Study of a Current and Voltage Polarization Sensor Network

Sensors based on polarization are suitable for application in power grids due to their excellent characteristics, such as high electrical insulation, non-magnetic saturation, oil-free, no risk of explosive failures, and high bandwidth. Utility companies are incorporating new technologies that are driving the evolution of electrical systems. Thus, it is interesting to evaluate the possibility of using polarization sensors in a network configuration. In this work, we present an experimental study of a current and voltage polarization sensor network applied to a medium voltage distribution grid. The current sensor is based on the Faraday effect, and the voltage sensor uses the Pockels effect. Both sensors use a 90° polarization degree between the two output ports to compensate for the various impairments on the measurements by applying the difference-over-sum. The network uses a DWDM topology centered at the 1550 nm range, and both current and voltage sensors in this work used this spectral band. We evaluated the sensor node in terms of accuracy according to IEC standard 61869-10 and IEC standard 61869-11. Considering that an important application of this sensor network is in the aerial cable of medium voltage networks, sensor node accuracy was also estimated in the presence of cable vibration. The calculated power budget of the proposed network indicates that reaching ten nodes of current and voltage sensors in a 10 km optical link is possible, which is enough for a medium urban voltage distribution network.

Sensor Modeling and Calibration Method Based on Extinction Ratio Error for Camera-Based Polarization Navigation Sensor

The performance of camera-based polarization sensors largely depends on the estimated model parameters obtained through calibration. Limited by manufacturing processes, the low extinction ratio and inconsistency of the polarizer can reduce the measurement accuracy of the sensor. To account for the challenges, one extinction ratio coefficient was introduced into the calibration model to unify the light intensity of two orthogonal channels. Since the introduced extinction ratio coefficient is associated with degree of polarization (DOP), a new calibration method considering both azimuth of polarization (AOP) error and DOP error for the bionic camera-based polarization sensor was proposed to improve the accuracy of the calibration model parameter estimation. To evaluate the performance of the proposed camera-based polarization calibration model using the new calibration method, both indoor and outdoor calibration experiments were carried out. It was found that the new calibration method for the proposed calibration model could achieve desirable performance in terms of stability and robustness of the calculated AOP and DOP values.

Efficient Localization Algorithm for Near-Field Noncircular Sources via Dual-Polarization Sensor Array

Noncircular signals are widely used in the area of radar, sonar, and wireless communication array systems, which can offer more accurate estimates and detect more sources. In this paper, the noncircular signals are employed to improve source localization accuracy and identifiability. Firstly, an extended real-valued covariance matrix is constructed to transform complex-valued computation into real-valued computation. Based on the property of noncircular signals and symmetric uniform linear array (SULA) which consist of dual-polarization sensors, the array steering vectors can be separated into the source position parameters and the nuisance parameter. Therefore, the rank reduction (RARE) estimators are adopted to estimate the source localization parameters in sequence. By utilizing polarization information of sources and real-valued computation, the maximum number of resolvable sources, estimation accuracy, and resolution can be improved. Numerical simulations demonstrate that the proposed method outperforms the existing methods in both resolution and estimation accuracy.

A Bio-Inspired Polarization Sensor with High Outdoor Accuracy and Central-Symmetry Calibration Method with Integrating Sphere

A bio-inspired polarization sensor with lenses for navigation was evaluated in this study. Two new calibration methods are introduced, referred to as “central-symmetry calibration” (with an integrating sphere) and “noncontinuous calibration”. A comparison between the indoor calibration results obtained from different calibration methods shows that the two proposed calibration methods are more effective. The central-symmetry calibration method optimized the nonconstant calibration voltage deviations, caused by the off-axis feature of the integrating sphere, to be constant values which can be calibrated easily. The section algorithm proposed previously showed no experimental advantages until the central-symmetry calibration method was proposed. The outdoor experimental results indicated that the indoor calibration parameters did not perform very well in practice outdoor conditions. To establish the reason, four types of calibration parameters were analyzed using the replacement method. It can be concluded that three types can be easily calibrated or affect the sensor accuracy slightly. However, before the sensor is used outdoors every time, the last type must be replaced with the corresponding outdoor parameter, and the calculation needs a precise rotary table. This parameter, which is mainly affected by the spectrum of incident light, is the main factor determining the sensor accuracy. After calibration, the sensor reaches an indoor accuracy of ±0.009° and a static outdoor accuracy of ±0.05° under clear sky conditions. The dynamic outdoor experiment shows a ±0.5° heading deviation between the polarization sensor and the inertial navigation system with a ±0.06° angular accuracy.

Disturbance analysis and performance test of the polarization sensor based on polarizing beam splitter

Purpose Multiple-source disturbances exist in the polarization sensor, which severely affect the sensor accuracy and stability. Hence, the disturbance analysis plays a vital role in improving the sensor orientation performance. This paper aims to present a novel sensor error model, a disturbances quantitative analysis, a calibration and performance test of polarization sensor based on a polarizing beam splitter. Design/methodology/approach By combining with the sensor coefficient errors, the Azimuth of Polarization (AoP) error model and the Degree of Polarization (DoP) error model are established, respectively. In addition, the multiple-source disturbances are classified, while the influence on the orientation accuracy is quantitative analyzed. Moreover, the least square optimization algorithm is employed to calibrate the sensor coefficients. Finally, an outdoor test is carried out to test the sensor long-term accuracy. Findings The theoretical analysis and numerical simulations illustrate that the sensor accuracy is closely related to the disturbances. To eliminate the influence of the disturbances, the least square optimization algorithm, which can minimize the sum of squares of the residual difference of AoP and DoP, is used to calibrate the sensor coefficients. The outdoor test indicates that the sensor can maintain long-term accuracy and stability. Originality/value The main contribution of this paper is to establish a novel sensor error model, where the sensor coefficient errors are introduced. In addition, the disturbances are classified and analyzed to evaluate the orientation accuracy of the sensor.

An autonomous navigation system integrated with air data and bionic polarization information

One of the most significant challenges for an unmanned aerial vehicle (UAV) is to autonomously navigate in complex environments, as the signals from the global positioning system (GPS) are subject to disturbance and interference. To improve the autonomy and availability of the UAV navigation system without GPS, we design a new autonomous navigation system and implement it for real applications in this paper, in which one integrates the inertial measurement unit (IMU), the bionic polarization sensor (BPS), and the air data system (ADS). The BPS can provide effective heading angle measurement, and the ADS is used to output information for continuous velocity and height. The combination of BPS and ADS is a solution the inertial error drift. Kalman filter is selected to estimate the error state of the integrated navigation system based on the measurements from the BPS and ADS, and then the estimation is used to correct the navigation system error in real time. The simulation and experimental results have shown that the new integrated navigation system can perform with high precision and autonomy without GPS signal.