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.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

The determinants of performance appraisal systems: A note (Do Brown and Heywood's results for Australia hold up for Britain?)

This article offers a replication for Britain of Brown and Heywood's analysis of the determinants of performance appraisal in Australia. Although there are some important limiting differences between our two datasets - the Australia Workplace Industrial Relations Survey (AWIRS) and the Workplace Employment Relations Survey (WERS) - we reach one central point of agreement and one intriguing shared insight. First, performance appraisal is negatively associated with tenure: where employers cannot rely on the carrot of deferred pay or the stick of dismissal to motivate workers, they will tend to rely more on monitoring, ceteris paribus. Second, employer monitoring and performance pay may be complementary. However, consonant with the disparate results from the wider literature, there is more modest agreement on the contribution of specific human resource management practices, and still less on the role of job control.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

Low-power radio telemetry: the potential for remote patient monitoring

Radio-based signalling devices will play an important role in future generations of remote patient monitoring equipment, both at home and in hospital. Ultimately, it will be possible to sample vital signs frompatients, whatever their location and without them necessarily being aware that a measurement is being taken. This paper reviews currentmethods for the transmission by radio of physiological parameters over ranges of 0.3, 3 and 30 m, and describes the radiofrequency hardware required and the carrier frequencies commonly used. Future developments, including full duplex systems and the use of more advanced modulation schemes, are described. The paper concludeswith a case studyof a humantemperature telemeter built to indicateovulation. Clinical results clearly show the advantage to be had in adopting radio biotelemetry in this instance.

Developments in Performance Monitoring of Concrete Exposed to Extreme Environments

The performance of the surface zone of concrete is acknowledged as a major factor governing the rate of deterioration of reinforced concrete structures as it provides the only barrier to the ingress of water containing dissolved ionic species such as chlorides which, ultimately, initiate corrosion of the reinforcement. In-situ monitoring of cover-zone concrete is therefore critical in attempting to make realistic predictions as to the in-service performance of the structure. To this end, this paper presents developments in a remote interrogation system to allow continuous, real-time monitoring of the cover-zone concrete from an office setting. Use is made of a multi-electrode array embedded within cover-zone concrete to acquire discretized electrical resistivity and temperature measurements, with both parameters monitored spatially and temporally. On-site instrumentation, which allows remote interrogation of concrete samples placed at a marine exposure site, is detailed, together with data handling and processing procedures. Site-measurements highlight the influence of temperature on electrical resistivity and an Arrhenius-based temperature correction protocol is developed using on-site measurements to standardize resistivity data to a reference temperature; this is an advancement over the use of laboratory-based procedures. The testing methodology and interrogation system represents a robust, low-cost and high-value technique which could be deployed for intelligent monitoring of reinforced concrete structures.

Low flow water quality in rivers; septic tank systems and high-resolution phosphorus signals

Rural point sources of phosphorus (P), including septic tank systems, provide a small part of the overall phosphorus budget to surface waters in agricultural catchments but can have a disproportionate impact on the low flow P concentration of receiving rivers. This has particular importance as the discharges are approximately constant into receiving waters and these have restricted dilution capacity during ecologically sensitive summer periods. In this study, a number of identified high impact septic systems were replaced with modern sequential batch reactors in three rural catchments during a monitoring period of 4 years. Sub-hourly P monitoring was conducted using bankside-analysers. Results show that strategic replacement of defective septic tank systems with modern systems and polishing filters decreased the low flow P concentration of one catchment stream by 0.032 mg TP L- 1 (0.018 mg TRP L- 1) over the 4 years. However two of the catchment mitigation efforts were offset by continued new-builds that increased the density of septic systems from 3.4 km- 2 to 4.6 km- 2 and 13.8 km- 2 to 17.2 km- 2 and subsequently increased low flow P concentrations. Future considerations for septic system mitigation should include catchment carrying capacity as well as technology changes.

Impacts of sewage outfalls on rocky shores: incorporating scale, biotic assemblage structure and variability into monitoring tools.

Coastal systems, such as rocky shores, are among the most heavily anthropogenically-impacted marine ecosystems and are also among the most productive in terms of ecosystem functioning. One of the greatest impacts on coastal ecosystems is nutrient enrichment from human activities such as agricultural run-off and discharge of sewage. The aim of this study was to identify and characterise potential effects of sewage discharges on the biotic diversity of rocky shores and to test current tools for assessing the ecological status of rocky shores in line with the EU Water Framework Directive (WFD). A sampling strategy was designed to test for effects of sewage outfalls on rocky shore assemblages on the east coast of Ireland and to identify the scale of the putative impact. In addition, a separate sampling programme based on the Reduced algal Species List (RSL), the current WFD monitoring tool for rocky shores in Ireland and the UK, was also completed by identifying algae and measuring percent cover in replicate samples on rocky shores during Summer. There was no detectable effect of sewage outfalls on benthic taxon diversity or assemblage structure. However, spatial variability of assemblages was greater at sites proximal or adjacent to sewage outfalls compared to shores without sewage outfalls present. Results based on the RSL, show that algal assemblages were not affected by the presence of sewage outfalls, except when classed into functional groups when variability was greater at the sites with sewage outfalls. A key finding of both surveys, was the prevalence of spatial and temporal variation of assemblages. It is recommended that future metrics of ecological status are based on quantified sampling designs, incorporate changes in variability of assemblages (indicative of community stability), consider shifts in assemblage structure and include both benthic fauna and flora to assess the status of rocky shores.

Quantifying contributions of domestic wastewater treatment systems to nutrient fluxes in stream headwaters

Effective implementation of the Water Framework Directive requires a reappraisal of conventional approaches to water quality monitoring. Quantifying the impact of domestic wastewater treatment systems (DWWTS) in Irish catchments is further complicated by high levels of natural heterogeneity. This paper presents a numerical model that couples attenuation to flow along different hydrological pathways contributing to river discharge; this permits estimation of the impact of DWWTS to overall nutrient fluxes under a range of geological conditions. Preliminary results suggest high levels of attenuation experienced
before DWWTS effluent reaches bedrock play a significant role in reducing its ecological impact on aquatic receptors. Conversely, low levels of attenuation in systems discharging directly to surface water may affect water quality more significantly, particularly during prolonged dry periods in areas underlain by low productivity aquifers (>60% of Ireland), where dilution capacity is limited.

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.

Intrusion Detection System for Network Security in Synchrophasor Systems

Synchrophasor systems will play a crucial role in next generation Smart Grid monitoring, protection and control. However these systems also introduce a multitude of potential vulnerabilities from malicious and inadvertent attacks, which may render erroneous operation or severe damage. This paper proposes a Synchrophasor Specific Intrusion Detection System (SSIDS) for malicious cyber attack and unintended misuse. The SSIDS comprises a heterogeneous whitelist and behavior-based approach to detect known attack types and unknown and so-called ‘zero-day’ vulnerabilities and attacks. The paper describes reconnaissance, Man-in-the-Middle (MITM) and Denial-of-Service (DoS) attack types executed against a practical synchrophasor system which are used to validate the real-time effectiveness of the proposed SSIDS cyber detection method.

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.