Search Results (101)

Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This study develops a data–model hybrid-driven simulation architecture that integrates a Federated Evolutionary Monte Carlo Optimization (FEMCO) algorithm for distribution network optimization. The model-driven module employs spectral clustering to decompose the network into multiple autonomous subsystems and performs distributed reconstruction through gradient descent. The data-driven module, built upon Long Short-Term Memory (LSTM) networks, learns temporal dependencies between load curves and operational parameters to enhance predictive accuracy. These two modules are fused via a Random Forest ensemble, while FEMCO jointly leverages Monte Carlo global sampling, Federated Learning-based distributed training, and Genetic Algorithm-driven evolutionary optimization. Simulation studies on the IEEE 33 bus distribution system demonstrate that the proposed framework reduces power losses by 25–45% and voltage deviations by 75–85% compared with conventional Genetic Algorithm and Monte Carlo approaches. The results confirm that the proposed hybrid architecture effectively improves convergence stability, optimization precision, and adaptability, providing a scalable solution for the intelligent operation and distributed control of modern power distribution systems. Full article

The reliability and lifetime of insulated gate bipolar transistors (IGBTs) are critical to ensuring the stability and safety of power electronic systems. IGBTs are widely used in electric vehicles, renewable energy systems, and industrial automation. However, their degradation over time poses a significant risk to system performance. Therefore, this paper proposes a data-driven approach based on a Long Short-Term Memory (LSTM) network optimized by a Genetic Algorithm (GA) to predict IGBT degradation. The study examines the health monitoring of insulated gate bipolar transistors from a device physics perspective. Degradation mechanisms that alter parasitics and electro-thermal stress produce characteristic changes in the turn-off overvoltage and the on-state voltage. Using power-cycling data from packaged half-bridge modules, an LSTM-based sequence model configured by a genetic algorithm search reduces error against an identically trained baseline (RMSE = 0.0073, MAE = 0.057, MAPE = 0.726%) under the shared protocol, with the clearest advantages in the early stage of degradation. These results support predictive maintenance and health management in power-electronic systems. Full article

Stray currents leaking from electrified DC rail systems cause the greatest corrosion risk to underground metal gas pipelines and can lead to pipeline wall perforation in a very short time. Leakage and gas explosion, and other direct and indirect effects, can even disrupt the stability of the energy system. Maintaining the reliability of gas pipelines, therefore, requires protecting them against corrosion caused by stray currents. It is therefore necessary to conduct field studies to identify sections of gas pipelines at risk and where protective installations should be installed. The paper discusses the most important field methods for assessing the risk of stray currents to gas pipelines: the potential of rail traction relative to ground, electric field gradients in the ground associated with stray current flow, correlation of gas pipeline potential and voltage of pipeline vs. the rail, and time-frequency analysis of the pipeline and rail potentials. A typical application case for each method is indicated, and the advantages and disadvantages of each research technique are identified. The criterion for selecting methods for this review was a short measurement duration (tens of minutes), after which it is possible to determine the level of the hazard to the gas pipeline caused by stray currents in the examined location. This is why these methods have an advantage over other research techniques that require long-term monitoring or exposure of probes or sensors. The review will be useful for cathodic protection personnel involved in the operation of gas pipelines and may be helpful in developing new methods for assessing the impact of stray currents. Full article

Photovoltaic systems are a core component of eco-friendly energy technologies and are now widely utilized across the world for power generation. However, solar modules that are continuously exposed to the external environment experience gradual performance degradation, which results in significant power loss and operational problems. Existing aging diagnostic methods such as current–voltage curve analysis and electroluminescence/photoluminescence testing have limitations in terms of real-time monitoring, quantitative evaluation, and applicability to large-scale power plants. To address these challenges, this study proposes a novel degradation detection method that utilizes the first-order derivative of the voltage–power curve of solar modules to extract key features. This method can estimate the number of degraded solar modules within a string and the degree of degradation, enabling early detection of subtle changes in electrical characteristics. In this study, we developed an AI model based on long short-term memory to classify normal and abnormal states and predict aging status, thereby supporting monitoring and early diagnosis. The model architecture was designed to reflect the characteristics of solar power systems, adopting a relatively shallow network due to the time-series data not being excessively long and the feature changes being clear. This design effectively mitigates the issues of overfitting and gradient vanishing, thereby positively contributing to the stability of model training. The training and validation results of the proposed long short-term memory model were verified through MATLAB simulations, confirming its effectiveness in learning and convergence. Full article

In regions with concentrated load centers in China, the AC transmission network is dense, leading to challenges such as difficulties in power flow control and excessive short-circuit currents. The scale effect of AC grids is approaching saturation, making it imperative to develop new AC/DC hybrid grid structures. To enhance the controllability, security, and stability of AC/DC hybrid power systems, a single-AC multi-DC hybrid grid structure is proposed in this paper. The operational characteristics of this grid are analyzed in terms of power flow control capability, N-1 overload, short-circuit current, frequency stability, voltage stability, and synchronous stability, and a method for constructing the single-AC multi-DC hybrid grid is presented. Finally, simulation analysis is conducted on a typical single-AC multi-DC case, and the results indicate that this hybrid grid structure can simultaneously satisfy the controllability, security, and stability requirements of AC/DC power systems, making it a highly promising grid configuration. Full article

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs. Full article

Single-phase ground (SPG) faults pose significant challenges in three-phase four-wire distribution systems due to their complex transient characteristics and the presence of multiple influencing factors. To solve the aforementioned issues, a comprehensive fault identification framework is proposed, which uses the TabNet deep learning architecture with hyperparameters optimized by Optuna. Firstly, a 10 kV simulation model is developed in Simulink to generate a diverse fault dataset. For each simulated fault, voltage and current signals from eight channels (L1–L4 voltage and current) are collected. Secondly, multi-domain features are extracted from each channel across time, frequency, waveform, and wavelet perspectives. Then, an attention-based fusion mechanism is employed to capture cross-channel dependencies, followed by L2-norm-based feature selection to enhance generalization. Finally, the optimized TabNet model effectively classifies 24 fault categories, achieving an accuracy of 97.33%, and outperforms baseline methods including Temporal Convolutional Network (TCN), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Capsule Network with Sparse Filtering (CNSF), and Dual-Branch CNN in terms of accuracy, macro-F1 score, and kappa coefficient. It also exhibits strong stability and fast convergence during training. These results demonstrate the robustness and interpretability of the proposed method for SPG fault detection. Full article

The complex electricity consumption situation on the customer side and large-scale wind and solar power generation have gradually shifted the traditional “source-follow-load” model in the power system towards the “source-load interaction” model. At present, the voltage regulation methods require excessive computing resources to accurately predict the fluctuating load under the new energy structure. However, with the development of artificial intelligence and cloud computing, more methods for processing big data have emerged. This paper proposes a new method for electricity consumption analysis that combines traditional mathematical statistics with machine learning to overcome the limitations of non-intrusive load detection methods and develop a distributed optimization of cloud–edge–device distribution networks based on electricity consumption. Aiming at problems such as overfitting and the demand for accurate short-term renewable power generation prediction, it is proposed to use the long short-term memory method to process time series data, and an improved algorithm is developed in combination with error feedback correction. The R² value of the coupling algorithm reaches 0.991, while the values of RMSE, MAPE and MAE are 1347.2, 5.36 and 199.4, respectively. Power prediction cannot completely eliminate errors. It is necessary to combine the consistency algorithm to construct the regulation strategy. Under the regulation strategy, stability can be achieved after 25 iterations, and the optimal regulation is obtained. Finally, the cloud–edge–device distributed coevolution model of the power grid is obtained to achieve the economy of power grid voltage control. Full article

This work presents a high-common-mode-rejection-ratio (CMRR) and high-gain FinFET-based bio-potential amplifier with a novel CMRR reduction technique. In this paper, a feedback buffer is used alongside a capacitively coupled chopper-stabilized circuit to reduce the common-mode signal gain, thus boosting the overall CMRR of the circuit. The conventional pseudoresistor in the feedback circuit is replaced with a tunable parallel-cell configuration of pseudoresistors to achieve high linearity. A chopper spike filter is used to mitigate spikes generated by switching activity. The mid-band gain of the chopper-stabilized amplifier is 42.6 dB, with a bandwidth in the range of 6.96 Hz to 621 Hz. The noise efficiency factor (NEF) of the chopper-stabilized amplifier is 6.1, and its power dissipation is 0.92 µW. The linearity of the parallel pseudoresistor cell is tested for different tuning voltages (V_tune) and various numbers of parallel pseudoresistor cells. The simulation results also demonstrate the pseudoresistor cell performance for different process corners and temperature changes. The low cut-off frequency is adjusted by varying the parameters of the parallel pseudoresistor cell. The CMRR of the chopper-stabilized amplifier, with and without the feedback buffer, is 106.9 dB and 100.3 dB, respectively. The feedback buffer also reduces the low cut-off frequency, demonstrating its multi-utility. The proposed circuit is compatible with bio-signal acquisition and processing. Additionally, a machine learning-based arrhythmia diagnosis model is presented using a convolutional neural network (CNN) + Long Short-Term Memory (LSTM) algorithm. For arrhythmia diagnosis using the CNN+LSTM algorithm, an accuracy of 99.12% and a mean square error (MSE) of 0.0273 were achieved. Full article

In view of the DC bus voltage fluctuation caused by the short-term periodic power demand of pulsed power loads (PPLs), this paper introduces a power allocation and tracking method for a hybrid energy storage system (HESS) with pulsed loads, aiming to improve the stability of the bus voltage. Firstly, a pulse power allocation and tracking method based on AC and DC components is proposed. Then, by introducing a current estimating method, a reference output current extraction from the AC component is obtained for model predictive control, which is used to control the supercapacitor converter, while the DC power is provided to inform the droop control to drive the battery converter. Finally, a frequency domain model is established to study the suppression effect of the control method on DC bus fluctuations, providing a reliable control scheme for HESS with pulsed power loads. Full article

The State of Charge (SOC) is vital for battery system management. Enhancing SOC estimation boosts system performance. This paper presents a combined framework that improves SOC estimation’s accuracy and stability for electric vehicles. The framework combines a Long Short-Term Memory (LSTM) network with an Adaptive Unscented Kalman Filter (AUKF). An Improved Arithmetic Optimization Algorithm (IAOA) fine-tunes the LSTM’s hyperparameters. Its novelty lies in its adaptive iteration algorithm, which adjusts iterations based on a threshold, optimizing computational efficiency. It also integrates a genetic mutation strategy into the AOA to overcome local optima by mutating iterations. Additionally, the AUKF’s adaptive noise algorithm updates noise covariance in real-time, enhancing SOC estimation precision. The inputs of the proposed method include battery current, voltage, and temperature, then producing an accurate SOC output. The predictions of LSTM are refined through AUKF to obtain reliable SOC estimation. The proposed framework is firstly evaluated utilizing a public dataset and then applied to battery packs on actual engineering vehicles. Results indicate that the Root Mean Square Errors (RMSEs) of the SOC estimations in practical applications are below 0.6%, and the Maximum Errors (MAX) are under 3.3%, demonstrating the accuracy and robustness of the proposed combined framework. Full article

This paper proposes a Hybrid Adaptive Learning-Based Control (HALC) algorithm for voltage regulation in grid-forming inverters (GFIs), addressing the challenges posed by voltage sags and swells. The HALC algorithm integrates two key control strategies: Model Predictive Control (MPC) for short-term optimization, and reinforcement learning (RL) for long-term self-improvement for immediate response to grid disturbances. MPC is modeled to predict and adjust control actions based on short-term voltage fluctuations while RL continuously refines the inverter’s response by learning from historical grid conditions, enhancing overall system stability and resilience. The proposed multi-stage control framework is modeled based on a mathematical representation using a control feedback model with dynamic optimal control. To enhance voltage stability, Lyapunov is used to operate across different time scales: milliseconds for immediate response, seconds for short-term optimization, and minutes to hours for long-term learning. The HALC framework offers a scalable solution for dynamically improving voltage regulation, reducing power losses, and optimizing grid resilience over time. Simulation is conducted and the results are compared with other existing methods. Full article

With the maturation of ultra-high-voltage direct current (UHVDC) technology, DC grids are taking on a more critical role in power systems. However, their impact on AC grids has become more pronounced, particularly in terms of frequency, short-circuit current level, and power flow control capabilities, which also affects the power supply reliability of the receiving-end grid. To comprehensively evaluate the balance between AC and DC strength at the receiving-end, this paper proposes a multidimensional assessment system that covers grid strength and operational security under various operating conditions. Furthermore, a rationality evaluation model for the AC/DC strong–weak balance relationship is developed based on the entropy weight method, forming a complete evaluation framework for assessing the AC/DC strong–weak balance in the receiving-end power grid. Finally, to address strength imbalances in grid, a structural optimization method for the receiving-end grid is designed by combining network decoupling techniques with modular multilevel converter-based HVDC (MMC–HVDC), serving as a strategy for enhancing grid stability. The proposed strategy is validated through simulations in a typical test system using PSD-BPA, demonstrating its effectiveness in optimizing power flow characteristics, improving system stability, reducing the risk of short-circuit current overloads and large-scale blackouts, and maintaining efficient system operation. Full article

With the increasing penetration of renewables, the importance of electrical energy storage (EES) for power supply stabilization is growing. The intermittency of renewable energy sources remains the main issue limiting their rapid integration; however, the development of high-capacity batteries capable of storing large quantities of energy offers a way to address this challenge. This article presents and describes dedicated algorithms for controlling the EES system to enable the provision of individual system services. Five services are planned for implementation: RES power stabilization; voltage regulation using active and reactive power; reactive power compensation; power stabilization of unstable loads; and power reduction on demand. The aim of this paper is to develop new, dedicated energy storage control algorithms for delivering these specific services. Additionally, the voltage regulation algorithm includes two operating modes: short-term regulation (voltage fluctuation stabilization) and long-term regulation (triggered by an operator signal). Full article

To enhance the operational stability of distribution networks during peak periods, this paper proposes a multi-timescale operational method considering load forecasting impacts. Firstly, the Crested Porcupine Optimizer (CPO) is employed to optimize the hyperparameters of long short-term memory (LSTM) networks for an accurate prediction of the next-day load curves. Building on this foundation, a multi-timescale optimization strategy is developed: During the day-ahead operation phase, a conservation voltage reduction (CVR)-based regulation plan is formulated to coordinate the control of on-load tap changers (OLTCs) and distributed resources, alleviating peak-shaving pressure on the upstream grid. In the intraday optimization phase, real-time adjustments of OLTC tap positions are implemented to address potential voltage violations, accompanied by an electrical distance-based control strategy for flexible adjustable resources, enabling rapid voltage recovery and enhancing system stability and robustness. Finally, a modified IEEE-33 node system is adopted to verify the effectiveness of the proposed multi-timescale operational method. The method demonstrates a load forecasting accuracy of 93.22%, achieves a reduction of 1.906% in load power demand, and enables timely voltage regulation during intraday limit violations, effectively maintaining grid operational stability. Full article