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Number of Volumes 4
Number of Issues 10
Number of Articles 63
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Submission statistics
Number of Submissions 124
Rejected Submissions 49
Accepted Submissions 64
Acceptance Rate 52
Time to Accept (Days) 55
Number of Indexing Databases 9
Number of Reviewers 915

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Reviewers

Acknowledging the scholars who reviewed submissions in 2025 and 2026.

Explore the list of reviewers contributing their expertise to the journal.

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  • Dr. Abbas Khanahmadi
  • Dr. Afshin Najafi
  • Dr. Ahmadreza Montazerolghaem
  • Dr. Ali Azamian
  • Dr. Ali Kuhestani
  • Dr. Ali Razzaghi
  • Dr. Ali Mokhtari
  • Dr. Ali Sedighimanesh
  • Dr. Alireza mohammadi Amidi
  • Dr. Alireza Joshan
  • Dr. Amirali Nouri
  • Dr. Anusha Gaddala
  • Dr. Araz Emami
  • Dr. Armineh Dastgiri
  • Dr. Asma Ahmadinejad
  • Dr. Behnam Ghalami Choobar
  • Dr. Ben Cao
  • Dr. Chaohui Zhao
  • Dr. Deepak Antony David
  • Dr. Ehsan Heydarian-Foroushani
  • Dr. Ensieh Davooddijam
  • Dr. Farhad Bayat
  • Dr. F. Mohammadmazadeh Shahir
  • Dr. Farzad Sedaghati
  • Dr. Fatemeh Sadat Lesani
  • Dr. Gholamreza Zare
  • Dr. Hadi Zayyani
  • Dr. Hamid Karimi
  • Dr. Hamid Reza Sezarvar
  • Dr. Hanaa ZainElDin
  • Dr. Hedayat Saboori
  • Dr. Hesam Rahbarimaghom
  • Dr. Hojat Moayyedri
  • Dr. Hossein Jokar
  • Dr. Hossein Yousefi
  • Dr. Islam M. Ibrahim
  • Dr. Javad Rahmianifard
  • Dr. K. Karthikumar
  • Dr. kamran Taghizad-Tavana
  • Dr. Kuppusamy Kanagaraj
  • Dr. Liupengfei Wu
  • Dr. M.H. Elkholy
  • Dr. Mahboubeh Shamsi
  • Dr. Mahdi Shademan
  • Dr. Mahdieh Monemi Bidgoli
  • Dr. Mehdi Gheisari
  • Dr. Mehran Haghighi
  • Dr. Mohammad Hossein Mousavi
  • Dr. Mohammad Sadeghi
  • Dr. Mohammad Shaterabadi
  • Dr. Mohammadamir Shirkhani
  • Dr. Mohammadhossein Tamimi
  • Dr. Mohammadreza Fattollahi
  • Dr. Navid Fahimi
  • Dr. P. Gowsikraja
  • Dr. Pezhman Bayat
  • Dr. Pooria Kianoush
  • Dr. Prabhakar Mahalingam
  • Dr. Rahim Zahedi
  • Dr. Ramin Ranjbarzadeh
  • Dr. Rasoul Ghani
  • Dr. Reza Ghanizadeh
  • Dr. Reza Noroozian
  • Dr. Roham Ghasemi
  • Dr. Roshan Alirezaei
  • Dr. S. Deepa
  • Dr. Saeed Alirejad
  • Dr. Saeed Hasanzadeh
  • Dr. Saeed Mirpour Marzuni
  • Dr. Salman Ahmad
  • Dr. Samir Toumi
  • Dr. Sarah Poormohammad
  • Dr. Selma Aouida
  • Dr. Senthilkumar Nangan
  • Dr. Setayesh Nazar
  • Dr. Seyed Hamid Khatami
  • Dr. S. H. Zahir Mamaghani
  • Dr. Seyed Mohammad Azimi
  • Dr. S. Mohammad Bornapour
  • Dr. Shahab Eslami
  • Dr. Shabir Mohammad
  • Dr. Shun Wang
  • Dr. Sina Ahmadi
  • Dr. Sina samadi gharehveran
  • Dr. Thi Thanh Thuy Pham
  • Dr. V. Nandagopal
  • Dr. Xuerr Lin
  • Dr. Yaser Damchi
  • Dr. Yonghong Xu
  • Dr. Yousef Amraoui

Reviewer acknowledgments for 2025–2026.

Global representation

Geographical Distribution of Reviewers

Countries and territories represented in the journal's international reviewer network.

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  • Iran
  • China
  • Japan
  • Tunisia
  • Vietnam
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  • United States
  • Canada
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  • Morocco

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Universities and academic institutions represented in PCDP's international collaboration network.

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  • Shanghai Dianji University
  • Shanghai University
  • Beijing Information Science and Technology University
  • Graphic Era University
  • York University
  • Egyptian Chinese University
  • University of Cincinnati
  • Dublin City University
  • The University of Hong Kong
  • Sidi Mohamed Ben Abdellah University
  • Mansoura University
  • Duy Tan University
  • Academy of People Security
  • University of Tunis El Manar
  • University of the Ryukyus
  • Universitat Politècnica de Catalunya (UPC)
  • Polytechnic School of Tunisia
  • Mohan Babu University
  • Vellore Institute of Technology
  • Hangzhou City University
  • Kongu Engineering College
  • SR University
  • Sri Venkateswara College of Engineering
  • University of Oklahoma Health Sciences Center

National academic network

Iranian Scientific Affiliations

Iranian universities and academic institutions represented in the journal's scientific network.

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  • Qom University of Technology
  • University of Tehran
  • Shiraz University
  • Yasouj University
  • Razi University
  • Kermanshah University of Technology
  • Shiraz University of Technology
  • Urmia University
  • University of Isfahan
  • University of Mohaghegh Ardabili
  • Shahid Bahonar University of Kerman
  • University of Tabriz
  • University of Zanjan
  • Iran University of Science and Technology
  • Hamedan University of Technology
  • Bu-Ali Sina University
  • University of Birjand
  • Isfahan University of Technology
  • K. N. Toosi University of Technology
  • Shahrood University
  • Ilam University
  • University of Guilan

University names are presented for informational purposes.

About the journal

PCDP welcomes original and unpublished manuscripts that are not under review in another journal. The journal publishes research in power electric, control, data processing, and computer sciences.

As a fully open-access journal, all published articles are freely accessible to readers worldwide, without subscription or access fees. PCDP is committed to the wide dissemination and exchange of scientific knowledge among researchers and practitioners.

The journal is fully sponsored by Qom University of Technology.

  • Open accessYes
  • H-index10
  • Citations331
  • i10-index11
  • Article typesResearch papers and review papers
  • Primary reviewApproximately 7 days
  • First-round reviewApproximately 30 days
  • Time to acceptanceApproximately 60 days
  • Peer-review policySingle-blind peer review
  • Reviewers per articleAt least 2
License: Creative Commons Attribution 4.0 International (CC BY 4.0) . Power, Control, and Data Processing Systems (PCDP) © 2024 by Qom University of Technology.

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PCDP is committed to following best practices in publication ethics. Its policies are based on the Core Practices from the Committee on Publication Ethics (COPE) and should be read alongside the PCDP Author and Reviewer Guides available through the website homepage.

Scientific association supporting the journal

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Power Engineering

Modified Metaheuristic Approach for Overcurrent Relay Coordination in Distribution Networks

Pages 1-17

https://doi.org/10.30511/pcdp.2026.2088443.1071

Ahad Jahandideh Shendi, Leila Mohammadian, Reza Ghanizadeh

Abstract Because conventional computational approaches falter under heavily constrained and nonlinear conditions, properly setting protective distribution relays remains an arduous task. While the metaheuristic algorithms have been applied to this problem, existing approaches typically optimize only time and current settings while assuming fixed relay characteristics, limiting the achievable coordination performance. This paper addresses this gap by proposing a unified Gravitational Search Algorithm (GSA) framework that simultaneously optimizes time multiplier settings, current settings, and relay characteristic selection from eight standard curves. By leveraging a combination of MATLAB for optimization and DIgSILENT for fault current estimation, this study evaluates its approach. The effectiveness of the technique is demonstrated through testing on both 30-bus and 9-bus distribution systems, where its performance is benchmarked against Informative Differential Evolution and Particle Swarm Optimization. Results demonstrate that the GSA-based approach achieves superior convergence and lower objective function values, with statistical analysis confirming its robustness and effectiveness in reducing relay operating times compared to the existing methods.

Electronic Engineering

Design and Implementation of a Safe Line-Following Robot with Automatic Climate and Lighting Control Systems

Pages 18-30

https://doi.org/10.30511/pcdp.2026.2091135.1082

Ali Razzaghi, Azin Rahimi Shateranloo, Negar Bakhtiari, Behzad Ebrahimi

Abstract This paper presents the design, implementation, and experimental evaluation of an autonomous line-following robot developed for efficient material transportation in dynamic industrial environments. Unlike conventional line-following robots that focus only on path tracking, the proposed system integrates automatic lighting control, onboard thermal management, and access security as core operational functions. These additional features enable the robot to maintain reliable performance under variable lighting and temperature conditions while preventing unauthorized use in shared workspaces. The navigation system is based on five TCRT5000 infrared sensors, which provide accurate line detection and stable maneuvering along complex trajectories, including sharp 90-degree turns. To enhance environmental adaptability, the robot automatically activates its lighting system in low-light conditions and employs a temperature-regulated cooling mechanism to protect onboard electronics and sensitive payloads. An access control layer is also incorporated to improve operational safety and reduce tampering risks. Experimental results demonstrate that the proposed robot achieves reliable path-following performance and robust subsystem response under different operating conditions. The combination of precise navigation, environmental adaptation, and security functions makes the system a practical and scalable solution for industrial automation and internal logistics in modern factory settings.

Power Engineering

Data-Driven Power Load Forecasting Using Time-Delay Cerebellar and Rough Gaussian Neural Networks

Pages 31-43

https://doi.org/10.30511/pcdp.2026.2093054.1091

Mohammadhossein Tamimi, Ehsan Shahi

Abstract Accurate load forecasting is recognized as a critical requirement for the secure and economical operation of modern power systems. In this study, two advanced data-driven neural architectures are presented and compared for forecasting the load behavior of Iran’s power grid. In the first approach, a Time-Delay Cerebellar Model Articulation Controller (TD-CMAC) is employed to capture temporal dependencies in hourly load series, and its performance is systematically benchmarked against a conventional Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Extreme Gradient Boosting (XGBoost). In the second approach, a rough Gaussian Neural Network (RGNN), trained by the Levenberg–Marquardt algorithm, is developed, in which rough set concepts are combined with Gaussian activation functions to enhance robustness under uncertainty. This RGNN is evaluated on power load time-series data, so that its generalization capability across regression tasks can be assessed. Both neural frameworks are trained on real load data from the Iranian power network. Their performance is quantified using cost functions, regression plots, and accuracy metrics, demonstrating that while the proposed RGNN provides exceptional robustness against fluctuations, both architectures offer complementary advantages depending on the forecasting horizon. The empirical findings indicate that the TD-CMAC achieves competitive and often superior accuracy compared with the MLP, and XGBoost for short-term load forecasting, whereas the Levenberg–Marquardt-trained RGNN provides effective and robust modeling for prediction in the presence of uncertainty. Overall, it is demonstrated that the combination of time-delay cerebellar structures with rough Gaussian neural modeling constitutes a powerful data-driven toolkit for power load forecasting in complex power systems.

Control Engineering

A VIKOR Optimization Approach Towards Predictive Torque Controller Design for Open-End Winding Induction Motors

Pages 44-51

https://doi.org/10.30511/pcdp.2026.2094243.1096

Vahid Moradi, Khosro Khandani, Mazdak Ebadi

Abstract Predictive torque control (PTC) is known as a straightforward approach for induction motor drives for which the control parameters can be directly included in the criterion measure. Nevertheless, choosing the weight factors of the criterion measure is an important challenge. In conventional PTC methods, the criterion measure includes the torque and stator flux control objectives which have different weight factors for each control objective. Tuning weight factors empirically results in a complicated control process. To successfully cope with this issue, this paper suggests the VIKOR method for optimization of the criterion measure. In this research, a new PTC scheme has been proposed using the VIKOR optimization algorithm for the selection of inverter switching vectors connected to an open-end winding induction motor (OEWIM). The purpose of controlling the switching vectors is to reduce the flux and torque ripples. The results are then compared with the conventional PTC and ranking methods. It is demonstrated that, with optimization by the VIKOR method, the flux and torque ripples have been remarkably reduced in comparison with the existing approaches. The proposed VIKOR-based PTC reduces the flux ripple by 78.5% (from 1.4 Wb to 0.3 Wb) and reduces the torque ripple by 58.3% (from 0.012 N·m to 0.005 N·m) compared to the conventional PTC method. Finally, a step-by-step implementation algorithm is presented and simulated using MATLAB which demonstrates favorable results.

Communication Engineering

Classification of Persian Miniature Painting Schools Using a Vision Transformer and Transfer Learning

Pages 52-61

https://doi.org/10.30511/pcdp.2026.2095440.1101

Darya Seifi Zarei, HamidReza Shahdoosti

Abstract The automated attribution of historical paintings to their school of origin is a long-standing challenge at the intersection of computer vision and art history. This challenge is especially acute for non-Western art traditions, such as Persian miniature painting, for which digitized and labelled image corpora remain comparatively small. This paper addresses the classification of Persian miniature paintings into five historical schools including, Herat, Qajar, Shiraz-e Avval, Tabriz-e Avval, and Tabriz-e Dovvom, using a Vision Transformer (ViT). Transfer learning is used to adapt a ViT-B/16 model, pre-trained on ImageNet-21k, to this task. Because the available dataset contains only 890 images, five complementary augmentation operations i.e. random-resized cropping, horizontal flipping, rotation, color jitter, and random erasing, are applied during training, together with Mixup as an additional sample-mixing regularizer. A progressive layer-freezing strategy keeps the first six Transformer encoder blocks fixed and fine-tunes only the last six blocks. To stabilize training on the small fine-tuning set, this strategy utilizes a classification head combined with label smoothing and a low, cosine-annealing learning rate. On a held-out test set of 178 images, the fine-tuned model reaches an overall accuracy of 92.13% and a weighted F1-score of 0.92, with three of the five schools classified at an F1-score above 0.94. These results indicate that a Vision Transformer, can reach competitive accuracy on a small, specialized cultural-heritage dataset when transfer learning and augmentation are used simultaneously.

Power Engineering

Deep Operator Learning-Based Prediction of Primal and Dual Variables in AC Optimal Power Flow

Pages 62-79

https://doi.org/10.30511/pcdp.2026.2096254.1104

Reza Mohammad Yousefi, Ahmad Salehi Dobakhshari

Abstract This paper proposes a deep operator learning framework for fast and accurate prediction of both primal and dual variables in alternating-current optimal power flow (AC-OPF). Unlike conventional neural-network surrogates that learn a fixed input--output mapping and often ignore dual information, the proposed method is designed to approximate the underlying operator that maps varying system operating conditions to optimal OPF solutions. The framework is built on a primal--dual DeepONet architecture, where branch and trunk networks jointly encode system conditions and query locations to predict generator dispatch, voltage states, and associated Lagrange multipliers.

To better capture optimization structure, a differentiable dual completion layer is incorporated to improve dual feasibility and complementary slackness, and a KKT-informed loss function is used to guide training toward physically consistent and optimization-aware solutions. The proposed model is evaluated on standard IEEE benchmark systems, where it achieves high prediction accuracy for both primal and dual variables, millisecond-level inference time, and significantly reduced KKT residuals compared with conventional learning-based OPF approximations. These results highlight the promise of deep operator learning as an efficient surrogate for real-time AC-OPF analysis and power-system operation.

Power Engineering

Energy Management in Residential Buildings Using Solar Photovoltaic Systems and Storage Batteries: Technical and Economic Analysis

Articles in Press, Accepted Manuscript, Available Online from 01 May 2027

https://doi.org/10.30511/pcdp.2026.2092292.1086

Hamid Reza Sezavar, hamid Karimi

Abstract The increase in electrical energy consumption in the building sector, along with frequent power outages, has made the need for distributed generation systems and renewable energy sources more evident than ever. One of the most suitable solutions to reduce dependence on the power grid and increase the reliability of energy supply in residential buildings is the use of solar photovoltaic systems with battery storage. This study presents a simulation framework for energy management in a residential building equipped with a solar system and a battery. In this model, the interactions among the building's electrical load, solar energy generation, battery energy storage, and energy exchange with the power grid are examined. To evaluate the system's economic performance, indicators such as net present value (NPV), internal rate of return (IRR), levelized cost of energy (LCOE), and payback period are calculated. A case study was conducted for a 5 kW solar system with a 10 kWh battery. The results show that the system's initial investment is about 4 billion Rials and generates an annual income of about 570 million Rials. Also, the payback period is about 7 years, and the internal rate of return is about 13%. The results show that although the use of batteries increases the payback period compared to the system without batteries, it significantly improves the reliability of energy supply and the building's energy independence.

Power Engineering

A Model-Free Predictive Torque Control for Induction Motors Without Weighting Factors

Articles in Press, Accepted Manuscript, Available Online from 01 March 2027

https://doi.org/10.30511/pcdp.2026.2093362.1092

Mahdi Bahmani

Abstract Model Predictive Torque Control (MPTC) has been widely employed in induction motor drives owing to its fast dynamic response and straightforward implementation. However, conventional MPTC suffers from two major limitations: the need for weighting factors and its dependence on an accurate machine model. Sequential Model Predictive Control (SMPC) has emerged as one of the simplest and most effective strategies by replacing the conventional multi-objective cost function with a sequence of single-objective cost functions. Nevertheless, conventional SMPC still relies on an accurate motor model and employs a fixed priority order for the cost functions, which may lead to suboptimal voltage vector selection under different operating conditions. To address these limitations, this paper proposes an adaptive model-free sequential predictive torque control strategy for induction motors. An ultra-local model combined with an Extended State Observer (ESO) is employed to estimate the system dynamics online, eliminating the dependence on machine parameters. Furthermore, an adaptive cost-function prioritization strategy is developed, in which the normalized torque and flux tracking errors are evaluated online to determine the order of sequential optimization. Simulation results demonstrate that the proposed method controls torque and flux effectively and improves robustness against parameter mismatches.

Power Engineering

Universal Feasibility Projection and Certification for Learning-Based OPF

Articles in Press, Accepted Manuscript, Available Online from 02 October 2026

https://doi.org/10.30511/pcdp.2026.2096241.1103

Reza Mohammad Yousefi, Ahmad Salehi Dobakhshari

Abstract Learning-based surrogates for alternating-current optimal power flow (AC-OPF) offer significant speed advantages for real-time power system operation, yet they often suffer from a lack of physical feasibility guarantees. Small prediction errors in neural networks can result in critical violations of AC power-flow equations and operational constraints, posing significant risks for practical deployment. This paper introduces a universal feasibility projection and certification (UFPC) framework designed to act as an architecture-agnostic trust layer for any neural OPF predictor. The framework employs a multi-stage hierarchical repair pipeline to transform arbitrary model outputs into physically valid operating points, ensuring strict adherence to network physics and engineering limits. To bridge the gap between fast inference and reliable operation, we further develop an explicit physics-based feasibility certificate that quantifies equality and inequality residuals alongside local numerical conditioning, enabling an automated decision logic to accept, refine, or invoke a conventional solver fallback. Extensive numerical experiments demonstrate that the proposed UFPC framework effectively enforces AC feasibility, provides reliable performance across diverse operating conditions, and maintains the computational efficiency required for real-time power system control.

Power Engineering

Designing a Grid-Connected Hybrid Micro-Grid: A Case Study in Hamedan

Volume 2, Issue 2, Spring 2025, Pages 1-10

https://doi.org/10.30511/pcdp.2025.2054269.1019

Sana Sadeghi, Adel Mohammadinia

Abstract Energy plays a critical role in the growth and development of societies, and universities are recognized as key drivers of economic progress. In light of declining fossil fuel reserves and global warming, adopting renewable energy sources, such as photovoltaic (PV) systems, has become increasingly essential. Furthermore, the advantages of distributed generation are now more apparent than ever. Many countries have developed various scenarios to eliminate fossil fuels from electricity generation, an approach that warrants serious consideration in Iran as well. This study investigates the techno-economic feasibility and optimal design of a hybrid photovoltaic/diesel/battery power system intended to supply electricity to an academic center located in western Iran. The technical aspects and economic viability of implementing such a system were evaluated using the HOMER (Hybrid Optimization of Multiple Energy Resources) simulation tool. Various configurations combining solar energy, battery storage, and diesel generators were analyzed and compared. The simulation results presented in this study demonstrate that, given the subsidized nature of energy carriers in Iran, the implementation of hybrid energy systems is not highly recommended at this time.

Power Engineering

Comprehensive Review of Cutting-Edge Lithium Battery Technologies Highlighting Nanotechnology, Safety Improvements and Sustainability

Volume 2, Issue 4, Autumn 2025, Pages 18-35

https://doi.org/10.30511/pcdp.2025.2066671.1036

Alireza Joshan

Abstract Lithium batteries have emerged as one of the most advanced energy storage technologies in recent decades, playing a crucial role in transforming industries such as electric vehicles, portable electronics, and renewable energy systems. Due to their high energy density, long lifespan, and lightweight design, lithium batteries have become a preferred alternative to older technologies like nickel-cadmium and lead-acid batteries. However, the development and optimization of these batteries face challenges such as safety concerns, performance degradation under extreme temperatures, and the limited availability of critical raw materials like lithium and cobalt. This article provides a comprehensive review of lithium battery technology, including recent advancements in active material structures, electrode design, and manufacturing processes. Nanotechnology, in particular, has played a significant role in enhancing the performance, safety, and sustainability of lithium batteries. The use of nanomaterials such as nanocomposites, nanoelectrolytes, and nanostructured electrodes has led to improvements in energy density, safety, and cycle life. Furthermore, the article addresses current technological challenges and explores innovative solutions, including the use of alternative materials, advanced thermal management techniques, and nanotechnology applications in cell design. The future outlook of this technology is discussed, with an emphasis on improving sustainability, reducing costs, and enhancing safety. This review aims to provide valuable insights for researchers, engineers, and policymakers, guiding the continued development of lithium batteries toward achieving clean and sustainable energy solutions.

Power Engineering

Emerging Trends and Advanced Techniques in Power System Optimization for Future Smart Grids

Volume 2, Issue 2, Spring 2025, Pages 26-38

https://doi.org/10.30511/pcdp.2025.2060096.1022

Alireza Joshan

Abstract This review article provides a comprehensive examination of power system optimization and smart grids, analyzing the pivotal role of emerging technologies in energy management. It begins with foundational concepts of power system optimization and smart grids, followed by a detailed discussion of artificial intelligence and data-driven techniques in energy management, including a review of recent literature on AI applications in future energy networks. The integration of renewable energy sources and distributed energy systems, stochastic energy scheduling in multi-microgrid systems, and multi-objective decision-making frameworks for energy hubs are also explored. Additionally, advanced optimization methods, cybersecurity challenges, and resilience in modern power networks are examined. Emerging trends in smart grid technologies and future outlooks on intelligent power systems are presented in the final sections, including a roadmap for intelligent multi-energy systems. This review offers an up-to-date and comprehensive perspective on advancements and challenges in the optimization and development of smart energy networks.

Control Engineering

The Formulation of a Linear Quadratic Regulator (LQR) for a System Characterized by Partial Differential Equations (PDE) Utilizing Reinforcement Learning Technique

Volume 3, Issue 1, Winter 2026, Pages 1-12

https://doi.org/10.30511/pcdp.2025.2065658.1034

Mahdi Rezvanitabar, Javad Sharifi, Meysam Yadegar

Abstract Reinforcement learning has emerged as a valuable tool in control theory and related areas, such as robotics and process control, facilitating prediction, identification, and management of complex systems. A key advantage of using reinforcement learning is its ability to derive optimal control policies without requiring a comprehensive understanding of the underlying system dynamics. In essence, RL develops control strategies through interaction with the environment. A common approach in controller design for these systems involves the use of approximation methods. Given the complexities associated with solving PDEs analytically, numerical techniques like the finite element method are typically employed for approximation. In this research, we begin by discretizing the PDE that characterizes the system's dynamics using an appropriate discretization technique. Following this step, we extract the discrete dynamics of the system. Since these discrete dynamics exhibit the Markov property where the future state depends only on the current state and the action taken the next phase involves designing a controller based on these derived dynamics. Recognizing that reinforcement learning focuses on optimizing future actions through data analysis, feedback from the optimal mode can be utilized as a viable option for controller design. This study will further explore LQR controller design for one-dimensional heat equation heat flow as a system whose dynamic is described with PDE.

Power Engineering

Integration of Artificial Intelligence and Blockchain: Combining Two Transformative Technologies in Iran's Power Sector

Volume 3, Issue 1, Winter 2026, Pages 13-27

https://doi.org/10.30511/pcdp.2025.2072324.1043

Alireza Joshan

Abstract The aim of this study is to examine how the integration of two innovative technologies Artificial Intelligence (AI) and Blockchain can transform and enhance the efficiency, security, and transparency of Iran’s electricity sector. Accordingly, this research adopts a descriptive–analytical approach based on a systematic review of national and international academic sources to identify the capacities, challenges, and strategies for integrating these technologies into the country’s energy system. The findings indicate that AI, through predictive analytics, adaptive learning, and process optimization, can make electricity network management smarter and more efficient. Conversely, blockchain technology provides a transparent, secure, and decentralized platform for energy data exchange. The synergy between these two technologies can lead to reduced energy losses, facilitation of peer-to-peer energy trading, enhanced data security, and improved grid sustainability. However, several barriers hinder this integration, including scalability limitations, high implementation costs, incompatibility with legacy infrastructure, legal and regulatory challenges, and a shortage of skilled professionals. Finally, the study proposes strategic measures such as developing supportive policies, employing deep learning–based smart contracts, adopting low-energy blockchain solutions, and expanding IoT-based energy management systems. These actions can pave the way for the establishment of an intelligent, efficient, and sustainable electricity infrastructure in Iran.

Computer Engineering

Empirical Evaluation of Well-known Farsi OCR Engines on the IDPL-PFOD Dataset

Volume 2, Issue 1, Winter 2025, Pages 35-47

https://doi.org/10.30511/pcdp.2025.2052926.1018

Fatemeh-sadat Hosseini, Elham Shabaninia, Hossein Nezamabadi-pour

Abstract Optical character recognition (OCR), also referred to as text recognition, extracts text from scanned documents and camera images. OCR has numerous applications in reading forms and cheques, converting archived documents to digital files, and reading books and papers. An accurate OCR system speeds up these processes by eliminating time-consuming manual tasks. Despite advancements, OCR remains challenging for languages like Farsi due to their complex structures and lack of diverse datasets for evaluation. IDPL-PFOD, a new synthetic Farsi dataset, addresses this gap by including various backgrounds, fonts, distortions, and blurs. This paper evaluates Tesseract and EasyOCR on IDPL-PFOD to highlight their limitations. Tesseract and EasyOCR achieve 84.41% and 73.28% overall accuracy, respectively. Tesseract outperforms EasyOCR in most cases but drops to 67.17% on textured backgrounds, while EasyOCR achieves 75.8%. Tesseract reaches its peak accuracy (92.45%) on plain backgrounds and shows robustness to Gaussian blur and slope distortion, with minimal accuracy drops. In contrast, EasyOCR performs poorly on salt-and-pepper noise (4.79%), and moderately on sinewave distortion and Gaussian blur (61.45% and 63.73%). These findings emphasize the need for training OCR engines on more diverse datasets to improve their performance under real-world conditions.

Power Engineering

Smart Homes Energy Management System Integrated with Renewable Energy Sources and Demand Response Programs

Volume 2, Issue 1, Winter 2025, Pages 6-13

https://doi.org/10.30511/pcdp.2025.719228

Mehran Haghighi, Mohammad Reza Masoudi, Milad Rahimipour Behbahani

Abstract Home energy management systems (HEMS) play an important role in optimizing energy consumption. These systems manage the electricity consumption of household appliances using demand response programs based on real-time prices. The main goal of these systems is to reduce electricity costs and increase energy efficiency. In this paper, a price-base demand response approach is proposed for a smart home with different types of home appliances, including electric storage and thermal storage systems. In the proposed method, various appliances are considered in the smart home modeled by the energy hub system. An objective function is developed for daily management simultaneously addressing electricity costs to provide comprehensive management for smart homes. The proposed model examines how the smart home energy system responds under various conditions. Additionally, stochastic optimization accounts for the probabilistic nature of demands, photovoltaic (PV), and wind energy. The simulation results indicate that the consumer's payment cost is 79 cents and the emissions cost is 7 cents. The numerical results demonstrate the effectiveness of this approach.

Power Engineering

Enhancing Power Quality in Distribution Networks Integrated with Distributed Generation Sources Using MOPSO and Soft Open Points (SOPs)

Volume 2, Issue 2, Spring 2025, Pages 20-25

https://doi.org/10.30511/pcdp.2025.2059172.1021

Raham Nazavar, Reza Ghanizadeh

Abstract This paper introduces a novel approach to improving voltage profiles in a 22-bus radial distribution network by integrating distributed generation (DG) sources with advanced optimization techniques. By leveraging multi-objective particle swarm optimization (MOPSO) alongside soft open points (SOP) devices, this study effectively reduces power losses, total harmonic distortion (THD), and voltage imbalance. The proposed method not only optimizes the allocation and sizing of DG units but also strategically deploys SOP devices to enhance operational flexibility. Simulation results conducted in MATLAB environment demonstrate that the integration of SOPs significantly improves the overall voltage profile, minimizes active power losses, and substantially reduces THD levels across the network. Furthermore, the coordinated optimization approach enhances the resilience and stability of the distribution system under varying load conditions. The improvement in power quality indices, including voltage regulation and harmonic performance, highlights the practical viability and technical effectiveness of combining MOPSO optimization techniques with SOP deployment for resilient and efficient distribution network management.

Power, Control, and Data Processing Systems

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