Hybrid Petri Net Model Research Articles

Cloud-Edge Collaboration-Based Distribution Network Reconfiguration for Voltage Preventive Control

The distribution network reconfiguration can realize the voltage preventive control of the distribution network under alert state, which solves the voltage security issues and maintains the safe operation of distribution network. However, since the great scale and complexity of distribution network would make the traditional centralized distribution network reconfiguration method have a heavy computing burden, a distribution network reconfiguration method based on cloud-edge collaborative architecture for voltage preventive control is proposed, which can reduce the huge computational pressure caused by the excessive concentration of computing tasks. In order to formulate the optimal topology reconfiguration strategy according to the specifics of voltage alerts, a differential hybrid Petri-net model with event-triggered strategy is constructed based on the cloud-edge collaborative architecture to characterize the logical relations of the solution process of the reconfiguration strategy. Considering the short time scale characteristic of preventive control, corresponding to the solution process described by the constructed model, an evaluation network based on graph convolutional neural network is proposed for the reachability discrimination of solutions to significantly reduce the number of candidate solutions, as well as a decision network based on multilayer perceptron is proposed for the selection of the optimal solution among the reachable solutions. Numerical tests are conducted on the modified IEEE 33-bus and IEEE 118-bus distribution systems to validate the effectiveness of the proposed method in dealing with voltage alert problems.

A multi-agent petri net model power management strategy for wind–solar-battery driven DC microgrid

In recent years, autonomous direct current microgrid has been widely investigated to improve its performance in terms of integrating distributed energy resources and power balance issues. In this paper, a multi-agent hybrid petri net model is developed to ensure power management in wind–solar-battery driven low-voltage direct current microgrid. The multi-agent system is a discrete-event dynamic process in which petri nets are employed as the modelling tools. Direct current microgrid is distinguished with a simple control and power management with respect to alternating current microgrid and poses several advantages such as higher conversion as well as transmission efficiency, higher reliability even in remote locations, convenient control, reduced cost and lesser filter effort due to absence of reactive power, phase synchronization, high inrush current etc. To evaluate the behaviour of the direct current microgrid with several distributed task, a multi-agent hybrid petri net adapting two-layer control strategy has been considered. The first layer deals with modelling and development of hybrid petri net for collecting all the possible information by coordinated switching control in between generating units. This information is communicated to the local control structure to select the mode intelligently. Finally, this coordinated control approach is experimentally verified in the direct current microgrid test bench. The test results validate effective mode transition and power sharing among all distributed energy resources and load.

Formal verification confirms the role of p53 protein in cell fate decision mechanism

The bio-cell cycle is controlled by a complex biochemical network of signaling pathways. Modeling such challenging networks accurately is imperative for the understanding of their detailed dynamical behavior. In this paper, we construct, analyze, and verify a hybrid Petri net (HPN) model of a complex biochemical network that captures the role of an important protein (namely p53) in deciding the fate of the cell. We model the behavior of the cell nucleus and cytoplasm as two stochastic and continuous Petri nets, respectively, combined together into a single HPN. We use simulative model checking to verify three different properties that capture the dynamical behavior of p53 protein with respect to the intensity of the ionizing radiation (IR) to which the cell is exposed. For each IR dose, 1000 simulation runs are carried out to verify each property. Our verification results showed that the fluctuations in p53, which relies on IR intensity, are compatible with the findings of the preceding simulation studies that have previously examined the role of p53 in cell fate decision.

In-silico modeling and analysis of the therapeutic potential of miRNA-7 on EGFR associated signaling network involved in breast cancer

BackgroundModeling is now increasingly used to get detailed insights into complex biological systems associated with different types of cancers. In order to understand one such complex system of cell signaling networks associated with breast cancer and to determine the potential impact of miRNA-7 inhibitor on breast cancer progression, we have tried to present a rather simple system of formalization by employing hybrid PN modeling, which consists of many important breast cancers associated oncogenes, proto-oncogenes and tumor-suppressor genes. The main objective of this study is to establish a model for the ease of understanding the mechanism of breast cancer progression and therapeutic potential of miRNA-7 against breast cancer. MethodologyA stepwise approach has been exploited including modeling of breast cancer-associated network, extraction of miRNA-7 associated inhibitory gene targets, model verification via hybrid petrinet modeling tool and analyses of simulation results. Results and discussionsThe approach was acquired to establish a highly accurate therapeutic potential of miRNA-7 against breast cancer. The results generated correlates with the experimental studies; the data verifies that the model is very useful in predicting the useful therapeutic abilities of different inhibitory agents which in our case was a miRNA. This study specifically analyses the levels of STAT3, RAS ER-alpha, P53, BRCA-1, and AKT, PI3K genes in both the presence and absence of miRNA-7 in breast cancer. Most of these oncogenes and their linked pathways were observed to be down-regulated under the increased activation influence of miRNA-7, contrarily the effect upon tumor-suppressor genes was opposite they were up-regulated under the increased activation of miRNA-7 while under the decreased activity level of miRNA-7 the tumor-suppressor genes were down-regulated accordingly with the normal cancerous mechanism while all the other oncogenes were found to be up-regulated in this case, this approach elaborated and verified the oncogenic potential of miRNA-7 against breast cancer. ConclusionThe hybrid petrinet (HPN) based modeling approach may help the biologists to understand how different inhibitor therapies work against breast cancer progression. The modeling approaches applied here can also provide a new path for scientists to use combination therapies of oncogenic and tumor-suppressor miRNAs against different cancers and other diseases associated with complex integrated cell signaling networks in biological systems.

Multiagent System-Based Integrated Design of Security Control and Economic Dispatch for Interconnected Microgrid Systems

Hybrid and intermittent characteristics of the distributed energy resources (DERs) bring great challenges to the security control and economic dispatch (ED) of the microgrids. To bypass these hurdles, this article proposes a multiagent system-based integrated design of security control and ED to guarantee the effective and economical operation of the interconnected microgrids. First, a hierarchical control scheme is constructed by two-level unit agents, in which the switching control and dynamic regulation are fully implemented with the corresponding hybrid behaviors based on the differential hybrid Petri-net (DHPN) model. Based on the DHPN model, a novel dynamic ED integrated with security control is proposed to overcome the issues that cannot be solved in conventional models. Furthermore, to reduce the computational complexity and unified the mathematical model of the DERs, the inverter-based power control strategy is converted to a predictive control model which can be decomposed into several subsystems. In the optimization process, all the subsystems are implemented in a fully distributed, communication free, and rolling optimization manner based on the distributed model predictive control (DMPC). The validity of the proposed design is demonstrated according to the simulation results in case studies.

Application of Hybrid Petri Nets for the Energy Dispatching of an Isolated Micro-Grid

Renewable energy-based autonomous installations sometimes have an energy deficiency or excess caused by the intermittence of renewable sources and the dynamics of the load. Storage systems are unable to cover load supply during long weather instability. In case of neighboring autonomous installations, some have a lack of energy while neighbors have overproduction. Hence, interconnecting installations via a micro-grid (MG) should allow supply installations experiencing an energy lack by sending them energy surplus from others. This article presents a Hybrid Petri Net (HPN) strategy for a micro-grid energy provider in order to make hourly decisions on dispatching energy between the connected installations. An HPN model combines discrete events (house energy state) and continuous events (energy flow) to cover the need of some installations by energy surplus offered by its neighbours. The algorithm has been validated for three connected houses with different load profiles. Results show that installations cover each other in energy without the need to refer to their batteries in case of a lack of energy.

Spatial quorum sensing modelling using coloured hybrid Petri nets and simulative model checking

BackgroundQuorum sensing drives biofilm formation in bacteria in order to ensure that biofilm formation only occurs when colonies are of a sufficient size and density. This spatial behaviour is achieved by the broadcast communication of an autoinducer in a diffusion scenario. This is of interest, for example, when considering the role of gut microbiota in gut health. This behaviour occurs within the context of the four phases of bacterial growth, specifically in the exponential stage (phase 2) for autoinducer production and the stationary stage (phase 3) for biofilm formation.ResultsWe have used coloured hybrid Petri nets to step-wise develop a flexible computational model for E.coli biofilm formation driven by Autoinducer 2 (AI-2) which is easy to configure for different notions of space. The model describes the essential components of gene transcription, signal transduction, extra and intra cellular transport, as well as the two-phase nature of the system. We build on a previously published non-spatial stochastic Petri net model of AI-2 production, keeping the assumptions of a limited nutritional environment, and our spatial hybrid Petri net model of biofilm formation, first presented at the NETTAB 2017 workshop. First we consider the two models separately without space, and then combined, and finally we add space. We describe in detail our step-wise model development and validation.Our simulation results support the expected behaviour that biofilm formation is increased in areas of higher bacterial colony size and density. Our analysis techniques include behaviour checking based on linear time temporal logic.ConclusionsThe advantages of our modelling and analysis approach are the description of quorum sensing and associated biofilm formation over two phases of bacterial growth, taking into account bacterial spatial distribution using a flexible and easy to maintain computational model. All computational results are reproducible.

Hybrid Petri Net scheduling model of household appliances for optimal renewable energy dispatching

Renewable energies represent one of the most promising and eco-friendly solutions for energy generation, which guarantees the sustainable development of society. Compared to fuel-based generators, the photovoltaic/Battery (PV/B) system is more suitable to supply domestic loads in sunny, isolated regions. However, given weather disturbances, a storage element is required so as to ensure the load supply autonomy. This work proposes an energy flow scheduling algorithm for a PV/B chalet installation in order to guarantee continuous supply of loads. The aim consists in the satisfaction of the loads demand, the maximization of the PV generation use, the limitation of the battery access time, and the battery protection against deep/over charge. The Timed Hybrid Petri Net (THPN) formalism is used to model the system operation considering the classification of loads into two priority levels. The developed algorithm computes several starting operation times of the secondary load, then selects the optimal one in order to satisfy the criteria cited above and guarantee continuous supply of the primary load. A comparative study of the calculated starting operation times demonstrates the efficiency of the optimal solution for a best PV/B power exploitation with a maximum battery lifetime and safety. The lost PV energy is nearly 26% with respect to an appropriate range of the battery State of Charge (SoC) from 100% to 21.76%. Such evaluation offers the opportunity to users to install more loads operating only during daylight in order to consume the PV lost energy.

AMS SoC Formal Verification based on Hybrid Scheme

This paper proposes for AMS SoC formal verification based on Hybrid Scheme combined with symbolic computing and LHPN model, FV-HS. The paper is concerned with a class of AMS designs, continuous-time AMS designs i.e., tunnel diode oscillator for research target. Firstly, Labeled Hybrid Petri Net model is established for safety property verification of tunnel diode oscillator, then mathematical expression for this model is extracted for efficiency enhancement, and then proof policy built in computer algebra Maple is applied to the corresponding LHPN model for tunnel diode oscillator to verify the property. The proposed method is implemented on tunnel diode oscillator and experiment results demonstrate the advantages of the proposed method over previous method. The proposed method overcomes the drawbacks of LHPN, makes full use of the merits of LHPN and symbolic computing, simplifies the workflow of algorithm and enhances the efficiency.

Intrusion Detection in Cyber-Physical Systems Based on Petri Net

Intrusion detection is a major concern in Cyber-Physical Systems (CPSs). In this paper, an algorithm based on Petri Net (PN) is proposed that simultaneously detects misuse and anomaly behavior of the system. The proposed anomaly detection method is applicable to Supervisory Control and Data Acquisition (SCADA) system at the highest level of CPSs. Neural First Order Hybrid Petri Net model (NFOHPN) with online fast Independent Component Analysis (ICA) is proposed for anomaly detection. It is shown that the use of distributed and multidisciplinary intrusion detection methods in different layers of CPSs increases security of the net against coordinated cyber-attacks. Simulation results and comparative studies based on the Defense Advanced Research Projects Agency (DARPA) evaluation datasets demonstrate that the proposed model can detect normal or malicious behavior with satisfying accuracy and at surprisingly high convergence speed.DOI: http://dx.doi.org/10.5755/j01.itc.47.2.16277

Modelling and analysis of hybrid stochastic timed Petri net

In this paper, an interesting Hybrid Stochastic Timed Petri Net (HSTPN) is proposed for a class of hybrid systems. The proposed HSTPN can be adopted to represent hybrid systems with discrete, continuous, conflicting, time-delay and stochastic characteristics simultaneously. The proposed HSTPN outperforms conventional hybrid Petri net models in terms of describing the scalability and immediacy of hybrid systems. Advantages of the HSTPN on describing hybrid system are verified by establishing some equivalent models of HPN and its derived models.

Modelling and simulation of energy consumption of ceramic production chains with mixed flows using hybrid Petri nets

Ceramic production chain consisting of discrete flow and continuous flow energy-intensive processes consumes substantial amounts of energy. This study aims to evaluate energy consumption performance and energy-saving potentials of the ceramic production chain. According to the energy consumption characteristics of manufacturing processes and process interaction constraints in a ceramic production chain, an approach integrating the first-order hybrid Petri net (FOHPN) model, an objective linear programming model and a sensitivity analysis is proposed. The FOHPN model will simulate the energy consumption patterns of the ceramic production chain. Meanwhile, multi-objective linear programming model and sensitivity analysis will suggest the optimal specific energy consumption (SEC) of the production chain and identify the influences of input parameters (i.e. production rate of a process) on the SEC in the optimal production scheme. Finally, a real case study from bathroom ceramic plant validates the approach. It provides a tool for modelling and simulation of energy consumption of ceramic production chains with mixed flows and helps operators to perform energy-saving actions in the ceramic enterprise.

Petri Net-Based Model of Helicobacter pylori Mediated Disruption of Tight Junction Proteins in Stomach Lining during Gastric Carcinoma.

Tight junctions help prevent the passage of digestive enzymes and microorganisms through the space between adjacent epithelial cells lining. However, Helicobacter pylori encoded virulence factors negatively regulate these tight junctions and contribute to dysfunction of gastric mucosa. Here, we have predicted the regulation of important tight junction proteins, such as Zonula occludens-1, Claudin-2 and Connexin32 in the presence of pathogenic proteins. Molecular events such as post translational modifications and crosstalk between phosphorylation, O-glycosylation, palmitoylation and methylation are explored which may compromise the integrity of these tight junction proteins. Furthermore, the signaling pathways disrupted by dysregulated kinases, proteins and post-translational modifications are reviewed to design an abstracted computational model showing the situation-dependent dynamic behaviors of these biological processes and entities. A qualitative hybrid Petri Net model is therefore constructed showing the altered host pathways in the presence of virulence factor cytotoxin-associated gene A, leading to the disruption of tight junction proteins. The model is qualitative logic-based, which does not depend on any kinetic parameter and quantitative data and depends on knowledge derived from experiments. The designed model provides insights into the tight junction disruption and disease progression. Model is then verified by the available experimental data, nevertheless formal in vitro experimentation is a promising way to ensure its validation. The major findings propose that H. pylori activated kinases are responsible to trigger specific post translational modifications within tight junction proteins, at specific sites. These modifications may favor alterations in gastric barrier and provide a route to bacterial invasion into host cells.

Scheduling of Crude Oil Operations in Refinery without Sufficient Charging Tanks Using Petri Nets

A short-term schedule for crude oil operations in a refinery should define and sequence the activities in detail. Each activity involves both discrete-event and continuous variables. The combinatorial nature of the scheduling problem makes it difficult to solve. For such a scheduling problem, charging tanks are a type of critical resources. If the number of charging tanks is not sufficient, the scheduling problem is further complicated. This work conducts a study on the scheduling problem of crude oil operations without sufficient charging tanks. In this case, to make a refinery able to operate, a charging tank has to be in simultaneous charging and feeding to a distiller for some time, called simultaneously-charging-and-feeding (SCF) mode, leading to disturbance to the oil distillation in distillers. A hybrid Petri net model is developed to describe the behavior of the system. Then, a scheduling method is proposed to find a schedule such that the SCF mode is minimally used. It is computationally efficient. An industrial case study is given to demonstrate the obtained results.

Multi-Agent System-Based Event-Triggered Hybrid Control Scheme for Energy Internet

This paper is concerned with an event-triggered hybrid control for the energy Internet based on a multi-agent system approach with which renewable energy resources can be fully utilized to meet load demand with high security and well dynamical quality. In the design of control, a multi-agent system framework is first constructed. Then, to describe fully the hybrid behaviors of all distributed energy resources and logical relationships between them, a differential hybrid Petri-net model is established, which is an original work. The most important contributions based on this model propose four types of event-triggered hybrid control strategies whereby the multi-agent system implements the hierarchical hybrid control to achieve multiple control objectives. Finally, the effectiveness of the proposed control is validated by means of simulation results.

Does Your Domestic Photovoltaic Energy System Survive Grid Outages?

Domestic renewable energy systems, including photovoltaic energy generation, as well as local storage, are becoming increasingly popular and economically feasible, but do come with a wide range of options. Hence, it can be difficult to match their specification to specific customer’s needs. Next to the usage-specific demand profiles and location-specific production profiles, local energy storage through the use of batteries is becoming increasingly important, since it allows one to balance variations in production and demand, either locally or via the grid. Moreover, local storage can also help to ensure a continuous energy supply in the presence of grid outages, at least for a while. Hybrid Petri net (HPN) models allow one to analyze the effect of different battery management strategies on the continuity of such energy systems in the case of grid outages. The current paper focuses on one of these strategies, the so-called smart strategy, that reserves a certain percentage of the battery capacity to be only used in case of grid outages. Additionally, we introduce a new strategy that makes better use of the reserved backup capacity, by reducing the demand in the presence of a grid outage through a prioritization mechanism. This new strategy, called power-save, only allows the essential (high-priority) demand to draw from the battery during power outages. We show that this new strategy outperforms previously-proposed strategies through a careful analysis of a number of scenarios and for a selection of survivability measures, such as minimum survivability per day, number of survivable hours per day, minimum survivability per year and various survivability quantiles.

Hybrid Petri Nets and Hybrid Automata for Modeling and Control of Two Adjacent Oversaturated Intersections

The problem of avoiding oversaturated phenomena between two adjacent intersections by intelligent traffic control strategy is addressed in this paper. The complex traffic behavior in this urban area is viewed as a dynamical hybrid system that can be modeled by hybrid Petri nets (HPNs). The property analysis of HPN model that gives an evaluation of the system performance is very limited. The translation of this model into hybrid automata (HA) can avoid this drawback. The interest of this translation is to profit from the both models advantages while avoiding their disadvantages that associate the modeling power of HPN with the analysis capacities of HA. The resulting model can capture an important aspect of the traffic flow dynamics where the oversaturated traffic conditions are presented by forbidden locations. A reachability analysis is performed to check this model. An optimal supervised controller synthesis algorithm is elaborated to get an optimal plan with coordinated traffic signals that satisfy the imposed constraints where the forbidden locations are suppressed. The experiment results show that the coordination traffic signal obtained by the proposed control approach outperforms those obtained using the widely used signal timing optimization software SYNCHRO under various demand scenarios from unsaturated to oversaturated.

FV-HS: Formal Verification for AMS SoC Based on Symbolic Computing

This paper proposes a novel methodology for AMS SoC formal verification based on Hybrid Scheme combined with symbolic computing and LHPN model, FV-HS. The paper is concerned with a class of AMS designs, continuous-time AMS designs i.e., tunnel diode oscillator for research target. Firstly, Labeled Hybrid Petri Net model is established for safety property verification of tunnel diode oscillator, then mathematical expression for this model is extracted for efficiency enhancement, and then proof policy built in computer algebra Maple is applied to the corresponding LHPN model for tunnel diode oscillator to verify the property. The proposed method is implemented on tunnel diode oscillator and experiment results demonstrate the advantages of the proposed method over previous method. The proposed method overcomes the drawbacks of LHPN, makes full use of the merits of LHPN and symbolic computing, simplifies the workflow of algorithm and enhances the efficiency.

MAS-Based Energy Management Strategies for a Hybrid Energy Generation System

An advanced control scheme for managing a hybrid energy generation system (HEGS) is presented in this paper. A hierarchical management and control architecture based on multi-agent systems (MAS) is discussed. MAS will account for the complex behavior of a hybrid energy supply system. The management and control strategies are implemented through a system of agents based on three tiers. The upper level agents develop overall energy management strategies for a hybrid energy supply system. The middle-level agents integrate coordinated switching controllers. The lower level agents are responsible for dealing with local control strategies. Coordinated switching controllers within the middle-level agents are designed as event-triggered hybrid controllers based on differential hybrid Petri-net (DHPN) models. The operation modes of distributed energy resources (DERs) can smoothly transfer in a coordinated manner due to the coordinated action of the switching controllers according to variation in operating conditions. Finally, simulation results from different scenarios verifying the feasibility of the proposed scheme are offered.

Modeling and Optimization of Hybrid Systems for the Tweeting Factory

In this paper, a predicate transition model for discrete-event systems is generalized to include continuous dynamics, and the result is a modular hybrid predicate transition model. Based on this model, a hybrid Petri net including explicit differential equations and shared variables is also proposed. It is then shown how this hybrid Petri net model can be optimized based on a simple and robust nonlinear programming formulation. The procedure only assumes that desired sampled paths for a number of interacting moving devices are given, while originally equidistant time instances are adjusted to minimize a given criterion. This optimization of hybrid systems is also applied to a real robot station with interacting devices, which results in about 30% reduction in energy consumption. Moreover, a flexible online and event-based information architecture called the Tweeting Factory is proposed. Simple messages (tweets) from all kinds of equipment are combined into high-level knowledge, and it is demonstrated how this information architecture can be used to support optimization of robot stations.