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.

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.

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.

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.

Three Projects of Intelligent Distribution Automation System in East Asia

Intelligent distribution automation system （IDAS） was developed based on distribution automation system that was installed in all distribution offices of Korea. IDAS was designed the combined system with the function of supervisory control and data acquisition （SCADA） and distribution automation system （DAS） for network operation from substation to high voltage customer, and it has been installed in Vietnam, China and Indonesia. This paper explains the project scope, system configuration, and the function of each sy stem.

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.

Mixed nonlinear dynamic voltage phasor tracing method based on WAMS/SCADA

By considering the static voltage characteristic of the load, we propose a WAMS/SCADA mixed nonlinear method to estimate the voltage of unobservable buses caused by topology change or phasor measurement unit (PMU) malfunction in a power system. By modeling the load characteristic with data from SCADA, we employed the Gauss-Seidel method to solve the nonlinear equations and estimate the voltage of unobservable buses with the high precision voltages of neighboring buses measured by a PMU. Simulations were carried out on the IEEE 39-bus system, and the results show that this novel method can dynamically and accurately trace the variation of the voltage phasor of the unobservable buses.