International Transactions on Electrical Energy Systems最新文献

This article proposes a battery-based hybrid power flow controller (B-HPFC), offering enhanced flexibility for power flow regulation and energy storage. First, the structure of B-HPFC is given, where the cascaded H-Bridge (CHB) is integrated with the phase-shifting transformer (PST). The batteries are connected to the modules of CHB. Doing so, the power flow can be adjusted by tuning the PST and CHB while the batteries can be charged or discharged by tuning CHB. Then, the hierarchical control method is given. The power flow control strategy operates as the outer loop, and the battery control strategy operates as the inner loop. Finally, the proposed structure and control method are verified by the hardware in the loop prototype. The results show that the power flow can be controlled smoothly while the SOC of batteries can be balanced.

In order to accurately evaluate and tap the mutual-aid capacity potential of interconnected power stations under the scenario of peak-to-peak compensation between new energy output and load demand, this paper uses Copula function to describe the correlation structure of wind, light, and load from the perspective of source–load matching, quantify the complementary degree of residual power and new energy output after source–load matching, and determine the feasible interval of mutual aid. On this basis, a space–time mutual-aid capacity optimization model with the goal of minimizing the total operating cost of the interconnected area is constructed. The model takes the principle of priority mutual aid in the station area and comprehensively considers the internal and external energy interaction constraints such as the transaction cost of purchasing and selling electricity with the superior power grid, the two-way power constraints of the tie line, the transformer capacity, and the renewable energy output constraints. Finally, the model is solved efficiently by the CPLEX solver of MATLAB. The simulation results of the example show that the proposed method can automatically establish cross-regional mutual power channels in the period of significant complementarity and significantly improve the renewable energy consumption level and overall operation economy of the station area while ensuring load power supply.

Wind energy conversion systems (WECSs) require robust and efficient control strategies to ensure optimal energy conversion. This study proposes a nonlinear and resilient control approach using a fractional-order proportional integral- and fractional-order proportional derivative (FOPI–FOPD) controller for direct power regulation of a doubly fed induction generator (DFIG)–based WECS. To meet the control objectives, two cascaded FOPI–FOPD controllers were designed, resulting in 12 parameters requiring precise tuning. To optimize these parameters, the Gazelle optimization algorithm (GOA) was employed, targeting the minimization of key performance-based cost functions: mean error (ME), mean absolute error (MAE), mean-square error (MSE), and integral time absolute error (ITAE). These functions integrate dynamic response criteria such as overshoot, rise time, and settling time. Simulation results highlight the effectiveness of the GOA-tuned FOPI–FOPD controller, particularly when using ITAE as the optimization criterion. The controller significantly reduces power ripples by 86.13% in active power and 75.66% in reactive power. It also improves transient response by reducing rise time by 0.035 ms, settling time by 0.3 ms, and completely eliminating overshoot. Moreover, the proposed strategies lower the current total harmonic distortion (THD) by approximately 21.43% compared to the basic strategy. The proposed ITAE–GOA–FOPI–FOPD technique ensures system stability and enhances performance across various operating conditions.

DC series arc faults (DC SAF) in photovoltaic (PV) systems can lead to electrical fires and electric shock hazards. Therefore, DC SAF modeling and detection is a significant process for ensuring the safety of PV panels and is necessary for producing PV systems in actual applications. Using real data, for the first time, this study presents a DC SAF modeling technique based on machine learning (ML) algorithms. Considering the unpredictable and nonlinear nature of such arcs and the application of ML in solving nonlinear and complex problems, multilayer perceptron, radial basis function, and support vector machine algorithms are used to model DC SAF in PV systems. The performance of proposed ML-based approaches is compared with well-known traditional models by using error indices, which are computed using a test data set. Finally, comprehensive evaluations and results of modeling demonstrate that proposed models based on ML methods remarkably improved modeling accuracy and generalization capability in DC SAF modeling.

One renewable energy (RE) source that shows promise for producing electrical energy is wind energy (WE). The coordination between the grid and WE conversion systems has become necessary due to high wind power penetration into the grid and varying wind speeds (VWSs). When incorporated into the grid, wind systems encounter challenging scenarios, including voltage fluctuations, power loss, and the troublesome dynamics of RE sources. Conventional PI control systems and fuzzy logic controllers (FLCs) face difficulties in resolving these problems. Applying hybrid artificial neural networks (ANN) will enhance the efficiency of the VWS system. The suggested controller can facilitate uninterrupted power transmission between generators and the grid, enabling a seamless connection with the grid. Here, it can all be facilitated by the constant voltage and power source supplied by a suggested controller. The training of hybridized ANNs with model predictive control (MPC) can minimize computing demands and device version errors. For ANN-MPC, the WE systems for DC microgrids are optimal. Simulink simulations in MATLAB/Simulink are conducted using the suggested hybrid ANN controller. The proposed ANN can consistently achieve better voltage balance and accuracy across various loading cases compared to conventional FLC and PID controllers. The outcomes demonstrate this. The outcomes of these simulations verify the efficiency of the ANN-based strategy. With an accuracy rate of 92.6% and a performance rate of 95.8%, the proposed hybrid ANN-MPC model outperforms similar current methods, as demonstrated by the experimental results.

With the continued growth in energy consumption, the installed capacity of clean energy, represented by wind power, is steadily increasing. However, the precise modeling of newly built wind farms is challenging due to a lack of data. Additionally, the dynamic updates of data associated with the wind farm’s operating conditions and the difficulty in capturing time-varying features further complicate accurate wind power forecasting. In response to these challenges, this paper proposes a wind power prediction method tailored for the data-scarce scenario of newly constructed wind farms. To prevent over-reliance on single-source domain data, a similarity measurement method combining Mahalanobis distance and dynamic time warping (DTW) is used to establish a multisource transfer learning-based pretrained model using a dilated convolutional neural network–bidirectional long short-term memory (DCNN–BiLSTM) network. Furthermore, to better capture the influence of time-varying scenario data on prediction accuracy, an online adaptive module-based prediction method is introduced to enhance the model’s generalization ability. Additionally, the elastic online deep learning (EODL) method is applied to address the issue of concept drift in dynamic streaming data, enabling quick adaptation to changes in data distribution. The proposed method is validated using data from a wind farm cluster in Northwestern China, demonstrating its superior ability to filter source domain data and provide more accurate power predictions.

Recent advancements in electric vehicles (EVs) and modern power systems offer broad opportunities for integrating renewable energy solutions. Solar photovoltaic (PV) systems, in particular, inherently avoid harmonic injection at the source due to the absence of alternating current (AC) power. However, consistently extracting maximum power from PV panels remains a technical challenge—especially under partial shading conditions where conventional algorithms struggle to locate the global maximum on the P–V curve. The recently introduced Jaya optimization algorithm has demonstrated improved performance through its reduced control variables and lower computational demand. Despite these advantages, its random nature often results in wide output fluctuations during transient periods, leading to limited exploitation near the global maximum. To overcome these drawbacks, this article introduces an enhanced Jaya algorithm designed to improve exploitation efficiency while tracking the global maximum power point (MPPT). A Luo DC–DC converter is employed due to its low output ripple, making it suitable for stable power conversion. Extensive simulations and experimental tests were conducted using 4S and 6S PV array configurations rated at 240 W and 360 W, respectively. The proposed method was benchmarked against seven other contemporary optimization algorithms and proved superior—achieving MPPT within 0.1 s and maintaining efficiency above 99% under all shading conditions. Further validation through statistical indices such as IAE, ITAE, ISE, and ITSE confirms the proposed approach’s robustness and suitability for real-time, fast renewable energy applications.

One of the most beneficial and effective methods for reducing the power losses of the distribution networks (DNs) is using distributed generations (DGs). The issue of optimal placement and sizing of DGs is a challenge that needs to be investigated carefully, as an improper location and sizing lead to a negative effect on the DN. In this work, an IEEE 33-bus is used as a test system for optimal placement and sizing of four DGs, three of them being photovoltaic (PV) sources and the fourth is a wind turbine (WT). The environmental data (irradiance, temperature, and wind speed) of Baghdad city (latitude: 33.29°, longitude: 44.38°) are used for training the artificial neural networks (ANNs) to forecast the day ahead values of the environmental variables for calculating the power production of PVs and WT. Particle swarm optimization (PSO) technique is used to optimize the location and sizing of the DGs. The operation cost of the system is optimized using genetic algorithm (GA) depending on the optimized sizing and placement of the DGs. Four electrical vehicles charging stations (EVCSs) are interconnected to the implemented DN with considering the uncertainty of hourly charging power demand using the queuing model. The optimal cost of the EVCSs is determined by using fuzzy logic system (FLS) to optimize the energy management of the daily power dispatch and peak power shifting to meet the peak power production of the DGs. The power losses are minimized by 50%, enhancing the voltage profile of the distribution system, and the operation cost is minimized by 19%. The annual operation cost saving of EVCSs is found to be 44.3%.

This paper investigates the application of reinforcement learning (RL) techniques for optimizing proportional–integral–derivative (PID) controller parameters in gas turbine speed control systems. The research employs the Rowen mathematical model as the foundational framework and introduces a novel approach utilizing twin-delayed deep deterministic policy gradient (TD3) algorithms. The methodology integrates machine learning with classical control theory to address the persistent challenges of maintaining optimal turbine speed during both transient startup phases and steady-state operations. Implementation was conducted using a simulation environment based on MATLAB/Simulink, with the General Electric 5001M heavy-duty gas turbine serving as the reference system. The RL agent was designed to interact with the simulated environment, continuously refining controller parameters to minimize performance metrics including integral error values, rise time, and settling characteristics. Comparative analysis between the proposed TD3-optimized PID controller and conventional tuning methods demonstrates significant performance enhancements across multiple control criteria. The optimized system achieved notable reductions in settling time, overshoot magnitude, and steady-state error, while also demonstrating improved disturbance rejection capabilities under variable load conditions and sensor noise.

With the increasing integration of renewable energy sources into the power system, challenges such as wind curtailment and operational flexibility are becoming more prominent. Therefore, this paper proposes a low-carbon optimised strategy for integrated energy system (IES) that considers the efficient use of hydrogen energy and the flexible operation of carbon capture power plant (CCPP)–methane reactor (MR)–hydrogen-doped combined heat and power (HCHP) combination. First, a model for the efficient utilisation of hydrogen energy containing wind power to hydrogen, hydrogen to thermoelectricity, gas-mixed hydrogen and hydrogen to methane was established. Secondly, the co-ordination mechanism among CCPP, HCHP and MR is explored, and the flexibility improvement of CCPP and HCHP is introduced by the liquid storage tank (LST) and Kalina cycle, respectively, and the joint CCPP-MR-HCHP flexible operation model is constructed. Finally, the integrated demand response (IDR) of electricity and heat is introduced, and a novel low-carbon optimisation model of the IES is established by integrating low-carbon and economic considerations. The simulation part of the example set up different scenarios for comparison, and the results showed that the introduction of an efficient hydrogen energy utilisation model can effectively improve the level of wind power consumption and reduce the total system cost and carbon emissions by about 11.35% and 24.73%, respectively. In addition, the proposed CCPP-MR-HCHP model can significantly improve the operational flexibility of the system, reducing the total system cost and carbon emissions by approximately 8.51% and 11.06%, respectively, compared to traditional operating modes.