Reduced Sensor Count Research Articles

Multi-objective shuffled frog leaping algorithm for deployment of sensors in target based wireless sensor networks

In this research, –coverage –connected problem is viewed as multi-objective problem and shuffling frog leaps algorithm is proposed to address multi-objective optimization issues. The shuffled frog leaping set of rules is a metaheuristic algorithm that mimics the behavior of frogs. Shuffled frog leaping algorithms are widely used to seek global optimal solutions by executing the guided heuristic on the given solution space. The basis for the success of this SFL algorithm is the ability to exchange information among a group of individuals which phenomenally explores the search space. SFL improves the overall lifespan of the network, the cost of connection among the sensors, to enhance the equality of coverage among the sensors and targets, reduced sensor count for increased coverage, etc. When it comes to coverage connectivity issues, each target has to be covered using k sensors to avoid the loss of data and m sensors connected enhance the lifespan of the network. When the targets are covered by k sensors then the loss of data will be reduced to an extended manner. When the sensors are connected with m other sensors then the connectivity among the sensors will not go missing and hence the lifespan of the network will be improved significantly. Therefore, the sensor node number in coverage indicates the total number of sensor nodes utilised to cover a target, and the number of sensor nodes in connected reflects the total number of sensor nodes that provide redundancy for a single failed sensor node. Connectivity between sensor nodes is crucial to the network’s longevity. The entire network backbone acts strategically when all the sensors are connected with one or the other to pertain to the connectivity of the network. Coverage is yet another key issue regarding the loss of data. The proposed algorithm solves the connectivity of sensors and coverage of targets problems without weighted sum approach. The proposed algorithm is evaluated and tested under different scenarios to show the significance of the proposed algorithm.

Bridgeless SEPIC PFC Converter for Low Voltage EV Applications With Reduced Sensor Count

Bridgeless SEPIC PFC Converter for Low Voltage EV Applications With Reduced Sensor Count

DC-Bus Ripple Elimination in Single-Phase Grid-Tied Differential Buck Inverter With Reduced Sensor Count

DC-Bus Ripple Elimination in Single-Phase Grid-Tied Differential Buck Inverter With Reduced Sensor Count

Maximum power point tracking‐based model predictive control with reduced sensor count for PV applications

AbstractThis paper discusses the MPPT based on finite‐set model predictive control (FS‐MPC) in photovoltaic (PV) systems. Generally, the FS‐MPC implementation needs more sensors in comparison with the traditional methods due to the existence of the prediction stage. However, it has a fast transient behaviour in case of fast‐changing atmospheric conditions. Thus, to make benefit from the FS‐MPC principle without increasing the system’s cost, two algorithms are developed to reduce the number of required sensors without altering the efficiency. First, an accurate model of the PV system including the losses is derived, which enables estimation of the output capacitor voltage. Another approach utilizing an extended Kalman filter (EKF) is proposed. The EKF takes advantage of the derived model of the system and estimates the PV current. In addition, practical PV system applications are considered to have an estimate for cost reduction with the proposed methods. The proposed methodologies are compared with the conventional FS‐MPC with full sensor utilization, where analysis and evaluation of the current‐ and voltage‐oriented FS‐MPC methods are presented. Moreover, robustness assessment of the proposed algorithms with sensor reduction against parameter variation is examined. All studied methods are validated in simulation and experimentally at different operating conditions.

Maximum Power Point Tracking-Based Model Predictive Control for Photovoltaic Systems: Investigation and New Perspective.

In this paper, a comparative review for maximum power point tracking (MPPT) techniques based on model predictive control (MPC) is presented in the first part. Generally, the implementation methods of MPPT-based MPC can be categorized into the fixed switching technique and the variable switching one. On one side, the fixed switching method uses a digital observer for the photovoltaic (PV) model to predict the optimal control parameter (voltage or current). Later, this parameter is compared with the measured value, and a proportional–integral (PI) controller is employed to get the duty cycle command. On the other side, the variable switching algorithm relies on the discrete-time model of the utilized converter to generate the switching signal without the need for modulators. In this regard, new perspectives are inspired by the MPC technique to implement both methods (fixed and variable switching), where a simple procedure is used to eliminate the PI controller in the fixed switching method. Furthermore, a direct realization technique for the variable switching method is suggested, in which the discretization of the converter’s model is not required. This, in turn, simplifies the application of MPPT-based MPC to other converters. Furthermore, a reduced sensor count is accomplished. All conventional and proposed methods are compared using experimental results under different static and dynamic operating conditions.

Estimating Lower Limb Kinematics Using a Lie Group Constrained Extended Kalman Filter with a Reduced Wearable IMU Count and Distance Measurements.

Tracking the kinematics of human movement usually requires the use of equipment that constrains the user within a room (e.g., optical motion capture systems), or requires the use of a conspicuous body-worn measurement system (e.g., inertial measurement units (IMUs) attached to each body segment). This paper presents a novel Lie group constrained extended Kalman filter to estimate lower limb kinematics using IMU and inter-IMU distance measurements in a reduced sensor count configuration. The algorithm iterates through the prediction (kinematic equations), measurement (pelvis height assumption/inter-IMU distance measurements, zero velocity update for feet/ankles, flat-floor assumption for feet/ankles, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints). The knee and hip joint angle root-mean-square errors in the sagittal plane for straight walking were and , respectively, while the correlation coefficients were and , respectively. Furthermore, experiments using simulated inter-IMU distance measurements show that performance improved substantially for dynamic movements, even at large noise levels ( m). However, further validation is recommended with actual distance measurement sensors, such as ultra-wideband ranging sensors.

Estimating Lower Limb Kinematics using Distance Measurements with a Reduced Wearable Inertial Sensor Count.

This paper presents an algorithm that makes novel use of distance measurements alongside a constrained Kalman filter to accurately estimate pelvis, thigh, and shank kinematics for both legs during walking and other body movements using only three wearable inertial measurement units (IMUs). The distance measurement formulation also assumes hinge knee joint and constant body segment length, helping produce estimates that are near or in the constraint space for better estimator stability. Simulated experiments have shown that inter-IMU distance measurement is indeed a promising new source of information to improve the pose estimation of inertial motion capture systems under a reduced sensor count configuration. Furthermore, experiments show that performance improved dramatically for dynamic movements even at high noise levels (e.g., σdist = 0.2 m), and that acceptable performance for normal walking was achieved at σdist = 0.1 m. Nevertheless, further validation is recommended using actual distance measurement sensors.

A Method for Sensor Reduction in a Supervised Machine Learning Classification System

Smart devices employing interconnected sensors for feedback and control are being rapidly adopted. Many useful applications for these devices are in markets that demand cost-conscious solutions. Traditional machine-learning-based control systems often rely on multiple measurements from many sensors to achieve performance targets. An alternative method is presented that leverages a time-series output produced by a single sensor. By using domain expert knowledge, the time-series output is discretized into finite intervals that correspond to the physical events occurring in the system. Statistical measures are taken across these intervals to serve as the features to the machine learning system. Additional features that decouple key physical metrics are identified, improving the performance of the system. This novel approach requires a more modest dataset and does not compromise performance. The resulting development effort is significantly more cost-effective than traditional sensor classification systems, not only due to the reduced sensor count, but also due to a significantly simplified and more robust algorithm development and testing step. Results are presented with the case study of a media-type classification system within a printing system, which was deployed to the field as a commercial product.

Dual-Loop Digital Control of a Three-Phase Power Supply Unit With Reduced Sensor Count

Widespread deployment of voltage source inverter units in high penetration applications such as distributed generation and stand-alone renewable energy conditioning imposes stringent demands on reliability and output voltage regulation. Reported literature provides numerous solutions for output voltage regulation, which includes twin aspects of command tracking and output impedance control. Most of these methods rely on the measurement of at least two separate variables per phase, which compromises overall reliability. This paper focuses on a dual-loop voltage regulation approach requiring only output voltage measurement. The outer loop of a control scheme is used to assure prompt tracking of the inverter output voltage, whereas the inner derivative feedback loop improves system stability by damping the output filter resonance. Digital realization of this derivative term is generally a challenge with many methods currently developed for resolving it. These methods are, however, still facing drawbacks, which have inclusively been explained in this paper. Subsequently, a new digital derivative is proposed whose performance is predicted analytically and then validated through experimental results with a 7.5-kVA inverter prototype.

FPGA-Based Fast Detection With Reduced Sensor Count for a Fault-Tolerant Three-Phase Converter

Fast fault detection (FD) and reconfiguration is necessary for fault tolerant power electronic converters in safety critical applications to prevent further damage and to make the continuity of service possible. The aim of this study is to minimize the number of the used additional voltage sensors in a fault tolerant three-phase converter. In this paper, first a practical implementation of a very fast FD scheme with reduced sensor number is discussed. Then, an optimization in this scheme is also presented to decrease the detection time. For FD, special time and voltage criterion are applied to observe the error in the estimated phase-to-phase voltages for a specific period of time. The proposed optimization is based on the fact that following a detectable fault, two line-to-line voltages will deviate from their respective estimated values. Fault detection is studied for a three-leg two-level fault tolerant converter. Control and FD systems are implemented on a single field-programmable gate array. First, hardware in the loop experiments are carried out to evaluate the implemented schemes. Then, fully experimental tests are performed. The results confirm good performance of the proposed detection schemes, the digital controller and the fault tolerant structure. It is shown that such methods can detect and locate a fault in a few tens of microseconds. In certain cases the optimized scheme can be faster up to 50%, and in the other cases they have the same detection time.

A multi-band shunt hybrid active filter with reduced sensor count

A Shunt Hybrid Active Filter (SHAF) is an attractive option for realizing low-cost harmonic compensation solutions. This paper proposes a SHAF with multiple harmonic compensation capability using a single Voltage Source Inverter and reduced sensor count. This strategy is apt for harmonic filtering solutions where low cost is the exclusive priority. In this paper, a new estimation approach is proposed to obviate requirement of a large number of sensors. Multiple Synchronous Reference Frames (MSRF) and low pass filters are used to measure 5th and 7th harmonic components separately from load as well as filter currents. Individual current controllers are designed for the 5th and 7th harmonic currents. Control is realized in the synchronously rotating, orthogonal (dq) reference frame. Performance of the controller is validated through simulation, using realistic plant and controller models. Experimental results are provided to corroborate the analytical and simulation results.