliability studies.

A New Stator Resistance Estimation Technique for Vector-Controlled PMSM Drive

This article presents new stator resistance (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> ) estimation techniques for a vector-controlled permanent magnet synchronous motor (PMSM) drive. These techniques give the indirect measurement of the temperature rise in the stator. An accurate estimation of the R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> will help in the condition monitoring of the PMSM machine. The various R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> estimation techniques proposed in this article are formulated using the model reference adaptive system (MRAS). Out of the various methods proposed, Þ - MRAS performs better than the remaining stator resistance estimation techniques since this is independent of the integrator, differentiator, and speed; offers robust and dynamic performance in all four modes of operation, including zero speed. The stability and sensitivity analysis is further carried out to ensure the robustness of the proposed estimation techniques. The proposed stator resistance estimation techniques are simulated in MATLAB/Simulink and experimentally validated through a dSPACE-1104-based laboratory prototype.

Robust real‐time parameter estimation for linear systems affected by external noises and uncertainties

SummaryA robust adaptive parameter estimation method, based on the application of a full‐order filter capable of rejecting exogenous disturbances, is proposed in this article. A linear matrix inequality condition is proposed to synthesize the desired robust filter, assuming the presence of a known input control with constraints. The filter uses the output of the system to estimate the desired signal that will be employed in the adaptive estimation procedure and, to assure robustness to exogenous noise and unstructured uncertainties, the guaranteed cost is minimized in the synthesis condition. The filtered signals are then applied to an adaptive procedure to estimate the unknown system's internal parameters, which is also proposed in this article. It is shown that lower values for the guaranteed cost from the disturbance input to the error output of the filter imply more accurate estimations of the parameters. The efficiency of the proposed estimation technique is illustrated through a simulated model and a physical system has been considered to validate real‐time estimation.

MagMonitor: Vehicle Speed Estimation and Vehicle Classification Through A Magnetic Sensor

Internet of Things (IoT) is playing an increasingly important role in Intelligent Transportation Systems (ITS) for real-time sensing and communication. In ITS, vehicle types, volume and speeds provide important information for road traffic management. However, the present methods for on-road traffic monitoring are lacking in providing cost-effective means to meet the demands. In this paper, we propose MagMonitor, a novel method for on-road traffic surveillance through a single small and easy-to-install magnetic sensor. The developed magnetic sensor system is wireless-connected, cost-effective, and environmental-friendly. First, a magnetic model of a moving vehicle is presented. The model employs multiple magnetic dipoles for modelling moving vehicle and varies depending on the on-road vehicle types. Through modelling of local magnetic field perturbations caused by moving vehicles, we extract the characteristics of magnetic waveforms for vehicle identification and speed estimation. The proposed model and estimation technique are validated with real field experimental data. Furthermore, we analyze and compare the performance of the proposed estimation technique with other speed estimation algorithms, which shows the superior accuracy of the proposed technique.

Racecar Longitudinal Control in Unknown and Highly-Varying Driving Conditions

This paper focuses on racecar longitudinal control with highly-varying driving conditions. The main factors affecting the dynamic behavior of a vehicle, including aerodynamic forces, wheel rolling resistance, traction force resulting from changing tire-road interaction as well as the occurrence of sudden wind gusts or the presence of persistent winds, are considered and assumed to have unknown models. By exploiting the theory on delayed input-state observers and using measurement data about the vehicle and wheel speeds, a dynamic filter that allows the online reconstruction of the above-mentioned unknown time-varying quantities is derived. Moreover, by exploiting the notion of effective tire radius, a reduced-degree-of-freedom model for the longitudinal vehicle dynamics is obtained, which is independent of the traction force and that enables, when used with the observer filter described above, an accurate speed control compensating for the resistance forces. One appealing feature of the proposed estimation and control method is that it requires no model information about such forces, for which, at the state-of-the-art, only heuristic approximations to be a-priori identified are available. Its effectiveness is shown via the simulation of scenarios where the car is required to execute aggressive maneuvers and the asphalt road surface abruptly changes from dry to wet, snowy, and icy. The evaluation also reveals that the proposed estimation technique outperforms standard solutions even in the presence of measurement noise.

Parameter Estimation of Fluctuating Two-Ray Model for Next Generation Mobile Communications

This paper focuses on the problem of parameter estimation for a Fluctuating Two-Ray (FTR) model in the context of wireless mobile communications. Precisely, the received signal is assumed to be the superposition of two dominant components (typically a direct plus a reflected path signal) in addition to diffusive secondary contributions. The signal components may be affected by random amplitude shadowing, statistically modeled by Nakagami- $m$ distribution, multiplied by unknown scaling factors with random uniform independent phases, whereas the diffusive component is assumed to follow the complex Gaussian distribution. Exploiting the method of moments, a $4\times 4$ nonlinear system is herein mathematically derived, which is very hard to be solved due to the strong nonlinearity. Therefore, a sequential procedure based on some prior information about the diffusive component power level is devised to solve it. The effectiveness of the proposed estimation technique is shown by evaluating the normalized root mean square errors as well as mean errors and standard deviations in several operating conditions of practical interest also considering the limit case of only one-ray in order to compare the proposed approach to simpler estimators, already presented in the literature. The results show the robustness of the new estimator even under a multipath model mismatch. Finally, the effectiveness of the proposed estimation procedure is confirmed through measured mmWave data.

The Option-Implied Asset Volatility Surface

This paper provides a simple way to obtain an option-implied asset volatility surface. The proposed estimation technique allows to estimate the unobservable asset volatility surface in the same fashion of what is done when equity volatility is extracted from options. Given a sample of 66 US firms, the asset volatility is first estimated at the firm level and then aggregated in order to study the properties of the market-wide asset volatility surface. Principal component analysis (PCA) is conducted on the weekly changes of the volatility surface both across the moneyness and the time-to-maturity dimension, as well as on the overall surface. Both across moneyness and maturity, the first three PCs are able to account for most of the variation and can be identified as level, slope/smirk and curvature factors respectively. When analysed in across the whole surface, the first two PCs account for most of the variation and represent a level and a skew factor. Finally, the joint evolution of the smirk and the slope of the surface is model-led as a Vector Auto-regressive model with exogenous variables. Both slope and smirk appear to be jointly auto-correlated and loading of the other market variables display the predicted sign.

New coding scheme for the state estimation and reference tracking of nonlinear dynamic systems over the packet erasure channel (IoT): Applications in tele-operation of autonomous vehicles

This paper presents a new technique for the state estimation and reference tracking of nonlinear dynamic systems over the packet erasure channel, which is an abstract model for transmission via the Internet, WiFi wireless network and ZigBee modules. A new encoder and decoder for real time state estimation of nonlinear dynamic systems at the end of communication link when the measurements are sent through the limited capacity erasure channel, are presented. Then, using the available results from control theory, a controller for reference tracking and hence the stability of the system is also designed. That is, for nonlinear systems, almost sure asymptotic state estimation and reference tracking techniques including an encoder, decoder and a controller are presented. The satisfactory performances of the proposed state estimation and control techniques are illustrated via computer simulations by applying the proposed techniques on the unicycle model, which represents the dynamics of autonomous vehicles.

In brief

PAGE 632 Chinese researchers propose a symbol cyclic reconstruction scheme for universal-filtered multi-carrier systems (UFMC). The overlap-add technique is used to lower the computational complexity of the system transmitter, with ISI cancellation used at the receiver to remove interference between neighbouring symbols. Simulation results show the superiority of the scheme over state-of-the-art systems. PAGE 616 Researchers from Iran present a monitoring technique for tracking the state-of-health of batteries, suitable for use in battery management systems. The method can detect early degradation of batteries by monitoring the switching and battery transitions in a battery management system. Experiments on a typical charge balancing circuit verify the performance of the technique. Block diagram of SCR-UFMC with multipath fading channels. PAGE 589 Indian researchers propose a novel estimation technique for a noise-influenced electronic circuit, unifying the Carleman linearisation with the Fokker-Planck equation for Itô's Stochastic Differential Equations. Numerical simulation results are presented for a benchmark tunnel diode oscillator circuit. The method may prove useful for estimating state trajectories under the influence of randomness when observations are not possible. Laboratory test bench. PAGE 612 A collaboration of researchers from Japan present an evaluative study on the current and luminance characteristics of powder electroluminescent devices prepared on gravure offset-printed invisible Ag-grid electrodes. Results show that the luminance is strongly dependent on the transparency and light-extraction efficiency of the Ag-grid electrodes. Flow of the proposed estimation technique.. PAGE 609 Researchers from China propose a wideband differential filtering power divider, using a three-lined coupled structure with multiple lumped elements. Compared with other differential filtering power dividers, the proposed design shows the advantages of a wide bandwidth, compact size and low insertion loss. A prototype validates the simulated results. Optical microscopy image of the emitted light from PELDs on the Ag-grid electrodes. Photograph of the proposed wideband differential filtering power divider.

A New Adaptive Mass Estimation Approach of Heavy Truck Based on Engine Torque Local Convex Minimum Characteristic at Low Speeds

This paper proposes a new mass estimation for a vehicle system, utilizing the characteristics of engine torque local convex minimum, where the mass can be estimated based on the driving forces and the longitudinal accelerations only. Fundamentally, this approach generally requires no other information about an aerodynamic effect, a road grade, or a rolling friction, which is usually demanded by the existing well-known longitudinal dynamics and adaptive filter-based estimation methods. The effectiveness of the proposed approach was evaluated and validated by both TruckSim/Simulink co-simulation and actual field test data. It is found that the proposed estimation technique is more favorable for a situation where the vehicle is exposed to low-speed regions. In addition to this new mass estimation strategy, other new and current existing methods were explored and are reviewed here. Moreover, this study suggested a guideline for a hybrid-type mass estimation strategy to predict a mass by combining a new method with an existing one for every speed.

Real-Time Implementation of Shunt Active Power Filter With Reduced Sensors

Performance of shunt active power filter (SAPF) depends on the effectiveness of control strategy, which requires information about voltages and currents obtained from sensors. Faults in these sensors cause SAPF to aggravate power quality problems instead of compensating it. Therefore, sensorless control of SAPF is a very attractive solution for its robust operation. This article proposes an Adaline-based control strategy that requires only source currents and dc voltage measurement, thus reducing source voltage and load current sensors. The proposed estimation techniques enable SAPF to work under highly dynamic load conditions where other similar approaches with reduced sensors cease to perform. A comparative analysis with the classical approach (using all sensors) under the same load conditions is done to show the effectiveness of the proposed techniques. The proposed control strategy is first simulated on MATLAB/Simulink platform and further verified on the OPAL-RT 5700 hardware-in-loop FPGA-based real-time platform.

Persistent and Rough Volatility

This paper introduces a discrete-time fractional stochastic volatility model (FSV) based on fractional Gaussian noise. The new model includes the standard stochastic volatility model as a special case and has the same limit as the fractional integrated stochastic volatility (FISV) model. A simulated maximum likelihood method, which maximizes the time-domain log-likelihood function calculated by the importance sampling technique, and a frequency-domain quasi maximum likelihood method (or Whittle) are employed to estimate the model parameters. Simulation studies suggest that, while both estimation methods can accurately estimate the model, the simulated maximum likelihood method outperforms the Whittle method. As an illustration, we fit the FSV and FISV models with the proposed estimation techniques to the S\&P 500 composite index over a sample period spanning 45 years. Our results reveal that the volatilities of the data series are persistent and rough.

CFO/Channel estimation algorithms for space‐time block coded orthogonal frequency division multiplexing wireless relay networks without using cyclic prefix of OFDM

This study presents a simple technique, which is used to estimate both channel state and carrier frequency offset (CFO) in decode-and-forward relay systems, where coded orthogonal frequency division multiplexing is employed without cyclic prefix. On the basis of the least-square criterion, we derive the estimate of CFO and channel with the aids of consecutive blocks of pilot symbols. The initially proposed estimation technique achieves a distinguished performance by using a two-dimensional complete search approach; however, the computation becomes more and more intensive when the search granularity becomes finer and finer. For this reason, we further present an iterative approach as an alternative solution, which provides a performance comparable with that of the complete search method, while greatly reducing the implementation complexity. Besides, we also derive a closed-form expression of the CFO estimate for the considered system, which allows us to avoid the exhaustive search during the estimation processing. Computer simulations have been conducted to verify the efficiency of the proposed schemes.

Control of a Three-Phase Grid-Connected Inverter Under Non-Ideal Grid Conditions With Online Parameter Update

Three-phase grid-connected inverter modeling depends on the equivalent resistance and inductance between the inverter and the grid. However, these parameters are not fixed during the operation of the inverter and vary with the operating conditions. In this paper, a new globally stable adaptive controller is proposed to estimate these values online and update the controller during its operation. A model reference adaptive system (MRAS) based on two fictitious quantities with no physical significance is utilized, namely $M = v \cdot i^*$ and $N = v^* \times i$ , where $v$ and $i$ are the steady-state values of voltage and current in synchronously rotating frame of reference. Detailed stability analysis of the proposed estimation technique has been presented using a Lyapunov energy function-based approach proving the global stability of the technique. Small-signal modeling and analysis of the proposed scheme has also been presented so as to understand the local dynamics of the system. The guidelines to select the parameter of the proposed MRAS system along with updating the controller are elaborated in this paper. Detailed simulation based on MATLAB/Simulink and PLECS, along with experimental studies through a Texas Instruments TMS320F28377S microcontroller, validates the feasibility of the proposed control architecture.

An Efficient Predictive Paradigm for Software Reliability

Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. The estimation of defects can be determined in the product development using many advanced statistical modelling techniques based on the empirical data obtained by the testing phases. The proposed estimation technique in this paper is a model which was developed using Rayleigh function for estimating effect of defects in Software Project Management. The present study offers to decide how many defects creep in to production and determine the effort spent in months. The estimation model was used on Software Testing Life Cycle (STLC) to complete product. The accuracy of the model explains the variation in spent efforts in months associated with number of defects. The model helps the senior management in estimating the defects, schedule, cost and effort.

Timing Analysis of Precedence Constraint Messages Scheduled With Slot Multiplexing Over Dynamic Segment of FlexRay

FlexRay offers high-speed reliable transmission in the vehicle domain. Thus, it is one of the most popular in-vehicle communication protocols for the applications that are safety-critical or applications with very low transmission time requirement. Due to the real-time constraint, the worst case end-to-end delay (EED) for such applications transmitting over a shared bus must be known. This paper addresses the problem of computation of EED for a given set of tasks with precedence constraint and corresponding messages, which are scheduled with slot multiplexing for event-triggered communication over the dynamic segment of FlexRay. First, computing precise delay has been shown to be an intractable problem. Subsequently, motivated by the intractability, an efficient estimation technique has been proposed to compute approximate EED. Unlike the common practice of considering the network in isolation, we present a comprehensive timing analysis, which considers the effects of task execution as well. Moreover, EED is allowed to be greater than interactivation time of task graph. The extensive simulation has been performed on the test cases generated with uniform as well as normal distribution. Note to Practitioners —In the past few decades, due to the large-scale use and sophistication of automotive electronic systems, various in-vehicle communication networks and protocols are also evolving. FlexRay is one such in-vehicle communication protocol, which provides the mechanism for the fault-tolerant high-speed communication over FlexRay bus. FlexRay is widely being employed for safety-critical applications in the automotive domain. For such applications, often the timing constraint is imposed with a bound on the worst case EED. Thus, timing properties of the designed network must be verified before applying it on the real domain such that the worst case EED for the propagation of an event at sensor end to actuator end is known. The proposed estimation technique can be used to compute approximate EED for an application scheduled with other applications over the dynamic segment of FlexRay. In addition, various guidelines for designing the network in order to reduce overestimation have also been presented.