High-Precision Rotor Position Estimation via Cascaded Model-Based and Data-Driven Estimator: An Integrated Method

The number of real-time supervisory control and data acquisition (SCADA) measurements in power distribution systems is scarce. This limits the reliability of state estimation (SE) results for distribution systems. Therefore, some studies seek to enhance the observability and SE accuracy of distribution systems by incorporating advanced metering infrastructure (AMI) data with the SCADA measurements. However, the hourly updated AMI data may be too coarse to capture system changes, especially in the presence of intermittent renewable energy sources. This issue is addressed by proposing a hybrid SE framework integrating a data-driven estimator and a model-based estimator. To be specific, the data-driven estimator combined with a topology identification method is presented to solve the DSSE problem between AMI scans, and the model-based estimator is employed to ensure robust estimation results against gross errors at a lower time scale. The proposed hybrid SE switches from the data-driven estimator to the model-based estimator once the AMI data is updated. Such a solution allows for capturing system changes at different time scales and improving the real-time and reliability of distribution system state estimation. Simulations are conducted on a sample distribution system to illustrate the characteristics of the proposed hybrid SE.

In this paper, we introduce a novel data-driven inverse dynamics estimator based on Gaussian Process Regression. Driven by the fact that the inverse dynamics can be described as a polynomial function on a suitable input space, we propose the use of a novel kernel, called Geometrically Inspired Polynomial Kernel (GIP). The resulting estimator behaves similarly to model-based approaches as concerns data efficiency. Indeed, we proved that the GIP kernel defines a finite-dimensional Reproducing Kernel Hilbert Space that contains the inverse dynamics function computed through the Rigid Body Dynamics. The proposed kernel is based on the recently introduced Multiplicative Polynomial Kernel, a redefinition of the classical polynomial kernel equipped with a set of parameters that allows for a higher regularization. We tested the proposed approach in a simulated environment, and also in real experiments with a UR10 robot. The obtained results confirm that, compared to other data-driven estimators, the proposed approach is more data-efficient and exhibits better generalization properties. Instead, with respect to model-based estimators, our approach requires less prior information and is not affected by model bias.

In order to improve the performance of the high-speed PMSM control system, a sensorless DPC (Direct Power Control) scheme for pump-kind applications is proposed. In the proposed DPC control strategy, a high-precision rotor position estimation is applied to obtain the accurate rotor position angle, which is the key factor for the DPC scheme. An experimental platform has been built, and the experimental results validate effectiveness of the proposed method.

In outdoor unmanned forklift unloading scenarios, pallet detection and localization face challenges posed by uncontrollable lighting conditions. Furthermore, the stacking and close arrangement of pallets also increase the difficulty of positioning a target pallet. To solve these problems, a method for high-precision positioning and rotation angle estimation for a target pallet using the BeiDou Navigation Satellite System (BDS) and vision is proposed. Deep dual-resolution networks (DDRNets) are used to segment the pallet from depth images and RGB images. Then, keypoints for calculating the position and rotation angle are extracted and further combined with the 3D point cloud data to achieve accurate pallet positioning. Constraining the pixel coordinates and depth coordinates of the center point of the pallet and setting the priority of the pallet according to the unloading direction allow the target pallet to be identified from multiple pallets. The positioning of the target pallet in the forklift navigation coordinate system is achieved by integrating BDS positioning data through coordinate transformation. This method is robust in response to lighting influences and can accurately locate the target pallet. The experimental results show that the pallet positioning error is less than 20 mm, and the rotation angle error is less than 0.37°, which meets the accuracy requirements for automated forklift operations.

The conventional state of charge (SOC) estimation model has several concerns, such as accuracy and reliability. In order to realize robust SOC estimation for embedded applications, this study focuses on three concerns of the existing SOC estimation model: accuracy, robustness, and practicality. In improving the estimation accuracy and robustness, this study took into account the dynamic of the actual SOC caused by the dynamic charging and discharging process. In practice, the charging and discharging processes have characteristics that must be considered to realize robust SOC estimation. The model-based SOC estimation developed based on the virtual battery model causes difficulties for real-time applications. Additionally, model-based SOC estimation cannot be reliably extrapolated to different battery types. In defining the behavior of various types of batteries, the model-based SOC estimation must be updated. Hence, this study utilized data-driven SOC estimation based on an artificial neural network (ANN) and measurable battery data. The ANN model, which has excellent adaptability to nonlinear systems, is utilized to increase accuracy. Additionally, using measurable battery data such as voltage and current signals, the SOC estimation model is suitable for embedded applications. Results indicate that estimating SOC with the proposed model reduced errors with respect to actual datasets. In order to verify the feasibility of the proposed model, an online estimation was out on the embedded system with the use of C2000 real-time microcontrollers. Results show that the proposed model can be executed in an embedded system using measurable battery data.

Chapter 9 - Data-driven state estimation in electric power systems

Lithium-ion battery state-of-health estimation is one of the vital issues for electric vehicle safety. In this work, a joint model-based and data-driven estimator is developed to achieve accurate and reliable state-of-health estimation. In the estimator, an increase in ohmic resistance extracted from the Thevenin model is defined as the health indicator to quantify the capacity degradation. Then, a linear state-space representation is constructed based on the data-driven linear regression. Furthermore, the Kalman filter is introduced to trace capacity degradation based on the novel state space representation. A series of battery aging datasets with different dynamic loading profiles and temperatures are obtained to demonstrate the accuracy and robustness of the proposed method. Results show that the maximum error of the Kalman filter is 2.12% at different temperatures, which proves the effectiveness of the proposed method.

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy.

A dataset for large prismatic lithium-ion battery cells (CALB L148N58A): Comprehensive characterization and real-world driving cycles.

This paper considers the problem of data-driven estimation with sparse measurements for a complex nonlinear system. While model-based nonlinear estimation methods are well known, state estimation from partial observations with unmodeled dynamics is less understood. Here we use a method for model-free estimation based on an echo-state network (ESN) where a reasonably accurate set of training data is available during the training period and some sparse measurements are obtained during the testing phase. The measurements are assimilated by an ensemble Kalman filter (EnKF) to improve the predictor's performance when compared to a free-running neural network architecture. The proposed method is applied to three systems: a low-dimensional chaotic system, a high-dimensional chaotic system, and a set of real-time traffic data.

To improve the accuracy of stator flux estimator in Direct Torque Control (DTC) of Interior Permanent-Magnet Synchronous Machine (IPMSM), a pulse response based rotor position estimator is proposed. The current ripples caused by active voltage vectors in DTC are utilized by the rotor position estimator. Then, the estimated rotor position serves as an input for a model-based stator flux estimator in order to steer the switching state generator. The proposed rotor position estimator exhibits a good accuracy without introducing additional test signals. This approach preserves many advantages of DTC in controlling electromagnetic torque and stator flux, such as the absence of a motion state sensor and pulse width modulation strategy. Furthermore, the effect of varying steady-state voltage vector on estimation of rotor position is studied. The rotor position estimation error has positive correlation with the rotational speed when the switching frequency remains constant. Simulation results verify the feasibility and effectiveness of the method in estimating the rotor position and controlling both the electromagnetic torque and the stator flux and the effects of varying steady-state voltage vector on rotor position estimation.

Centimeter-level localization and precise rotation angle estimation for flatbed trucks pose significant challenges in unmanned forklift automated loading scenarios. To address this issue, the study proposed a method for high-precision positioning and rotation angle estimation of flatbed trucks using the BeiDou Navigation Satellite System (BDS) and vision technology. First, an unmanned forklift equipped with a Time-of-Flight (ToF) camera and a dual-antenna mobile receiver for BDS positioning collected depth images and localization data near the front and rear endpoints of the flatbed. The Deep Dual-Resolution Network-23-slim (DDRNet-23-slim) model was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform. The algorithm then computed the set of intersection points of the lines. A neighborhood feature vector was designed to identify the endpoint of a flatbed from a set of intersection points using feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BDS positioning. A rotation angle estimation was then performed using the endpoints at the front and rear. Experiments showed that the endpoint positioning error was less than 3 cm, and the rotation angle estimation error was less than 0.3°, which verified the validity and reliability of the method.

The high-precision roll attitude estimation of the decoupled canards relative to the projectile body based on the bipolar hall-effect sensors is proposed. Firstly, the basis engineering positioning method based on the edge detection is introduced. Secondly, the simplified dynamic relative roll model is established where the feature parameters are identified by fuzzy algorithms, while the high-precision real-time relative roll attitude estimation algorithm is proposed. Finally, the trajectory simulations and grounded experiments have been conducted to evaluate the advantages of the proposed method. The positioning error is compared with the engineering solution method, and it is proved that the proposed estimation method has the advantages of the high accuracy and good real-time performance.

State of charge (SOC) estimation has always been a hot topic in the field of both power battery and new energy vehicle (electric vehicle (EV), plug-in electric vehicle (PHEV) and so on). In this work, aiming at the contradiction problem between the exact requirements of EKF (extended Kalman filter) algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period, a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed. The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery. Through the combination of data-based and model-based SOC estimation method, the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods, and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment (such as in an EV or PHEV). A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

PurposeThe purpose of this paper is to propose a high-precision attitude solution to solve the attitude drift problem caused by the dispersion of low-cost micro-electro-mechanical system devices in strap-down inertial navigation attitude solution of micro-quadrotor.Design/methodology/approachIn this study, a three-stage attitude estimation scheme that combines data preprocessing, gyro drifts prediction and enhanced unscented Kalman filtering (UKF) is proposed. By introducing a preprocessing model, the quaternion orientation is calculated as the composition of two algebraic quaternions, and the decoupling feature of the two quaternions makes the roll and pitch components independent of magnetic interference. A novel real-time based on differential value (DV) estimation algorithm is proposed for gyro drift. This novel solution prevents the impact of quartic characteristics and uses the iterative method to meet the requirement of real-time applications. A novel attitude determination algorithm, the pre-process DV-UKF algorithm, is proposed in combination with UKF based on the above solution and its characteristics.FindingsCompared to UKF, both simulation and experimental results demonstrate that the pre-process DV-UKF algorithm has higher reliability in attitude determination. The dynamic errors in the three directions of the attitude are below 2.0°, the static errors are all less than 0.2° and the absolute attitude errors tailored by average are about 47.98% compared to the UKF.Originality/valueThis paper fulfils an identified need to achieve high-precision attitude estimation when using low-cost inertial devices in micro-quadrotor. The accuracy of the pre-process DV-UKF algorithm is superior to other products in the market.