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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Md Muzakkir Quamar Khalid Abdullah Khan Bo Liu Muhammad Khalid Department of Control & Instrumentation King Fahd University of Petroleum & Minerals Dhahran KSA IRC for Smart Mobility and Logistics KFUPM Dhahran KSA Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran KSA IRC for Renewable Energy and Power Systems KFUPM Dhahran KSA

Utilization of wind energy source is among the most preferred alternative electricity generation. Typically, proportinal-integral-derivative (PID) controllers are implemented for pitch angle control of the wind turbines. The parameters of such PID controllers are derived by the designer based on experience which is subjected to being non-optimal. Therefore, a more effective controller is needed. In this paper, an optimal fractional order PID controller (FOPID) is designed. Further the results of PID and FOPID controller is compared. The gain parameters of both the PID and FOPID controller is obtained using heuristic optimization technique (genetic algorithm). The results obtained shows the effectiveness of optimal FOPID over optimal PID under normal conditions as well as under the effect of disturbances.

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Intelligent Fault Diagnosis for Low-Voltage power Network

Intelligent Fault Diagnosis for Low-Voltage Power Network

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Hamza M. Anwar Md Shafiullah M. A. Abido Electrical Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Control & Instrumentation Engineering Department KFUPM Dhahran Saudi Arabia IRC for Renewable Energy and Power Systems KFUPM Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence (JRCAI) KFUPM Dhahran Saudi Arabia

Faults are common and potentially dangerous problems in low-voltage power system networks, e.g., distribution networks. Upon discovery, they must be dealt with immediately to prevent further damage to the distribution system and quickly return electricity to consumers. This paper proposes a hybrid fault diagnosis method combining a signal processing technique with deep learning models. Faults are applied in a four-node test distribution feeder developed using MATLAB and Simulink. Different measurement noise levels are implemented, along with varying load and fault parameters. The short-time Fourier transform (STFT) is used on the feeder’s current signals as a feature extraction tool. These features are then used to train deep learning models to detect, classify, and locate faults. Various parameters of the models are varied to find the optimum ones, which are used to obtain the results. The findings show that the model performed exceptionally well in fault detection and classification and satisfactorily in fault location.

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Optimal power Generation of Variable Wind Turbine System Using Linear Quadratic Regulator

Optimal Power Generation of Variable Wind Turbine System Usi...

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Energy Technologies for Future Grids (ETFG), IEEE International Conference on

作者： Khalid Abdullah Khan Md Muzakkir Quamar Muhammad Majid Gulzar Muhammad Khalid Electrical Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran KSA IRC for Renewable Energy and Power Systems KFUPM Dhahran KSA SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran KSA Department of Control & Instrumentation KFUPM Dhahran KSA IRC for Smart Mobility and Logistics KFUPM Dhahran KSA

Wind energy source is considered as a viable alternative power generation for establishing a sustainable electricity grid. However, numerous challenges associated with variable and unpredictable output power generation hinder its potential. In this paper, regulation of output power generation of a multi-input-multi-output variable speed wind turbine is performed using linear quadratic regulator (LQR). Considering different wind speed scenarios, the operating range of the wind turbine is achieved. The optimal operation is determined based on the trade-off between the power conversion based on the mechanical fatigue due to the variable loading conditions experienced by the wind generation system. Based on the results obtained the developed LQR controller proves to regulate optimal performance of the variable speed wind turbine.

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Biologically Inspired Algorithm for Adaptive Convergence in Multi-Agent System

Biologically Inspired Algorithm for Adaptive Convergence in ...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Muhammad Majid Gulzar Muhammad Maaruf Khalid Khan Fahad Alismail Muhammad Khalid Department of Control & Instrumentation Engineering King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) King Fahd University of Petroleum & Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals

Numerical realization of optimal consensus control is important for multi-agent systems. In this study we formulate a nonlinear multi-agent system based on Cucker-Smale model and develop consensus control to resolve the dynamic problem of flocking. The validation of the framework is performed with random initial position and velocities considering two cases, that is, 5 and 13 birds. Based on the results obtained and presented for 40 iterations it is observed that the agent's position and velocities have successfully converged.

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Current Sharing control Method for Fuel Cell Hybrid Electric Vehicles

Current Sharing Control Method for Fuel Cell Hybrid Electric...

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IEEE PES Asia-Pacific power and Energy engineering Conference (APPEEC)

作者： Muhammad Maaruf Georgios Konstantinou Muhammad Khalid Control and Instrumentation Engineering Department Center for Smart Mobility and Logistics SDAIA-KFUPM Joint Research Center for Artificial Intelligence School of Electrical Engineering and Telecommunications UNSW Sydney Sydney Australia Electrical Engineering Department Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

This paper considers fuel cell hybrid electric vehicles (FCHEV) including a proton exchange membrane (PEM) fuel cell as the main energy source, and lithium-ion battery and ultra-capacitor as energy storage systems to support the fuel cell. The two important objectives are to share the current demand of the traction motor among the energy sources and maintain the stability of the DC-bus voltage. The current sharing is based on an energy management scheme. A passivity-based control (PBC) approach is proposed to achieve current tracking based on the energy management and regulate the DC-bus voltage. The PBC is a nonlinear control approach that guarantees the stability of a closed-loop system by making it passive. The feasibility of the proposed control method is evaluated in MATLAB/Simulink.

关键词： Protons Lithium-ion batteries Fuel cells Traction motors Hybrid electric vehicles Resource management Fuels

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power system stability enhancement through optimal PSS design

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 9卷

作者： Islam, Md. Rashidul Azam, Md. Samiul Hossen, Md. Saber Islam, Mohammad Saiful Worku, Muhammed Y. Shahriar, Mohammad Shoaib Shafiullah, Md. Department of Electrical and Electronic Engineering International Islamic University Chittagong Chittagong 4318 Bangladesh Interdisciplinary Research Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Electrical Engineering University of Hafr Al-Batin Hafr Al Batin 31991 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia

Low-frequency oscillations (LFO) are generated in interconnected electric networks because of the weak tie lines between the parts of the networks. Such oscillations have been a significant challenge for engineers that may lead to system instability if appropriate measures are not taken. This article proposes two new metaheuristic algorithms, the jellyfish search algorithm (JSA) and the tunicate swarm algorithm (TSA), to design the power system stabilizers (PSS) for single machine infinite bus (SMIB) and multimachine power system (MPS) networks. The proposed method uses the JSA and TSA to dampen out the LFOs by modifying the vital parameters of lead-lag type conventional PSS (CPSS). A damping ratio-based objective function improves both models' system damping. These methods are tested on an SMIB system and two distinct MPS networks exposed to the three-phase fault under various loading conditions. The effectiveness of the suggested approach has been studied by comparing the system eigenvalues and minimum damping ratio (MDR) produced from JSA and TSA-tuned PSS and the CPSS. In addition, time domain simulation results are compared, demonstrating that the new approach outperforms the standard techniques, such as particle swarm optimization (PSO) and backtracking search algorithm (BSA). In the SMIB system, MDR produced by JSA and TSA-based PSS is 2.55 times higher than that of CPSS. Similarly, in comparison to PSO and BSA-based PSS, JSA, and TSA-tuned PSS offer >1.4 times greater MDR in the Two Area Four Machine network and up to 8.4 times larger MDR for the IEEE-39 Bus network. Furthermore, the efficacy of the suggested JSA and TSA approaches' prediction capacity is validated by satisfying the values of statistical performance metrics. © 2024 The Author(s)

关键词： Damping ratio Dynamic stability Jellyfish search algorithm Low frequency oscillation power system stabilizers Tunicate swarm algorithm

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Improving the Frequency Response of High Renewable Penetrated Islanded Microgrids

Improving the Frequency Response of High Renewable Penetrate...

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IEEE PES Asia-Pacific power and Energy engineering Conference (APPEEC)

作者： Muhammad Maaruf Sulaiman Ahmad Muhammad Khalid Control and Instrumentation Engineering Department Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Electrical Engineering Department Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The integration of renewable energy sources (RESs) into microgrid weakens the inertia of the microgrid and negatively affects its frequency stability. Recently, this problem is rectified using the derivative virtual inertia (VI) control strategy in the form of an energy storage system (ESS) to improve the inertia and frequency stability of the microgrid. The derivative VI controller calculates the rate of change of the frequency deviation (RoCoF) for injecting active power into the microgrid during periods of contingencies. However, the main problem of the derivative VI controller is that the damping effect is neglected in its design. As such, it might give an unsatisfactory performance in a microgrid with low damping. Therefore, this paper proposes an improved derivative VI controller that can emulate both inertia and damping concurrently. A differential evolution (DE) algorithm has been utilized to optimize the virtual inertia and damping coefficients. The performance of the proposed VI controller is validated through simulation studies under different RESs and loads. Moreover, performance comparison between the proposed VI controller and the existing VI control techniques shows that the frequency response is more robust and accurate under the action of the proposed VI controller.

关键词： Damping Renewable energy sources Microgrids Stability analysis Frequency response Transient analysis Frequency control

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Cucker-Smale Model Based Consensus Algorithm for Enhanced Convergence in Communication Network of Distributed Energy systems

Cucker-Smale Model Based Consensus Algorithm for Enhanced Co...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Muhammad Majid Gulzar Tayyab Jadoon Khalid A. Khan Muhammad Khalid Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Center for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia Department of Electrical Engineering University of Central Punjab Lahore Pakistan Electrical Engineering Department King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia

Consensus problems and algorithms are being increasingly applied in problems associated with the management of renewable energy integration in power systems. The concept of numerous microgrid integration needs appropriate coordination and communication, and optimization infrastructure that can be achieved with the multi-agent system formation solved with consensus algorithms. The primary objective of a consensus algorithm is to facilitate the process by which a group of distributed agents may come to a consensus over a quantity of interest by sharing information with one another through a communication network. In this respect, faster convergence quantifies the efficiency of the consensus algorithm. In this paper, Cucker-Smale flocking model is developed for the consensus algorithm. By using this model, consensus can be achieved by randomly choosing the initial positions and velocities of the birds with a minimum number of iterations. The proposed methodology is tested and validated on a ring and mesh communication network topologies with 3 and 12 nodes. The results obtained validate the efficiency of fast convergence of information sharing among the nodes.

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Multi-objective Optimization of A CCHP System Model Integrated with P2G for Decarbonization

Multi-objective Optimization of A CCHP System Model Integrat...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Pengyu Wei Olusola Bamisile Dongsheng Cai Jian Li Humphrey Adun Qi Huang School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control University of Electronic Science and Technology of China Chengdu China Energy Systems Engineering Department Cyprus International University KKTC Mersin Turkey

To realize the dual carbon policy, the goal of energy saving, and emission reduction, it is important to develop ways to better promote the low carbon economic operation of energy systems. Therefore, this research proposes an optimal operation strategy considering the electric-gas-cooling-heat demand response. Considering the demand response of each participant, an integrated energy system model with photovoltaic, wind energy, and traditional grid as energy supply. Also, electric energy, natural gas, cooling, and heating subsystems with co-production are established. With minimizing the overall system operation cost and optimizing environmental protection, the improved particle swarm optimization algorithm is used to solve the multi-constrained objective optimization problem. Finally, the optimized model is tested. The results from this study show that the energy system under cogeneration can find a balance between economy and environmental friendliness, and provide a reference for the subsequent energy supply configuration.

关键词： Photovoltaic systems Renewable energy sources Wind energy systems operation System integration Demand response Particle swarm optimization

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Narrowband-IoT Based Integrated Framework for Monitoring Pipeline Condition in Oil and Gas Industry

Narrowband-IoT Based Integrated Framework for Monitoring Pip...

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International Conference on control, Automation and Diagnosis (ICCAD)

作者： Md Muzakkir Quamar Khalid Abdullah Khan Muhammad Khalid Department of Control & Instrumentation King Fahd University of Petroleum & Minerals Dhahran KSA IRC for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran KSA Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran KSA IRC for Renewable Energy and Power Systems King Fahd University of Petroleum & Minerals Dhahran KSA SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran KSA

The recent global development of smart devices in the field of low-power wide area network (LPWAN) technology and cloud computing has enabled transition of numerous industries towards smart solutions. An accelerated growth was experienced in the field of oil and gas (O&G) industry, for the various operation of upstream, downstream, fault management, and monitoring that is expected to increase exponentially. The advanced solutions facilitated by LPWAN and cloud have great advantages over manual wired/wireless such as decreased infrastructure cost, reliable and fast communication systems, ubiquitous access to business applications and flexible business model. In LPWAN, narrowband internet-of-things (NB-IOT) is one the most promising technologies that offers a huge benefit in terms of QoS, Latency, reliability, range, cost and battery lifetime. Given the advantages of NB-IOT, in this paper we propose a framework for utilizing the NB-IOT with the major cloud technologies available in the market for fault detection and monitoring in the O&G industry.

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