Distributed Generation & Alternative Energy Journal

Evaluation and Assessment of Power Plant Fuel Management Reliability Using Fuzzy Multi-criteria Decision-making

2026-04-14T04:18:39+02:00

Effective fuel management plays a vital role in the provision of power generation services with minimum associated risk. However, the results obtained from the application of conventional evaluation approaches have been unable to handle the issues of uncertainty in fuel reliability evaluation. To address these challenges, the study develops a novel approach to fuel reliability evaluation based on a hybrid fuzzy MCDM approach, which combines Fuzzy AHP with Fuzzy TOPSIS with sensitivity analysis to validate the results. The proposed approach makes use of the Global Power Plant Dataset and expert judgments for the assessment of the four different fuels: Coal, Gas, Oil, and Biomass, based on the criteria of fuel availability, fuel supply stability, capacity reliability, fuel diversity, and operational risk. Fuzzy AHP is applied for the calculation of the criteria weights in the presence of uncertainty, and Fuzzy TOPSIS is applied for the computation of the reliability scores of the alternatives. Sensitivity analysis is carried out for the assessment of the stability of the results with different criteria weights. The results showed that the reliability score for Oil-based plants is the highest at 0.765, followed by Coal at 0.544, Gas at 0.475, and Biomass at 0.440. The results also show that the overall reliability depends not only on the dominance in any particular factor but also on the overall performance in all the factors. The proposed framework proves to be a powerful decision support tool in the evaluation of fuel management reliability.

A Health Condition Assessment and Safety Early Warning Framework for Hydropower Equipment Using Multi-Source Data Fusion and Decision Support Systems

2026-06-15T18:47:43+02:00

Hydropower plants heavily depend on the performance of turbine generators, where unexpected equipment failure can cause downtime, loss, and even safety issues in the process. The traditional condition monitoring methods mainly focus on vibration signals detected by a single sensor, which is not integrated with multiple sources and lacks the ability to perceive the risk at the plant level. In addition, the majority of the studies do not consider the equipment level health condition and structural risk factors to develop unified decision support for safety issues. To address the issues, the paper proposes a framework for the integrated health assessment and safety early warning framework, which is developed by integrating the equipment level fault diagnosis model based on the CNN-LSTM network and the structural risk assessment model developed by the GloHydroRes dataset. The novelty of the proposed multi-level data fusion framework integrates equipment-level and plant-level information for safety assessment that incorporates multi-channel vibration and torque signals along with structural attributes such as dam height, reservoir volume, and installed capacity to derive a unified safety risk index. Experimental validation using the proposed approach on the SEU multi-sensor dataset achieved high classification performance with Accuracy of 0.9634, Precision of 0.8642, Recall of 0.9613, F1-score of 0.9065, and MCC of 0.8662. Moreover, multi-class ROC analysis indicated high performance with AUC values of 0.9936 (Low Risk), 0.9930 (Medium Risk), and 0.9997 (High Risk), outperforming individual CNN, LSTM, and Random Forest approaches. The unified safety index revealed that 46.6% of samples were classified as Low Risk, 34.1% of samples were classified as Medium Risk, and 19.3% of samples were classified as High Risk, thus validating the efficacy of the proposed decision support mechanism. Compared with traditional fault detection methods, the proposed approach improves the reliability of fault prediction results, reduces false alarm rates, and enables proactive maintenance decisions based on risk considerations.

Development of Adaptive Power Dispatch Automation System Based on Artificial Intelligence and Machine Learning

2026-05-30T17:36:09+02:00

Faced with the challenges of high grid uncertainty, multi-timescale dynamic coupling, and operational efficiency bottlenecks caused by the high proportion of renewable energy integration, traditional dispatching methods struggle to achieve rapid response and global optimization. To address this issue, this study first constructs a joint perception model integrating graph attention networks and temporal convolutions to accurately extract the spatiotemporal dynamic features of the power grid. Next, a competitive collaborative decision-making framework based on multi-agent deep reinforcement learning is designed to achieve distributed collaborative control of power sources, grid, load, and storage. Then, a high-fidelity digital twin environment is introduced to perform online security verification and rolling optimization of agent strategies. Finally, a lightweight online update mechanism oriented towards concept drift is deployed. Experiments show that the developed AI-ADS (Artificial Intelligence-based Adaptive Dispatch System) increases the renewable energy absorption rate to 95.5%, reduces the dispatch response time to an average of 2.8 seconds, and lowers weekly operating costs by 21.5%, validating its effectiveness in improving grid resilience and economy through a “perception-decision-verification-evolution” intelligent closed loop.

Application Research of Intelligent Inspection Technology Based on Multi-Source Data Fusion in Digital Power Grid Construction

2026-04-25T15:28:56+02:00

Intelligent inspection technology has become a key support to ensure the safe and stable operation of the power grid. In view of the shortcomings of traditional inspection methods in terms of efficiency, accuracy and adaptability, this paper proposes a multi-source fusion architecture for intelligent inspection for digital power grids. By integrating multi-source heterogeneous information such as Unmanned Aerial Vehicle (UAV) remote sensing data, sensor timing information, GIS geographic data and equipment operation and maintenance text, combined with dynamic threshold adjustment, spatiotemporal correlation analysis and cascade attention mechanism, a complete processing process covering data collection, feature extraction, multi-source fusion and decision output is constructed. Experimental verification shows that the model’s F1 score in fault detection reaches 94.8%. Moreover, the performance retention rate in a 5 dB strong noise environment reaches 85.4%, the inference speed reaches 98 frames/second, and the practicality score is 0.83, the generalization ability across data sets is 89.6%, and the scalability retention rate on a thousand-node scale is 93.6%, which significantly optimizes the timeliness of inspections and reduces the consumption of human resources. The multi-source fusion method can not only effectively improve the accuracy of power grid equipment condition monitoring and fault prediction, but also enhance the robustness and practicability of the system in complex environments, and promote the inspection technology to be adaptive and reliable. direction evolution.

Distribution System Security Protection Model based on OCSVM Combined with DPoS

2026-04-10T17:57:23+02:00

With the rapid development of information technology in the power field, information security threats have gradually penetrated into the power system. To solve the low efficiency and insufficient accuracy of intrusion detection in multi-microgrid power distribution systems, this study constructs an intrusion detection method for distributed power grids based on One Class Support Vector Machine (OCSVM). Meanwhile, blockchain technology and the Delegated Proof of Stake (DPoS) are combined to build a collaborative detection model. In the collaborative security protection model, the study uses the triggering and polling mechanism to generate proposals, and optimizes the judgment conditions of the DPoS algorithm to improve the applicability to power distribution systems. The attack detection rate of the improved OCSVM model proposed in the study was 4.55% higher than that of the traditional Support Vector Machine (SVM). For model training efficiency, the OCSVM was 6.1 minutes faster than that of the SVM. In the test sample data, the highest accuracy of the OCSVM-DPoS model was 0.989, and the average accuracy was as high as 0.933. In addition, the OCSVM-DPoS model had an average detection rate of 93.46% for tampering attacks and 93.50% for replay attacks, which were also higher than those of other models. The proposed method has high performance in intrusion detection of multi-microgrid distribution systems and has good application prospects in the power grid security management.

A New Controller Design for Eliminating the Negative Impedance Increase Effect Caused by Constant Power Loads in DC Microgrids

2026-02-03T22:22:10+01:00

The growing use of power electronic loads in DC microgrids has turned out to be a significant source of stability challenges, mainly due to the detrimental increased impedance behaviour of Constant Power Loads (CPLs) that can cause the danger of especially high and low DC bus voltage fluctuations and even voltage collapse. The proposed control strategy is based on a continuous-time Model Predictive Control (MPC) approach. A Disturbance Observer (DOB) is integrated to enhance robustness. This combination effectively mitigates the negative incremental impedance effect caused by constant power loads (CPLs) and improves DC bus voltage stability. A detailed nonlinear model of a solar Photovoltaic (PV)-battery-based DC microgrid providing power for a CPL is constructed, and the unstable condition is analytically expressed. Simulation results reveal that, during open-loop operation, the voltage at the DC bus drops from 400 V to around 265 V with a voltage deviation of nearly 135 V and a negative incremental impedance of approximately $-$ 27 $Ω$ , which indicates a voltage collapse tendency. The DC bus voltage goes back to its reference value with virtually no steady-state error, smaller overshoot, and faster settling time, all while voltage oscillations are effectively suppressed when the MPC-DOB controller is in place. The comparative results also indicate better damping, smoother control current profiles, and improved robustness against sudden changes of the CPL power, which altogether prove that the proposed control strategy has a very positive impact on the stability of DC microgrid operation.

Harmonic Disturbance Identification and Suppression Method Under Compound Power Quality Disturbance

2026-05-16T20:47:46+02:00

High photovoltaic penetration introduces coupled power-quality disturbances whose overlapping RMS, harmonic, and transient signatures are difficult to distinguish under noise. This study develops a field-calibrated diagnosis-and-mitigation workflow for distributed photovoltaic systems. A balanced 17-class dataset is synthesized within IEEE Std 1159-2019 phenomenon ranges. The 50 Hz signals are sampled at 2 kHz for 0.2 s, and additive white Gaussian noise is applied. Magnitude and signed S-transform maps form complementary inputs to a two-channel convolutional neural network. Its learned representation and posterior vector are fused with physically interpretable time, frequency, and time-frequency descriptors. A Logistic-map chaos search then tunes a decision-tree ensemble and probability-fusion weights. Under the unified main condition of 20 dB SNR, 200 samples per class, and stratified source-isolated five-fold cross-validation, the method achieves 97.59% $\pm$ 0.38% accuracy. Ablation and benchmark tests distinguish the contributions of CNN feature learning, conventional ensemble classification, and chaos-optimized fusion. The diagnostic output is mapped to a harmonic-bearing decision and an order-resolved spectrum descriptor, which guide passive target orders and active-current sharing in an equivalent hybrid active power filter (HAPF) model. Using the same field-calibrated PCC waveform, the HAPF reduces current THD from 6.550% to 0.973% and lowers active-converter RMS current from 60.18 A for APF-only operation to 26.40 A, a 56.1% reduction. The source-isolation rule also prevents leakage between training and testing operating conditions. The workflow therefore links reproducible compound-disturbance diagnosis with lower-capacity harmonic mitigation and is directly relevant to power-quality management in distributed photovoltaic generation.

Distributed Energy Storage Scheduling Optimization Based on Improved Multi-agent Deep Deterministic Policy Gradient Algorithm

2026-05-04T11:19:13+02:00

The core of current energy storage scheduling optimization is to achieve multi-timescale collaborative decision-making through intelligent algorithms to improve system economy and reliability, and accelerate its evolution towards market-oriented and large-scale applications. This study is designed to develop a scenario-adaptive intelligent scheduling system. It skips the need for accurate long-term future time-series predictions, and directly leverages real-time observable information at the current decision point, such as renewable energy output, power load, electricity price and energy storage status, to complete dynamic optimal scheduling. For a single independent microgrid, an electric-hydrogen hybrid architecture is constructed, and an improved deep deterministic policy gradient algorithm with attenuated random noise is proposed. The scheduling strategy is optimized through online interaction with the target network and a soft update mechanism. For multiple interconnected microgrids, a centralized training and decentralized execution framework is adopted to achieve multi-agent collaborative optimization and autonomous decision-making. The results show that in a single microgrid scenario, the research method achieves a renewable energy utilization efficiency of 98.77% and a average operating cost of 0.381 yuan/kWh; in a multi-microgrid scenario, the average operating cost is 0.389 yuan/kWh. The research indicates that the two types of algorithms are respectively adapted to single-microgrid internal optimization and multi-microgrid collaborative scheduling, providing scenario-based solutions for distributed energy storage optimization.

Load Center Calculation and Substation Site Selection Considering Uncertainty of Distributed Power Sources

2026-05-14T15:33:25+02:00

The integration of high-penetration distributed generation changes spatial load distribution from static demand dominance to bidirectional source-load fluctuation, making traditional fixed load-moment planning insufficient to balance positioning accuracy, investment redundancy, and low-carbon accommodation. Therefore, a DLC-FCS model integrating probabilistic DG output correction, dynamic load-center calculation, weighted Voronoi service boundaries, and fuzzy comprehensive evaluation is constructed. The case results show that the comprehensive load-center positioning error decreases from 594.3 m to 483.2 m, with a reduction of 18.7%. The total planning cost decreases from USD 20.55 million to USD 18.02 million, with a reduction of 12.3%. Meanwhile, renewable energy accommodation increases by 13.7%, and carbon emissions and average outage duration decrease by 14.4% and 35.6%, respectively. This not only significantly reduces system investment costs to improve power supply reliability, but also reduces environmental pollution. This research provides power system planners with an effective tool to deal with the challenges brought by the uncertainty of distributed power sources and achieve economical, reliable and sustainable development of the power system.

Research on Cross-Domain Defect Diagnosis and Model Generalization of Power Equipment for High-Penetration Distributed Generation Scenarios

2026-05-20T04:36:29+02:00

With the construction of the new power system, the penetration rate of distributed renewable energy in power distribution networks has gradually increased, bringing new characteristics to the new power system, including bidirectional power flow in equipment operation and frequent operating condition fluctuations. Faced with increasingly complex operating conditions and frequent fluctuations of equipment, the traditional defect diagnosis methods for power system equipment lack generalization ability for scenario migration. Therefore, this paper focuses on the defect diagnosis of power equipment and model generalization under the scenario of high penetration of distributed renewable energy. Based on the analysis of multi-source heterogeneous detection data, a cross-domain defect diagnosis framework integrating domain adaptation and feature alignment is constructed. Firstly, domain-invariant features under different operation scenarios are extracted, and combined with the data augmentation strategy, the problems of sparse and unbalanced distribution of typical defect samples in high-penetration scenarios are alleviated. Secondly, the results are verified based on multi-scenario platforms and measured data, and the diagnosis accuracy and transferability of the proposed method are compared with those of traditional models. The verification results demonstrate that the research in this paper can provide novel technical ideas for improving the state perception capability of power equipment in the complex distribution network environment of the new power system.