Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy

Distributed State Estimation With Phasor Measurement Units (Pmu) For Power Systems

Wide-area monitoring for the power system is a key tool for preventing the power system from system wide failure. State Estimation (SE) is an essential and practical monitoring tool that has been widely used to provide estimated values for each quantity within energy management systems (EMS) in the control center. However, monitoring larger power systems coordinated by regional transmission operators has placed an enormous operational burden on current SE techniques. A distributed state estimation (DSE) algorithm with a hierarchical structure designed for the power system industry is much more computationally efficient and robust especially for monitoring a wide-area power system. Moreover, considering the deregulation of the power system industry, this method does not require sensitive data exchange between smaller areas that may be competing entities. The use of phasor measurement units (PMUs) in the SE algorithm has proven to improve the performance in terms of accuracy and converging speed. Being able to synchronize the measurements between different areas, PMUs are perfectly suited for distributed state estimation. This dissertation investigates the benefits of the DSE using PMU over a serial state estimator in wide area monitoring. A new method has been developed using available PMU data to calculate the reference angle differences between decomposed power systems in various situations, such as when the specific PMU data of the global slack bus cannot be obtained. The algorithms were tested on six bus, I standard 30 bus and I 118-bus test cases. The proposed distributed state estimator has also been implemented in a test bed to work with a power system real-time digital simulator (RTDS) that simulates the physical power system. PMUs made by SEL and GE are used to provide real-time inputs to the distributed state estimator. Simulation results demonstrated the benefits of the PMU and distributed SE techniques. Additionally a constructed test bed verified and validated the proposed algorithms and can be used for different smart grid tests

A Method Based on Weighted Least Squares for Estimating Voltage of Distribution Network System Integrated with Distributed Generations Using Remote Measurement Data

Voltage quality of a distribution network system is one of the most important issues. To evaluate the voltage in real-time, this paper proposes a method of voltage estimation in distribution network systems which are integrated with distributed generations (DGs). In the proposed method, the weighted least squares (WLS) method is used to optimize the objective function of the error measurement which is built by the network configuration, real-time and pseudo measurements at the main substation and DG buses. The proposed method is verified by simulation case studies of the 17-bus radial distribution network integrated with DGs including a solar power plant and two wind power plants. The simulation results in this work confirm that the proposed method is high accuracy for voltage estimation

Data Quality Management in Large-Scale Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are cross-domain, multi-model, advance information systems that play a significant role in many large-scale infrastructure sectors of smart cities public services such as traffic control, smart transportation control, and environmental and noise monitoring systems. Such systems, typically, involve a substantial number of sensor nodes and other devices that stream and exchange data in real-time and usually are deployed in uncontrolled, broad environments. Thus, unexpected measurements may occur due to several internal and external factors, including noise, communication errors, and hardware failures, which may compromise these systems quality of data and raise serious concerns related to safety, reliability, performance, and security. In all cases, these unexpected measurements need to be carefully interpreted and managed based on domain knowledge and computational models. Therefore, in this research, data quality challenges were investigated, and a comprehensive, proof of concept, data quality management system was developed to tackle unaddressed data quality challenges in large-scale CPSs. The data quality management system was designed to address data quality challenges associated with detecting: sensor nodes measurement errors, sensor nodes hardware failures, and mismatches in sensor nodes spatial and temporal contextual attributes. Detecting sensor nodes measurement errors associated with the primary data quality dimensions of accuracy, timeliness, completeness, and consistency in large-scale CPSs were investigated using predictive and anomaly analysis models via utilising statistical and machine-learning techniques. Time-series clustering techniques were investigated as a feasible mean for detecting long-segmental outliers as an indicator of sensor nodes’ continuous halting and incipient hardware failures. Furthermore, the quality of the spatial and temporal contextual attributes of sensor nodes observations was investigated using timestamp analysis techniques. The different components of the data quality management system were tested and calibrated using benchmark time-series collected from a high-quality, temperature sensor network deployed at the University of East London. Furthermore, the effectiveness of the proposed data quality management system was evaluated using a real-world, large-scale environmental monitoring network consisting of more than 200 temperature sensor nodes distributed around London. The data quality management system achieved high accuracy detection rate using LSTM predictive analysis technique and anomaly detection associated with DBSCAN. It successfully identified timeliness and completeness errors in sensor nodes’ measurements using periodicity analysis combined with a rule engine. It achieved up to 100% accuracy in detecting potentially failed sensor nodes using the characteristic-based time-series clustering technique when applied to two days or longer time-series window. Timestamp analysis was adopted effectively for evaluating the quality of temporal and spatial contextual attributes of sensor nodes observations, but only within CPS applications in which using gateway modules is possible

Multi-agent system for monitoring temperature in sensing surfaces including hard and soft sensors

In the digital transformation era, the collection of data assumes a crucial relevance. In some applications, the use of real sensors to measure the target parameters is constrained by technical or economical limitations. In such situations, it is required to use alternative techniques based on soft sensors that acquire data by estimating the measurement of a variable through the correlation of the data acquired by the neighbouring sensors. However, the co-existence of real and soft sensors requires a computational infra-structure that integrates these heterogeneous data sources and supports the synchronisation of the monitoring system based on the inputs of different measurement nodes. Multi-agent systems provide this distributed infra-structure for the data collection, ensuring modularity, scalability and reconfigurability capabilities. This paper introduces a multi-agent system approach to create a modular and scalable sensing system, based on a diversity of real and soft sensors, to support the monitoring of temperature in thin-film sensing surfaces. The proposed approach was experimentally tested in a plastic injection process, presenting promising results in terms of accuracy and response time, and allowing to obtain more sampling points through the use of computational techniques to complement the real data.The work reported in this paper was supported by ONSURF - Mobilizar Competências Tecnológicas em Engenharia de Superfícies, Projeto nº POCI-01-0247-FEDER-024521.info:eu-repo/semantics/publishedVersio

Computer Vision Based Robotic Polishing Using Artificial Neural Networks

Polishing is a highly skilled manufacturing process with a lot of constraints and interaction with environment. In general, the purpose of polishing is to get the uniform surface roughness distributed evenly throughout part’s surface. In order to reduce the polishing time and cope with the shortage of skilled workers, robotic polishing technology has been investigated. This paper studies about vision system to measure surface defects that have been characterized to some level of surface roughness. The surface defects data have learned using artificial neural networks to give a decision in order to move the actuator of arm robot. Force and rotation time have chosen as output parameters of artificial neural networks. Results shows that although there is a considerable change in both parameter values acquired from vision data compared to real data, it is still possible to obtain surface defects characterization using vision sensor to a certain limit of accuracy. The overall results of this research would encourage further developments in this area to achieve robust computer vision based surface measurement systems for industrial robotic, especially in polishing proces

Data Analytics and Wide-Area Visualization Associated with Power Systems Using Phasor Measurements

As power system research becomes more data-driven, this study presents a framework for the analysis and visualization of phasor measurement unit (PMU) data obtained from large, interconnected systems. The proposed framework has been implemented in three steps: (a) large-scale, synthetic PMU data generation: conducted to generate research-based measurements with the inclusion of features associated with industry-grade PMU data; (b) error and event detection: conducted to assess risk levels and data accuracy of phasor measurements, and furthermore search for system events or disturbances; (c) oscillation mode visualization: conducted to present wide-area, modal information associated with large-scale power grids. To address the challenges due to real data confidentiality, the creation of realistic, synthetic PMU measurements is proposed for research use. First, data error propagation models are generated after a study of some of the issues associated with the unique time-synchronization feature of PMUs. An analysis of some of the features of real PMU data is performed to extract some of the statistics associated with data errors. Afterwards, an approach which leverages on existing, large-scale, synthetic networks to model the constantly-changing dynamics often observed in real measurements is used to generate an initial synthetic dataset. Further inclusion of PMU-related data anomalies ensures the production of realistic, synthetic measurements fit for research purposes. An application of different techniques based on a moving-window approach is suggested for use in the detection of events in real and synthetic PMU measurements. These fast methods rely on smaller time-windows to assess fewer measurement samples for events, classify disturbances into global or local events, and detect unreliable measurement sources. For large-scale power grids with complex dynamics, a distributed error analysis is proposed for the isolation of local dynamics prior any reliability assessment of PMU-obtained measurements. Finally, fundamental system dynamics which are inherent in complex, interconnected power systems are made apparent through a wide-area visualization of large-scale, electric grid oscillation modes. The approach ensures a holistic interpretation of modal information given that large amounts of modal data are often generated in these complex systems irrespective of the technique that is used

The Comparison Study Among Optimization Techniques In Optimizing A Distribution System State Estimation

State estimation considered the main core of the Energy Management System and plays an important role in stability analysis, control and monitoring of electric power systems. The state estimator actually depends on many factors, such as data sensitive regarding the sensors accuracy, the availability of raw data, the network database accuracy, and the time skew of data. Many researchers already been studied multi-area power system state estimation and most of them investigation of state estimation schemes including different state estimators for each a central coordinator and control area. Therefore, accurate and timely efficient state estimation algorithm is a prerequisite for a stable operation of modern power grids. This thesis introduce an intelligent decentralized State Estimation method based on Firefly algorithm for distribution power systems. The mathematical procedure of distribution system state estimation which utilizing the information collected from available measurement devices in real-time. A consensus based static state estimation strategy for radial power distribution systems is proposed in this research. This thesis concentrates on the balanced systems. There are buses acting as agents using which we can evaluate the local estimates of the entire system. Therefore each measurement model reduces to an underdetermined nonlinear system and in radial distribution systems, the state elements associated with an agent may overlap with neighboring agents. The states of these systems are first estimated through centralized approach using the proposed algorithm to compare with weighted least squares technique. At the end, the result will presented the application of the developed approach to a network based on IEEE 13 bus, 14 bus and 33 bus test System. The result a proved to be computational efficient and accurately evaluated the impact of distributed generation on the power system. From the result, it can observe that for decentralized is faster and less error for both WLS and FA. In addition, FA show faster and less error than WLS for both centralized and decentralized. In addition, the proposed FA show faster with increasing the number of buses