Adaptive backstepping droop controller design for multi-terminal high-voltage direct current systems

Wind power is one of the most developed renewable energy resources worldwide. To integrate offshore wind farms to onshore grids, the high-voltage direct current (HVDC) transmission cables interfaced with voltage source converters (VSCs) are considered to be a better solution than conventional approaches. Proper DC voltage indicates successive power transfer. To connect more than one onshore grid, the DC voltage droop control is one of the most popular methods to share the control burden between different terminals. However, the challenges are that small droop gains will cause voltage deviations, while higher droop gain settings will cause large oscillations. This study aims to enhance the performance of the traditional droop controller by considering the DC cable dynamics. Based on the backstepping control concept, DC cables are modelled with a series of capacitors and inductors. The final droop control law is deduced step-by-step from the original remote side. At each step the control error from the previous step is considered. Simulation results show that both the voltage deviations and oscillations can be effectively reduced using the proposed method. Further, power sharing between different terminals can be effectively simplified such that it correlates linearly with the droop gains, thus enabling simple yet accurate system operation and control.

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.

Secure data networks for electrical distribution applications

Smart Grids are characterized by the application of information communication technology (ICT) to solve electrical energy challenges. Electric power networks span large geographical areas, thus a necessary component of many Smart Grid applications is a wide area network (WAN). For the Smart Grid to be successful, utilities must be confident that the communications infrastructure is secure. This paper describes how a WAN can be deployed using WiMAX radio technology to provide high bandwidth communications to areas not commonly served by utility communications, such as generators embedded in the distribution network. A planning exercise is described, using Northern Ireland as a case study. The suitability of the technology for real-time applications is assessed using experimentally obtained latency data.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

The Size Distribution of US Banks and Credit Unions

This study examines the firm size distribution of US banks and credit unions. A truncated lognormal distribution describes the size distribution, measured using assets data, of a large population of small, community-based commercial banks. The size distribution of a smaller but increasingly dominant cohort of large banks, which operate a high-volume low-cost retail banking model, exhibits power-law behaviour. There is a progressive increase in skewness over time, and Zipf’s Law is rejected as a descriptor of the size distribution in the upper tail. By contrast, the asset size distribution of the population of credit unions conforms closely to the lognormal distribution.

Long-Term Economic Model for Allocation of FACTS Devices in Restructured Power System Integrated Wind Generation

This study proposes an approach to optimally allocate multiple types of flexible AC transmission system (FACTS) devices in market-based power systems with wind generation. The main objective is to maximise profit by minimising device investment cost, and the system's operating cost considering both normal conditions and possible contingencies. The proposed method accurately evaluates the long-term costs and benefits gained by FACTS devices (FDs) installation to solve a large-scale optimisation problem. The objective implies maximising social welfare as well as minimising compensations paid for generation re-scheduling and load shedding. Many technical operation constraints and uncertainties are included in problem formulation. The overall problem is solved using both particle swarm optimisations for attaining optimal FDs allocation as main problem and optimal power flow as sub-optimisation problem. The effectiveness of the proposed approach is demonstrated on modified IEEE 14-bus test system and IEEE 118-bus test system.

Analysis of the Second Harmonic Effect on Power Amplifier Intermodulation Products

A theoretical analysis is reported in this paper to investigate the effect that a second harmonic signal which might be present at an amplifier’s input has on generating additional intermodulation products, particularly the third-order intermodulation (IM3) products. The analysis shows that the amplitude of an extra generated IM3 component is equal to the product of the fundamental amplitude, the second harmonic amplitude, and the second order Taylor series coefficient. The effect of the second order harmonic on the IM3 is examined through a simulated example of a 2.22-GHz 10-W Class-EF amplifier whereby the IM3 levels have been reduced by 2-3 dB after employing a second harmonic termination stub at the input.

Distribution and visual impact of postoperative refractive error after cataract surgery in rural China: study of cataract outcomes and up-take of services report 4.

PURPOSE: To model the possible impact of using average-power intraocular lenses (IOLs) and evaluate the postoperative refractive error in patients having cataract surgery in rural China.SETTING: Rural Guangdong, China.METHODS: Patients having cataract surgery by local surgeons were examined and visual function was assessed 10 to 14 months after surgery. Subjective refraction at near and distance was performed bilaterally by an ophthalmologist. Patients had a target refraction of -0.50 diopter (D) based on ocular biometry.RESULTS: Of the 313 eligible patients, 242 (77%) could be contacted and 176 (74% of contacted patients, 56% overall) were examined. Examined patients had a mean age of 69.4 +/- 10.5 years. Of the 211 operated eyes, 73.2% were within +/-1.0 D of the target refraction after surgery. The best presenting distance vision was in patients within +/-1.0 D of plano and the best presenting near vision, in those with mild myopia (<-1.0 D to > or =2.0 D) (P= .005). However, patients with hyperopia (>+1.0 D) reported significantly better adjusted visual function than those with emmetropia or myopia (<-1.0 D). When the predicted use of an average-power IOL (median +21.5 D) was modeled, predicted visual acuity was significantly reduced (P= .001); however, predicted visual function was not significantly altered (P>.3).CONCLUSIONS: Accurate selection of postoperative refractive error was achieved by local surgeons in this rural area. Based on visual function results, aiming for mild postoperative myopia may not be suitable in this setting. Implanting average-power IOLs significantly reduced postoperative presenting vision, but not visual function.

Parallel Time Series Analysis and Income Inequality: A New Approach to Comparative Case Studies

This case study deals with the role of time series analysis in sociology, and its relationship with the wider literature and methodology of comparative case study research. Time series analysis is now well-represented in top-ranked sociology journals, often in the form of ‘pooled time series’ research designs. These studies typically pool multiple countries together into a pooled time series cross-section panel, in order to provide a larger sample for more robust and comprehensive analysis. This approach is well suited to exploring trans-national phenomena, and for elaborating useful macro-level theories specific to social structures, national policies, and long-term historical processes. It is less suited however, to understanding how these global social processes work in different countries. As such, the complexities of individual countries - which often display very different or contradictory dynamics than those suggested in pooled studies – are subsumed. Meanwhile, a robust literature on comparative case-based methods exists in the social sciences, where researchers focus on differences between cases, and the complex ways in which they co-evolve or diverge over time. A good example of this is the inequality literature, where although panel studies suggest a general trend of rising inequality driven by the weakening power of labour, marketisation of welfare, and the rising power of capital, some countries have still managed to remain resilient. This case study takes a closer look at what can be learned by applying the insights of case-based comparative research to the method of time series analysis. Taking international income inequality as its point of departure, it argues that we have much to learn about the viability of different combinations of policy options by examining how they work in different countries over time. By taking representative cases from different welfare systems (liberal, social democratic, corporatist, or antipodean), we can better sharpen our theories of how policies can be more specifically engineered to offset rising inequality. This involves a fundamental realignment of the strategy of time series analysis, grounding it instead in a qualitative appreciation of the historical context of cases, as a basis for comparing effects between different countries.

Optimal alarms systems and its application to financial time series

This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.

Integer-valued APARCH processes in the analysis of time series of counts

The Asymmetric Power Arch representation for the volatility was introduced by Ding et al.(1993) in order to account for asymmetric responses in the volatility in the analysis of continuous-valued financial time series like, for instance, the log-return series of foreign exchange rates, stock indices or share prices. As reported by Brannas and Quoreshi (2010), asymmetric responses in volatility are also observed in time series of counts such as the number of intra-day transactions in stocks. In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric overdispersion. Basic probabilistic and statistical properties are summarized and parameter estimation is discussed. A simulation study is presented to illustrate the proposed model. Finally, an empirical application to a set of data concerning the daily number of stock transactions is also presented to attest for its practical applicability in data analysis.

Analysis of annuli in otoliths, age distribution and growth rates of the Namibian hake (Merluccius capensis)

Dissertação mest., Gestão da Água e da Costa, Universidade do Algarve, 2007

Structure, tissue distribution and estrogen regulation of splice variants of the sea bream estrogen receptor alpha gene

Estrogen actions are mainly mediated by specific nuclear estrogen receptors (ERs), for which different genes and a diversity of transcript variants have been identified, mainly in mammals. In this study, we investigated the presence of ER splice variants in the teleost fish gilthead sea bream (Sparus auratus), by comparison with the genomic organization of the related species Takifugu rubripes. Two exon2-deleted ERα transcript variants were isolated from liver cDNA of estradiol-treated fish. The ΔE2 variant lacks ERα exon 2, generating a premature termination codon and a putative C-terminal truncated receptor, while the ΔE2,3* variant contains an in-frame deletion of exon 2 and part of exon 3 and codes for a putative ERα protein variant lacking most of the DNA-binding domain. Both variants were expressed at very low levels in several female and male sea bream tissues, and their expression was highly inducible in liver by estradiol-17β treatment with a strong positive correlation with the typical wild-type (wt) ERα response in this tissue. These findings identify novel estrogen responsive splice variants of fish ERα, and provide the basis for future studies to investigate possible modulation of wt-ER actions by splice variants.

Five gonadotrophin-releasing hormone receptors in a teleost fish: isolation, tissue distribution and phylogenetic relationships

Gonadotrophin-releasing hormone (GnRH) is the main neurohormone controlling gonadotrophin release in all vertebrates, and in teleost fish also of growth hormone and possibly of other adenohypophyseal hormones. Over 20 GnRHs have been identified in vertebrates and protochoordates and shown to bind cognate G-protein couple receptors (GnRHR). We have searched the puffer fish, Fugu rubripes, genome sequencing database, identified five GnRHR genes and proceeded to isolate the corresponding complementary DNAs in European sea bass, Dicentrachus labrax. Phylogenetic analysis clusters the European sea bass, puffer fish and all other vertebrate receptors into two main lineages corresponding to the mammalian type I and II receptors. The fish receptors could be subdivided in two GnRHR1 (A and B) and three GnRHR2 (A, B and C) subtypes. Amino acid sequence identity within receptor subtypes varies between 70 and 90% but only 50–55% among the two main lineages in fish. All European sea bass receptor mRNAs are expressed in the anterior and mid brain, and all but one are expressed in the pituitary gland. There is differential expression of the receptors in peripheral tissues related to reproduction (gonads), chemical senses (eye and olfactory epithelium) and osmoregulation (kidney and gill). This is the first report showing five GnRH receptors in a vertebrate species and the gene expression patterns support the concept that GnRH and GnRHRs play highly diverse functional roles in the regulation of cellular functions, besides the ‘‘classical’’ role of pituitary function regulation.