International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026)
On behalf of the Local Committee, we are delighted to invite you to the International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026), to be hosted by the School of Electrical Engineering and the School of Artificial Intelligence at Shenyang University of Technology, Liaoning, China. The conference will be held in a hybrid format (both in-person and online) from July 29-31, 2026, at the Shenyang Sun Lion Marriott Hotel (Renaissance Hotel). Conference Focus Ingenio 2026 serves as a forum for researchers and industry experts to share innovative solutions that bridge advanced technology with real-world operational needs. The conference encourages submissions that explore applications beyond controlled laboratory environments, fostering exchange between theory-driven and data-driven approaches. We aim to facilitate meaningful discussions among young researchers, industry professionals, and research agencies in fields such as Computer Science, Information Technology, Artificial Intelligence, Electrical Engineering, Electronics, and Communication Engineering. Join us to share insights, collaborate, and contribute to realistic and impactful outcomes in energy, intelligent systems, and cloud computing.
Hierarchical Temporal Diffusion for Dispatch-Oriented Multi-Energy Scenario Generation
Multi-energy scenario generation should capture intraday dynamics, cross-channel dependence, and dispatch-sensitive risks. This paper proposes Hierarchical Temporal Diffusion (HTD) for joint PV, electricity, cooling, and heat scenario generation. The framework contains three coupled components. A hierarchical temporal decomposer separates each channel into trend, periodic, and fluctuation components. A weather-conditioned coupling graph learns cross-channel dependencies from meteorological and temporal conditions. A bounded decision feedback module builds dispatch-risk pressure from electric-equivalent demand, import exposure, ramp stress, and PV surplus. It then corrects generated scenarios within anchor constraints and channel-wise trust regions. Experiments show that the TDD-WCG generator improves joint statistical quality, while HTD with BDF improves dispatch-related risk metrics with a controlled trade-off in marginal accuracy and coverage. 你可以做些什么？ 你是怎样分析图片的？ 你能帮我翻译这段英文吗？
Copyright (c) 2026 International Conference on Energy, Intelligence Systems, and Cloud Computing (Ingenio 2026)
Measured-Data EV Hosting Capacity Assessment andDegradation-Aware V2G Reassessment under SpatioTemporal Charging Uncertainty
Reliable electric-vehicle (EV) hosting-capacity (HC) assessment requires the timing and location of charging demand to be represented jointly. This paper presents a probabilistic framework based on measured public charging data and uses it to reassess HC after degradation-aware vehicle-to-grid (V2G) dispatch. Station-hour records are converted into node-hour scenarios by combining empirical daily profiles with scenario-specific spatial allocations. For each scenario, power flow is solved using the backward/forward sweep method, and the results are checked against bus-voltage, transformer, and main feeder current limits. A Stackelberg V2G model is then solved iteratively, and HC is recomputed from the dispatched net load. The case study uses data from 97 representative days recorded at 92 public charging stations in Jiaxing, China. The mean HC is 1.10 MW, with P5 and P95 values of 0.89 and 1.42 MW, respectively. The measured EV peak is about 74% of the mean HC. V2G increases HC by only 2.8% because the measured public-charging profile is relatively flat; applying the same procedure to a sharp-peak benchmark yields a much larger gain. The capacity contribution of V2G therefore depends on load shape, connection location, and the binding network constraint. The framework provides a common basis for charging-station siting and feeder-reinforcement decisions.
Real-Time Object Detection of Photovoltaic Defects via Aerial Thermography: Evaluating Convolutional Neural Network and Transformer Architectures
The rapid expansion of large-scale photovoltaic plants requires efficient predictive maintenance strategies to ensure optimal energy generation. Traditional manual thermal inspections are time-consuming and subjective, driving the need for automated solutions via Unmanned Aerial Vehicle thermography. This paper presents a comparative study of state-of-the-art Deep Learning architectures for the automated object detection of thermal anomalies. The study focuses on critical defects, e.g., hotspots, open-circuit, and string open-circuit utilizing a specialized, manually curated dataset of aerial thermal images from 50 MWp operational solar plants. Three distinct architectures were evaluated to determine the optimal trade-off between diagnostic precision and computational cost: a Convolutional Neural Network (YOLOv11), a vision-language hybrid (YOLO-World), and an end-to-end real-time Vision Transformer. Experimental results demonstrate that Transformer achieves superior boundary precision and recall (mAP50-95 of 0.80), effectively mitigating false negatives for diffuse micro-anomalies. Conversely, YOLOv11 delivers unrivaled inference agility (4.43 ms per image), making it the optimal choice for massive, high-speed inspections constrained by edge-computing hardware. YOLO-World serves as a highly capable intermediate alternative, offering strong small-object detection with lower computational overhead than pure Transformers. These findings validate the practical applicability of these models for industrial UAV inspections, enabling operators to deploy specific architectures tailored to their unique hardware constraints and operational requirements.
Fault Detection Method for Grounding Fault by using Dual-Switched Grounding Detector in Isolated System
Electric vehicle high-voltage circuits commonly suffer two prominent technical bottlenecks: faint signals generated by ground faults and poor performance in pinpointing faulty segments. Targeting these practical limitations, this study develops an original fault localization scheme that combines sequential switching grounding detection with wavelet packet transform and support vector machine (WPT-SVM) algorithms. For the detection architecture proposed in this study, two controllable grounding test terminals are installed on the direct current bus and the drive motor branch circuit respectively; these terminals are switched to link with a unified grounding resistor in turn. Transient voltage waveforms captured in this process are used to reflect distinct fault signatures corresponding to each grounding access route. Wavelet packet decomposition is then adopted to compute normalized frequency band energy and energy entropy as fault characteristic parameters. Feature vectors extracted per grounding switching cycle are concatenated and input into a support vector machine for accurate fault region discrimination. Simulation validations show the proposed scheme can effectively distinguish ground faults in different high-voltage branches while retaining high classification accuracy and reliability under large transition resistance faults.
A Hybrid Machine Learning Framework for Predictive Maintenance Routing Optimization in Floating Offshore Wind Farms
Operation and maintenance (O&M) activities for floating offshore wind farms remain one of the main techno-economic barriers to large-scale commercial deployment. Long offshore distances, severe metocean conditions, degradation of critical components, limited vessel availability, and the increasing adoption of tow-to-port repair strategies significantly reduce the effectiveness of conventional time-based preventive maintenance approaches. This study presents an integrated decision support framework combining machine learning, stochastic degradation modelling, and maritime routing optimization to minimize the expected maintenance cost of floating offshore wind farms. The main contribution lies in the formulation of a disaggregated cost function that takes into account different items. The proposed framework is solved through a hybrid scheme. First, an XGBoost predictor estimates the short-term probability of failure for each turbine. Second, a cost priority index is used to identify candidate turbines for intervention. Finally, feasible maintenance routes for a heterogeneous fleet composed of Crew Transfer Vessels (CTV), Service Operation Vessels (SOV), and towing vessels are planned. A simulated case study based on Hywind Scotland floating wind farm and consisting of 14 floating wind turbines rated at 15MW and located 30km from the base port is developed to evaluate the proposed methodology. Three maintenance strategies are compared: purely corrective maintenance, threshold-based maintenance and Heuristic index-based maintenance. Results indicate that the proposed heuristic policy reduces thetotal annual maintenance cost by 30%-45% compared with conventionalmaintenance strategies. These improvements are mainly driven by lower production losses, reduced deterioration state, and more efficient vessel utilization
Improving E-Bike Trip Efficiency through perfect duration and Battery Consumption Prediction Using Machine Learning Techniques and Power BI Analytics
Global warming and rise in temperature in cities due to vehicle pollution are the main causes to increase the usage of pollution free Electrical Bikes. These vehicles operate on batteries; therefore it is needed to estimate the batteries power consumption and enhance the trip efficiency. This paper illustrates about data-driven approach for estimating the time required for trips as well as battery usage in e-bikes share systems. Feature engineering has been assigned to expand the dataset through environmental, temporal, and operational factors. Several machine learning models such as Linear Regression, Decision Tree, and Random Forest regression have been built and evaluated via performance measures including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-Squared (R²) score. Out of all the tested models, the random forest regression executed best with RMSE of 290.02 and R² score of 0.75. In addition, Power Business Intelligence (Power BI) has been used for interactive data visualization. The proposed approach intensifies operational efficiency and supports sustainable urban mobility planning.
A Stackelberg game bidding model for a combined heat and power virtual power plant considering carbon emission flow
By aggregating distributed energy resources efficiently and flexibly, a virtual power plant (VPP) is capable of altering the traditional pattern of local consumption. However, existing bidding models and scheduling strategies have notable deficiencies in addressing multi-agent conflicts of interest and the allocation of carbon emission responsibilities. To overcome the aforementioned drawbacks, a bidding model for a combined heat and power virtual power plant (CHP-VPP) is developed in the present study. Based on the overall transaction information and energy flow direction, the competitive relationship between the operator and the consumer aggregator are clarified. To trace the carbon footprint in the power delivery network, a carbon‑emission flow model is additionally adopted, which enables the equitable allocation of emission duties from the internal production plants to the demand side. In this model, the operator acts as the game leader in formulating pricing strategies, while the consumer aggregator, as the follower, responds to the price signals with the objective of maximizing its own revenue, forming a Stackelberg game framework. In addition, the emission‑related cost is included in the objective function of the sub‑level aggregator. A mechanism offering incentives for carbon abatement is also proposed to motivate the on‑site generators and consumers to act jointly within a game‑theoretic framework, which can fully unlock the demand‑side's carbon‑cutting capability. Case study results demonstrate that the proposed model can effectively reduce carbon emissions while improving system economic performance, ultimately achieving coordinated low-carbon operation on both the supply and demand sides.
Distributed Secondary Cooperative Control Strategy for Multiple VSG/GFM Converters Based on Consensus Algorithm
Aiming at the problems of frequency deviation, unequal power sharing, and line impedance mismatch in the parallel operation of multiple virtual synchronous generator /grid forming(VSG/GFM)converters, this paper proposes a distributed secondary cooperative control strategy based on consensus algorithm. This method equates multiple converters as a multi-agent system, achieves frequency restoration and active power allocation proportionally to rated capacity through information interaction among neighboring nodes. Simulation results demonstrate that the proposed method exhibits favorable stability and power sharing performance under conditions of sudden load change.
Data-driven Surge Modelling of Floating Offshore Wind Turbines with Various Input Selection for Condition Monitoring
Floating offshore wind turbines (FOWTs) rely heavily on mooring systems to maintain position and ensure safe operation under complex environmental loading conditions. However, accurate modeling and condition monitoring of mooring dynamics remain challenging due to strong nonlinear behavior, coupled multi-degree-of-freedom interactions, and the limited availability of high-quality experimental data. This study proposes a data-driven modeling framework for condition monitoring of FOWT mooring systems under limited data conditions. A time-delay neural network (TDNN) is developed to capture the nonlinear dynamic relationships between environmental excitations, platform motions, and surge response which closely correlates with mooring forces. The model is evaluated using the normalized root mean square error (NRMSE) under wave-only, wind-only, and wave–wind coupled scenarios. Results indicate that appropriate selection of input features significantly influences pre diction accuracy. In wave-only cases, pitch motion provides the most rele vant additional information for improving surge prediction. In wind-only cases, the combination of heave and pitch yields the best performance, as these motions contain useful low-frequency dynamics correlated with surge. However, in wave–wind coupled cases, model performance deterio rates due to increased system complexity and limited data length, which reduces generalization capability. Overall, the proposed framework high lights the importance of physically informed input selection and demon strates its effectiveness for data-driven condition monitoring of FOWT mooring systems using limited experimental measurements.
Low-Frequency Oscillation Suppression Strategy forGrid-Forming Converters Based on Active-Power-LoopVirtual Damping Control
With the rapid growth of renewable energy penetration, grid-forming converters (GFMs) have become essential for the stable operation of new power systems. However, the virtual synchronous generator (VSG) features strong coupling between power and voltage-current control loops due to power electronic multi-timescale dynamics, which easily induces negative damping and low-frequency oscillations (LFOs). Under the commonly adopted constant DC-voltage assumption, the interactions among the active-power and reactive power loops, together with the influence of the fast voltage-current control loops, may still result in insufficient damping and LFO. To solve this issue, this paper proposes a virtual damping control (VDC) strategy for the active power loop. A reduced-order small-signal model considering the coupling between the active-power and reactive-power loops is established, while the voltage-current inner-loop dynamics are neglected under the assumption that they are much faster than the outer-loop dynamics. A VDC scheme employing band-pass filtering and phase compensation is designed to generate supplementary power modulation, providing damping torque to suppress oscillations. Eigenvalue analysis and time-domain simulations validate the proposed method. Compared with traditional virtual impedance (VI) control, the proposed VDC effectively suppresses LFO and improves the damping performance and dynamic stability of the GFM system
Performance Analysis of an Air-Source Heat Pump andSolid Thermal Energy Storage Staged Series HeatingSystem in Severe Cold Regions
In regions with severe cold, both air-source heat pumps (ASHP) and solid thermal energy storage (STES) systems have performance limitations when used for heating in isolation. This paper addresses MW-scale heating demands by developing a model for the series-connected, coordinated operation of ASHPs and STES, and analyses system performance under different ambient and intermediate temperatures. The results indicate that a decrease in ambient temperature leads to a significant drop in the ASHP’s coefficient of performance (COP), with a particularly marked decline below–15 ℃. The ASHP-STES system optimizes heat pump operating conditions through staged heating; under conditions of–20 ℃ to–25 ℃, total energy consumption is reduced by approximately 1.05% and 10.78% compared with ASHP and STES-ASHP systems, respectively. Its total power consumption exhibits a U-shaped variation with intermediate temperature, reaching an optimum in the 80–85 ℃ range, resulting in energy savings of 1.4% to 5.1% compared with the ASHP system. In contrast, the STES-ASHP system consistently fails to demonstrate any energy efficiency advantage. When the ambient temperature exceeds–10 ℃, standalone ASHP heating is optimal; when it falls below–15 ℃, the ASHP-STES system offers greater energy-saving benefits.
Optimal configuration of diversified energy storage for renewable energy peak regulation and frequency response
Faced with the uncertainty and volatility that large-scale integration of renewable energy brings to the power grid, implementing combined electricity heat scheduling within the park is highly beneficial. Therefore, a comprehensive energy storage configuration method is proposed for the park based on various types of energy storage resources, which includes wind turbine (WT), photovoltaic unit (PV), electro-chemical energy storage (ECES), and solid-state electric thermal storage (SETS). To maximize the profit of the park, revenue from the electricity market, ancillary services, and capacity market is considered to develop a configuration method and profit, while calculating investment and operation costs. Compared with traditional methods, four different configuration scenarios are selected for simulation calculations based on energy spatiotemporal conversion characteristics, and energy storage facilities with ancillary service capabilities can effectively improve the park's economy. STES cannot obtain sufficient revenue under the current pricing mode. However, the combined electric-heat energy storage can increase the flexibility of the park, its economic feasibility still needs to be judged based on the actual situation.
Early Detection and Severity Estimation of Non-Condensable Gas Faults in Water-Cooled Chiller Using Physics-Informed Machine Learning Model
Non-condensable gases Contamination (NC) in water-cooled chillers reduces thermodynamic efficiency, leads to higher power consumption for the compressor, and lowers system life span; however, such problems are challenging to identify in low levels of severity via traditional rule-based approaches. In this paper, an FDD algorithm of three-layer architecture integrating Gordon-Ng Thermodynamic Residuals, COP calculation, and RBF-SVM classification for identifying different NC concentrations at varying operating conditions. The method is validated on two independent datasets: a 58‑ton ASHRAE RP‑1043 laboratory chiller and a 338‑ton OpenStudio EnergyPlus simulation with synthetic NC faults injected via a custom OpenStudio measure. On the RP‑1043 data, the framework achieves a detection rate of 85.2% at 1% NC, rising to 100% at 5% NC, with a false alarm rate of 12.3% and SVM accuracy of 87.7%. On the 338‑ton simulation, detection reaches 99.6% at 2% NC and 100% at ≥3% NC, with 0.0% false alarms and 98.2% accuracy. The Gordon–Ng power residual on the larger chiller is nearly perfectly linear (~10 kW per 1% NC; R² ≈ 1.00), enabling continuous severity estimation from compressor power alone. Results show that physics‑informed residuals provide highly discriminative features for robust NC detection even at clinically low contamination levels, and that signal‑to‑noise ratio scaling with chiller capacity is a key calibration factor for field deployment. Future work will extend the pipeline to additional RP‑1043 faults, investigate multi‑fault concurrent detection, and pursue live BACnet/MQTT integration with commercial building management systems.
Parameter Optimization and Mechanism Analysis of Coordinated Adaptive Inertia-Damping Control for Virtual Synchronous Generator
Fixed-parameter virtual synchronous generator (VSG) control is subject to a trade-off between inertial support and damping performance under transient disturbances. To address this issue while avoiding the high computational cost of black-box optimization, this paper presents a deterministic parameter-optimization method for coordinated adaptive inertia-damping control. The post-disturbance response is first divided into acceleration and recovery intervals according to the time-domain evolution of angular-frequency deviation and RoCoF, and a continuous piecewise adaptive law is then formulated to allocate virtual inertia and damping to the corresponding transient stages. An augmented root-locus analysis is used to determine the small-signal stability boundary of the adaptive coefficients. Within this boundary, the inertia and damping coefficients Kj and Kd are selected through a control-variable time domain simulation procedure. MATLAB/Simulink results show that the proposed method reduces active-power overshoot and improves frequency support under bidirectional step disturbances compared with fixed-parameter and single-parameter adaptive VSG controls. The proposed design has an explicit stability basis and a simple algebraic structure, making it suitable for digital implementation in converter-interfaced microgrids.
Transformer–LSTM Framework for Energy Consumption Prediction in Smart Energy Systems using Time-Series Deep Learning
Energy consumption forecasting is a key element in intelligent energy management systems and a smart grid environment. By accurately predicting electricity consumption, we can maximize energy utilization efficiency and reduce operating costs. This makes it important to predict energy consumption for developing a smart energy system. Traditional forecasting methods fail to adequately characterize the temporal dependencies and non-linearity within long time-series data. The proposed framework in this paper aims to use a deep learning approach to establish a Transformer-LSTM model to perform short-term energy consumption forecasting. This Transformer-LSTM model combines the self-attention mechanism within Transformer networks and the sequence learning property within LSTMs to improve the forecasting performance and enhance temporal feature extraction. The proposed framework is evaluated using PJM Hourly Energy Consumption data and UCI Household Electric Power Consumption data and compared with existing models: LSTM, GRU, CNN LSTM and Transformer models. Evaluating by MAE, RMSE, MAPE and R² Score respectively, the proposed hybrid framework demonstrated competitive forecasting performance across both datasets. The proposed model achieved the highest R² score and lowest RMSE on the UCI dataset, while maintaining comparable forecasting accuracy to recurrent and hybrid baselines on the PJM dataset. The proposed framework demonstrated competitive forecasting performance across both datasets while maintaining robust prediction accuracy on the PJM dataset. The approach can support intelligent decision making within smart grids, smart buildings, cloud-based energy management systems, etc.
Reinforcement-Learning-Based Fault-TolerantControl for a 2-DoF UAV System
This paper investigates fault-tolerant control for a 2-DoF Quanser Aero2 UAV system subject to propeller damage. An LQR controller with integral action (LQI) is first designed as the baseline con troller under healthy conditions. Propeller faults are then introduced experimentally using single-cut, middle-cut, and symmetric-cut blade damage patterns. Hardware-in-the-loop results show that single-cut and middle-cut faults are largely tolerated by the LQI controller, whereas symmetric-cut faults cause significant thrust loss and actuator saturation. To improve performance under partial actuator degradation, a Soft Actor-Critic reinforcement-learning framework combined with an Extended Kalman Filter is proposed to estimate actuator effectiveness loss and compensate the control input. Experimental results show improved tracking performance and reduced overshoot for partially de graded symmetric-cut cases.
Data Resource Library for AHU of HVAC/ACMV Condition Monitoring: A Case Study Using AI-Driven Intelligence Approach
Air Handling Units (AHU) classic leakage of outdoor-air dampers generates energy increments in the cooling-system and this energy increments is used for threshold-based Fault Detection and Diagnosis (FDD). For tropical buildings equipped with gas-fired reheats the thermodynamic effect on heating and reheat load resulting from excess outdoor air enthalpy leads to loss of such characteristic energy signatures, making it impossible for FDD systems based on fan electric power to identify the fault. This work generates a public-accessible labeled data library on a particular type of fault occurring in a tropical climate gas-reheat VAV AHU. Energy Plus 23.2 and OpenStudio 3.7.0 simulation, with modifications of the ORNL Flexible Research Platform for Kuala Lumpur climate and with fault levels of severity between 20% and 50%, yield 16,680 hours analyzed based on Fault Impact Ratio calculation, sensitivity ranking, and Random Forest classification. The fault manifests as an ambiguous signal pattern: no fan electricity sensitivity (0.00%), 2.5% growth in cooling coil electricity consumption, up to 85.8% reduction in hot water pump electricity, and up to 88.66% decrease in boiler natural gas consumption, rendering both fan-based and energy threshold FDD ineffective. Electrical-signal based FDD attains 71.47% balanced accuracy while employing the practical signal group yields 95.49%. This dataset is introduced as an open access FDD dataset for Southeast Asia. Thermal and latent energy signatures of the hot water loop and cooling coil, respectively, can be used to diagnose the fault without any direct airflow measurements.
Bidding Strategy for New Energy Stations considering Wind and Solar Uncertainty in the Energy-Frequency Modulation Ancillary Service Market
With the increasing penetration of renewable energy sources in the power grid due to the high proportion of renewable energy integration, a system architecture is proposed for joint participation of wind, solar, and electric energy storage in both the energy market and frequency regulation ancillary service mar ket. To improve the implementation accuracy of day-ahead schedules, a deviation penalty mechanism is introduced. Considering uncertainties in wind power, solar irradiance amplitude, a day-ahead and real-time coordinated bidding model based on typical scenarios is constructed to maximize the overall expected economic benefit of the integrated system. Simulation verification proves that compared to separate bidding for wind and solar power, joint bidding can mitigate bidding deviation penalties caused by wind power fluctuations through the flexible ad justment characteristics of electric energy storage, thereby enhancing the overall revenue of the joint system.
Optimal Scheduling of Source-Load Dual Layer Based on Node Carbon Potential and Green Certificate-Carbon Joint Trading Mechanism
To address the issues of insufficient load-side responsiveness and the lack of effective synergy between the renewable energy certificate market and carbon trading scheme on the power production side, this paper presents a bilevel optimal dispatching framework that integrates both supply and demand sides, centered on node carbon potential guidance and a mechanism linking green certificate and carbon trading. From the demand-side, nodal carbon potential values are computed using carbon emission flow theory and used as a direct steering signal to drive two types of demand response, thereby unlocking the full carbon abatement potential of the demand sector. On the power production side, a coordinated framework is set up to integrate green certificate transactions with carbon allowance trading. Green certificates, with their carbon reduction functions, can be used to offset a portion of the carbon allowance purchase volume, carbon trading costs are effectively reduced, enabling the synergistic operation of the two markets. Based on the above analysis, this paper constructs a bi-level optimization model. the upper layer problem targets the minimization of overall power production costs under the constraints of the coupled green certificate and carbon emission trading rules, and the lower level aims to minimize user electricity expenses under the guidance of nodal carbon potential signals. Finally, simulation and validation are conducted using the IEEE 14-node system. The results show that the presented scheduling scheme can fully tap into the carbon reduction potential on the load side, enhance the interactivity of market mechanisms on the generation side.
Harmonic Suppression Strategy for PV–Storage Grid Connected VSG System Based on Adaptive Virtual Harmonic Impedance
To address the grid-connected current distortion caused by back ground grid harmonics in virtual synchronous generator (VSG)-controlled PV storage systems, an adaptive virtual harmonic impedance-based harmonic sup pression strategy is proposed. First, an equivalent impedance model of the VSG grid-connected system is established to analyze the influence of background harmonics on the grid-connected current. Then, a hybrid generalized integrator composed of cascaded TOGI and SOGI is employed to extract the fundamental and harmonic currents, based on which the virtual harmonic impedance is constructed. Furthermore, an adaptive virtual impedance control method is designed according to the harmonic content variation of the grid-connected current to realize online adjustment of the virtual inductance. Simulation results demonstrate that the proposed method can effectively reduce the harmonic content of the grid connected current, improve power quality, and provide satisfactory dynamic performance.
Research on Online Monitoring of Transformer Short Circuit Impedance Based on VMD Combined with VFFRLS
Aiming at the problems of significant non-stationary characteristics and complex strong noise interference in the electrical signals of the primary and secondary sides of transformers during actual operation, to achieve high precision, real-time and reliable online impedance monitoring, this paper proposes a joint signal processing and parameter identification scheme integrating Variational Mode Decomposition (VMD) and Variable Forgetting Factor Recursive Least Squares (VFF-RLS). First, VMD is used to adaptively decompose the noisy voltage and current signals at multiple scales, which effectively suppresses noise and accurately extracts pure fundamental components. In the signal preprocessing stage, the superiority of VMD in fundamental component extraction and anti-interference is verified through comparative analysis with the method combining wavelet threshold denoising and windowed interpolation. Subsequently, based on the classic T-type equivalent circuit model of the transformer, the VFF-RLS algorithm is adopted to complete the online identification of dynamic short-circuit impedance. Simulation results under three representative operating conditions and with SNRs of 40–60 dB show that the short-circuit impedance identification error can be maintained within 1%, demonstrating high accuracy and stability. This scheme provides an efficient and feasible technical support for the health assessment of transformer insulation and winding status, early fault warning, and safe operation and maintenance.
Transformer DGA Fault Diagnosis Based on SMOTEand SSA-Optimized XGBoost
In the daily operation and maintenance of power systems, the condition monitoring of oil-immersed transformers has always been a significant issue. Dissolved Gas Analysis (DGA) is one of the effective methods for detecting faults in oil-immersed power transformers. However, in practical applications, factors such as the uneven distribution of faulty samples, inappropriate selection of feature dimensions, and difficulty in determining the parameters of the classifier limit the accuracy of traditional diagnostic methods, making the diagnostic results vulnerable to interference. This paper presents a hybrid fault diagnosis scheme that integrates the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling of minority class samples, feature importance selection based on LightGBM (Light Gradient Boosting Machine), and the eXtreme Gradient Boosting classifier (XGBoost) optimized by the Sparrow Search Algorithm. The experimental results on the public dataset show that the proposed method achieved an accuracy of 97.42%, a Macro-F1 score of 97.36%, and a Kappa coefficient of 0.9677. Compared to the unoptimized XGBoost, SSA optimization improved accuracy by 0.42% and Macro-F1 by 0.48%, This demonstrates that automatic hyperparameter tuning is effective. The proposed method also outperforms SVM (80.26%), Random Forest (93.56%), and PCA + SSA-LightGBM (88.41%). In addition, the ablation experiment further validated the role played by each component.
Fast thermal-state sensing for solid thermal storage in heavy-oil recovery
Renewable-electricity substitution in heavy-oil thermal recovery requires rapid knowledge of the thermal state inside high-temperature solid thermal storage units, where dense internal sensing is difficult to deploy. This study develops a sparse-measurement soft-sensing method that combines a one dimensional convolutional neural network with a bidirectional long short-term memory network (CNN-BiLSTM). A field-calibrated multiphysics heat-transfer model was first used to generate temperature data over complete charging and discharging processes. The model then used 60-step historical sequences from five thermocouple features as inputs. The 1D-CNN extracted local thermal response features among measurement points, whereas the BiLSTM captured thermal inertia and temporal lag within the available historical window. The target was the volumetric average temperature, which represents the stored thermal inventory and thermal state of charge for steam-supply scheduling and heat-supply-capacity assessment. Local maximum temperature should still be monitored by independent protection sensors. On a chronological test set, five repeated training runs yielded a mean absolute error of 5.95 °C, a root mean square error of 7.43 °C and an R² of 0.996, with a 95% confidence interval of 6.51-8.36 °C for RMSE. Under 5% input noise, the RMSE was 7.56 °C and R² remained 0.996. The mean single-sample response time, including normalisation, forward inference and inverse normalisation, was approximately 23.94 ms. These results indicate that the method can support online state estimation and operational monitoring of large solid thermal storage systems within the tested operating range.
Optimal Design and Techno-Economic Analysis of an On-Grid Renewable Energy Microgrid for a 1MW Hypothetical Hospital in Eskisehir (Türkiye)
This study covers the design and optimization for the on-grid renewable micro-grid network with solar photovoltaic energy, battery storage system, inverter, and grid interconnectivity, all with a view to efficiently tackle the energy requirements while viewing sustainability. A few configurations were analyzed in this study by using HOMER simulation software so that this paper could present the most cost-effective and reliable solution against the base-case of the government incentivized LCOE of the Turkish national power grid. The optimal architecture: 11,841 kW solar photovoltaic; 6,162 kW converter with grid interconnection-at net present cost of $2.69M to realize an LCOE of $0.01111/kWh. Then, the percentage of renewable penetrations would come to about 82.8%, reducing the environmental impact by the reduction in the carbon dioxide emissions by more than 50%, without sacrificing operational reliability. Economic analysis for these types of solar-powered net-metered systems shows huge savings from the base case of only-grid use: 14.1% IRR, simple payback in 6.7 years, and a discounted payback period of 8.85 years. The analysis of energy production per month shows very clearly that this kind of system is perfectly able to satisfy fully day-to-day demand but with some excess energy returned to the grid. The research work hereby establishes the technical and economic viability of the renewable energy microgrid system in efficient and sustainable power generation.
Optimal Reactive Power Dispatch in Distribution Networks with Distributed Generation
To address the issue that the uncertainty of renewable energy output and load fluctuations may cause voltage limit violations after grid connection, severely impacting the stable operation of power systems, a dynamic reactive power optimization mathematical model for distribution networks with distributed generators (DGs) is established. The model accounts for the daily switching limits of DG integration locations and transformer tap ratios, with system active power losses and voltage deviations serving as the objective functions. A scenario reduction method combining Monte Carlo sampling and K-means clustering is employed to significantly reduce the computational complexity of the optimization. The improved whale optimization algorithm is applied to conduct simulation analyses on the IEEE 33-bus. Simulation results demonstrate that the proposed model and method can satisfy system operational planning requirements while enhancing both economic efficiency and operational stability.
Resonance Suppression Strategy for Grid-FormingInverters Based on Grid-Voltage Feedforward
With the growing reach and adoption of renewable energy, the grid impedance rises significantly, resulting in weak and extremely weak grid conditions that seriously threaten grid stability. Consequently, grid-forming inverters, capable of providing voltage and frequency support, have garnered widespread attention. However, under weak grid conditions, Delays from digital control alters the output impedance performance grid-forming inverters to exhibit negative damping characteristics. Resonance problems at mid and high frequencies arise from the coupling between this output impedance and grid impedance. To address this problem, this paper introduces the passivity theory from the perspective of inverter output impedance and proposes an impedance reshaping strategy based on grid voltage feedforward, transforming the inverter output impedance from active to passive. First, an impedance model of the grid forming inverter is established to analyze the mechanism of resonance generation, and the non-passive characteristics of the output impedance are verified based on passivity theory. Second, through an analysis of the inverter impedance, a novel voltage feedforward function is derived and designed.By appropriately configuring the feedforward parameters, Satisfying passivity conditions at all frequencies, the inverter output impedance achieves resonance suppression. Ultimately, a 10 kW inverter prototype verifies the accuracy of theoretical derivations and the feasibility of the presented control schem.
Research on Circulating Current Control Strategy for Parallel VSG Based on Adaptive Virtual Impedance
To address the circulating current and uneven power distribution issues in multi-virtual synchronous generator (VSG) parallel systems caused by incon sistent line impedances, an improved control strategy introducing dynamic vir tual impedance is proposed. By analyzing the active power-frequency and reac tive power-voltage control characteristics of VSG, the influence mechanism of line impedance differences on power distribution accuracy and circulating cur rent is revealed. In the proposed strategy, adaptive virtual impedance is intro duced to enhance the inductive feature of the output impedance, achieving power decoupling, and a voltage feedback integral link is combined to compensate for the voltage drop caused by virtual impedance. Simulation results show that this strategy can effectively suppress the circulating current between parallel VSGs, achieve precise sharing of active and reactive power, and improve system stabil ity and power quality.
Low-Frequency Electromechanical Oscillation Suppression of Hybrid Synchronous Sources Based on a Dual-Input Power System Stabilizer Strategy
With the large-scale integration of renewable energy, hybrid power systems comprising virtual synchronous generators (VSGs) and synchronous generators (SGs) are increasingly susceptible to low-frequency electromechanical oscillations (LFEOs) caused by the dynamic interaction between virtual and physical inertia. This paper investigates the oscillatory characteristics and damping behavior of a VSG-SG hybrid synchronous-source system. A Phillips-Heffron small-signal model is developed to identify the dominant modes and reveal the virtual-physical power-angle coupling mechanism. A dual-input power system stabilizer (PSS2A) is then incorporated into the SG excitation system, and its LFEO suppression mechanism is analyzed using the damping-torque method. Eigenvalue analysis and time domain simulations under an applied disturbance show that PSS2A shifts the dominant oscillatory poles farther into the left half-plane, increases the damping ratio, and markedly reduces the power- and angle-oscillation amplitudes. The results demonstrate that PSS2A enhances the damping of SG-dominated modes while weakening the dynamic interaction between the SG rotor angle and the VSG virtual power angle. This study provides a theoretical basis for applying conventional dual-input stabilizers in VSG-SG hybrid power systems.
A Capacitor Current Feedback Compensation Strategy for Enhancing Resonance Suppression Capability
Against the backdrop of high proportions of renewable energy and power electronic equipment, resonance stability has emerged as a critical issue. This paper takes grid-forming inverter (GFMI) as the research object, considering that the internal digital delay will affect the inner-loop self-stability of the inverter, the double-loop of the GFMI is modeled and analyzed in the discrete domain to obtain the fastest dynamic performance and the optimal damping ratio. In addition, aiming at the external resonance problem of the interaction between the inverter and the grid impedance, a mapping model reflecting the effect of harmonic voltage disturbance of the inverter and the grid on the resonance excitation of the grid-connected current is established, and a capacitor current feedback compensation (CCFC) resonance suppression strategy is proposed to enhance the damping capacity of the system. The simulation results indicate that the proposed strategy significantly suppresses the resonance phenomenon of the system and improves the power quality.
Novel Virtual Power Synchronous Damping Control for Low-Frequency Oscillations in GFVSG-SG Interactive System
Affected by the complex dynamic coupling between power electronic devices and synchronous machines, the grid-forming virtual synchronous gener ator-synchronous generator (GFVSG-SG) interactive system suffers from weak inherent damping and severe low-frequency oscillations under grid disturbances, endangering system stable operation. To address this problem, this paper pro poses a novel virtual power synchronous damping (VPSD) control for oscillation suppression. Firstly, the small-signal dynamic model of the GFVSG-SG interac tive system is established to analyze the formation mechanism of low-frequency oscillation (LFO) modes. On this basis, the VPSD controller with an additional damping feedback loop is designed to improve the system equivalent damping. Taking the typical SOTA control methods as the comparison benchmark, time domain simulations are carried out under typical disturbance conditions. The re sults show that compared with existing SOTA control schemes, the proposed VPSD control can better suppress LFOs, optimize dynamic transient response, and effectively enhance the small-signal stability and operational robustness of the GFVSG-SG interactive system.