International Transactions on Electrical Energy Systems最新文献

Brushless DC motor (BLDCM) drives often experience significant current and torque ripples, which negatively affect the overall performance. Moreover, the dynamic characteristics of BLDCMs such as low mechanical oscillations and tight speed regulation can introduce undamped AC components that destabilize the DC-link supply (DCLS). This study presents a novel compensation strategy to address DCLS instability in BLDCM drives. A quasiproportional-resonant compensator (QPRC) is proposed to enhance DCLS stability. This paper explores a QPRC-based stabilization approach for the DCLS in an in-front zeta converter (IFZC)–assisted BLDCM drive. The IFZC is mainly employed for voltage regulation while the QPRC is integrated to suppress the undamped AC signals in the DC link. A hardware prototype has been developed to validate the proposed control strategy. Experimental results confirm that the suggested stabilization strategy effectively enhances DC-link stability and improves the overall performance of the BLDCM drive.

Multienergy distribution network (MEDN) with high penetration photovoltaics (PVs) may suffer from sharp voltage fluctuations and increased network losses. Existing methods struggle to achieve voltage control due to challenges such as high interarea communication latency and difficulties in power flow modeling caused by low coverage of measurement devices. To address these issues, this paper proposes a multiagent deep reinforcement learning (MADRL) method to realize the collaborative optimization of controllable devices, including hybrid energy storage system (HESS) and PV inverters. Furthermore, under the framework of decentralized partially observable Markov decision processes (Dec-POMDP), we integrate cross-agent attention (CAA) and factored value networks to enhance perception capabilities and improve value function fitting. The proposed method explicitly assigns credit to agents and dynamically captures electrical coupling relationships between agents and buses. The improved IEEE 33-bus and IEEE 141-bus distribution systems were used as case studies to compare with mainstream MADRL. Experimental results demonstrate that after offline deployment, the agents achieve global voltage control based solely on limited local observations within each zone, without relying on a complete power flow model or interarea communication. The comparative experiments verify the effectiveness, robustness, and scalability of this method.

Bangladesh intends to build a 5 GW transmission super-highway from Moheshkhali to Madunaghat to Bhulta to evacuate a bulk amount of power. This study presents the particle swarm optimization (PSO) method for estimating line parameters. The optimum value for inductance is 0.3152 mH/km and capacitance is 3.57 × 10⁻² μF/km. A hexa-bundle conductors are designed using triangle properties. Three types of ACSR conductors such as Moose, Cardinal, and Tern are examined for the proposed EHVAC line. At 75°C, the line resistances of these three conductors are 0.0057, 0.0061, and 0.0074 Ω/km while line losses are 0.0006346, 0.0006791, and 0.0008238 MW/km. The conductor surface gradient is the permissible limit to suppress the audible noise. The value of surface gradient shows 7.4406 kV/cm for Tern conductor while using six conductors per bundle. The results indicate that the hexa bundle Moose, Cardinal, and Tern are promising for the proposed EHVAC line. The transmission capacity based on natural condition, normal condition, and emergency condition is examined for the proposed line. The natural loading (SIL) represents 6232.52 MW of the line for the optimal values of L and C. The corona loss is recorded as 0.422, 0.420, and 0.416 kW/km/phase when the line is made of Moose, Cardinal, and Tern conductors with subconductor spacing of 500 mm. The corona loss is insignificant in fair weather condition. The techno-economic analysis is presented using an economic model. The GDP-based long-term forecasting model is developed to compute the cost and benefit of the transmission system. Future cash flow is estimated using discounted cash flow method. The key economic parameters such as ENPV, EIRR, DPP ensure the economic viability of the high-voltage transmission line project. The life cycle cost of the proposed line is $1701.91 million, while the ENPV of the project is $1577.18 million. The results yield valuable information for the future 765 kV transmission line projects of Bangladesh Power’s grid.

Modern agricultural parks possess substantial photovoltaic (PV) resources, yet there is often hesitation to invest in PV and storage systems (PV–storage systems) due to economic considerations. This study introduces a method to boost the operational economic viability of agricultural parks with a high proportion of PV storage integration through cascaded fuzzy control. This strategy is designed to enhance the expected economic returns, thereby increasing the propensity to invest in PV-storage systems. The method involves a primary fuzzy controller, termed the “microgrid energy assessment module,” which uses a cloud model to determine the membership values based on the park’s PV power generation, load demand, and energy storage status. This assessment estimates the current energy status of the agricultural park microgrid. A secondary fuzzy controller, the “reference power transaction resolution module,” calculates the reference power transactions based on the energy status assessment provided by the primary controller and time-of-use (TOU) electricity pricing. In addition, this study leverages an adaptive genetic algorithm to optimize the fuzzy rule table, thereby refining the control strategy for economic improvement of the park. The park’s cloud-based controller can then utilize these reference power transactions, in conjunction with the storage system’s capacity constraints, to proactively manage the buying and selling of electricity, thus enhancing the park’s operational economic viability. Practical experiments conducted in an agricultural park in China, using an installed cloud controller, side sensor, and optical storage machine, demonstrate the feasibility of the proposed control method. Historical operational data simulation analysis further validates that the implementation of this method can significantly enhance the economic performance of agricultural parks with high PV storage integration. This facilitates faster recovery of investment costs, increased profitability, and supports the development of low-carbon, energy-autonomous agricultural parks.

Against the backdrop of the continuous development of ship informatization, joint scheduling for the entire fleet within a region brings numerous advantages. The optimization of scheduling problems for such a regional fleet, in addition to considering the number of orders completed, reducing operational costs, and further reducing carbon emissions, is also a key research point to address the increasingly severe climate change. This study establishes maritime scheduling strategies for container transport fleets considering energy management. It simulates a shipping company’s operations to meet freight demands among multiple ports. Utilizing reinforcement learning (RL) to choose the optimal scheduling strategy for each individual ship, the study ultimately derives the optimal operational plan for the shipping company. During the process of completing each navigation, every ship will attempt to control its voyage speed for reducing carbon emissions and operating costs. Double deep Q-learning (DDQN) is used to improve the performance of the RL algorithm, and an additional Q Rank network is used to reduce the action and state space. Ultimately, this paper validates the superiority of the model using a case study that includes multiple ports and ships.

Multiport DC-DC converters are essential for modern renewable energy systems where the integration of multiple energy sources and dynamic loads demands flexible, reliable, and efficient power management. However, conventional two-port converter topologies face significant limitations when addressing high-power applications exceeding 600 W, particularly under fluctuating input and load conditions. To overcome these challenges, this paper proposes a novel four-port nonisolated bidirectional DC-DC converter designed specifically for solar photovoltaic (PV) and grid-integrated energy systems. The converter supports multiple operating modes—Single Input Triple Output (SITO), Single Input Double Output (SIDO), Double Input Double Output (DIDO), and Single Input Single Output (SISO)—allowing adaptable power flow between solar PV, energy storage systems (ESS), DC loads, and AC loads via an inverter. A key innovation of this work lies in the use of a cost-effective Arduino UNO microcontroller to govern the MOSFET-based switching system. Compared to conventional control techniques, the Arduino-based controller significantly reduces complexity, cost, and component count while improving switching efficiency. The converter architecture further minimizes switching losses by employing fewer switches, enhancing overall performance for high-power applications. The system is simulated under both open-loop and closed-loop configurations using PSIM and Proteus software to evaluate functionality across various operational states and load conditions. A hardware prototype is developed to experimentally validate the simulation results under real-world constraints, including switching losses, voltage drops, and parasitic effects. The comparative analysis reveals a 10% average deviation between simulation and hardware results, which is within acceptable limits for practical deployment. Across all operating modes, the converter maintains stable power delivery, demonstrating high reliability and system adaptability. The results confirm that the proposed four-port converter is well-suited for solar PV-powered systems, energy storage integration, and electric vehicle (EV) applications, offering enhanced scalability, control simplicity, and energy transfer efficiency.

The study optimizes the location and capacity of soft open points (SOPs) and renewable energies-based distributed generators (REDGs) in two IEEE distribution power grids (DPGs) with 33 and 69 nodes for reducing power loss, energy loss, and total electric purchase cost by using war strategy optimization (WSO). The previous studies put one SOPs device each on several predetermined selections while the locations of SOPs are optimally determined in the study. So, WSO can find smaller losses than in previous studies for the two grids over 1 h. Then, the WSO is run to reduce 1-day energy loss and grid energy purchase cost for the IEEE 69-node DPG. The grid with renewable power sources (RPSs) and SOPs can reduce the energy loss by 3638.6229 kWh (about 96.1%) compared to the original grid and 1257.2779 kWh (about 89.49%) compared to the grid with RPSs. In addition, the grid with SOPs and RPSs can reach a 3869.9684 and $246.0011 smaller cost than the grid without and with RPSs. The cost reduction is about 61.44% and 9.2% of the total cost of the grids. So, optimal connection and capacity determination of SOPs and REDGs in DPGs is essential to reduce energy loss and energy purchase costs from conventional power grids.

The dependability and robustness of electric vehicle (EV) charging infrastructure, which functions as a pivotal nexus between consumers and the electrical network, play a crucial role in enhancing the efficiency of EV usage and ensuring the safe management of the power grid. Therefore, it is highly urgent to develop an effective and robust anomaly detection system to provide early warnings of potential risks and address the issue of imbalanced data distribution. In this paper, a conditional variational autoencoder (CVAE) is employed to construct an anomaly detection model for charging pile data. In contrast to other anomaly detection methodologies, the method proposed in this study demonstrates more desirable performance. Furthermore, this paper extends the investigation by modifying the architecture of the CVAE to facilitate supervised learning. The reconfigured network structure yields enhanced detection accuracy, obtaining better anomaly detection performance when evaluated on the charging pile dataset.

The port-integrated energy system (PIES) presents a transformative pathway for decarbonizing port operations through multienergy synergies. Power-to-hydrogen technology converts surplus renewable energy into green hydrogen, which is stored and reconverted to electricity via fuel cells during supply shortages. However, joint optimization of heterogeneous ports, with divergent resource endowments and load profiles, remains challenging. This study proposes an optimal scheduling method for electricity–hydrogen–heat–integrated energy systems accounting for port-specific energy characteristics and establishes a multienergy coupling model of PIES with vessel-based mobile hydrogen storage and a cooperative optimization framework incorporating energy consumption dynamics. Simulation results demonstrate that multiport joint dispatch reduces total operating costs by 2.3%–8.2%, compared to isolated schemes, while hydrogen-powered vessels enable spatiotemporal arbitrage and serve dual roles as mobile energy carriers/temporary storage units, with hydrogen interchange critically constrained by vessel logistics scheduling.

This study proposes an advanced hybrid fault-tolerant control (FTC) architecture for permanent magnet synchronous motors (PMSMs) operating under speed sensor faults (SSFs), integrating model predictive control (MPC), third-order sliding mode control (TOR-SMC), and a model reference adaptive system (MRAS). The key innovation lies in the synergistic combination of MPC’s predictive optimization with the robustness of TOR-SMC and the real-time adaptive estimation capability of MRAS, enabling reliable operation in the presence of sensor degradation or failure. A residual-based fault detection mechanism is embedded to monitor discrepancies between actual and estimated rotor speeds, enabling rapid fault identification and seamless transition to observer-based control. The proposed hybrid control system is designed within a hierarchical architecture, wherein MPC optimizes inverter switching actions, TOR-SMC ensures robust disturbance rejection and chattering suppression, and MRAS delivers high-fidelity speed estimation. Simulation studies under various operating scenarios—encompassing step changes in speed, variable torque loads, and fault scenarios—demonstrate that the system achieves a maximum speed estimation error of 1.8%, speed tracking accuracy of 97.6%, and a fault detection time of less than 2.5 ms, which is 41.3% faster than extended Kalman filter (EKF)–based schemes. Quantitatively, the proposed method reduces torque ripples by 32.5%, current overshoot by 35.7%, and transient response time by 27%, while improving overall fault tolerance by 63% compared to conventional FTC approaches. The TOR-SMC module contributes to a 78% reduction in chattering and ensures stable electromagnetic torque behavior even under dynamic torque disturbances. In parallel, MRAS offers faster convergence (2.5 ms) and smoother transitions compared to SMO and EKF observers, maintaining control integrity despite sensor anomalies. This comprehensive and modular FTC approach addresses a critical vulnerability in PMSM drive systems and is particularly well-suited for deployment in electric vehicles, aerospace systems, and renewable energy platforms, where high reliability, real-time responsiveness, and robustness to sensor degradation are paramount. The results confirm the proposed hybrid MPC–TOR-SMC–MRAS framework as a scalable and high-performance solution for next-generation motor control systems under fault-prone​ environments.