The design of a navigation guidance and control system for an unmanned surface vehicle for environmental monitoring

Maintaining the ecosystem is one of the main concerns in this modern age. With the fear of ever-increasing global warming, the UK is one of the key players to participate actively in taking measures to slow down at least its phenomenal rate. As an ingredient to this process, the Springer vehicle was designed and developed for environmental monitoring and pollutant tracking. This special issue paper highlighted the Springer hardware and software architecture including various navigational sensors, a speed controller, and an environmental monitoring unit. In addition, details regarding the modelling of the vessel were outlined based mainly on experimental data. The formulation of a fault tolerant multi-sensor data fusion technique was also presented. Moreover, control strategy based on a linear quadratic Gaussian controller was developed and simulated on the Springer model.
Gaussian controller is developed and simulated on the Springer model.

Characterization of melanoidins from a glucose-glycine model system

The properties of melanoidins prepared from glucose and glycine (GG) were investigated by a three step purification protocol consisting of dialysis, gel filtration at high ionic strength and ion metal affinity chromatography. The high molecular weight fraction obtained in the GG system is responsible for 80% of the total brown colour and its antioxidative ability was about 1/4 of that of Trolox measured by the inhibition of linoleic acid oxidation. GG melanoidins have good affinity towards Cu (II) (32% bound to the resin) while it is much lower towards Pb (II) (10%) and Fe (II) (5%). Capillary zone electrophoresis analysis suggests that GG melanoidins are positively charged, although no signal was observed analysing melanoidins by matrix-assisted laser desorption-ionisation time-of-flight mass spectrometry (MALDI-TOF/MS).

Measurement-based Method for Wind Farm Power System Oscillations Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This study presents a measurement-based method for the early detection of power system oscillations, with consideration of mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet-based support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in frequency bands, whereas the SVDD method is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude, or that are resonant, can be alarmed to the system operator, to reduce the risk of system instability. The proposed method is exemplified using measured data from a chosen wind farm site.

An Integrated Sensing System for Monitoring the Corrosion Activity in Concrete Blocks Exposed at Hangzhou Bay Bridge

This paper discusses the importance of integrated sensing systems comprising techniques that give different types of data from a structure exposed to the marine environment so that its service life could reliably be predicted. For this purpose, a novel sensor combination was designed and installed in concrete panels which were exposed to Hangzhou Bay Bridge in China. The integrated sensor probe was used to monitor the cover concrete as well as the reinforcement. The sensor probes were connected to a monitoring station, which enabled access and control of the data remotely from Belfast, UK. The initial data obtained from the monitoring station gives interesting information on the early age properties of concrete and distinct variations in these properties with different types of concrete. This paper also reports the variation in electrical properties of different concrete samples and environmental data in response to the marine exposure condition at Hangzhou bay bridge.

Measurement-based Method for Wind Farm Power System Oscillation Monitoring

The global increase in the penetration of renewable energy is pushing electrical power systems into uncharted territory, especially in terms of transient and dynamic stability. In particular, the greater penetration of wind generation in European power networks is, at times, displacing a significant capacity of conventional synchronous generation with fixed-speed induction generation and now more commonly, doubly-fed induction generators. The impact of such changes in the generation mix requires careful monitoring to assess the impact on transient and dynamic stability. This paper presents a measurement based method for the early detection of power system oscillations, with attention to mode damping, in order to raise alarms and develop strategies to actively improve power system dynamic stability and security. A method is developed based on wavelet transform and support vector data description (SVDD) to detect oscillation modes in wind farm output power, which may excite dynamic instabilities in the wider system. The wavelet transform is used as a filter to identify oscillations in different frequency bands, while SVDD is used to extract dominant features from different scales and generate an assessment boundary according to the extracted features. Poorly damped oscillations of a large magnitude or that are resonant can be alarmed to the system operator, to reduce the risk of system instability. Method evaluation is exemplified used real data from a chosen wind farm.

Commissioning and Evaluation of a Fiber-Optic Sensor System for Bridge Monitoring

This paper describes the design, commissioning, and evaluation of a ?ber-optic strain sensor system for the structural health monitoring of a prestressed concrete posttensioned box girder railway bridge in Mumbai, India, which shows a number of well-documented structural problems. Preliminary laboratory trials to design the most appropriate sensor system that could be readily transported and used on site are described, followed by a description of load tests on the actual bridge undertaken in collaboration with Indian Railways and using locomotives of known weight. Results from the load tests using the optical system are compared with similar results obtained using electrical resistance strain gages. Conclusions are summarized concerning the integrity of the structure and for the future use of the sensor system for monitoring bridges of this type. Crack width measurements obtained during the load tests are also described.

The performance of a cathodic protection system in reinforced concrete structure: Monitoring and Service Life Modelling

The use of cathodic protection in reinforced concrete is becoming increasingly common with such systems being installed on a number of structures throughout the United Kingdom and Ireland. However the prescribed design lives (or service life) of each cathodic protection system vary widely. The aim of this project was to assess the effectiveness of a sacrificial anode cathodic protection system and to predict its design life through a series of laboratory based experiments. The experimental plan involved casting a number of slabs which represented a common road bridge structure. The corrosion of the steel within the experimental slabs was then accelerated prior to installation of a cathodic protection system. During the experiment corrosion potential of the steel reinforcement was monitored using half-cell measurement. Additionally the current flow between the cathodic protection system and the steel reinforcement was recorded to assess the degree of protection. A combination of theoretical calculations and experimental results were then collated to determine the design life of this cathodic protection system. It can be concluded that this sacrificial anode based cathodic protection system was effective in halting the corrosion of steel reinforcement in the concrete slabs studied. Both the corrosion current and half-cell potentials indicated a change in passivity for the steel reinforcement once sacrificial anodes were introduced. The corrosion current was observed to be sensitive to the changes to the exposure environment. Based on the experimental variables studied the design life of this sacrificial anode can be taken as 26 to 30 years.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.