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Matti Lehtonen received the master's and Licentiate degrees in electrical engineering from Helsinki University of Technology, Finland, in 1984 and 1989, respectively, and the Doctor of Technology degree from Tampere University of Technology, Finland, in 1992. He was with VTT Energy, Espoo, Finland, from 1987 to 2003, and since 1999 has been a full Professor and head of power systems and high voltage engineering group's at Aalto University, Espoo, Finland. His research interests include power system planning and assets management, power system protection including earth fault problems, harmonic related issues, power cable insulation, and polymer nanocomposites. He is an Associate Editor for Electric Power Systems Research, and IET Generation, Transmission & Distribution.
Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6% taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model.
Arsalan Najafi; Mahdi Pourakbari-Kasmaei; Michal Jasiniski; Matti Lehtonen; Zbigniew Leonowicz. A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems. Energy 2021, 238, 121661 .
AMA StyleArsalan Najafi, Mahdi Pourakbari-Kasmaei, Michal Jasiniski, Matti Lehtonen, Zbigniew Leonowicz. A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems. Energy. 2021; 238 ():121661.
Chicago/Turabian StyleArsalan Najafi; Mahdi Pourakbari-Kasmaei; Michal Jasiniski; Matti Lehtonen; Zbigniew Leonowicz. 2021. "A medium-term hybrid IGDT-Robust optimization model for optimal self scheduling of multi-carrier energy systems." Energy 238, no. : 121661.
Optimal power flow (OPF), a mathematical programming problem extending power flow relationships, is one of the essential tools in the operation and control of power grids. To name but a few, the primary goals of OPF are to meet system demand at minimum production cost, minimum emission, and minimum voltage deviation. Being at the heart of power system problems for half a century, the OPF can be split into two significant categories, namely optimal active power flow (OAPF) and optimal reactive power flow (ORPF). The OPF is spontaneously a complicated non-linear and non-convex problem; however, it becomes more complex by considering different constraints and restrictions having to do with real power grids. Furthermore, power system operators in the modern-day power networks implement new limitations to the problem. Consequently, the OPF problem becomes more and more complex which can exacerbate the situation from mathematical and computational standpoints. Thus, it is crucially important to decipher the most appropriate methods to solve different types of OPF problems. Although a copious number of mathematical-based methods have been employed to handle the problem over the years, there exist some counterpoints, which prevent them from being a universal solver for different versions of the OPF problem. To address such issues, innovative alternatives, namely heuristic algorithms, have been introduced by many researchers. Inasmuch as these state-of-the-art algorithms show a significant degree of convenience in dealing with a variety of optimization problems irrespective of their complexities, they have been under the spotlight for more than a decade. This paper provides an extensive review of the latest applications of heuristic-based optimization algorithms so as to solve different versions of the OPF problem. In addition, a comprehensive review of the available methods from various dimensions is presented. Reviewing about 200 works is the most significant characteristic of this paper that adds significant value to its exhaustiveness.
Ehsan Naderi; Hossein Narimani; Mahdi Pourakbari-Kasmaei; Fernando Cerna; Mousa Marzband; Matti Lehtonen. State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints. Processes 2021, 9, 1319 .
AMA StyleEhsan Naderi, Hossein Narimani, Mahdi Pourakbari-Kasmaei, Fernando Cerna, Mousa Marzband, Matti Lehtonen. State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints. Processes. 2021; 9 (8):1319.
Chicago/Turabian StyleEhsan Naderi; Hossein Narimani; Mahdi Pourakbari-Kasmaei; Fernando Cerna; Mousa Marzband; Matti Lehtonen. 2021. "State-of-the-Art of Optimal Active and Reactive Power Flow: A Comprehensive Review from Various Standpoints." Processes 9, no. 8: 1319.
In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.
Fernando Cerna; Mahdi Pourakbari-Kasmaei; Luizalba Pinheiro; Ehsan Naderi; Matti Lehtonen; Javier Contreras. Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement. Energies 2021, 14, 3624 .
AMA StyleFernando Cerna, Mahdi Pourakbari-Kasmaei, Luizalba Pinheiro, Ehsan Naderi, Matti Lehtonen, Javier Contreras. Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement. Energies. 2021; 14 (12):3624.
Chicago/Turabian StyleFernando Cerna; Mahdi Pourakbari-Kasmaei; Luizalba Pinheiro; Ehsan Naderi; Matti Lehtonen; Javier Contreras. 2021. "Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement." Energies 14, no. 12: 3624.
In this paper, the resilient-constrained generation and transmission expansion planning (RCGTEP) considering natural disasters such as earthquake and flood is presented. The proposed model contains a two objective problem that the first objective function minimizes the construction and operation costs of resiliency sources (RSs) and, in the second objective function, the minimization of the expected energy not supplied (EENS) due to the outage of the system against the mentioned natural disasters is formulated. Also, the proposed model is constrained to linearized AC optimal power flow (AC-OPF) equations, the planning and operating of RSs constraints such as generation units, hardening transmission lines, parallel and series FACTS devices, resiliency constraints and power system angular stability against earthquakes and floods in the presence of critical and non-critical loads. In order to achieve the best compromise response, the RCGTEP single-objective problem is formulated based on the ε constraint-based Pareto optimization. In addition, this model has the uncertainty of RSs availability against natural disasters, so stochastic programming is used for RCGTEP. Then, the Benders decomposition (BD) method is used to solve the proposed problem and achieve the optimal solution in the shortest possible time. Finally, by implementing the proposed model on an IEEE standard transmission network, the numerical results confirm the capability of this method in improving the economic, operation, angular stability and resiliency indices of the power system simultaneously.
Hamidreza Hamidpour; Sasan Pirouzi; Sheila Safaee; Mohammadali Norouzi; Matti Lehtonen. Multi-objective resilient-constrained generation and transmission expansion planning against natural disasters. International Journal of Electrical Power & Energy Systems 2021, 132, 107193 .
AMA StyleHamidreza Hamidpour, Sasan Pirouzi, Sheila Safaee, Mohammadali Norouzi, Matti Lehtonen. Multi-objective resilient-constrained generation and transmission expansion planning against natural disasters. International Journal of Electrical Power & Energy Systems. 2021; 132 ():107193.
Chicago/Turabian StyleHamidreza Hamidpour; Sasan Pirouzi; Sheila Safaee; Mohammadali Norouzi; Matti Lehtonen. 2021. "Multi-objective resilient-constrained generation and transmission expansion planning against natural disasters." International Journal of Electrical Power & Energy Systems 132, no. : 107193.
Power systems are stretched across thousands of miles of diverse territories, often in remote locations, to generate and transfer the energy to geographically dispersed customers. The system is therefore subjected to a wide range of natural hazards which could potentially damage critical system components and cause interruption of electricity supply in some areas. To improve system resilience against natural hazards, management frameworks are required to identify hazardous areas and prioritize reinforcement activities in order to take the most out of the limited resources. Landslide is a natural disaster that involves the breakup and downhill flow of rock, mud, water, and anything caught in the path. It is a phenomenon frequently occurred in some parts of the world that could result in the failure of power transmission networks. Consequently, in this paper, a novel approach has been proposed that quantifies the landslide hazard, its damage to power system components, and the impacts on the overall system performance to prioritize reinforcement activities and mitigate the landslide vulnerability. The proposed approach is applied to a real power system and the obtained results are discussed in detail.
Rahim Ghorani; Sajjad Fattaheian-Dehkordi; Mehdi Farrokhi; Mahmud Fotuhi-Firuzabad; Matti Lehtonen. Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide. IEEE Access 2021, 9, 80300 -80309.
AMA StyleRahim Ghorani, Sajjad Fattaheian-Dehkordi, Mehdi Farrokhi, Mahmud Fotuhi-Firuzabad, Matti Lehtonen. Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide. IEEE Access. 2021; 9 (99):80300-80309.
Chicago/Turabian StyleRahim Ghorani; Sajjad Fattaheian-Dehkordi; Mehdi Farrokhi; Mahmud Fotuhi-Firuzabad; Matti Lehtonen. 2021. "Modeling and Quantification of Power System Resilience to Natural Hazards: A Case of Landslide." IEEE Access 9, no. 99: 80300-80309.
This paper presents an intensive measurement and analysis of monopolar ionized fields in bundled high voltage direct current (HVDC) conductors using the finite difference method based on the full multigrid technique. The positive feature of this study is that it considers the comprehensive representation of the bundle conductor, unlike the existing studies that approximate the bundle conductor with an equivalent conductor radius. Firstly, the proposed method is compared with previous experimental results. Secondly, a flexible laboratory model for the bundled HVDC conductors is constructed. Thirdly, the laboratory model is exploited to validate the numerically computed current density distribution on the ground plane and corona current for different bundles’ numbers and different distances between bundles. Bundles of one, two, and four conductors are adopted in the experimental setup. For the same applied voltage, the results verified that the corona current decreases by increasing the bundles’ number and/or minimizing the spacing between bundles. Consequently, the obtained results confirmed that corona power losses can be minimized, without needing the traditional procedures that involve increasing either the conductor radius or its height above the ground. The results of the proposed numerical approach concurred well with the present and past laboratory results.
Mohamed A. Abouelatta; Sayed A. Ward; Ahmad M. Sayed; Karar Mahmoud; Matti Lehtonen; Mohamed M.F. Darwish. Measurement and assessment of corona current density for HVDC bundle conductors by FDM integrated with full multigrid technique. Electric Power Systems Research 2021, 199, 107370 .
AMA StyleMohamed A. Abouelatta, Sayed A. Ward, Ahmad M. Sayed, Karar Mahmoud, Matti Lehtonen, Mohamed M.F. Darwish. Measurement and assessment of corona current density for HVDC bundle conductors by FDM integrated with full multigrid technique. Electric Power Systems Research. 2021; 199 ():107370.
Chicago/Turabian StyleMohamed A. Abouelatta; Sayed A. Ward; Ahmad M. Sayed; Karar Mahmoud; Matti Lehtonen; Mohamed M.F. Darwish. 2021. "Measurement and assessment of corona current density for HVDC bundle conductors by FDM integrated with full multigrid technique." Electric Power Systems Research 199, no. : 107370.
Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision–making about the GIS status.
Mahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Diaa-Eldin A. Mansour; Matti Lehtonen; Mohamed M. F. Darwish. Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning. IEEE Access 2021, 9, 1 -1.
AMA StyleMahmoud Elsisi, Minh-Quang Tran, Karar Mahmoud, Diaa-Eldin A. Mansour, Matti Lehtonen, Mohamed M. F. Darwish. Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleMahmoud Elsisi; Minh-Quang Tran; Karar Mahmoud; Diaa-Eldin A. Mansour; Matti Lehtonen; Mohamed M. F. Darwish. 2021. "Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning." IEEE Access 9, no. : 1-1.
Recently, the penetration of energy storage systems and photovoltaics has been significantly expanded worldwide. In this regard, this paper presents the enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load. DC–DC and DC–AC converters are coordinated and controlled to achieve DC voltage stability in the microgrid. To achieve such an ambitious target, the system is widely operated in two different modes: stand-alone and grid-connected modes. The novel control strategy enables maximum power generation from the photovoltaic system across different techniques for operating the microgrid. Six different cases are simulated and analyzed using the MATLAB/Simulink platform while varying irradiance levels and consequently varying photovoltaic generation. The proposed system achieves voltage and power stability at different load demands. It is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode. In general, the proposed battery converter control introduces a stable operation and regulated DC voltage but with few voltage spikes. The merit of the integrated DC microgrid with batteries is to attain further flexibility and reliability through balancing power demand and generation. The simulation results also show the system can operate properly in normal or abnormal cases, thanks to the proposed control strategy, which can regulate the voltage stability of the DC bus in the microgrid with energy storage systems and photovoltaics.
Dina Emara; Mohamed Ezzat; AlMoataz Abdelaziz; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems. Electronics 2021, 10, 1261 .
AMA StyleDina Emara, Mohamed Ezzat, AlMoataz Abdelaziz, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems. Electronics. 2021; 10 (11):1261.
Chicago/Turabian StyleDina Emara; Mohamed Ezzat; AlMoataz Abdelaziz; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems." Electronics 10, no. 11: 1261.
Increasing the integration of distributed energy resources (DERs) inspired by environmental and governmental incentives, beside the introduction of multi-agent structure perspective have led to a paradigm shift of operational conditions in distribution systems. In this context, the traditional concept of fit and forget in the distribution grid management would not be efficient in the modern distribution systems and consequently new mechanisms should be developed in order to enable the distribution system operator (DSO) to efficiently manage the grid congestion caused by peak power output of DERs or load demands requests. In this paper, the Stackelberg game concept is employed to develop an incentive-based mechanism that facilitates the contribution of local flexible resources operated by independent agents in the congestion management of distribution grids. Therefore, the proposed bi-level formulation would result in congestion alleviation in the multi-agent system; while the objectives associated with DSO and independent agents are fulfilled. Additionally, the developed framework enables DSO to reconfigure the network to decrease the operational costs associated with the congestion alleviation procedure. Finally, the strong-duality concept is utilized in order to combine the twolevel problem into a one-level problem and the obtained model is implemented on IEEE-37 bus test system to investigate its effectiveness in congestion management in the distribution system.
Sajjad Fattaheian-Dehkordi; Mehdi Tavakkoli; Ali Abbaspour; Mahmud Fotuhi-Firuzabad; Matti Lehtonen. An Incentive-Based Mechanism to Alleviate Active Power Congestion in a Multi-Agent Distribution System. IEEE Transactions on Smart Grid 2021, 12, 1978 -1988.
AMA StyleSajjad Fattaheian-Dehkordi, Mehdi Tavakkoli, Ali Abbaspour, Mahmud Fotuhi-Firuzabad, Matti Lehtonen. An Incentive-Based Mechanism to Alleviate Active Power Congestion in a Multi-Agent Distribution System. IEEE Transactions on Smart Grid. 2021; 12 (3):1978-1988.
Chicago/Turabian StyleSajjad Fattaheian-Dehkordi; Mehdi Tavakkoli; Ali Abbaspour; Mahmud Fotuhi-Firuzabad; Matti Lehtonen. 2021. "An Incentive-Based Mechanism to Alleviate Active Power Congestion in a Multi-Agent Distribution System." IEEE Transactions on Smart Grid 12, no. 3: 1978-1988.
The nonlinearities of the robotic manipulators and the uncertainties of their parameters represent big challenges against the controller design. Moreover, the tracking of regular and irregular trajectories with fewer overshoots, short settling time, and small steady-state error is the main target for the robotic response. The model predictive control (MPC) is an efficient controller to handle the performance requirements. However, the conventional MPC requires the linearization of the system model. The linearization of the model does not cover all dynamics of the robotic system. Thus, this paper introduces the nonlinear MPC (NLMPC) as a proper control method for the nonlinear systems instead of the conventional MPC. Specifically, this work proposes the use of NLMPC for controlling robotic manipulators. However, the NLMPC gains need proper tuning to attain good performance rather than the conventional methods. The neural network algorithm (NNA) considers a sufficient adaptive intelligent technique that can be utilized for this purpose. The restriction in a local optimum reveals the main issue versus artificial intelligence techniques. This paper suggests a new improvement to reinforce the exploration behavior of the NNA to overcome the local restriction issue. This modification is carried out by utilizing the polynomial mutation as an effective method to promise the exploration manner of the intelligence techniques. The proposed system can estimate all states from only the output to reduce the cost of the required sensors to measure all states. The results confirm the superiority of the proposed systems with the estimator with negligible change in the output response. The proposed modified NNA (MNNA) is evaluated with the main NNA, genetic algorithm-based PID control scheme, besides the cuckoo search algorithm-based PID control scheme from other works. The results confirm the robustness and effectiveness of the suggested MNNA-based NLMPC to track regular and irregular trajectories compared with other techniques.
Mahmoud Elsisi; Karar Mahmoud; Matti Lehtonen; Mohamed M. F. Darwish. Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators. IEEE Access 2021, 9, 64278 -64290.
AMA StyleMahmoud Elsisi, Karar Mahmoud, Matti Lehtonen, Mohamed M. F. Darwish. Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators. IEEE Access. 2021; 9 ():64278-64290.
Chicago/Turabian StyleMahmoud Elsisi; Karar Mahmoud; Matti Lehtonen; Mohamed M. F. Darwish. 2021. "Effective Nonlinear Model Predictive Control Scheme Tuned by Improved NN for Robotic Manipulators." IEEE Access 9, no. : 64278-64290.
Power distribution networks are transitioning from passive towards active networks considering the incorporation of distributed generation. Traditional energy networks require possible system upgrades due to the exponential growth of non-conventional energy resources. Thus, the cost concerns of the electric utilities regarding financial models of renewable energy sources (RES) call for the cost and benefit analysis of the networks prone to unprecedented RES integration. This paper provides an evaluation of photovoltaic (PV) hosting capacity (HC) subject to economical constraint by a probabilistic analysis based on Monte Carlo (MC) simulations to consider the stochastic nature of loads. The losses carry significance in terms of cost parameters, and this article focuses on HC investigation in terms of losses and their associated cost. The network losses followed a U-shaped trajectory with increasing PV penetration in the distribution network. In the investigated case networks, increased PV penetration reduced network costs up to around 40%, defined as a ratio to the feeding secondary transformer rating. Above 40%, the losses started to increase again and at 76–87% level, the network costs were the same as in the base cases of no PVs. This point was defined as the economical PV HC of the network. In the case of networks, this level of PV penetration did not yet lead to violations of network technical limits.
Samar Fatima; Verner Püvi; Ammar Arshad; Mahdi Pourakbari-Kasmaei; Matti Lehtonen. Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks. Energies 2021, 14, 2405 .
AMA StyleSamar Fatima, Verner Püvi, Ammar Arshad, Mahdi Pourakbari-Kasmaei, Matti Lehtonen. Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks. Energies. 2021; 14 (9):2405.
Chicago/Turabian StyleSamar Fatima; Verner Püvi; Ammar Arshad; Mahdi Pourakbari-Kasmaei; Matti Lehtonen. 2021. "Comparison of Economical and Technical Photovoltaic Hosting Capacity Limits in Distribution Networks." Energies 14, no. 9: 2405.
Condition assessment of insulating oil is crucial for the reliable long-term operation of power equipment, especially power transformers. Under thermal aging, critical degradation in oil properties, including chemical, physical, and dielectric properties, occurs due to the generation of aging byproducts. Ultraviolet-visible (UV-Vis) spectroscopy was recently proposed for the condition assessment of mineral oil. However, this absorption technique may involve all electronic states of the investigated material which typically yield a broad spectrum, and thus cannot precisely reflect the electronic structure of aged oil samples. It also cannot be implemented as an online sensor of oil degradation. In this paper, photoluminescence (PL) spectroscopy is introduced, for the first time, for effective condition assessment of insulating oil. The PL technique involves emission processes that only occur between a narrow band of electronic states that are occupied by thermalized electrons and consequently yields a spectrum that is much narrower than that of the absorption spectrum. Aged oil samples with different aging extents were prepared in the laboratory using accelerated aging tests at 120 °C, under which 1 day of laboratory aging is equivalent to approximately 1 year of aging in the field. These aged samples were then tested using PL spectroscopy with a wavelength ranging from 150 nm to 1500 nm. Two main parameters were evaluated for quantitative analysis of PL spectra: The full width at half-maximum and the enclosed area under the PL spectra. These parameters were correlated to the aging extent. In conjunction with PL spectroscopy, the aged oil samples were tested for the dielectric dissipation factor as an indication of the number of aging byproducts. Interestingly, we find a correlation between the PL spectra and the dielectric dissipation factor. The results of PL spectroscopy were compared to those of UV-Vis spectroscopy for the same samples and the parameters extracted from PL spectra were compared to the aging b-products extracted from UV-Vis spectra. Finally, the corresponding physical mechanisms were discussed considering the obtained results and the spectral shift for each spectrum. It was proved that PL spectroscopy is a promising technique for the condition assessment of insulating oil when compared to conventional transformer oil assessment measuring techniques and even to other optical absorption techniques.
Abdelrahman Alshehawy; Diaa-Eldin Mansour; Mohsen Ghali; Matti Lehtonen; Mohamed Darwish. Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil. Processes 2021, 9, 732 .
AMA StyleAbdelrahman Alshehawy, Diaa-Eldin Mansour, Mohsen Ghali, Matti Lehtonen, Mohamed Darwish. Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil. Processes. 2021; 9 (5):732.
Chicago/Turabian StyleAbdelrahman Alshehawy; Diaa-Eldin Mansour; Mohsen Ghali; Matti Lehtonen; Mohamed Darwish. 2021. "Photoluminescence Spectroscopy Measurements for Effective Condition Assessment of Transformer Insulating Oil." Processes 9, no. 5: 732.
Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.
Mostafa Al-Gabalawy; Karar Mahmoud; Mohamed Darwish; James Dawson; Matti Lehtonen; Nesreen Hosny. Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries. Applied Sciences 2021, 11, 3609 .
AMA StyleMostafa Al-Gabalawy, Karar Mahmoud, Mohamed Darwish, James Dawson, Matti Lehtonen, Nesreen Hosny. Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries. Applied Sciences. 2021; 11 (8):3609.
Chicago/Turabian StyleMostafa Al-Gabalawy; Karar Mahmoud; Mohamed Darwish; James Dawson; Matti Lehtonen; Nesreen Hosny. 2021. "Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries." Applied Sciences 11, no. 8: 3609.
In modern power systems, power transformers are considered vital components that can ensure the grid’s continuous operation. In this regard, studying the breakdown in the transformer becomes necessary, especially its insulating system. Hence, in this study, Box–Behnken design (BBD) was used to introduce a prediction model of the breakdown voltage (VBD) for the transformer insulating oil in the presence of different barrier effects for point/plane gap arrangement with alternating current (AC) voltage. Interestingly, the BBD reduces the required number of experiments and their costs to examine the barrier parameter effect on the existing insulating oil VBD. The investigated variables were the barrier location in the gap space (a/d)%, the relative permittivity of the barrier materials (εr ), the hole radius in the barrier (hr), the barrier thickness (th), and the barrier inclined angle (θ). Then, only 46 experiment runs are required to build the BBD model for the five barrier variables. The BBD prediction model was verified based on the statistical study and some other experiment runs. Results explained the influence of the inclined angle of the barrier and its thickness on the VBD. The obtained results indicated that the designed BBD model provides less than a 5% residual percentage between the measured and predicted VBD. The findings illustrated the high accuracy and robustness of the proposed insulating oil breakdown voltage predictive model linked with diverse barrier effects.
Sherif Ghoneim; Sobhy Dessouky; Ahmed Boubakeur; Adel Elfaraskoury; Ahmed Abou Sharaf; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects. Processes 2021, 9, 657 .
AMA StyleSherif Ghoneim, Sobhy Dessouky, Ahmed Boubakeur, Adel Elfaraskoury, Ahmed Abou Sharaf, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects. Processes. 2021; 9 (4):657.
Chicago/Turabian StyleSherif Ghoneim; Sobhy Dessouky; Ahmed Boubakeur; Adel Elfaraskoury; Ahmed Abou Sharaf; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Accurate Insulating Oil Breakdown Voltage Model Associated with Different Barrier Effects." Processes 9, no. 4: 657.
This paper proposes a distributionally robust optimization method for assessing economic benefits of transmission expansions with thyristor controlled series compensation devices. To accommodate the uncertainty of wind generations and electrical vehicle loads, a distributionally robust optimization method is proposed to use historical data instead of predefined distribution probabilities. In the proposed framework, firstly, a confidence set for unknown distributions are constructed based on the historical data. Then, based on worst-case distributions within a constructed confidence set, the optimal results of transmission expansion planning and allocation of thyristor controlled series compensation device are obtained. In order to take advantage of the flexible alternative current transmission devices (FACTS) in both economic and technical considerations, the optimal allocation of thyristor controlled series compensation is integrated into the transmission expansion planning. This requires AC-based transmission expansion planning model. AC power flow suffers from high computational complexity. To tackle this issue, the linearized AC power flow model is utilized. Furthermore, to solve the optimization problem, the combined technique including Bender's decomposition and cut-and-column are utilized. The numerical results on the Garver 6-bus and IEEE 118-bus systems demonstrate the effectiveness of the proposed method in comparison with scenario-based stochastic and robust optimization methods. Besides, the impacts of the thyristor controlled series compensation on total cost reduction has been investigated.
Mohammad Sadegh Mokhtari; Mohsen Gitizadeh; Matti Lehtonen. Optimal coordination of thyristor controlled series compensation and transmission expansion planning: Distributionally robust optimization approach. Electric Power Systems Research 2021, 196, 107189 .
AMA StyleMohammad Sadegh Mokhtari, Mohsen Gitizadeh, Matti Lehtonen. Optimal coordination of thyristor controlled series compensation and transmission expansion planning: Distributionally robust optimization approach. Electric Power Systems Research. 2021; 196 ():107189.
Chicago/Turabian StyleMohammad Sadegh Mokhtari; Mohsen Gitizadeh; Matti Lehtonen. 2021. "Optimal coordination of thyristor controlled series compensation and transmission expansion planning: Distributionally robust optimization approach." Electric Power Systems Research 196, no. : 107189.
Recently, the use of diverse renewable energy resources has been intensively expanding due to their technical and environmental benefits. One of the important issues in the modeling and simulation of renewable energy resources is the extraction of the unknown parameters in photovoltaic models. In this regard, the parameters of three models of photovoltaic (PV) cells are extracted in this paper with a new optimization method called turbulent flow of water-based optimization (TFWO). The applications of the proposed TFWO algorithm for extracting the optimal values of the parameters for various PV models are implemented on the real data of a 55 mm diameter commercial R.T.C. France solar cell and experimental data of a KC200GT module. Further, an assessment study is employed to show the capability of the proposed TFWO algorithm compared with several recent optimization techniques such as the marine predators algorithm (MPA), equilibrium optimization (EO), and manta ray foraging optimization (MRFO). For a fair performance evaluation, the comparative study is carried out with the same dataset and the same computation burden for the different optimization algorithms. Statistical analysis is also used to analyze the performance of the proposed TFWO against the other optimization algorithms. The findings show a high closeness between the estimated power–voltage (P–V) and current–voltage (I–V) curves achieved by the proposed TFWO compared with the experimental data as well as the competitive optimization algorithms, thanks to the effectiveness of the developed TFWO solution mechanism.
Mokhtar Said; Abdullah Shaheen; Ahmed Ginidi; Ragab El-Sehiemy; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer. Processes 2021, 9, 627 .
AMA StyleMokhtar Said, Abdullah Shaheen, Ahmed Ginidi, Ragab El-Sehiemy, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer. Processes. 2021; 9 (4):627.
Chicago/Turabian StyleMokhtar Said; Abdullah Shaheen; Ahmed Ginidi; Ragab El-Sehiemy; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer." Processes 9, no. 4: 627.
To achieve a successful integration of fluctuating renewable power generation, the power-to-heat (P2H) conversion is seen as an efficient solution that remedies the issue of curtailments as well as reduces carbon emissions prevailing in the district heating (DH) sector. Concurrently, the need for storage is also increasing to maintain a continuous power supply. Hence, this paper presents a MILP-based model to optimize the size of thermal storage required to satisfy the annual DH demand of a community solely by P2H conversion employing renewable energy. The DH is supplied by the optimal operation of a novel 2-km deep well heat pump system (DWHP) equipped with thermal storage. To avoid computational intractability, representative time steps with varying time duration are chosen by employing hierarchical agglomerative clustering that aggregates adjacent hours chronologically. The value of demand response and the effect of interannual weather variability are also analyzed. Numerical results from a Finnish case study show that P2H conversion utilizing small thermal storage in tandem with the DWHP is able to cover the annual DH demand, thus leading to a carbon-neutral DH system and, at the same time, mitigating the curtailment of excessive wind generation. Compared with the annual DH demand, an average thermal storage size of 29.17 MWh (2.58%) and 13.99 MWh (1.24%) are required in the business-as-usual and the demand response cases, respectively.
Arslan Bashir; Andreas Lund; Mahdi Pourakbari-Kasmaei; Matti Lehtonen. Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities. Energies 2021, 14, 1911 .
AMA StyleArslan Bashir, Andreas Lund, Mahdi Pourakbari-Kasmaei, Matti Lehtonen. Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities. Energies. 2021; 14 (7):1911.
Chicago/Turabian StyleArslan Bashir; Andreas Lund; Mahdi Pourakbari-Kasmaei; Matti Lehtonen. 2021. "Optimizing Power and Heat Sector Coupling for the Implementation of Carbon-Free Communities." Energies 14, no. 7: 1911.
In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.
Ahmed Bendary; AlMoataz Abdelaziz; Mohamed Ismail; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System. Sensors 2021, 21, 2269 .
AMA StyleAhmed Bendary, AlMoataz Abdelaziz, Mohamed Ismail, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System. Sensors. 2021; 21 (7):2269.
Chicago/Turabian StyleAhmed Bendary; AlMoataz Abdelaziz; Mohamed Ismail; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System." Sensors 21, no. 7: 2269.
This paper presents a hierarchically distributed algorithm for the execution of distribution state estimation function in active networks equipped with some phasor measurement units. The proposed algorithm employs voltage-based state estimation in rectangular form and is well-designed for large-scale active distribution networks. For this purpose, as the first step, the distribution network is supposed to be divided into some overlapped zones and local state estimations are executed in parallel for extracting operating states of these zones. Then, using coordinators in the feeders and the substation, the estimated local voltage profiles of all zones are coordinated with the local state estimation results of their neighboring zones. In this regard, each coordinator runs a state estimation process for the border buses (overlapped buses and buses with tie-lines) of its zones and based on the results for voltage phasor of border buses, the local voltage profiles in non-border buses of its zones are modified. The performance of the proposed algorithm is tested with an active distribution network, considering different combinations of operating conditions, network topologies, network decompositions, and measurement scenarios, and the results are presented and discussed.
Mohammad Gholami; Ali Tehrani-Fard; Matti Lehtonen; Moein Moeini-Aghtaie; Mahmud Fotuhi-Firuzabad. A Novel Multi-Area Distribution State Estimation Approach for Active Networks. Energies 2021, 14, 1772 .
AMA StyleMohammad Gholami, Ali Tehrani-Fard, Matti Lehtonen, Moein Moeini-Aghtaie, Mahmud Fotuhi-Firuzabad. A Novel Multi-Area Distribution State Estimation Approach for Active Networks. Energies. 2021; 14 (6):1772.
Chicago/Turabian StyleMohammad Gholami; Ali Tehrani-Fard; Matti Lehtonen; Moein Moeini-Aghtaie; Mahmud Fotuhi-Firuzabad. 2021. "A Novel Multi-Area Distribution State Estimation Approach for Active Networks." Energies 14, no. 6: 1772.
Power transformers are considered important and expensive items in electrical power networks. In this regard, the early discovery of potential faults in transformers considering datasets collected from diverse sensors can guarantee the continuous operation of electrical systems. Indeed, the discontinuity of these transformers is expensive and can lead to excessive economic losses for the power utilities. Dissolved gas analysis (DGA), as well as partial discharge (PD) tests considering different intelligent sensors for the measurement process, are used as diagnostic techniques for detecting the oil insulation level. This paper includes two parts; the first part is about the integration among the diagnosis results of recognized dissolved gas analysis techniques, in this part, the proposed techniques are classified into four techniques. The integration between the different DGA techniques not only improves the oil fault condition monitoring but also overcomes the individual weakness, and this positive feature is proved by using 532 samples from the Egyptian Electricity Transmission Company (EETC). The second part overview the experimental setup for (66/11.86 kV–40 MVA) power transformer which exists in the Egyptian Electricity Transmission Company (EETC), the first section in this part analyzes the dissolved gases concentricity for many samples, and the second section illustrates the measurement of PD particularly in this case study. The results demonstrate that precise interpretation of oil transformers can be provided to system operators, thanks to the combination of the most appropriate techniques.
Sayed Ward; Adel El-Faraskoury; Mohamed Badawi; Shimaa Ibrahim; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors. Sensors 2021, 21, 2223 .
AMA StyleSayed Ward, Adel El-Faraskoury, Mohamed Badawi, Shimaa Ibrahim, Karar Mahmoud, Matti Lehtonen, Mohamed Darwish. Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors. Sensors. 2021; 21 (6):2223.
Chicago/Turabian StyleSayed Ward; Adel El-Faraskoury; Mohamed Badawi; Shimaa Ibrahim; Karar Mahmoud; Matti Lehtonen; Mohamed Darwish. 2021. "Towards Precise Interpretation of Oil Transformers via Novel Combined Techniques Based on DGA and Partial Discharge Sensors." Sensors 21, no. 6: 2223.