Improvement of Power Quality in Primary Distribution Systems Based on Static-Var Compensators

A flexible AC Transmission Systems (FACTS) is a new technology offers a fast and reliable control over the transmission and distribution parameters like voltage and phase angle between the sending end voltage and receiving end voltage. Distribution static synchronous compensator (DSTATCOM) is a second generation member of the (FACTS) controllers. Reactive power compensation is an important aspect in the control of distribution systems. Reactive current in addition to increasing the distribution system losses, introduces voltage drop at lead point and finally it causes poor power quality in power systems. Primary radial distribution feeders have high resistance to reactance ratio, which causes voltage drop and high power loss in radial distribution systems. providing high demanding power to entire load while maintaining voltage magnitude at acceptable range is one of the major system constraints, using of capacitor banks to improve the reactive power have not quite enough at high reactive power feeder, because it have more slow in step by step response and have not capable to generate continuously variable reactive power. In some primary distribution feeders at the state of Khartoum, it is observed that there are some appropriate drops in voltage and quality service at high reactive power loads although some individual capacitor banks have been connected at these feeders, from here we researched for a new technology to overcome this problem. A steady-state model of (DSTATCOM) is proposed and developed to compensate the reactive power by using a Voltage Source Converter (VSC) with Pulse Width Modulation (PWM) and cascade control of four direct proportional integral controllers (PI) in synchronous reference frame. The detailed modeling and control design of (DSTATCOM) with specific typical radial primary distribution feeders (industrial, commercial, residential) are presented and implemented along necessary mathematical model equations in the Matlab software. Simulation schemes of (DSTATCOM) are done with help of control block diagrams and stability analysis. Load flow is an important method for analysis, operation and planning studies of any power system in a steady-state condition. In this research backward forward sweeps load flow method (Kirchhoff’s Laws) has been proposed rather than Newton-Raphson and Fast decoupled methods because the distribution feeders have a high R/X ratio which make the systems are ill-conditioned iterations analysis. (DSTATCOM) was installed on specific typical radial feeders at the (DSTATCOM) which using for distribution lines and load compensation has been the subject of considerable interest beside it is a new technology for a good power quality solution. Keywords: Power System, Flexible AC Transmission Systems (FACTS) Devices, STATCOM, PI Controller DOI: 10.7176/ISDE/13-1-05 Publication date:May 31st 202

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks

Multi-Objective Optimization in Metabolomics/Computational Intelligence

The development of reliable computational models for detecting non-linear patterns encased in throughput datasets and characterizing them into phenotypic classes has been of particular interest and comprises dynamic studies in metabolomics and other disciplines that are encompassed within the omics science. Some of the clinical conditions that have been associated with these studies include metabotypes in cancer, in ammatory bowel disease (IBD), asthma, diabetes, traumatic brain injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few. The traction in this domain is attributable to the advancements in the procedures involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of a wide abundance of datasets. Throughput datasets generated by modern 1H NMR spectrometers are often characterized with features that are uninformative, redundant and inherently correlated. This renders it di cult for conventional multivariate analysis techniques to e ciently capture important signals and patterns. Therefore, the work covered in this research thesis provides novel alternative techniques to address the limitations of current analytical pipelines. This work delineates 13 variants of population-based nature inspired metaheuristic optimization algorithms which were further developed in this thesis as wrapper-based feature selection optimizers. The optimizers were then evaluated and benchmarked against each other through numerical experiments. Large-scale 1H NMR-linked datasets emerging from three disease studies were employed for the evaluations. The rst is a study in patients diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked by a distinctive facial appearance, learning disabilities, and gigantism culminating in tall stature and macrocephaly, also referred to as cerebral gigantism. Another study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative condition marked by intracellular accrual of cholesterol and complex lipids including sphingolipids and phospholipids in the endosomal/lysosomal system. The third study involved sore throat investigation in human (also known as `pharyngitis'); an acute infection of the upper respiratory tract that a ects the respiratory mucosa of the throat. In all three cases, samples from pathologically-con rmed cohorts with corresponding controls were acquired, and metabolomics investigations were performed using 1H NMR technique. Thereafter, computational optimizations were conducted on all three high-dimensional datasets that were generated from the disease studies outlined, so that key biomarkers and most e cient optimizers were identi ed in each study. The clinical and biochemical signi cance of the results arising from this work were discussed and highlighted

Modelling mechanisms of change in crop populations

Computer -based simulation models of changes occurring within crop populations when subjected to agents of phenotypic change, have been developed for use on commonly available personal computer equipment. As an underlying developmental principle, the models have been designed as general -case, mechanistic, stochastic models, in contrast to the predominantly empirically- derived, system -specific, deterministic (predictive) models currently available. A modelling methodology has evolved, to develop portable simulation models, written in high - level, general purpose code, allowing for use, modification and continued development by biologists with little requirement for computer programming expertise.The initial subject of these modelling activities was the simulation of the effects of selection and other agents of genetic change in crop populations, resulting in the computer model, PSELECT. Output from PSELECT, specifically phenotypic and genotypic response to phenotypic truncation selection, conformed to expectation, as defined by results from established analogue modelling work. Validation of the model by comparison of output with the results from an experimental -scale plant breeding exercise was less conclusive, and, owing to the fact that the genetic basis of the phenotypic characters used in the selection programme was insufficiently defined, the validation exercise provided only broad qualitative agreement with the model output. By virtue of the predominantly subjective nature of plant breeding programmes, the development of PSELECT resulted in a model of theoretical interest, but with little current practical application.Modelling techniques from the development of the PSELECT model were applied to the simulation of plant disease epidemics, where the modelled system is well characterised, and simulation modelling is an area of active research. The model SATSUMA, simulating the spatial and temporal development of diseases within crop populations, was developed. The model generates output which conforms to current epidemiological theory, and is compatible with contemporary methods of temporal and spatial analysis of crop disease epidemics. Temporal disease progress in the simulations was accurately described by variations of a generalised logistic model. Analysis of the spatial pattern of simulated epidemics by frequency distribution fitting or distance class methods was found to give good qualitative agreement with observed biological systems.The mechanistic nature of SATSUMA and its deliberate design as a general case model make it especially suitable for the investigation of component processes in a generalised plant disease epidemic, and valuable as an educational tool. Subject to validation against observational data, such models can be utilised as predictive tools by the incorporation of information (concerning crop species, pathogen etc.) specifically relevant to the modelled system. In addition to its educational use, SATSUMA has been used as research tool for the examination of the effect of spatial pattern of disease and disease incidence on the efficiency of sampling protocols and in parameterising a general theoretical model for describing the spatio -temporal development of plant diseases

Allocation of dump load in islanded microgrid using the mixed-integer distributed ant colony optimization with robust backward\forward sweep load flow

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReliable planning and operation of droop-controlled islanded microgrids (DCIMGs) is fundamental to expand microgrids (MGs) scalability and maximize renewable energy potential. Employing dump loads (DLs) is a promising solution to absorb excess generation during off-peak hours while keeping voltage and frequency within acceptable limits to meet international standards. Considering wind power and demand forecast uncertainties in DCIMG during off-peak hours, the allocation of DL problem was modelled as two problems, viz., deterministic and stochastic. The former problem was tackled using four highly probable deterministic generation and demand mismatch scenarios, while the latter problem was formulated within scenario based stochastic framework for uncertainty modelling. The mixed-integer distributed ant colony optimization (MIDACO) was introduced as a novel application in microgrids to find the optimal location and size of DL as well as the optimal droop setting for distributed generation (DG). Furthermore, to enhance the convergence of the proposed optimization technique, three robust and derivative free load flow methods were developed as novel extensions of the original backward\forward sweep (BFS) for grid-connected MGs. The three load flow methods are called special BFS, improved special BFS, and general BFS. The first two methods rely on one global voltage variable distributed among all DGs, while the latter has more general approach by adopting local voltage at each generating bus. The deterministic multi-objective optimization problem was formulated to minimize voltage and frequency deviation as well as power losses. Inversely, the stochastic multi-objective problem with uncertainty was formulated to minimize total microgrid cost, maximum voltage error, frequency deviation, and total energy loss. The proposed method was applied to the IEEE 33-, 69-, and 118-test systems as modelled in MATLAB environment and further validated against competitive swarm and evolutionary metaheuristics. Various convergence tests were considered to demonstrate the efficacy of the proposed load flow methods with MIDACO’s non-dominated solution. Likewise, different optimization parameters were utilized to investigate their impact on the solution. Moreover, the advantage of multi-objective optimization against single objective was provided for the deterministic optimization problem, while the effect of load model and droop response were also investigated. The obtained results in chapter 5 and 6 further demonstrate the fundamental role of DL in voltage and frequency regulation while minimizing costs and energy losses associated with DCIMG operation. Accordingly, an improved voltage and frequency profiles for the system after DL inclusion were attained in Figure 6.9 and Figure 6.10, respectively. To demonstrate the competitiveness of DL-based energy management system (EMS) against storage-based EMS, a brief cost benefit analysis considering hot water demand was also provided

Advances in Plant Breeding Strategies : Nut and Beverage Crops

Peer reviewe

A numerical framework for solving PDE-constrained optimization problems from multiscale particle dynamics

In this thesis, we develop accurate and efficient numerical methods for solving partial differential equation (PDE) constrained optimization problems arising from multiscale particle dynamics, with the aim of producing a desired time-dependent state at the minimal cost. A PDE-constrained optimization problem seeks to move one or more state variables towards a desired state under the influence of one or more control variables, and a set of constraints that are described by PDEs governing the behaviour of the variables. In particular, we consider problems constrained by one-dimensional and two-dimensional advection-diffusion problems with a non-local integral term, such as the associated mean-field limit Fokker-Planck equation of the noisy Hegselmann-Krause opinion dynamics model. We include additional bound constraints on the control variable for the opinion dynamics problem. Lastly, we consider constraints described by a two-dimensional robot swarming model made up of a system of advection-diffusion equations with additional linear and integral terms. We derive continuous Lagrangian first-order optimality conditions for these problems and solve the resulting systems numerically for the optimized state and control variables. Each of these problems, combined with Dirichlet, no-flux, or periodic boundary conditions, present unique challenges that require versatility of the numerical methods devised. Our numerical framework is based on a novel combination of four main components: (i) a discretization scheme, in both space and time, with the choice of pseudospectral or fi nite difference methods; (ii) a forward problem solver that is implemented via a differential-algebraic equation solver; (iii) an optimization problem solver that is a choice between a fi xed-point solver, with or without Armijo-Wolfe line search conditions, a Newton-Krylov algorithm, or a multiple shooting scheme, and; (iv) a primal-dual active set strategy to tackle additional bound constraints on the control variable. Pseudospectral methods efficiently produce highly accurate solutions by exploiting smoothness in the solutions, and are designed to perform very well with dense, small matrix systems. For a number of problems, we take advantage of the exponential convergence of pseudospectral methods by discretising in this way not only in space, but also in time. The alternative fi nite difference method performs comparatively well when non-smooth bound constraints are added to the optimization problem. A differential{algebraic equation solver works out the discretized PDE on the interior of the domain, and applies the boundary conditions as algebraic equations. This ensures generalizability of the numerical method, as one does not need to explicitly adapt the numerical method for different boundary conditions, only to specify different algebraic constraints that correspond to the boundary conditions. A general fixed-point or sweeping method solves the system of equations iteratively, and does not require the analytic computation of the Jacobian. We improve the computational speed of the fi xed-point solver by including an adaptive Armijo-Wolfe type line search algorithm for fixed-point problems. This combination is applicable to problems with additional bound constraints as well as to other systems for which the regularity of the solution is not sufficient to be exploited by the spectral-in-space-and-time nature of the Newton-Krylov approach. The recently devised Newton-Krylov scheme is a higher-order, more efficient optimization solver which efficiently describes the PDEs and the associated Jacobian on the discrete level, as well as solving the resulting Newton system efficiently via a bespoke preconditioner. However, it requires the computation of the Jacobian, and could potentially be more challenging to adapt to more general problems. Multiple shooting solves an initial-value problem on sections of the time interval and imposes matching conditions to form a solution on the whole interval. The primal-dual active set strategy is used for solving our non-linear and non-local optimization problems obtained from opinion dynamics problems, with pointwise non-equality constraints. This thesis provides a numerical framework that is versatile and generalizable for solving complex PDE-constrained optimization problems from multiscale particle dynamic

Biologically-inspired double skin facades for hot climates: a parametric approach for performative design

La Biomimicry è una scienza applicata che studia le forme, i materiali, i sistemi e i processi naturali per individuare soluzioni applicabili anche a problemi umani. Tale scienza trova applicazione in molti campi, quali l’agricoltura, la medicina, l’ingegneria e l’architettura. Grazie ai progressi compiuti nella modellazione parametrica, ad oggi sono disponibili potenti strumenti che, oltre alla simulazione energetica, consentono di esplorare le potenzialità delle soluzioni tratte dal mondo naturale nella progettazione architettonica, superando i limiti della semplice imitazione della forma. Una delle maggiori sfide per gli architetti negli ultimi anni è la riduzione della domanda energetica del costruito. Per i climi caldi, le esigenze di ventilazione e raffrescamento sono pertanto fattori cruciali per migliorarne la prestazione energetica. La tesi di ricerca affronta il problema della progettazione e dell’efficienza energetica dell’involucro edilizio in contesti climatici caldi, quale l’Egitto. A tal fine, è stato definito e applicato un approccio progettuale biomimetico-computazionale, per studiare e analizzare i comportamenti adattivi di termoregolazione di vari organismi naturali. In particolare, il lavoro di ricerca esplora possibili soluzioni architettoniche, ispirate a caratteristiche biologiche, per l’involucro di un edificio per uffici, con l’obiettivo di ridurre la domanda energetica per il raffrescamento. L’involucro dell’edificio è stato modellato parametricamente utilizzando Grasshopper Visual Programming Language per Rhino 3D Modeller, applicando inoltre alcuni algoritmi evolutivi multi-obiettivo per ottimizzare la soluzione architettonica rispetto al duplice obiettivo di diminuire i carichi di raffrescamento e mantenere un buon livello di illuminazione naturale. In tal modo, la riduzione dei carichi di raffreddamento non comporta un incremento dei consumi elettrici per l'illuminazione artificiale. Le prestazioni termiche dell’edificio sono state valutate con il software EnergyPlus. La soluzione architettonica esplorata è una facciata a doppia pelle ispirata a vari principi della natura. Le prestazioni della soluzione proposta sono state confrontate con quelle di un edificio per uffici esistente a Il Cairo. Il modello dell’edificio è stato ricostruito sulla base di planimetrie e specifiche sui materiali presenti; inoltre la disponibilità di dati sui consumi energetici per il raffrescamento dell’edificio ha permesso di valutare l’accuratezza della prestazione energetica calcolata con il software di modellazione. La soluzione progettuale è stata comparate anche rispetto alle prestazioni di una tipica facciata a doppia pelle. Inoltre le prestazioni termiche calcolate con EnergyPlus sono state confrontate con quelle ottenute con software di simulazione fluidodinamica computazionale (CFD), più accurati nel calcolo delle facciate a doppia pelle. Tale comparazione ha permesso di identificare il grado di errore e l’appropriatezza dell’uso di EnergyPlus nelle fasi iniziali della progettazione. La facciata a doppia pelle proposta consente una diminuzione della domanda di raffrescamento fino al 13,4%, migliorando al tempo stesso il livello di illuminazione naturale, che spesso costituisce uno dei maggiori limiti per l’applicazione di tale sistema. La ricerca termina con una sintesi dei risultati ottenuti e una valutazione complessiva del processo di progettazione presentato, degli strumenti di progettazione/simulazione utilizzati e delle prestazioni dell’involucro proposto, discutendone vantaggi e limiti. Sulla base delle sperimentazioni e dei risultati conseguiti, sono state individuate linee guida e raccomandazioni per la progettazione delle facciate a doppia pelle nei climi caldi. Inoltre viene fornita una matrice che raccoglie tutte le idee biomimetiche esplorate e analizzate, che rappresenta una mini-banca dati per architetti o designer interessati a questo approccio progettuale nell’affrontare i problemi di termoregolazione del costruito. Infine, la differenza di accuratezza tra i risultati di EnergyPlus e quelli dello strumento CFD è risultata trascurabile.Biomimicry is an applied science that derives inspiration for solutions to human problems through the study of natural designs, materials, structures and processes. Many fields of study benefit from biomimetic inspirations, such as agriculture, medicine, engineering, and architecture. Technological advances in parametric and computational design software in addition to environmental simulation means offer very useful tools in order to explore the potential of nature’s inspirations in architectural designs that does not just mimic shapes and forms. Energy efficiency is one of the major and growing concerns facing architects. Cooling and ventilation needs are critical factors that affect energy efficiency especially in hot climates. This thesis addresses the problem of designing building skins that are energy efficient in the context of hot climates such as that in Egypt. The research attempts to define and apply a biomimetic-computational design approach to study and analyse natural organisms in terms of their behaviour regarding thermoregulation. Aiming to decrease cooling loads, the research explores possible architectural solutions for a biologically inspired skin system for office buildings. The building’s skin is parametrically designed using Grasshopper Visual Programming Language for Rhino 3D Modeller, and it is optimised using multi-objective evolutionary algorithms which are particularly important in the attempt of finding a range of solutions that reduce cooling loads while maintaining daylight needs. Consequently, the reduction in cooling loads should not be at the expense of increased energy consumption in artificial lighting. Simulations regarding the thermal performance were performed using EnergyPlus. A Double-Skin Façade (DSF) is proposed based on inspirations from nature. In order to evaluate the performance of the proposal, it is compared to the performance of the skin of an existing office building in Cairo acting as a reference case. Data regarding the reference case such as the building drawings, material specifications and annual cooling consumption were obtained in order to build its digital model and assess its accuracy. The proposed design is also evaluated by comparing it to a typical flat DSF. The obtained results regarding the thermal performance of the proposed building skin are verified by comparing them to results of more accurate simulations performed using Computational Fluid Dynamics (CFD). The aim is to know the degree of error as well as the appropriateness of using EnergyPlus for geometrically-complex DSFs in early design phases when CFD is not practical. The proposed DSF was able to decrease cooling loads by up to 13.4% while improving daylight performance at the same time which is often one of the main challenges of using DSFs. The research criticises the presented design approach as a whole, the design/simulation tools used and the performance of the proposed skin discussing their benefits and limitations. Based on the design experimentation and results, general guidelines and recommendations for DSF design in hot climates are presented. Additionally, the research presents a compiled matrix of the biomimetic ideas explored and analysed in order to serve as a mini-data bank for architects or designers interested in this design approach in addressing thermoregulation problems. Finally, the comparison between EnergyPlus and CFD software results showed minor differences