Bond Graph Research Articles

Enhanced thermal modeling of power transformers: A comparison of bond graph and IEEE methods considering external environmental factors

The optimal and secure operation of electrical power system is largely affected by the reliability of power transformers in operational and economic terms. The continuous monitoring of power transformer is crucial for ensuring the quality of electrical power supply. Hence, the thermal modeling of power transformer is very essential to prevent their failure. This paper aims to design thermal models of operating power transformer by considering the two external influencing parameters of solar irradiation and wind flow. This paper presents a Bond Graph (BG) method-based design of thermal models for power transformer under operating condition by comparison with the results obtained from IEEE Std. based thermal models and also with the practical readings. The models have been implemented on a 5MVA, 33/11 kV power transformer running at 33 kV Substation, Sardarpura, Jodhpur, by initially considering the input hourly dataset of load and ambient temperature. The designed thermal models were subsequently updated by integrating the impact of solar radiation and wind incident on the surface of power transformer. Comparative study of all the designed thermal models has been done that indicates improved accuracy of the model if the impact of solar radiation and wind flow are included. Also, the effectiveness of the BG based thermal models in accurately predicting the system behaviour validates their applicability for real world engineering applications.

Modeling and Simulation of an Integrated Synchronous Generator Connected to an Infinite Bus through a Transmission Line in Bond Graph

Most electrical energy generation systems are based on synchronous generators; as a result, their analysis always provides interesting findings, especially if an approach different to those traditionally studied is used. Therefore, an approach involving the modeling and simulation of a synchronous generator connected to an infinite bus through a transmission line in a bond graph is proposed. The behavior of the synchronous generator is analyzed in four case studies of the transmission line: (1) a symmetrical transmission line, where the resistance and inductance of the three phases (a,b,c) are equal, which determine resistances and inductances in coordinates (d,q,0) as individual decoupled elements; (2) a symmetrical transmission line for the resistances and for non-symmetrical inductances in coordinates (a,b,c) that result in resistances that are individual decoupled elements and in a field of inductances in coordinates (d,q,0); (3) a non-symmetrical transmission line for resistances and for symmetrical inductances in coordinates (a,b,c) that produce a field of resistances and inductances as individual elements decoupled in coordinates (d,q,0); and (4) a non-symmetrical transmission line for resistances and inductances in coordinates (a,b,c) that determine resistances and inductance fields in coordinates (d,q,0). A junction structure based on a bond graph model that allows for obtaining the mathematical model of this electrical system is proposed. Due to the characteristics of a bond graph, model reduction can be carried out directly and easily. Therefore, reduced bond graph models for the four transmission line case studies are proposed, where the transmission line is seen as if it were inside the synchronous generator. In order to demonstrate that the models obtained are correct, simulation results using the 20-Sim software are shown. The simulation results determine that for a symmetrical transmission line, currents in the generator in the d and q axes are −25.87 A and 0.1168 A, while in the case of a non-symmetrical transmission line, these currents are −26.14 A and 0.0211 A, showing that for these current magnitudes, the generator is little affected due to the parameters of the generator and the line. However, for a high degree of non-symmetry of the resistances in phases a, b and c, it causes the generator to reach an unstable condition, which is shown in the last simulation of the paper.

Efficiency-Centered Fault Diagnosis of In-Service Induction Motors for Digital Twin Applications: A Case Study on Broken Rotor Bars

The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor parameters and loss sources. We introduce a bond graph as a fault diagnosis network, linking loss sources, motor parameters, and faults. This interconnected approach is the key aspect of our proposed diagnostic method and aims to be used in fault diagnosis as a general method. A case study of a broken rotor bar is used to validate the proposed method using a dataset of five motors. Among these, one motor operates without failure, while the remaining four exhibit broken rotor faults categorized as 1, 2, 3, and 4. The proposed method achieves 99.99% precision in identifying one to four defective rotor bars in IMs. Comparative analysis demonstrates good performance compared to vibration-based FDD approaches. Moreover, our methodology is computationally efficient and aligned with Industry 4.0 requirements.

The Bond Graph Modeling and Experimental Verification of a Hydraulic Inertial Vibration Isolator Including Nonlinear Effects

The Bond Graph Modeling and Experimental Verification of a Hydraulic Inertial Vibration Isolator Including Nonlinear Effects

Modelling and Diagnosis of Hybrid Dynamic Systems by a Multi-Model Approach

Fault diagnosis is an essential task in ensuring the smooth operation of complex dynamic systems. The consequences of faults can be serious, leading to loss of life, harmful emissions to the environment, high repair costs and economic losses caused by unplanned production line stoppages. The work developed in this paper concerns the modeling and diagnosis of faults (sensor faults, system faults, actuator faults) in hybrid dynamic systems using our multi-model approach (which combines two sub-models, one continuous and the other discrete). The aim is to integrate three well-known tools in the literature: the Bond Graph, the Observer and the Timed Automata, to design a global diagnostic model. The hybrid dynamic system is modeled by connecting the tools for the continuous part, i.e. the bond graph and the observer, to the timed automata for the discrete part. The resulting model is used for fault diagnosis in two stages: The first is fault detection by analyzing the residuals generated by the system output and that of the observer. The second step involves fault localization, which results from analysis of the signature matrix and temporal identification of the system. The proposed method combines the advantages of these tools to obtain the best performance, particularly in the fault location phase. The simulation results prove the effectiveness of the proposed model for the hybrid dynamic system. Moreover, these results also evaluate the performance of the proposed diagnostic approach while reducing non-detections, detection delays and false alarms.

Modeling and Implementation of PID Controller in Vehicle Ride Comfort Improvement Using Bond Graph

The proportional integral derivative (PID) controller method is a famous and influential approach used in a variety of engineering domain systems. This paper presents Bond Graph modelling and implementation of PID controller in the active quarter-vehicle suspension system for the purpose of achieving better ride comfort. Bond graph is a better tool for modelling multi-domain energy systems with control hardware associated with it. SYMBOL SHAKTI software is used to simulate the active vehicle suspension system, which is theoretically represented as a 4-DOF human Bio-dynamic model combined with a 3-DOF quarter automobile system. The suggested PID Controller's performance is contrasted with that of the passive model. To measure the effectiveness of the suggested PID controller suspension system, time domain evaluations of systems performance criteria are conducted. The findings show that a significant improvement in ride comfort is offered by the suggested Bond graph PID controller model of the active vehicle suspension.

Modeling and control of a novel mechatronic model of a 750 kW-FSWT with power-splitting transmission using RBF neural network: a bond graph approach

This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package.

An integrated Petri net-pseudo bond graph model for nuclear hazard assessment

A pseudo-bond graph is presented to model the heat transferred from the fuel rods to the coolant via its cladding in a generic nuclear reactor case study. Simulations performed using this model are used to explore the temperatures of the core components under ordinary and emergency scenarios, considering various conditions of coolant supply and reactor power output. The model is combined with a timed stochastic Petri net to produce a hybrid model, in which the reactor operation and fault status is determined by the Petri net and fed into the bond graph to examine the resulting impact on core temperatures, which in turn are fed back into the Petri net process. The results predict the distribution of the reactor operational durations before a disruption occurs. The model provides the temperature profiles attained by the cladding and fuel components, indicating a low probability of dangerous temperatures.

Modeling the circumduction mechanism of thumb metacarpal through muscle-tendons actuation using bond graph

Modeling the circumduction mechanism of thumb metacarpal through muscle-tendons actuation using bond graph

Intelligent vector control for a novel mechatronic modeling of a 1.5MW variable speed WECS using the bicausality concept under a real wind flow: A Bond Graph Approach

Enhancing the dynamic behaviors of Wind Energy conversion Systems (WECS) based on a doubly fed induction machine (DFIG) is one of the most attractive challenging in the field of maximizing renewable energy's output power, improving its quality and ensuring its efficient grid integration. For this purpose, a novel mechatronic modelling of a 1.5 MW variable-speed WECS based on a doubly fed induction machine using the Bond graph Approach is proposed, and three robust control strategies are displayed to regulate: the DFIG's active and reactive powers for the networks side converter (NSC), maximizing the extracted power and controlling the pitch angle for the load area. The flux oriented vector control laws presented in this contribution are based upon the independent control of magnetic flux and the electromagnetic torque inside the DFIG machine. They are derived from the integrated model's Inverse Bond Graph (IBG). This robust control strategy based on the bicausality concept enabled us to elaborate the Vector Control model that relies on a Radial Basis Function RBF-VC to be trained online with an optimal dataset under some real wind profiles. The robustness and effectiveness of the proposed model and RBF-VC controller are verified. The simulation results were conducted to confirm the validity of the proposed technique. Hands-on experience is carried out by means of the 20-sim software package.

A review of the diverse applications of bond graphs in biology and physiology

Computational biology and physiology is an interdisciplinary endeavour, requiring input from biologists, physiologists, mathematicians, chemists, engineers and clinicians. These systems are composed of complex phenomena across disparate temporal and spatial scales, and a holistic understanding of system behaviour typically requires the application of advanced multi-scale models. While many modelling techniques have been used, the bond graph (BG) is the only approach for modelling physical systems, where ‘causality’ is represented graphically. Additionally, the BG approach with its intrinsic properties allows the modular construction of models and verifying the conservation of mass and energy algorithmically. The BG approach has been widely used in engineering and, more recently, has been increasingly applied to biology and physiological systems. In this review, we briefly introduce the concepts and strengths of BG modelling. Following this, we review the history of BGs in modelling cellular mechanisms, biochemical reactions and musculoskeletal and cardiovascular systems. Then, current developments in BG software are reviewed, and opportunities and perspectives on the future application of BGs are discussed.

Fault diagnosis of landing gear retraction system with bond graph under uncertain conditions.

Given the lack of sufficient historical data for aircraft landing gear retractor systems, a model-based fault diagnosis approach is needed to overcome this data deficiency. Meanwhile, inherent uncertainties are inevitable in engineering practice, and it is a great challenge to construct a model that accurately reflects the complexity of the actual system under uncertain conditions. Due to the urgent need for reliable model-based diagnostic methods and the need to cope with inherent uncertainties, this paper proposes an improved fault diagnostic method aimed at increasing the diagnostic efficiency of the landing gear retractor system, a critical component in aircraft take-off and landing operations. Due to a lack of historical data, the model-based fault diagnosis method can solve the problem of lack of data. The proposed uncertainty method addresses the challenge of multiple sources of uncertainty by using subsystems to reduce complexity. Fault diagnosis is achieved by comparing residuals with thresholds derived from a diagnostic bond graph (DBG) model. To address the problem of limited fault data, we modeled and simulated the landing gear retractor system using AMESim®. In addition, the linear fractional transform (LFT) approach has been used to resolve parametric uncertainties, but is unable to resolve system structural uncertainties. Therefore, we also analyzed the comparative fault diagnosis results derived from the linear fractional transformation-DBG (LFT-DBG) and the subsystem-DBG approaches. The experimental results support the effectiveness of the subsystem approach in improving fault diagnosis accuracy and reliability, highlighting its potential as a viable diagnostic strategy in aerospace engineering applications.

A hybrid model for shop floor simulation and work-in-process feedback control

In this paper, a smart hybrid model for shop floor simulation and control is proposed based on dynamic modelling and control theory’s analytical tools. The model is smart because the production levels are automatically adjusted based on feedback loops, considering shop floor current states (i.e., it is a closed-loop model). It is hybrid because it deals with discrete and continuous transitions, and integrates two approaches: switched arrival systems (SAS) and bond graphs (BG). This novel integrated SAS + BG model is applied to a production system in the food industry and four production control rules (well-known from the operations management literature) are tested: pure push and pull rules and extended push and pull rules. The results showed that the SAS is effective to dynamically represent intermittent production systems, and its use, coupled with bond graphs, allows the definition of capacity adjustment and production control rules that are effective to control the work in process (WIP). The extended pull rule outperformed the remaining ones, corroborating prior literature. This paper contributes with a novel approach that is modular and can be extended to represent the closed-loop dynamics of different configurations of multi-product multi-station production systems, with a disaggregated level of detail.

Multi-physics system modelling based on bond graph theory for offshore hydrogen production

Hydrogen is expected to take a major role in the energy sector. To address the increasing demand, specially designed systems for dedicated hydrogen production, e.g., an offshore hydrogen wind turbine, are developed. Especially in these multi-physics systems, each aspect of the entire system requires expert knowledge for modelling and sometimes specific simulation tools. A key challenge lies in the integration of component models into the full system model. We propose a method based on bond graph theory to identify single components and their interfaces for dynamic simulation. This top-down approach leads to a structured processes for the development of multi-physics systems with a diverse team. We present a design of a new 15 MW green hydrogen offshore wind turbine to study the performance in turbulent wind conditions. The single components are decoupled in their development and therefore, can be tested independently or combined with other components. All component models are utilized as Functional Mock-Up Units and combined in the full model with Co-Simulation. The development method is applicable to other complex energy systems.

A Study on Fishing Vessel Energy System Optimization Using Bond Graphs

Recently, environmental regulations have been strengthened due to climate change. This change comes in a way that limits emissions from ships in the shipbuilding industry. According to these changes, the trend of ship construction is changing installing pollutant emission reduction facilities such as scrubbers or applying alternative fuels such as low sulfur oil and LNG to satisfy rule requirements. However, these changes are focused on large ships. Small ships are limited in size. So, it is hard to install large facilities such as scrubbers and LNG propulsion systems, such as fishing boats that require operating space. In addition, in order to apply the pure electric propulsion method, there is a risk of marine distress during battery discharge. Therefore, the application of the electric–diesel hybrid propulsion method for small ships is being studied as a compromised solution. Since hybrid propulsion uses various energy sources, a method that can estimate effective efficiency is required for efficient operation. Therefore, in this study, a Bond graph is used to model the various energy sources of hybrid propulsion ships in an integrated manner. Furthermore, based on energy system modeling using the Bond graph, the study aims to propose a method for finding the optimal operational scenarios and reduction ratios for the entire voyage, considering the navigation feature of each different maritime region. In particular, the reduction gear is an important component at the junction of the power transmission of the hybrid propulsion ship. It is expected to be useful in the initial design stage as it can change the efficient operation performance with minimum design change.

Bond graph realizations of state space linear time-invariant multivariable descriptions

Given a state space description of a linear time-invariant multivariable system, a similarity transformation is applied to get a controllability canonical form. This transformation assures a Bond Graph (BG) realization and avoids the use of active bonds. Active or signal bonds do not preserve the continuity of power flows and consequently, energy conservation properties are lost. The state matrix of the system in transformed coordinates contains an antisymmetric matrix associated with the storage field and the rest is associated with the dissipative field. Thus, in general, the dissipative field is implemented with a multiport element, and in order to give physical meaning, the storage field is implemented with one-port elements. The resulting BG model does not have zero-order causal paths, that is, algebraic loops between the dissipative elements. The results are applied to the BG realization of a linear robust multivariable controller that is designed for a two mass system modelled in BG.

Prior knowledge-infused Self-Supervised Learning and explainable AI for Fault Detection and Isolation in PEM electrolyzers

In this paper, a novel Fault Detection and Isolation (FDI) method for Proton Exchange Membrane (PEM) electrolyzers is presented. The challenge of limited availability of labeled fault data is addressed through the utilization of Bond Graphs (BG) and Self-Supervised Learning (SSL). The Linear Fractional Transformation-Bond Graph (LFT-BG) model is employed to generate uncertain residuals and pseudo labels, enabling the training of a deep neural network through self-supervised learning. Additionally, the BG-XAI method is introduced, leveraging eXplainable AI (XAI) and structural equations from Bond Graphs to provide explanations for the decisions made by the deep learning model. The superior performance of the proposed approach, particularly in dealing with limited labeled data, is demonstrated through comparative assessments against various state-of-the-art SSL methods. The demo code of the proposed method is available in this repository: https://github.com/mohan696matlab/SSL_based_Hybrid_FDI.

MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning.

Accurately predicting molecular metabolic stability is of great significance to drug research and development, ensuring drug safety and effectiveness. Existing deep learning methods, especially graph neural networks, can reveal the molecular structure of drugs and thus efficiently predict the metabolic stability of molecules. However, most of these methods focus on the message passing between adjacent atoms in the molecular graph, ignoring the relationship between bonds. This makes it difficult for these methods to estimate accurate molecular representations, thereby being limited in molecular metabolic stability prediction tasks. We propose the MS-BACL model based on bond graph augmentation technology and contrastive learning strategy, which can efficiently and reliably predict the metabolic stability of molecules. To our knowledge, this is the first time that bond-to-bond relationships in molecular graph structures have been considered in the task of metabolic stability prediction. We build a bond graph based on 'atom-bond-atom', and the model can simultaneously capture the information of atoms and bonds during the message propagation process. This enhances the model's ability to reveal the internal structure of the molecule, thereby improving the structural representation of the molecule. Furthermore, we perform contrastive learning training based on the molecular graph and its bond graph to learn the final molecular representation. Multiple sets of experimental results on public datasets show that the proposed MS-BACL model outperforms the state-of-the-art model. The code and data are publicly available at https://github.com/taowang11/MS.

Bond-Graph model based stepper motor output characteristics simulation implemented in LabVIEW environment

In the industry, simulations are of great importance. They enable measurements to be made in different conditions about a virtual device, which are highly comparable to measurements made in a real life scenarios. Because of their wide range of usage in lower power drive systems, where precision and simplicity is a must, the subject of study is a permanent magnet stepper motor. For precise positioning purposes, it is essential to know the positioning behaviour of these devices. The model construction process involved an intermediate step, which consisted of creating the Bond-Graph of the motor based on pre-defined models available in the literature in this field. In the next step, the Bond-Graph model was converted to a block diagram of the motor. This permitted the direct implementation of the motor model in LabVIEW visual programming environment. The preliminary steps allows us to check and confirm the functionality and correctness of the model. This article covers in detail the model conversion and implementation steps of the simulation. At the end, the functionality of the simulation was tested.

Wheel Torque Distribution Control Strategy for Electric Vehicles Dynamic Performance With an Electric Torque Vectoring Drive Axle

The ability of active chassis control for dynamic performance improvement of electric vehicles with centralized drive system is limited duo to the driving torque between two-side wheels always equally distributed. To solve this problem, this paper proposes a novel electric torque vectoring (TV) drive-axle and its torque distribution control strategy, which can realize the function of arbitrary distribution of the left/right wheel driving torque in the form of centralized drive, thereby improving the dynamic performance of the vehicle. First, the structure and torque distribution principle of TV drive-axle are analyzed theoretically. Subsequently, an accurate mathematical model of TV drive-axle is established based on bond graph theory. Next, a wheel torque distribution control strategy of TV drive-axle is proposed based on hierarchical control approach to improve the vehicle’s handling stability. Wherein, the target yaw rate motion tracking controller as the upper-level controller is designed based on the second-order super-twist sliding mode control (SS-SMC) algorithm, and the wheel drive anti-slip controller in lower-level is used to restrict possible wheels slipping. Finally, the off-line simulation and hardware-in-the-loop (HIL) experiment results suggest that the proposed novel TV drive-axle and control strategy can significantly improve the handling stability and steering flexibility of the vehicle.