Real Time Control System for Metro Railways Using PLC & SCADA

This paper proposes to adopt SCADA and PLC technology for the improvement of the performance of real time signaling&train control systems in metro railways.The main concern of this paper is to minimize the failure in automated metro railways system operator and integrate the information coming from Operational Control Centre(OCC),traction SCADA system,traction power control,and power supply system.This work presents a simulated prototype of an automated metro train system operator that uses PLC and SCADA for the real time monitoring and control of the metro railway systems.Here,SCADA is used for the visualization of an automated process operation and then the whole opera-tion is regulated with the help of PLC.The PLC used in this process is OMRON(NX1P2-9024DT1)and OMRON’s Sysmac studio programming software is used for developing the ladder logic of PLC.The metro railways system has deployed infrastructure based on SCADA from the power supply system,and each station’s traction power control is connected to the OCC remotely which commands all of the stations and has the highest command priority.An alarm is triggered in the event of an emergency or system congestion.This proposed system overcomes the drawbacks of the current centralized automatic train control(CATC)system.This system provides prominent benefits like augmenting services which may enhance a network’s full load capacity and networkflexibility,which help in easy modification in the existing program at any time.

A call for enhanced data-driven insights into wind energy flow physics

With the increased availability of experimental measurements aiming at probing wind resources and wind turbine operations,machine learning(ML)models are poised to advance our understanding of the physics underpinning the interaction between the atmospheric boundary layer and wind turbine arrays,the generated wakes and their interactions,and wind energy harvesting.However,the majority of the existing ML models for predicting wind turbine wakes merely recreate Computational fluid dynamics(CFD)simulated data with analogous accuracy but reduced computational costs,thus providing surrogate models rather than enhanced data-enabled physics insights.Although ML-based surrogate models are useful to overcome current limitations associated with the high computational costs of CFD models,using ML to unveil processes from experimental data or enhance modeling capabilities is deemed a potential research direction to pursue.In this letter,we discuss recent achievements in the realm of ML modeling of wind turbine wakes and operations,along with new promising research strategies.

THE SYSTEM OF MODEL CONTROL AND DATA ACQUISITION IN THE HUAIHE RIVER FLOOD CONTROL MODEL

The area of the Huaihe river flood control model is 4000m2. A computer system is employed to control models and collect experimental data.It can control 7 inflows which simulating 9 typical years flood process, a tail water level, 6 sluices, 11 outlets of breaking dike, and collect the water level of 37 points and velocity of 32 points.After running long time,the system has ben proved that it possesses steady character and exact accuracy.It is essential precondition for the experimental study of the Huaibe river find control model.

Networking dual-surveillance/dual-pair-tele-paths for critical urban areas

Providing a reliable and efficient communication infrastructure for critical regions is generally a strategy used to enhance regional safety or to sustain regional development. For such purposes, the spider-web network is prototyped as a cellular telecommunication infrastructure for its advantages including being interference-free, possessing fault-tolerance, having security management, and countering radio's multipath effects. Besides, it is used to develop area-based-street-SCADA (supervisory control and data acquisition) networks composed of regular optimal degree-3 prototyped sub-networks with the concepts of dual-surveillance, operational order and Hamiltonian laceability, and it flexibly fits various circulation configurations. Moreover, it can offer integrative response capabilities even after two paths' SCADA networks have been ruined.

Designing an Intelligent Control Philosophy in Reservoirs of Water Transfer Networks in Supervisory Control and Data Acquisition System Stations

In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.

Risk assessment framework for power control systems with PMU-based intrusion response system

Cyber threats are serious concerns for power systems.For example,hackers may attack power control systems via interconnected enterprise networks.This paper proposes a risk assessment framework to enhance the resilience of power systems against cyber attacks.The duality element relative fuzzy evaluation method is employed to evaluate identified security vulnerabilities within cyber systems of power systems quantitatively.The attack graph is used to identify possible intrusion scenarios that exploit multiple vulnerabilities.An intrusion response system(IRS)is developed to monitor the impact of intrusion scenarios on power system dynamics in real time.IRS calculates the conditional Lyapunov exponents(CLEs)on line based on the phasor measurement unit data.Power system stability is predicted through the values of CLEs.Control actions based on CLEs will be suggested if power system instability is likely to happen.A generic wind farm control system is used for case study.The effectiveness of IRS is illustrated with the IEEE 39 bus system model.

Development of an Internet and Fuzzy Based Control System of Manufacturing Process

The aim of this work is to develop an Internet and fuzzy based control and data acquisition system for an industrial process plant which can ensure remote running and fuzzy control of a cement factory. Cases studies of the proposed system application in three cement factories in Algeria, SCAEK（Setif）, SCIMAT（Batna）, and SCT（Tebessa）, are discussed. The remote process control consists of alarms generated during running of the processes while maintaining and synchronizing different regulation loops thus ensuring automatic running of processes smoothly. In addition, fuzzy control of the kiln and the other two mills ensures that the system is operational at all times with minimal downtime. The process control system contains different operator station（OP）, alarms table and a provision to monitor trends analysis. The operator can execute any operation according to his authorised access assigned by the system administrator using user administration tool. The Internet technology is used for human security by avoiding all times presence of operators at site for maintenance. Further, in case of a breakdown, the problem would be remotely diagnosed and resolved avoiding requirement of an expert on site thus eliminating traveling cost, security risks, visa formalities, etc. These trips are difficult to organize（costs, visas, risks）. So the enterprise can reduce downtimes and travel costs. In order to realize a process control system guided by operators in the main control room or through Internet, the process control is based on programming in PCS 7 utilizing Cemat library and Fuzzy Control＋＋ Siemens tools.

Wind turbine fault detection based on SCADA data analysis using ANN

Wind energy is one of the fast growing sources of power production currently, and there is a great demand to reduce the cost of operation and maintenance. Most wind farms have installed supervisory control and data acquisition （SCADA） systems for system control and logging data. However, the collected data are not used effectively. This paper proposes a fault detection method for main bearing wind turbine based on existing SCADA data using an artificial neural network （ANN）. The ANN model for the normal behavior is established, and the difference between theoretical and actual values of the parameters is then calculated. Thus the early stage of main bearing fault can be identified to let the operator have sufficient time to make more informed decisions for maintenance.