Sotdhipong Phichaisawat

The evolution of intelligent power grids into integrated cyberphysical systems has unveiled significant vulnerabilities within their digital measurement frameworks, with false data injection (FDI) attacks presenting substantial threats to grid stability by potentially inciting cascading failures via manipulated control interventions. Although the existing technique of the Variable Dummy Value Model with a nonlinear functional form (NF-VDVM) enhances the detection of FDI, it continues to be susceptible to adaptive adversaries who capitalize on deterministic patterns. This study introduces a Dummy-Variable Model with randomized coefficients (RP-VDVM) that fundamentally fortifies security measures through the implementation of dynamic coefficient randomization, effectively eliminating predictable attack vectors. Rigorously assessed on the IEEE 14-bus and other bus systems using authentic PJM load profiles, RP-VDVM exhibits remarkable 95% detection accuracy under a 5% attack intensity (representing a 2.1 times enhancement compared to NF-VDVM 45%) while maintaining a false negative rate 2% according to North American Electric Reliability Corporation (NERC) standards. An analysis of computational complexity validates the model’s theoretical scalability to more extensive systems, with anticipated execution durations of 480 milliseconds (ms) for 300-bus networks. The proposed model is also compared to other machine learning detectors in order to position it as a viable, high-performance resolution for AC power networks that surpasses both conventional state estimation methods and machine learning-based detection systems in security-critical scenarios.

Photovoltaic systems have been under development for many years. However, their energy production is low compared to other systems. From 2016 to 2022, the research goal was to develop a fault diagnosis that evolves from inverter functional analysis to comprehensive monitoring systems. Currently, it is a major challenge to propose a commercially viable product. This review article focuses on fault diagnosis algorithms on the PV DC side for real-world applications. We analyzed 100 studies published between 2017 and 2024, considering nine factors PV array size on the DC side, data collection method, number of data sets, the fault itself, fault location, diagnosis accuracy, diagnosis time, input data for the algorithm and the diagnosis algorithm used. We summarize the strengths and weaknesses of each study in terms of practical implementation and highlight new technological trends. In addition, we discuss how new technologies for PV fault diagnosis on the DC side are tested and evaluated. Our contribution is intended to guide research in the field of PV fault diagnostics and help ensure that it can be used commercially in the future.

Accurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to enhance electricity demand prediction. The research analyzes key temporal patterns in historical load, renewable energy generation, and weather data to improve forecasting precision. RNN-based LSTM, with its gated memory structure, effectively captures long-term dependencies and mitigates vanishing gradient issues, making it highly suitable for modeling complex time-series data in power systems. Its ability to retain crucial past information across extended sequences enables precise load forecasting, even in the presence of fluctuations and intermittent renewable energy sources. To validate the effectiveness of the proposed technique, the model was implemented and tested using MATLAB/Simulink, ensuring realistic simulation of power system dynamics. Through a comprehensive comparative analysis, RNN-LSTM is found to exhibit superior predictive accuracy, achieving the lowest RMSE (2.2889), MAE (1.1041), and MAPE (1.538%), while also demonstrating faster convergence, lower inference time, and higher stability. Furthermore, LSTM demonstrates superior convergence speed, lower inference time (0.00098 s), and high stability (0.9091), which confirms its robustness. Compared to other deep learning techniques such as GRU, standard RNN, and CNN, RNN-LSTM outperforms in capturing long-range dependencies and learning from temporal patterns effectively. The convergence analysis further confirms the robustness of RNN-LSTM, making it the most suitable model for dynamic and complex power system environments. These findings underscore the significance of RNN-based LSTM in enhancing load forecasting reliability, ultimately contributing to improved grid stability, energy management, and operational efficiency.

Sustainable microgrids (MGs) provide a decentralized and environmentally sustainable solution to contemporary energy challenges, offering improved resilience and reduced carbon emissions. The effective optimization of renewable energy-based MGs requires the precise calibration of energy generation, storage, and distribution systems to maximize efficiency, reliability, and sustainability. This research presents a priority-based renewable energy system with a backup supply designed for grid-tied operation, aiming to optimize renewable resource utilization while ensuring system reliability and contributing to grid stability. The primary objective of this study is to develop an advanced optimization algorithm tailored to renewable energy-based MGs, with the goals of enhancing energy efficiency, maintaining grid stability, and minimizing operational costs. We introduce the Compact Cyclone-Based Manta Ray Foraging Optimization (C-CMRFO) algorithm, specifically designed for application in sustainable MGs to improve energy distribution efficiency, optimize resource allocation, and facilitate the integration of renewable energy sources. The proposed algorithm ensures precise voltage and frequency regulation, thereby supporting grid stability, and is characterized by rapid convergence, allowing for quick adaptation to dynamic grid conditions and efficient real-time operations. The effectiveness of the C-CMRFO algorithm is validated through extensive MATLAB/Simulink simulations, which demonstrate significant improvements in energy distribution efficiency, optimized resource utilization, and accurate voltage and frequency tracking. Additionally, the algorithm’s fast convergence rate is confirmed, enabling swift response to fluctuating grid conditions. Practical validation on a test bed further corroborates the consistency and reliability of the proposed control strategy in optimizing energy management and enhancing power distribution system performance. The close alignment between experimental results and simulation outcomes underscores the robustness and dependability of the proposed approach.

Grid-forming inverters represent a transformative advancement in power grid management, providing a flexible solution for optimizing energy systems and enhancing grid stability amid the increasing integration of renewable energy and growing grid complexity. This control strategy focuses on regulating voltage and frequency, enabling decentralized energy sources, such as renewables, to synchronize effectively with the island grid, ensuring a stable power supply. This study aims to develop a control algorithm for decentralized grid-forming inverters utilizing renewable energy resources and diesel generators. The proposed system incorporates five distributed generators: a fuel cell, wind turbine, photovoltaic panel, battery energy storage system, and diesel generator. An adaptive fuzzy-based droop controller is employed to meet the study’s objectives, with one component tracking reference voltage and frequency and another addressing the load’s active and reactive power demands. Validation through Matlab/Simulink demonstrates effective voltage and frequency regulation at the point of common coupling, supported by compelling simulation results. The study’s alignment with expected outcomes highlights the system’s reliability and robustness in maintaining grid stability amid dynamic load conditions and renewable energy integration. Furthermore, practical assessments on a test bed validate consistent active and reactive power sharing across all microgrids, reinforcing the proposed control strategy’s effectiveness in optimizing energy management and enhancing power distribution system performance.

This research examines the viability of incorporating a second-life lithium-ion battery energy storage system with a solar photovoltaic power source to facilitate energy acquisition in two irrigation scenarios at a durian farm located in Thailand. The first option entails selecting the battery with suitable dimensions to effectively harness solar energy following morning irrigation, while the second option involves sizing the battery specifically for storing solar energy solely for the purpose of powering the motor pump. This study conducts an analysis on the degradation of the battery, focusing on the influence of specific environmental conditions and its specification. The degradation analysis employs polynomial functions as an alternative to exponential semi-empirical stress models for fitting stress models. This approach demonstrates a robust fit, as evidenced by a high R-squared value of approximately 1. The economic analysis considers various factors, including solar irradiance derating factors, battery deterioration rate, and photovoltaic power ratings for all scenarios. The sensitivity analysis evaluates the variations in irradiance and costs associated with the battery. When solar radiation levels surpass 500 W/m2 and the cost of the battery remains below $100/kWh, the estimated payback period is approximately 5 years over the duration of the 10-year project lifespan.

Grid-tied microgrids play a crucial role by connecting renewable energy sources to the main power grid, contributing to sustainability and resilience in a balanced and effective manner. However, the dynamic interplay between the intermittent nature of renewable energy sources and the volatility of load fluctuations presents a multifaceted array of intricate energy management complexities. This study aims to formulate optimization techniques for energy management systems based on renewable energy resources and standalone diesel systems. The proposed system consists of a wind turbine, a photovoltaic system, a standalone diesel generator, and a battery energy storage system, along with flexible and non-flexible loads tied to the local grid. Battery energy storage acts as a primary backup system, while diesel generators act as a standalone secondary backup system. The performance of the proposed optimization technique is validated using Matlab/Simulink, substantiating its performance and robustness, thus affirming its pragmatic suitability for real-world implementation. A comparison has been made with other optimization techniques and found that the proposed technique gives enhanced efficiency, improved resource allocation, load scheduling, and greater adaptability to varying demand and supply dynamics. Moreover, the proposed system exhibits a superior ability to achieve optimal energy utilization and realize noteworthy cost savings in comparison to the alternatives that underwent evaluation.