Power systems
Hamid Reza Sezavar; Saeed Hasanzadeh
Abstract
Insulator pollution levels are critical for ensuring the operational stability and safety of power transmission systems. Traditional methods for detecting pollution are often invasive, inaccurate, and time-consuming. To address these issues, this study investigates the application of Artificial Intelligence ...
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Insulator pollution levels are critical for ensuring the operational stability and safety of power transmission systems. Traditional methods for detecting pollution are often invasive, inaccurate, and time-consuming. To address these issues, this study investigates the application of Artificial Intelligence (AI), specifically Gradient Boosting Machines (GBM), to classify insulator pollution levels based on Partial Discharge (PD) characteristics. We utilize a combination of time-domain and frequency-domain features extracted from PD signals to train a predictive model. The results indicate that the proposed model achieves a high classification accuracy, averaging between 92% and 95% across various contamination levels. Furthermore, the study analyzes the model's sensitivity to environmental factors, including humidity and Hydrophobicity Class (HC), revealing important insights that could influence classification performance. By employing this AI-driven approach, we aim to significantly enhance the efficiency of power grid maintenance, ultimately contributing to the long-term stability and reliability of transmission systems. The findings from this research underscore the potential of AI in revolutionizing pollution assessment methods and optimizing maintenance practices in power infrastructure.
Power systems
Mahdi HassanniaKheibari; Zivar Rigi
Abstract
As power systems rapidly expand and the demand for uninterrupted power supply to network loads increases, ensuring the safe and stable operation of these systems has become crucially important. However, conducting dynamic stability assessments with detailed dynamic models is nearly impossible in today’s ...
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As power systems rapidly expand and the demand for uninterrupted power supply to network loads increases, ensuring the safe and stable operation of these systems has become crucially important. However, conducting dynamic stability assessments with detailed dynamic models is nearly impossible in today’s complex power networks. The introduction of Phasor Measurement Units (PMUs) has paved the way for new stability evaluation techniques that rely on real-time measurement data. A common limitation of most measurement-based techniques is their vulnerability to noise in the data. While some newer methods offer improved noise resistance, they are often hindered by high computational demands and slow processing times, limiting their practical use. This paper developed a measurement-based method that uses power spectral density (PSD) and cross-spectral density (CSD) to achieve a more precise estimation of low-frequency oscillations in power systems. Simulation results on the IEEE 14-bus and 39-bus test systems, tested under both noisy and noise-free conditions, show that the proposed method yields more accurate frequency and oscillation shape estimates, even when measurement noise is present. Additionally, the Prony algorithm, a well-known measurement-based method, is also implemented, and its high sensitivity to noisy data is demonstrated.
Power systems
Hamed Shadfar; Hamid Reza Izadfar
Abstract
Calculating the current of the rotor bars in a squirrel cage induction motor (SCIM) using stator data is difficult, but it is very useful. With the calculation of the rotor bars’ current, analysis and investigation of some different parameters of the motor can be done, precisely. One of the faults ...
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Calculating the current of the rotor bars in a squirrel cage induction motor (SCIM) using stator data is difficult, but it is very useful. With the calculation of the rotor bars’ current, analysis and investigation of some different parameters of the motor can be done, precisely. One of the faults faced by SCIM is the broken rotor bar (BRB) fault. The breakage of one or more bars causes a change in the healthy bars' current and the motor's behavior and parameters. This paper introduces the method of calculating the rotor bars’ current in the SCIM in two healthy and defective states (breakage of 1 bar and 2 adjacent bars) using the multiple-coupled circuit (MCC) model. In addition to currents, some important parameters, such as speed, magnetic field, etc., will be calculated. For validation of results, a two-pole SCIM with a nominal specification of 1.1 kW, 220/380 V, and 50 Hz is subjected to experimental testing. The results are confirmed by practical tests and simulations using the Ansys Maxwell software.
Power systems
AmirHossein Babaali; Mohammad Taghi Ameli
Abstract
Short-term voltage stability (STVS) varies with operating conditions of power networks, making its accurate assessment a critical challenge. This paper investigates a multi-class, data-driven approach to STVS evaluation. A dynamic index is employed to categorize voltage magnitude variations into three ...
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Short-term voltage stability (STVS) varies with operating conditions of power networks, making its accurate assessment a critical challenge. This paper investigates a multi-class, data-driven approach to STVS evaluation. A dynamic index is employed to categorize voltage magnitude variations into three classes: stable, alert, and unstable. A significant obstacle in data-driven methods is missing measurement data, typically caused by sensor failures or communication delays. To address this issue, we propose two complementary solutions. First, a Bidirectional Gated Recurrent Unit (Bi-GRU) network with an attention mechanism is designed to recover data loss due to sensor failures. This method leverages both temporal trends and historical system information to reconstruct missing values with high accuracy. Second, a variable-length sliding window (VLSW) algorithm combined with a Bi-GRU is introduced to mitigate data loss arising from communication delays. The VLSW algorithm enhances data diversity and enables fast recovery. Simulation results on IEEE 39-bus and IEEE 118-bus test systems demonstrate that the proposed framework effectively identifies multi-class STVS under missing data conditions and remains robust against long-range data losses. Finally, validation on a real-world local network further confirms the practicality and robustness of the proposed approach.
Power systems
Seyed Fariborz Zarei; Saeed Hasanzadeh
Abstract
This paper presents a comprehensive approach to the design of Resolution Bandwidth (RBW) filters specifically for Electromagnetic Interference (EMI) applications. We propose a mathematical modeling method that accurately captures the characteristics of standard RBW filters, which are essential for precise ...
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This paper presents a comprehensive approach to the design of Resolution Bandwidth (RBW) filters specifically for Electromagnetic Interference (EMI) applications. We propose a mathematical modeling method that accurately captures the characteristics of standard RBW filters, which are essential for precise EMI noise measurements. The proposed approach utilizes paired complementary second-order filters with symmetrical cutoff frequencies to ensure compliance with CISPR-16 standards. The methodology underscores the importance of aligning theoretical models with real-world filter behavior, ensuring that the resulting models are both accurate and reliable. By establishing a robust framework for RBW filter design, the method enables optimized EMI system performance and the implementation of appropriate filtering solutions. Validation is carried out through simulations using a 150 kHz signal with a dynamically ramped amplitude increase, demonstrating high accuracy and strong performance under both transient and steady-state conditions. Despite the challenging test scenarios, the results confirm that the proposed filter model remains accurate and effective even under worst-case EMI conditions.
Power systems
Mehran Haghighi; Hamid Karimi; Shahram Jadid
Abstract
A home energy management system optimizes the electrical demand of household appliances according to price-based demand response programs. In this paper, we proposed a price-based demand response for a smart home with different types of appliances according to customer satisfaction, which also used electric ...
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A home energy management system optimizes the electrical demand of household appliances according to price-based demand response programs. In this paper, we proposed a price-based demand response for a smart home with different types of appliances according to customer satisfaction, which also used electric and thermal storage systems. In the proposed method, various appliances were considered in the smart home modeled by the energy hub system. A multi-objective daily management is proposed which considered electricity costs and customer satisfaction simultaneously to provide comprehensive management for smart homes. This paper presents a multi-objective optimization approach that not only minimizes operating costs and maximizes customer satisfaction but also reduces environmental impacts through emission reduction, creating a more sustainable energy management system. After solving the multi-objective problem, the technique for order preference by similarity to the ideal solution was used to rank the solutions, considering the preference of the decision-maker. The proposed model investigates the response of the smart home energy system in different conditions. Also, stochastic optimization was applied to model the probabilistic nature of demands, PV, and wind energy. The simulation results demonstrate that the proposed method reduces the consumer dissatisfaction by 79.9% and reduces the range from 199 to 40
Power systems
Hamid Reza Sezavar
Abstract
This paper presents a comparative study on the application of artificial intelligence for optimizing External Lightning Protection Systems (ELPS) in photovoltaic power (PV) plants. The research addresses the critical need for advanced protection systems in solar installations, which are particularly ...
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This paper presents a comparative study on the application of artificial intelligence for optimizing External Lightning Protection Systems (ELPS) in photovoltaic power (PV) plants. The research addresses the critical need for advanced protection systems in solar installations, which are particularly vulnerable to lightning strikes due to their expansive outdoor configurations. Through a detailed comparative analysis, the study evaluates multiple AI approaches, including metaheuristic algorithms and machine learning models. The investigation reveals that metaheuristic algorithms often have lower accuracy compared to modern AI techniques. All comparisons are based on a multi-level optimization framework, systematically addressing air termination design, grounding system configuration, and overall system integration. The results show superiority in sensitivity analysis in the transformer model. Compared to other models, the random forest (RF) model, along with the artificial neural network (ANN) model, has a higher speed in data analysis. However, physics-informed neural networks (PINN) achieve remarkable improvements, delivering 93% protection coverage with only 3.2% grounding error while significantly reducing design convergence times.
Power systems
Javad Rahmani-Fard
Abstract
This paper presents a comprehensive investigation into the design principles and operational characteristics of dual three-phase permanent magnet (PM) machines. The study focuses on optimizing the winding arrangement and slot-pole combinations for enhanced performance and reliability. Through detailed ...
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This paper presents a comprehensive investigation into the design principles and operational characteristics of dual three-phase permanent magnet (PM) machines. The study focuses on optimizing the winding arrangement and slot-pole combinations for enhanced performance and reliability. Through detailed analysis, an optimal configuration is proposed, and a dual three-phase machine based on this design is developed. The operational behavior of the machine is thoroughly examined under healthy conditions, with particular attention given to its thermal performance to ensure it can sustain high power density and output power without compromising reliability. The effectiveness of the proposed design and thermal analysis is validated through advanced simulation results, which demonstrate the motor's robust performance, efficiency, and ability to maintain stable operation under demanding conditions. Under natural cooling, the dual three-phase motor operates safely within its thermal limits, with a maximum winding temperature of 139.99℃, below the 180℃ insulation limit, and a maximum magnet temperature of 105.62℃, below the 150℃ limit. This research highlights the potential of dual three-phase PM machines for applications requiring high reliability and performance.
Power systems
Abbas-Ali Zamani
Abstract
The large-scale integration of renewable generation into microgrids can lead to decreased inertia, resulting in high rates of change of frequency and frequency instability. This issue is even more complex in islanded MGs that incorporate a high proportion of RGs and need to deliver power to loads in ...
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The large-scale integration of renewable generation into microgrids can lead to decreased inertia, resulting in high rates of change of frequency and frequency instability. This issue is even more complex in islanded MGs that incorporate a high proportion of RGs and need to deliver power to loads in islanded mode. To address this problem, a virtual inertia control scheme can be employed to enhance system inertia and maintain frequency stability. In this article, we propose a novel control strategy named the optimal nonlinear fractional-order PI-based virtual inertia controller, which integrates a nonlinear fractional-order PI controller into the conventional VIC loop. The designed ONFOPI+VI controller, which considers both inertia and damping properties, is optimized using the Coot optimization algorithm. Furthermore, an alternative control methodology, denoted as OFOPI+VI, has been developed to analyze and evaluate the outcomes obtained from the proposed ONFOPI+VI control structure. This paper compares the performance of the proposed ONFOPI+VI strategy to that of the OFOPI+VI and other VIC techniques for different RG and load variations under various scenarios. Simulation results and detailed analyses confirmed that the ONFOPI+VI controller significantly outperformed conventional methods, yielding at least a 30% improvement in IAE and a 20% improvement in ITAE compared to other control techniques.
Power systems
Milad Niaz Azari; Iraj Ahmadi; Hossein Aboulqasemi
Abstract
In this paper, the loss of excitation fault (LOE) as one of the most common fault in synchronous generator is analyzed and the methods for detecting this fault are investigated. Then, the performance of the power system equipped with STATCOM is simulated in the Matlab / Simulink software and the effects ...
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In this paper, the loss of excitation fault (LOE) as one of the most common fault in synchronous generator is analyzed and the methods for detecting this fault are investigated. Then, the performance of the power system equipped with STATCOM is simulated in the Matlab / Simulink software and the effects of the generator performance on the resistor and its derivatives in the generator terminal are analyzed. A new method for LOE detection based on derivative of resistance is proposed. To illustrate the efficiency of this method various sizes and conditions for generator load are considered. The simulation results show that the amount of resistance time derivative in all cases, whether with or without STATCOM, behaves the same as a new criterion for detecting the LOE of an effective and useful method that is faster and more accurate than conventional methods. Simulation results in different amount and type of the loads shows the validity of the proposed method.
Power systems
Reza Karimi; Abbas Ketabi; Seyyed Mohammad Nobakhti
Abstract
The utilization of distributed generation (DG) sources in distribution systems has experienced significant growth due to their numerous advantages. Despite benefits such as voltage support and reduced losses, DG integration has introduced substantial challenges to distribution system protection, impairing ...
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The utilization of distributed generation (DG) sources in distribution systems has experienced significant growth due to their numerous advantages. Despite benefits such as voltage support and reduced losses, DG integration has introduced substantial challenges to distribution system protection, impairing the performance of conventional protection schemes. Variations in fault current levels, especially during islanding conditions, and bidirectional fault current flow are among the factors influencing the operation of traditional protection schemes. Under such conditions, directional overcurrent relays may not operate as intended. Moreover, coordinating multiple overcurrent relays is often challenging and can lead to increased operating times of protective relays. This paper proposes a directional comparison protection scheme for protecting lines and zones in active distribution systems based on the calculation of incremental active power transient energy. The proposed scheme is capable of detecting faults on microgrid lines at both low and medium voltage levels and is adaptable to changes in microgrid configuration. To prevent the directional protection scheme from operating during load switching transients, a differential protection scheme based on the calculation of transient energy of current signals is employed. The proposed methods offer the advantages of ease of calculation and high accuracy. An AC active distribution system incorporating inverter-based DG sources is implemented in the PSCAD-EMTDC software to simulate various fault types. The simulation results are then transferred to MATLAB for the implementation of the proposed algorithms.
Power systems
Hamed Shadfar; Hamid Reza Izadfar
Abstract
One of the main components of a power transformer is the transformer insulation system, namely, transformer insulation oil and transformer insulation paper. Any failure can cause the transformer to fail temporarily or permanently. As a result, regular and non-destructive monitoring of transformers is ...
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One of the main components of a power transformer is the transformer insulation system, namely, transformer insulation oil and transformer insulation paper. Any failure can cause the transformer to fail temporarily or permanently. As a result, regular and non-destructive monitoring of transformers is of particular importance for early detection and prediction of any faults. Frequency response analysis (FRA) is known as a high-accuracy, fast, economical, and non-destructive method for diagnosing the condition of a transformer, which can be used independently or as a complementary method to ensure the results of other diagnostic tests and based on the operational conditions of the transformer, it can be implemented in two methods, online and offline. This paper provides an in-depth discussion of measuring and interpreting FRA results and the ability of this method to detect and locate power transformer faults, especially insulation faults, which have been given less attention in the past. The information confirmed in this survey is expected to provide an important roadmap for future research in monitoring the condition of transformer insulation systems
Power systems
Reza Ghanizadeh; Hamed Azadrou
Abstract
Bearing-less induction motors (BLIMs) are suitable candidates for high-speed applications but suffer from low torque density and complex control issues due to the interaction of torque and levitation forces. To address these challenges, this paper presents a new control strategy that combines vector ...
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Bearing-less induction motors (BLIMs) are suitable candidates for high-speed applications but suffer from low torque density and complex control issues due to the interaction of torque and levitation forces. To address these challenges, this paper presents a new control strategy that combines vector control and direct torque control (DTC) for torque management, alongside a novel force control method based on finite element analysis (FEA). The proposed approach minimizes interference between torque and force magnetic fields by employing a parallel winding structure and distinct control units for torque and force. Simulation results demonstrate that the proposed method significantly reduces torque ripple and improves steady-state performance compared to conventional vector control and DTC. Furthermore, the force control unit outperforms a dual field-oriented control (FOC) method in regulating rotor position, offering better suspension force control and faster stabilization. This work contributes to the development of more efficient control strategies for BLIMs, enhancing their performance in industrial applications.
Power systems
Meysam Feili; Mohammad Taghi Aameli
Abstract
The prominent role of natural gas networks in mitigating the intermittency of renewable energy resources has highlighted the importance of integrated operation between electricity and gas grids. Additionally, energy storage systems, such as batteries and hydrogen, play a crucial role in power balancing ...
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The prominent role of natural gas networks in mitigating the intermittency of renewable energy resources has highlighted the importance of integrated operation between electricity and gas grids. Additionally, energy storage systems, such as batteries and hydrogen, play a crucial role in power balancing and energy management. Previous research on the synergy between electricity and natural gas systems has primarily focused on the operational constraints of each grid. Only a few studies have explored market-driven models, such as peer-to-peer (P2P) energy trading, for the integrated operation of these networks. Furthermore, the limited studies that have implemented the peer-to-peer (P2P) market model for the integrated operation of power and natural gas grids have been conducted in two distinct phases: scheduling and trading. This paper introduces a stochastic P2P market-based optimization model for the coupled operation of natural gas and electricity grids, considering smart grid technologies such as power-to-gas (P2G) storage, batteries, and demand response (DR). Also, the presented framework incorporates alternating current (AC) power flow, natural gas steady-state model, and the power grid usage fee through the electrical distance model. The simulation results indicate that the proposed method significantly decreases total operating costs, reduces power losses, improves network component synergy, and enhances the performance of both networks.
Power systems
Gholamreza Memarzadeh; Farshid Keynia; Faezeh Amirteimoury; Rasoul Memarzadeh; Hossein Noori
Abstract
In recent years, there has been a significant increase in the utilization of renewable resources for electricity generation. Consequently, accurate short-term forecasting of renewable power production has become crucial for power system operations. However, Renewable Power Production Forecasting (RPPF) ...
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In recent years, there has been a significant increase in the utilization of renewable resources for electricity generation. Consequently, accurate short-term forecasting of renewable power production has become crucial for power system operations. However, Renewable Power Production Forecasting (RPPF) presents unique challenges due to the intermittent and uncertain nature of renewable energy sources. This paper proposes a novel approach to short-term RPPF. The proposed model integrates various techniques, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The aim is to enhance the accuracy and predictive performance of renewable power production forecasts. The suggested hybrid model employs the Modified Relief-Mutual Information (MRMI) feature selection technique to identify the most influential input data for prediction. Subsequently, the combined model generates a 24-hour ahead RPP prediction using a weighted output approach. By capitalizing on the strengths of each individual model, the combined method mitigates their weaknesses, thereby improving the overall efficiency of the forecasting process. The accuracy and performance of the proposed method are evaluated through two case studies involving solar farm power generation at the Mahan, Iran and Rafsanjan, Iran sites. The results demonstrate the effectiveness of the hybrid model in enhancing the accuracy of short-term RPPF. By combining multiple forecasting methods and utilizing the MRMI feature selection technique, the proposed method significantly improves prediction accuracy.
Power systems
Ali Riki; Mahmoud Oukati Sadegh; Omid Narouei
Abstract
The concept of an energy hub (EH) has been utilized to address the issue of performing concurrent operations of various energy generation and transmission infrastructures. One of the primary concerns for investors is the efficient utilization of EH to effectively manage energy carriers, particularly ...
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The concept of an energy hub (EH) has been utilized to address the issue of performing concurrent operations of various energy generation and transmission infrastructures. One of the primary concerns for investors is the efficient utilization of EH to effectively manage energy carriers, particularly in transactions with the upstream grid. In this paper the proposed smart energy hubs (SEH) manage dispatchable generation, i.e. Combined Cooling, Heat, and power (CCHP), and non-dispatchable generation, i.e. Photovoltaic (PV). SEHs consider Ice Storage Conditioner (ISC) as well as Thermal Energy Storage System (TESS) as the Energy Storage System (ESS). To mitigate dependence on gas and electricity utility companies, a peer-to-peer (P2P) energy sharing strategy has been executed. The implementation of demand response (DR) is directed toward shiftable electrical loads. The thermodynamic model of heating and cooling loads is developed with flexibility as integrated demand response (IDR) based on the desired temperature. The objective of optimization is to minimize operation and environmental costs.. The flexibility constraint serves in particular to enhance the flexibility of the interrelationships between MG and the upstream network. The suggested model incorporates the probabilistic nature of PV generation as well as the electrical, thermal, and cooling demands in various scenarios. The proposed model is a Mix Integer Non-Linear Problem (MINLP), which was solved using SCIP solver in GAMS software. Implementation of the proposed framework on the typical EHs shows the impact of P2P transactive energy and flexibility constraint performance on elements such as operation costs, emissions and flexibility of the system.
Power systems
Saeed Javadi; Ali Hesami Naghshbandy
Abstract
Correct information about a power system’s dynamic variables is important and necessary for protection and control issues. Today's power systems, which differ from past systems, face new challenges due to converter-based resources. A solution to these challenges is dynamic state estimation in short ...
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Correct information about a power system’s dynamic variables is important and necessary for protection and control issues. Today's power systems, which differ from past systems, face new challenges due to converter-based resources. A solution to these challenges is dynamic state estimation in short time intervals, such as the time domain. This paper simulates a standard 68-bus system in the presence of converter-based resources with a high penetration percentage in DIgSILENT software and compares the performance of four Bayesian filters in estimating the dynamic variables of the synchronous generators of the system using values in the time domain with each other. The four types of filters used include extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter.The MATLAB software suite was used for the comparison of the performance of the four filter types in different scenarios, including the presence of measurement and processing noise, extreme noise, network fault, data missing, state estimation time by each filter, and the comparison of time domain method with other methods such as phasor domain. Finally, the advantages and disadvantages of each were identified.
Power systems
Fatemeh Keramati; Hamid Reza Mohammadi
Abstract
Concerning the increasing application of plug-in electric vehicles (PEVs), planning PEV fast-charging stations (PEVF-CS) has become an important research topic. Regarding the reactive power compensation capability, the optimal planning of PEVF-CS reduces voltage deviation and power loss in the distribution ...
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Concerning the increasing application of plug-in electric vehicles (PEVs), planning PEV fast-charging stations (PEVF-CS) has become an important research topic. Regarding the reactive power compensation capability, the optimal planning of PEVF-CS reduces voltage deviation and power loss in the distribution network. Also, one of the basic requirements for expanding electric transportation is the optimal placement of accessible PEVF-CSs, considering the geographic information data. Therefore, the optimal placement of PEVF-CS requires attention to different geographical criteria and power distribution network constraints. In this sense, this paper aims to propose an approach that integrates the Geographic Information System (GIS) technique, Multi-Criteria Decision-Making (MCDM) method, and Mixed-Integer Nonlinear Programming to find the optimal location of a PEVF-CS in Kabul city. The first stage is decision analysis based on the GIS technique and the MCDM approach. The second stage is suitability analysis of the power distribution network constraints to improve power quality. This paper considers ten different suitability criteria, and the Technique for Order Preference Similarity to Ideal Solution (TOPSIS) is applied to rank the different candidate locations. The analysis identified Junction 4 as the optimal choice and demonstrated a significant 3.6% reduction in power loss during peak hours, decreasing from 1071 kW to 1032 kW. These results demonstrate the effectiveness of our approach in optimizing PEVF-CS placement to enhance power quality and reduce the power loss.
Power systems
Farhad Amiri; Mohammad Hassan Moradi
Abstract
: In the context of frequency stability in a two-area microgrid, it is crucial to address the fluctuations in frequency caused by load disturbances. To achieve this, an effective load-frequency control (LFC) system, which serves as the secondary control, must be implemented. However, the presence of ...
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: In the context of frequency stability in a two-area microgrid, it is crucial to address the fluctuations in frequency caused by load disturbances. To achieve this, an effective load-frequency control (LFC) system, which serves as the secondary control, must be implemented. However, the presence of renewable energy sources such as wind turbines and photovoltaic systems adds complexity to the operation of the LFC system due to their inherent uncertainty. To enhance the performance of the LFC system in the two-area microgrid, this paper proposes a reduction in the number of controllers employed, aiming for a less complex structure. Specifically, Model Predictive Control (MPC) is utilized for LFC, and the weight parameters of the MPC controller are determined using Craziness-based Particle Swarm Optimization (CRPSO). The proposed method is compared with alternative approaches, including PID controller optimized with Social Spider Optimization (SSO), Fractional Order Fuzzy PI (FOFPI), and conventional MPC. The effectiveness of the proposed method is evaluated in various scenarios, considering load variations and the presence of distributed microgrid generation resources. The results demonstrate that the proposed method outperforms the other controllers in terms of speed of response, reduction of overshoot and undershoot, and overall complexity. Importantly, the proposed method significantly improves the frequency stability of the two-area microgrid. The simulation and analysis are conducted using MATLAB software, providing a comprehensive understanding of the system dynamics and the performance of the proposed controller.
Power systems
Sasan Pirouzi; Mahmoud Zadehbagheri; Rohollah Rashidi
Abstract
In this article, the robust scheduling of the distribution network is presented considering electric vehicles, distributed generation, and energy storage, in which the energy management of the mentioned elements is considered, and also only one scenario is needed. The proposed deterministic problem is ...
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In this article, the robust scheduling of the distribution network is presented considering electric vehicles, distributed generation, and energy storage, in which the energy management of the mentioned elements is considered, and also only one scenario is needed. The proposed deterministic problem is an optimization problem whose objective function is equal to minimizing energy cost. Also, the limitations of the problem are equal to the power flow equations of the network, the limitations of the technical indicators of the network such as the voltage of the buses and the passing power of the lines, the operation equations of electric vehicles, energy storages, and distributed generation. It is worth mentioning that the mentioned problem is non-linear. In the following, to achieve the global optimal point with a high solution speed, the linear model of the mentioned problem is presented with a very low calculation error. In this research, the uncertainty parameters of the problem are equal to active and reactive loads, energy prices, parameters of electric vehicles, and renewable productions. Finally, to simplify the decision-making of the distribution network operator, a robust model of the mentioned problem was presented. Finally, the proposed problem is applied to the IEEE standard 33-bus radial distribution network using GAMS optimization software, and then the capabilities of the proposed design are evaluated.
Power systems
Morteza Behbahanipour; Seyed Fariborz Zarei; Mohammadhadi Shateri
Abstract
This paper proposes an impedance-based approach for locating short-circuit faults in active distribution networks (DNs). This topic is a crucial task for operators, especially in grids with inverter-based distributed generators (IBDGs). Various methods have been proposed in this research area, including ...
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This paper proposes an impedance-based approach for locating short-circuit faults in active distribution networks (DNs). This topic is a crucial task for operators, especially in grids with inverter-based distributed generators (IBDGs). Various methods have been proposed in this research area, including traveling waves, impedance-based methods, and artificial intelligence (AI) techniques. Among them, the impedance-based scheme provides a simple and efficient feature that could be used in AI-based techniques. In this paper, an enhanced fault localization method based on impedance estimation is introduced. This method comprises two main components: (i) fault distance determination and (ii) faulty section identification. When developing the proposed method, the modeling of inverter-based resources under symmetrical and asymmetrical faults is considered, which includes the impact and behavior of such sources in the proposed approach. Unlike conventional impedance-based methods, the proposed approach does not require network information such as structure, lines, load data, or voltage and current measurements along the feeder at multiple points. The proposed method can be utilized as a feature in AI-based techniques, significantly enhancing accuracy and reducing the complexity of such techniques. To validate the efficacy of the proposed approach, various series of time-domain case studies are performed, in addition to mathematical proofs. The results demonstrate the effectiveness of the proposed scheme in accurately locating faults with varying resistances at different positions in the presence of inverter-based distributed generators.
Power systems
Ramezan Havangi; Fatemeh Karimi
Abstract
Battery Management System (BMS) including measurements errors that causes decrease in the quality of calculated State of the Charge (SOC). It will limit the accurate estimation of the SOC that is a critical challenge in some of the engineering fields such as medical science, robotics, ...
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Battery Management System (BMS) including measurements errors that causes decrease in the quality of calculated State of the Charge (SOC). It will limit the accurate estimation of the SOC that is a critical challenge in some of the engineering fields such as medical science, robotics, navigation and industrial applications. These facts implies on the significance of SOC estimation from battery measurements that is the matter of the literature through the recent years. Due to the dependency of the EKF to the system model, the change in the battery parameters and noise information cause losing performance in the SOC estimation over the time. In this paper, we assume that the battery parameters including internal resistance and capacitor and also the noise information are varying over the time. To solve that, two separate on-line identification algorithms for parameters and noise information are introduced. In more details, a Recursive Least Square (RLS) algorithm is used to identify the resistance and capacitor values. Moreover, the process and measurement noise covariance are estimated based on iterative noise information identification algorithm. Then all of the updated values are used in the EKF algorithm. This paper aims to address the issue of uncertainty in SOC estimation by proposing two algorithms. The first algorithm focuses on identifying deterministic uncertainty, which refers to uncertainty in model parameters. To address the challenge of uncertain model parameters, RLS is introduced.
Power systems
Saeed Abazari
Abstract
This study examines stability improvement of the power system which includes Double Feed Induction Generators (DFIGs) and Static Series Synchronous Compensator (SSSC). The proposed nonlinear controller is designed based on the terminal sliding mode control theory. A sliding mode observer is also developed ...
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This study examines stability improvement of the power system which includes Double Feed Induction Generators (DFIGs) and Static Series Synchronous Compensator (SSSC). The proposed nonlinear controller is designed based on the terminal sliding mode control theory. A sliding mode observer is also developed to remove the need to access the information of all the state variables. The final closed-loop of the power system modeling is robust against parameter variations and uncertainties. The limitations of the control signals in the process of controller design are also considered. The application of such a method increases the stability margins and results in higher robustness degrees. A comparison with other nonlinear approaches such as back-stepping and feedback linearization approaches is carried out. The results show the faster and more reliable convergence rate of the power system-controlled trajectories to reach back to the equilibrium point after occurring a sudden fault. The results are obtained by performing a simulation on the standard 39-Bus, 10 machines NEW ENGLAND power system.
Power systems
Amir Ghaedi; Reza Sedaghati; Mehrdad Mahmoudian; Shahriyar Bazyari
Abstract
Due to the problems associated with overhead lines, underground XLPE cables are increasingly being used in power systems. The main cause of deterioration in these cables is insulation failure, primarily arising from the partial discharge phenomenon. One of the main challenges in online PD detection is ...
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Due to the problems associated with overhead lines, underground XLPE cables are increasingly being used in power systems. The main cause of deterioration in these cables is insulation failure, primarily arising from the partial discharge phenomenon. One of the main challenges in online PD detection is the presence of various noises in the environment that must be eliminated. In recent years, various types of noise with different distributions, such as impulse noises generated by power electronic devices, have been introduced into the power system. Therefore, denoising techniques should be employed to filter out the noises and interferences present in the detected PD signal. Due to the non-stationary nature of PDs, this paper suggests using the wavelet transform method, which covers both the time and frequency domains, to remove various noises from PDs. Consequently, to determine the suitable mother wavelet transform, threshold, and number of decompositions, the characteristics of PD signals occurring in the cables are investigated through experimental tests. Additionally, because different noises exist in substations, the background noise at the measurement site is recorded as a reference noise to be used in the application of the wavelet-based noise removal process. This method is examined on a sample cable, and the results are discussed. Moreover, using the suggested method, the detection of PD signals in several 20 kV substations in Iran is carried out through the use of high-frequency current transformers connected to shield wires, oscilloscopes with high-frequency bandwidth, and MATLAB software.
Power systems
Mehdi Shafiee; Abbas-Ali Zamani; Mehdi Sajadinia
Abstract
In power systems planning, economic load dispatch considering the uncertainty of renewable energy sources is one of the most important challenges that researchers have been concerned about. Complex operational constraints, non-convex cost functions of power generation, and some uncertainties make it ...
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In power systems planning, economic load dispatch considering the uncertainty of renewable energy sources is one of the most important challenges that researchers have been concerned about. Complex operational constraints, non-convex cost functions of power generation, and some uncertainties make it difficult to solve this problem through conventional optimization techniques. In this article, an improved dynamic differential annealed optimization (IDDAO) meta-heuristic algorithm, which is an improved version of the dynamic differential annealed optimization (DDAO) algorithm has been introduced. This algorithm has been used to solve the economic emission load dispatch (EELD) problem in power systems that include wind farms, and the performance of the proposed technique was evaluated in the IEEE 40-unit and 6-unit standard test systems. The results obtained from numerical simulations demonstrate the profound accuracy and convergence speed of the proposed IDDAO algorithm compared to conventional optimization algorithms including, PSO, GSA, and DDAO, while independent runs indicate the robustness and stability of the proposed algorithm.
