Loop Approach Research Articles

Reconfiguration of distribution system with distributed generation using an adaptive loop approach

Abstract A new concept of switches selection in the meta-heuristic optimization process of optimal distribution network reconfiguration has been proposed. Based on the adaptive set of selectable candidates, the proposed concept determines the switch status. This approach prevents the creation of unfeasible solutions (non-radial and unconnected configurations), and significantly reducing the number of searches and accelerating the optimization process. Unfeasible solutions, created by meta-heuristic optimization rules, can be corrected by means of the proposed adaptive loop concept. The correct parts of the unfeasible solution are retained, while only the defective parts are replaced by the adaptively formed loops from the currently available conditions that respect the correct switching operations. In this way, the basic characteristics of the optimization process have been retained to the greatest possible extent. Tests were performed on a two different size standard distribution networks.

An active learning method combining Kriging and accelerated chaotic single loop approach (AK-ACSLA) for reliability-based design optimization

To achieve an optimal design of complicated structures with stochastic parameters, the reliability-based design optimization (RBDO) usually needs to handle the nested double optimization loops, which results in unbearable computational cost. In this paper, a new active learning method for RBDO combining with Kriging metamodel and accelerated chaotic single loop approach (AK-ACSLA) is developed, in which the most probable learning function (MPLF) is proposed to search the most probable point instead of the limit state function in entire design space with an active learning behavior. To ensure the high efficiency, the system’s most probable learning function (SMPLF) is further constructed to solve the RBDO problem of series system with multiple probabilistic constraints, and then the ACSLA is proposed by taking full advantage of chaos feedback control methodology for guaranteeing the validity of AK-ACSLA. Nonlinear mathematical examples and complex RBDO engineering examples illustrate the high efficiency and accuracy of AK-ACSLA through comparison with both existing gradient-based methods and active learning methods.

An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries

In this paper, an improved state of charge (SOC) estimation method of Lithium-Ion battery is developed based on a cubature Kalman filter (CKF) supported by experimental data. Firstly, a first-order RC model and corresponding fractional order model are established to evaluate the estimation accuracy of different models. Secondly, model parameters are identified through a custom Hybrid Pulse Power Characteristic (HPPC) experiment based on the Sequential Quadratic Programming (SQR) method. Then, a CKF algorithm is used to estimate the battery SOC under different battery models with no prior knowledge of initial SOC. The results show that the proposed CKF method has a better estimate robustness rather than Extended Kalman filter (EKF) and the fractional order model can achieve higher accuracy while it consumes more computing resources compared with equivalent circuit models. SOC estimation error of CKF algorithms is less than 3%. Thirdly, a battery management unit in the loop approach is applied to verify the accuracy of estimation. Last but not least, in order to reduce the estimation error due to battery degradation and battery model errors, a fuzzy controller is constructed to modified the gain coefficient of Kalman. The proposed improved method can minimize the estimation error of SOC by 2%.

Global maximum power point tracking using variable sampling time and p-v curve region shifting technique along with incremental conductance for partially shaded photovoltaic systems

This paper proposes modifications to Incremental Conductance (INC) concept to track global maximum power point under partial shading conditions of photovoltaic (PV) systems. The proposed algorithm determines the p-v curve as a combination of regions, and tracks global peak by shifting operating point from one region to another without using any nature inspired intelligent techniques like particle swarm optimization (PSO) and other related algorithms. Additionally, a variable sampling time concept is introduced in the algorithm for faster tracking of global peak under extreme partial shading conditions arising due to random shading pattern of PV modules. MATLAB /SIMULINK platform was used to model PV module in conjunction with SEPIC converter for battery charging application. Proposed algorithm was tested under different shading patterns to observe the tracking speed and accuracy of the tracker. Finally, the concept behind proposed algorithm was validated on TMS320F28027 LAUNCHPAD DSP board using Process-in- loop (PIL) approach for practical applications. Results demonstrate the usefulness of proposed algorithm in terms of less computational time as compared to latest reported work in this field.

Reliability-Based Design Optimization of Load Sharing Systems Using SSI-Markov Models

This paper presents a novel single loop approach to design the components of the load sharing systems by optimally allocating the failure probabilities to each component, thereby satisfying the overall system reliability requirement. The Reliability–Based Design Optimization (RBDO) of load sharing systems is computationally intensive due to the dynamic nature of component failure probabilities, since the failure of one component will vary the failure probabilities of other working components. Many RBDO methods have been successfully utilized to design individual components, however using these methods for handling system level reliability constraints is still a challenging task. This is because of a drop in accuracy and computational efficiency, especially when considering a load sharing system, where there is dependency in failure probabilities of components. The key idea is to integrate Stress–Strength Interference (SSI) theory with discrete (or) continuous time-discrete state Markov model for the reliability assessment of system, with the states being the condition of components (working/failed). This method takes advantage of the state transition probability matrix to represent the dynamic nature of the system performance. A numerical example of a simple load sharing system with two I-Beams is presented to illustrate and evaluate the performance of the proposed methodology.

Assessing the alteration of physicochemical characteristics in composted organic waste in a prototype decentralized composting facility.

This article presents the pilot experience of an integrated biowaste management system developed in Tinos island, Greece, which promoted source separation and decentralized composting in a prototype unit. This system was introduced as a new-to-the-area of implementation and innovation, since landfilling of mixed municipal solid waste has been the common practice in Tinos island, as in many other areas of insular and mainland Greece. The biowaste management system was implemented through a bring scheme that aimed at motivating the public to separate at source the organic fraction of MSW. The system was monitored on an input-output basis of critical parameters used to assess the purity of separately collected biowaste, the treatment efficiency of the prototype unit, the quality characteristics of compost produced, and public's awareness and participation. Results showed that biowaste source separation was practiced effectively by citizens, giving high-purity feed (> 98%). Compost samples were examined in comparison with the proposed EU End-of-Waste (EoW) quality criteria and fulfilled the requirements set. More specifically, the average values of compost samples regarding heavy metal content were 72% lower than the EoW limit value for Cd, 43% lower for Ni, 38% lower for Pd, 24% lower for Cu, and 36% lower for Zn. Examined composts also met the EoW criteria for phytotoxicity and pathogenic or parasitic microorganisms, while they showed an approx. 15% decrease in initial organic matter content. Moreover, this study analyzed the carbon balances and the degree that composting can sequestrate carbon. Overall, this study demonstrated that the development and operation of on-island, decentralized composting, when properly practiced, is a sustainable option in order for islands and remote areas to adopt a closed loop approach to the biowaste management problem, in line with the circular economy principles.

Metric-guided regularisation parameter selection for statistical iterative reconstruction in computed tomography

As iterative reconstruction in Computed Tomography (CT) is an ill-posed problem, additional prior information has to be used to get a physically meaningful result (close to ground truth if available). However, the amount of influence of the regularisation prior is crucial to the outcome of the reconstruction. Therefore, we propose a scheme for tuning the strength of the prior via a certain image metric. In this work, the parameter is tuned for minimal histogram entropy in selected regions of the reconstruction as histogram entropy is a very basic approach to characterise the information content of data. We performed a sweep over different regularisation parameters showing that the histogram entropy is a suitable metric as it is well behaved over a wide range of parameters. The parameter determination is a feedback loop approach we applied to numerically simulated FORBILD phantom data and verified with an experimental measurement of a micro-CT device. The outcome is evaluated visually and quantitatively by means of root mean squared error (RMSE) and structural similarity (SSIM) for the simulation and visually for the measured sample (no ground truth available). The final reconstructed images exhibit noise-suppressed iterative reconstruction. For both datasets, the optimisation is robust where its initial value is concerned. The parameter tuning approach shows that the proposed metric-driven feedback loop is a promising tool for finding a suitable regularisation parameter in statistical iterative reconstruction.

A causal loop approach to uncover interrelationship of student online interaction and engagement and their contributing factors

Advances in technology reinforce the imperative to obtain further insight into the factors that impact online interaction in online environments. Even though past researchers have extracted factors impacting student online interaction and engagement, there is a lack of research that uncovers the dynamics of these relationships and investigates the impact of a comprehensive set of factors on student online interaction at the same time. Thus, this paper seeks to fill this gap by employing a causal loop approach to uncover the interrelationships of these factors that contribute to a positive impact on students’ learning outcomes, and to evaluate satisfaction and engagement in online courses by focussing on students’ online interaction. To this end, a rich qualitative data set was obtained from an online focus group consisting of students from a large online course, and a thematic analysis was conducted resulting in identifying different factors that played a role in the topic under study. More importantly, causal loop modelling was used to model these factors and their causal interrelationships.

Smart Grid Simulation Including Communication Network: A Hardware in the Loop Approach

Smart grids are complex electrical systems made up of power devices and communication systems that interact with each other, both locally through measures and generally through the exchange of information on communication networks. In this article, the simulation of a smart grid including the emulation of the communication network behavior together with the physical hardware devices is proposed using hardware-in-the-loop methodology. The original aspect of the technique is related to the fact that the models are generated in a tool that is independent of the real-time simulator using a model-based design approach, and then the code is generated and deployed in a real-time simulator coupled with hardware devices such as low voltage protection units. The proposed framework is used to test the protection coordination that employs the IEC-61850 communication protocol. A test case is shown demonstrating the coordination of switches and the emulation of the PV system in hardware-in-the-loop, focusing on the implementation of the interface algorithm over Ethernet using the UDP communication protocol.

Evaluating Low Speed Limit of Back-EMF Observer for Permanent Magnet Synchronous Motor

Abstract The Back-EMF (BEMF) observer represents a solution for sensorless control of Permanent Magnet Synchronous Motor (PMSM) for middle to high speed range. It is a very robust sensorless method for given speed range and it is often used not only in automotive industry but in overall transport systems. For zero and low speed, an open loop approach (known as scalar control) is often used in low-end applications. However, this method is much less efficient because the position of the stator flux is not known, thus the torque angle cannot be controlled. This yields to lower torque produced for given stator current. Therefore, the aim is to control the motor in closed loop from as low speed as possible. This article will focus on low speed limit of BEMF Observer and will offer approaches how to lower this limit. The observer limits were evaluated under several conditions. The low speed limit of original structure of observer was used as a benchmark to evaluate performance of proposed enhancements and settings. The effect of tracking observer bandwidth will be presented as well as the bandwidth of speed loop. Moreover, real measured voltages were fed into the BEMF Observer instead of demanded values to see if the low speed performance can be improved.

Work loop dynamics of the pigeon (Columba livia) humerotriceps demonstrate potentially diverse roles for active wing morphing.

Control of wing shape is believed to be a key feature that allows most birds to produce aerodynamically efficient flight behaviors and high maneuverability. Anatomical organization of intrinsic wing muscles suggests specific roles for the different motor elements in wing shape modulation, but testing these hypothesized functions requires challenging measurements of muscle activation and strain patterns, and force dynamics. The wing muscles that have been best characterized during flight are the elbow muscles of the pigeon (Columba livia). In vivo studies during different flight modes revealed variation in strain profile, activation timing and duration, and contractile cycle frequency of the humerotriceps, suggesting that this muscle may alter wing shape in diverse ways. To examine the multifunction potential of the humerotriceps, we developed an in situ work loop approach to measure how activation duration and contractile cycle frequency affected muscle work and power across the full range of activation onset times. The humerotriceps produced predominantly net negative power, likely due to relatively long stimulus durations, indicating that it absorbs work, but the work loop shapes also suggest varying degrees of elastic energy storage and release. The humerotriceps consistently exhibited positive and negative instantaneous power within a single contractile cycle, across all treatments. When combined with previous in vivo studies, our results indicate that both within and across contractile cycles, the humerotriceps can dynamically shift among roles of actuator, brake, and stiff or compliant spring, based on activation properties that vary with flight mode.

Online Validation of Relay based Identification and Controller Design with an Anti-Reset Windup for a Binary Distillation Column

Sequential auto-tuning based model identification is a closed loop approach, which has an extensive benefit compared to conventional methods because of user defined manipulated gain to the process within the verge of instability. Model parameters estimation of multi input multi output process is challenging because of existence of multivariable interaction among the variables. In this paper ideal relay is used as a sequential basis for binary distillation column in real time. Obtaining sustained oscillations in conventional methods is based on trial and error, benefit of relay is that oscillations can be generated as scaling of user defined gains. Predictive PI control algorithm is implemented. Results depicts the efficiency of methodology and importance of anti-reset term in the algorithm.

Online Validation of Relay based Identification and Controller Design with an Anti-Reset Windup for a Binary Distillation Column

Sequential auto-tuning based model identification is a closed loop approach, which has an extensive benefit compared to conventional methods because of user defined manipulated gain to the process within the verge of instability. Model parameters estimation of multi input multi output process is challenging because of existence of multivariable interaction among the variables. In this paper ideal relay is used as a sequential basis for binary distillation column in real time. Obtaining sustained oscillations in conventional methods is based on trial and error, benefit of relay is that oscillations can be generated as scaling of user defined gains. Predictive PI control algorithm is implemented. Results depicts the efficiency of methodology and importance of anti-reset term in the algorithm.

Contribution to Circular Economy options of mixed agricultural wastes management: Coupling anaerobic digestion with gasification for enhanced energy and material recovery

Anaerobic digestion (AD) is an established process for the treatment of organic wastes and the production of renewable energy. However, high amounts of digestate produced by AD plants require enhancement for further use. This study investigates a conceptual model for the digestate enhancement by using a downstream gasification. It is based on a ‘systemic approach’ considering the interactions of every contributing process into the dual system. The digestate was provided by an Italian AD plant, that treats mixed agricultural wastes of pig manure (43%), cow manure (20%), maize and triticale silages (25%), and cereal bran (12%). Digestate air gasification experiments were conducted, in a downdraft fixed-bed reactor, at temperature range from 750 °C to 850 °C, with λ varying from 0.14 to 0.34. Results have shown that gasification of digestate at 850 °C with λ = 0.24, increased producer gas yield (65.5 wt %), and its LHV (2.88 MJ Nm−3). The gas is classified as medium heating value fuel, suitable to generate electricity of 971 kWhel day−1 to enhance the AD plant's economic viability. A carbonaceous material rich in macronutrients (P, K, Ca, Mg) was produced, with R50 = 0.48, suitable for carbon sequestration. The study offers a resource closed loop approach of converting AD digestate into energy and soil fertilizer. Useful suggestions for policy makers and business can be drawn.

Multilevel delay lock loop approach for wall clutter mitigation in through‐the‐wall radar imaging

Wall clutter mitigation is important in through-the-wall imaging (TWI) because the target information is often buried in the wall's reflections, especially when the target is located close to the wall. A new wall clutter mitigation method is proposed based on the multilevel delay lock loop (M-DLL) technique. The M-DLL structure uses the M-sequences’ phase information sequentially remove the direct wave, wall's primary echo and the multiple echoes, then the rest part of the signal is used to probe and estimate the position of the target. The M-DLL can obtain the precise correlation of the signal, and the rest target echo has an enhanced signal-to-noise ratio. This method is used in TWI as a new technique, and the results compared with other methods show that it has an excellent detectability and a good anti-jamming performance.

Modelling and performance analysis of different multilevel inverter topologies using PSO-MPPT technique for grid connected photovoltaic systems

This paper proposes a new control structure for two multilevel three-phase inverter topologies for photovoltaic (PV) systems connected to the grid. This control scheme includes the use of the space vector pulse wide modulation (SVPWM) technique to control the Diode Clamped Inverter (DCI) and cascade inverter topologies and the integration of the particle swarm optimisation (PSO) technique to operate the PV system at the Maximum Power Point (MPP). A Field-Programmable Gate Array (FPGA) implementation of PSO based Maximum Power Point Tracking (MPPT) is proposed to overcome the problem of MPP tracking under partial shading conditions. This MPPT technique is validated under various PV array configurations in order to evaluate the behaviour of each PV configuration under non-uniform irradiance. A SVPWM control strategy is used in order to generate gate control signals for the inverter and implemented for both DCI and cascade inverter topologies. Then, a comparative study of photovoltaic systems with these inverter topologies is carried out in the Matlab/Simulink environment and evaluated on the basis of MPPT, harmonic distortion, cost, advantages, and disadvantages. In order to test the practical implementation of the proposed control structure, FPGA/Simulink-based Hardware in the Loop approach has been used to bring the obtained results as close as possible to reality and with a minimum of constraints. Based on the analysis of the obtained results, some experimental parameters are summarized and a comparison table is obtained.

A novel compliance-pressure loop approach to quantify arterial compliance in systole and in diastole

Arterial compliance has been recognized as a critical parameter in governing pulsatile flow dynamics. It has traditionally been assumed constant throughout the cardiac cycle and its computation has been based either on the classic Windkessel model (C) in diastole or the stroke volume over pulse pressure (Cv) method in systole. Other methods using area (Cam) or two-area (Ctam) and exponential (C(P)exp1) methods were used for the cardiac cycle. We proposed a novel compliance-pressure loop (CPP loop) approach for the quantification of arterial compliance and compared it to existing linear and nonlinear methods. Experimental data were gathered in 5 dogs and blood pressure levels were varied (systolic pressure of 100 mmHg–185 mmHg) with induced hypertension and vasodilation. Results showed the limited regime of validity of C (Control:0.4681 ± 0.1270 ml/mmHg, MTX:0.3015 ± 0.1264 ml/mmHg and NTP:1.8323 ± 0.7207 ml/mmHg) and Cv (Control:0.3583 ± 0.0158 ml/mmHg, MTX:0.2602 ± 0.1275 ml/mmHg and NTP:0.4131 ± 0.0589 ml/mmHg), Cam (Control:0.4175 ± 0.0505, MTX:0.3086 ± 0.1568 and NTP:1.4181 ± 0.4812) and Ctam (Control: 0.2064 ± 0.0228 ml/mmHg, MTX:0.1967 ± 0.0884 ml/mmHg, NTP:0.0881 ± 0.0375 ml/mmHg) and that C(P)exp1 underestimates the arterial compliance compared to our method (Control:0.2233 ± 0.0168 ml/mmHg vs 0.4481 ± 0.0515 ml/mmHg, MTX:0.1976 ± 0.0964 ml/mmHg vs 0.3273 ± 0.1443 ml/mmHg and NTP: 0.2177 ± 0.0273 ml/mmHg vs 1.9990 ± 1.8221 ml/mmHg at mean arterial pressure). The CPP method based on the exponential method is superior, as it provides continuous compliance variations and CPP loop area can be readily visualized from hypotension to hypertension conditions. We conclude that the concept of using compliance-pressure loop is advantageous as it can afford continuous and accurate tracking of the dynamic arterial behavior despite greatly varying blood pressure levels.

Exploring temple floral refuse for biochar production as a closed loop perspective for environmental management

Religious faith and ritual activities lead to significant floral offerings production and its disposal as waste to the nearby open lands and water bodies. These activities result into various social and environmental nuisances because of their high organic content. Alternatively, it can be used as valuable resources for various biochemical and thermo-chemical processes. Floral refuse has been utilized in natural dye extraction, however, the residual solid refuse is of significant environmental concern due to its nutrient rich nature. This study explores the potential utilization of solid residue of temple floral refuse after natural dye extraction by thermo-chemical decomposition of it. The slow pyrolysis of solid residue was performed at 350 °C and 500 °C, and the biochar yield of 42 and 36% was obtained, respectively. TGA-DTG analysis was performed to observe the thermo-chemical behaviour of floral refuse. The biochar products were further characterized by FTIR, SEM, EDX, BET, XRD, and RAMAN spectroscopy to observe the impact of pyrolysis temperature (PT) on the resulting material, i.e. biochar and its possible application measures. EDX results revealed the presence of various macro-nutrients such as C, N, P, K Ca and Mg in different proportions which showed its soil amelioration potential. Moreover, based on the SEM and BET results, biochar prepared at 500 °C was further explored for adsorption of methylene blue dye at various dose and pH conditions. Based on Langmuir (R2 = 0.98) and Freundlich (R2 = 0.97) isotherms, it is found as a potential adsorbent material for removal of methylene blue dye. The results revealed that biochar conversion of colour extracted floral refuse can be a vital option for quick and efficient management of it in a closed loop approach.

Development of a new method for joint damping identification in a bolted lap joint

Mechanical joints have considerable effects on dynamic behaviour of machine tools; thus, joint damping identification is important in studying the dynamics of mechanical structures. Due to the difficulties in analysis of microslip friction, the experimental prediction of microslip joint dynamics is of great importance. In this study, a new experimental approach is proposed to determine the damping of bolted lap joints. Because of the complex nature of the mechanical joints, the lap joint is isolated through the addition of a mechanical resonator, which consists of a lumped mass and spring, to the bolted structure. The frequency response function (FRF) of this system is used for joint damping identification. This approach is used for bolted structures under both translational and torsional excitations and overcomes difficulties associated with slip boundaries identification in the joint interface. The method is verified by comparing the obtained results with those of the hysteresis loop approach.

Data-Driven Modeling of a Commercial Photovoltaic Microinverter

We present a data-driven modeling (DDM) approach for static modeling of commercial photovoltaic (PV) microinverters. The proposed modeling approach handles all possible microinverter operating modes, including burst mode. No prior knowledge of internal components, structure, and control algorithm is assumed in developing the model. The approach is based on Artificial Neural Network (ANN) and Fast Fourier Transform (FFT). To generate the data used to train the model, a Power Hardware in the Loop (PHIL) approach is applied. Instantaneous inputs-outputs data are collected from the terminals of a commercial PV microinverter at time domain. Then, the collected data are converted to the frequency domain using Fast Fourier Transform (FFT). The ANNs that are the core of the DDM are developed in frequency domain. The outputs of the ANNs are then converted back to time domain for validation and use in system level simulation. The comparison between measured and simulated data validates the performance of the presented approach.