The topology of low-voltage distribution networks is complex and variable, and smart meters are mainly deployed on the customer side, resulting in insufficient transparency of the network topology and difficulty in accurately obtaining impedance parameters. Therefore, this paper proposes a topology identification method for low-voltage distribution networks with latent nodes considering impedance parameter constraints. Firstly, an equivalent electrical distance matrix generation method considering impedance parameter constraints is proposed based on the branch linear current model. Then, the impedance information is used to set the connection relationship criterion for judging node pairs, and the topology is generated step by step from the user node to the root node by an iterative recursive method, while obtaining more accurate distribution line impedance parameters. Finally, the effectiveness of the proposed method is verified based on actual data and simulation design of low-voltage distribution network.

Nonintrusive load monitoring (NILM) is an economical technology for promoting demand-side management (DSM) by offering electricity usage details. Driven by the fact that most existing NILM works do not support either real-time response or plug-and-play, this article proposes a real-time unsupervised NILM method based on novel graph signal processing (GSP) concepts. Instead of traditional features such as power variation and steady-state sequence, more informative state transition sequences (STSs) are featured. STSs of various types of loads are adaptively extracted based on specifically defined anchor points. After each falling STS (FSTS) referring to load switching-off event is paired with the optimal rising STS (RSTS) in milliseconds, such STS pairs are labeled as identified loads based on an undirected graph with introducing a penalty term in graph total variation regularization. In addition, the scaling factor selection and negative samples are designed to enhance NILM performance. Validation on three publicly available datasets shows the proposed method generally outperforms seven state-of-the-art benchmarks in three NILM metrics and real-time performance.

Power equipment malfunctions pose a threat to the safe operation of the entire power grid, making the rapid and accurate identification of power equipment faults a key focus of research in the electric power field. Existing methods based on artificial intelligence and expert systems face issues such as strong dependence on sample data and difficulties in acquiring expert knowledge. In response, this paper proposes a device fault detection method based on TDA, leveraging TDA's capability in feature recognition. First, the time-series data is mapped to a high-dimensional space, allowing the application of topological methods for analysis. Betti curves are used to analyze the topological structure of the time series, revealing its topological features at different scales. Then, in addition to the topological features, statistical features of the time series are incorporated to enhance the expressive power of the features. Finally, these topological and statistical features are integrated into a feature space, and a machine learning classifier is used for fault type recognition and classification. The method has shown promising results in experiments.

Non-intrusive load monitoring (NILM) is a technique that involves analyzing changes in voltage and current flowing through the main feeder to determine which appliances are in operation and their energy consumption. With the increasing amount and diversity of electric loads nowadays, it is becoming increasingly important to extract unique load signatures and build robust classification models for NILM. Topological data analysis (TDA) studies the properties of space that are preserved under continuous deformations, which can reveal the structure and relationships within complex datasets, such as networks, graphs, and manifolds. In this paper, we use TDA as feature extractor to mine vast of non-linear shape features from Voltage-Current (V-I) trajectory for appliance identification. Then, an adaptive feature selection method based on mutual information (MI) is proposed, as a result, the selected subset of features is more discriminative for the adopted dataset. Further, using selected features, the strategy of Frienemy Indecision Region Dynamic Ensemble Selection (FIRE-DES), which adaptively selects combination of classifiers according to data-samples by considering the improvement by indecision region, is employed for non-intrusive appliance classification. By such fusion of TDA and technique of ensemble learning, the experimental results on two public datasets prove efficacy of proposed method in both identification accuracy and computation time.

In smart meters, loose contact at screw terminals can lead to prolonged overheating and arcing, posing significant fire hazards. To mitigate these risks through early fault detection, this study proposes a data-driven framework integrating the Local Outlier Factor (LOF) and Multiple Linear Regression (MLR) algorithms. Voltage differentials, extracted from operational data collected via a simulated multi-meter metering enclosure, are leveraged to diagnose terminal contact degradation. Specifically, LOF identifies arc faults, characterized by abrupt and transient voltage deviations, by detecting outliers in voltage differentials, while MLR quantifies contact resistance through regression analysis, enabling precise loose contact detection, a condition associated with gradual and persistent voltage changes due to increased resistance. Extensive validation demonstrates the framework’s robustness, outperforming conventional centralized methods in diagnostic accuracy and adaptability to diverse load conditions.

Non-Intrusive Load Monitoring (NILM) involves identifying and analyzing electrical loads through monitoring fluctuations in physical parameters like voltage and current. The escalating diversity and quantity of modern electrical devices have underscored the critical need for effective feature extraction and robust classification modeling in NILM. While deep learning has advanced feature extraction, these methods often produce features with ambiguous mathematical meanings and high computational costs. This study introduces h-vectors from combinatorial commutative algebra as a novel feature extraction tool for NILM. By leveraging mathematical principles, this approach rapidly extracts nonlinear, shape-based features from active power signals, offering clear interpretability. Experimental validation on the PLAID public dataset reveals that our method achieves faster load identification while significantly improving accuracy for complex appliances compared to state-of-the-art techniques. Thus, the proposed framework represents a balanced NILM solution, harmonizing computational efficiency, accuracy, and interpretability—a key advancement for practical deployment.

Non-intrusive load monitoring (NILM) is an advanced technology for intelligent energy management. Although graph signal processing (GSP) concepts have been applied to NILM in an unsupervised way, the performance of such solutions remains unstable and undesirable. In this paper, a new unsupervised NILM framework is proposed. The original state transition sequence (STS) extraction method is first improved. Then, the operational duration is introduced as a novel time-wise feature. This feature is fused with power-wise features and utilized in both pairing and clustering processes. Results on open-access residential and industrial datasets indicate that the proposed method significantly outperforms other benchmarks, making it promising for practical implementation.

Non-intrusive load monitoring (NILM) is an advanced technique for demand side management. While graph signal processing (GSP) based unsupervised methods have been explored in NILM, such solutions remain insufficient for practical deployment due to their unstable performance and limited adaptability. In this paper, an improved GSP-based NILM approach is proposed. State transition sequences (STS) are first precisely extracted, representing the switching on/off process of appliances. Next, an STS pairing method is proposed to select the optimal rising STS (RSTS) for each extracted falling STS (FSTS), where both power-related and time-related features are utilized. Subsequently, periodical features are extracted with a two stage periodicity analysis algorithm. Finally, the feature vector for each pair is constructed and GSP-based clustering is performed using the corresponding feature vector. Results on open-access datasets indicate that the proposed method significantly outperforms other benchmarks, making it promising for real-world deployment.

Equipment fault diagnosis is crucial to ensure safe operation of industrial systems. The development of artificial intelligence has led to increasing applications of data-driven methods for fault diagnosis. However, such methods often require a large amount of labeled data for training and are tend to be sensitive to noise. To address these challenges, a novel model using topological data analysis (TDA)--topological incremental fast Fourier transform(TIFF)--for equipment fault diagnosis is proposed in this paper, leveraging the capability of TDA in extracting geometric features of dataset after continuous deformations of topology space. First, for time series signal of equipment operation, persistent homology fluctuating series is constructed by calculating the topological Wasserstein distance between two successive windows of the raw signal, which contains more condensed information on its fault pattern. Then, the time-frequency domain features of the raw signals are obtained by concatenating the frequency domain features derived from fast Fourier transformation and time domain features derived from statistical analysis on both of the persistent homology fluctuating and the raw signal series. Finally, a machine learning based equipment fault diagnosis framework is proposed. By feeding the time-frequency domain features extracted by TDA method, a model based on support vector machine(SVM) classifier after supervised training is established to give identification result for type of fault. The proposed method is tested on two publicly available datasets under different conditions and the results prove that it outperforms three state-of-the-art benchmarks in noisy settings and with few-shot.

Non-intrusive Load Monitoring (NILM) is a technology that identifies and analyzes electrical loads by examining changes in physical quantities such as voltage and current. With the increasing number and diversity of electrical loads today, extracting distinctive features and constructing corresponding classification models has become an increasingly important issue for NILM. Although technological advancements have introduced deep learning algorithms to improve traditional feature extraction methods, the features extracted by these methods often lack clear mathematical significance and require considerable computation time. Topological Data Analysis (TDA) studies spatial properties that remain invariant under continuous deformations, which can reveal the structures and relationships within complex datasets such as networks, graphs, and manifolds. In this paper, we use TDA as a feature extraction method to quickly extract a large number of nonlinear shape features with clear mathematical significance from active power to identify loads. Experimental results on the public dataset PLAID, compared with other methods, demonstrate that our method can quickly identify loads and significantly improve the identification results for certain complex appliances. Therefore, the proposed method in this paper is a novel NILM approach that balances computational speed, accuracy, and interpretability.

The design and development of BIM industrial building construction system has become an important field in the construction of industrial buildings, which brings significant benefits by improving production and construction efficiency, reducing errors and rework costs, and optimizing life cycle mana

Efficient decision-making method contributes to economical and reliable operation of electric vehicle aggregators (EVAs). In this paper, a data-driven multi-stage decision-making framework is proposed for an EVA, where bidding and disaggregation are jointly optimized in energy and balancing markets. For reducing computation complexity, online disaggregation optimization is replaced by dynamic disaggregation strategy selection. Based on the chronological relationship, the multi-stage decision-making problem of EVA is formulated into a single Markov decision process and solved by a novel reinforcement learning (RL) method. The proposed RL develops a customized actor network with composite architecture to support both continuous and discrete action outputs for co-optimizing bidding and disaggregation strategy selection. Through competitive experiments conducted on a real-world dataset against four state-of-the-art benchmarks, the economic superiority of the proposed method is verified.

Load forecasting has always held a pivotal position within the strategic planning and day-to-day management of electric utilities, encompassing both transmission and distribution entities. As technology evolves, economic conditions fluctuate, and myriad other influential factors come into play, the significance of accurate load forecasting continues to escalate. Moreover, as emerged as an indispensable tool for modern power systems, particularly in nations where the power sector operates within a deregulated framework. In our paper, we propose a data-driven method based on topological data analysis and we apply it to predict load level in a span of 15 days quarterly hours. Our result show it is feasible to use topological data analysis as feature extractor in load forecasting

Higher alcohol synthesis directly from syngas is highly desirable as one of the efficient non-petroleum energy conversion routes. Co0–CoO catalysts showed great potential for this reaction, but the alcohol selectivity still needs to be improved and the crystal structure effect of CoO on catalytic behaviors lacks investigation. Here, a series of tetrahedrally coordinated CoO polymorphs were prepared by a thermal decomposition method, which consisted of wurtzite CoO and zinc blende CoO with varied contents. After diluting with SiO2, the catalyst showed excellent performance for higher alcohol synthesis with ROH selectivity of 45.8% and higher alcohol distribution of 84.1 wt % under the CO conversion of 38.0%. With increasing the content of wurtzite CoO, the Co0/Co2+ ratio gradually increased in the spent catalysts, while the proportion of highly active hexagonal close packed cobalt in Co0 decreased, leading to first decreased then increased CO conversion. Moreover, the higher content of zinc blende CoO in fresh catalyst facilitated the retention of more Co2+ sites in spent catalysts, promoting the ROH selectivity but slightly decreasing the distribution of higher alcohols. The catalyst with 40% wurtzite CoO obtained the optimal performance with a space time yield toward higher alcohols of 7.9 mmol·gcat–1·h–1.

Electrocatalytic CO2 reduction is an effective way to close the global carbon cycle. Copper is a promising metal for CO2 conversion to multiple products but suffers from low selectivity. Here commercial copper foil is exploited as an efficient catalyst for CO2 reduction with tunable product distributions through electrochemical processing. Ultrafine Cu nanoparticles were generated with dominant (111) facets on the 2D Cu foil surface through cathodic corrosion, showing CH4 selectivity up to 69.6 %. Meanwhile, C(2+ )products became dominant with a selectivity up to 66 % on the same Cu (111) nanoparticles when supported on electrodeposited copper dendrites. The in-situ Raman spectroscopy indicated that the high CO coverage and local pH created by the hierarchical structure contributed to the product transformation from C-1 to C2+. This study demonstrates that electrochemical processing could be employed as a promising method to control and tune the product selectivity of CO2 electroreduction. (C) 2022 Elsevier Ltd. All rights reserved.

Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large scale nonlinear programming problem is solved using genetic algorithm, simulated annealing and mixed integer second-order cone programming method. The feasibility and effectiveness of the proposed algorithm have been verified.

In the face of the radical revolution of energy systems, there is a gradually held consensus regarding the adoption of distributed renewable energy resources, represented by Photovoltaic (PV) and wind generation. Consequently, the distributed Energy Storage Systems (ESSs) have become increasingly important in the distribution networks, as they provide the arbitrage and ancillary services. Determining the optimal installation site and the capacity of the distributed ESSs will defer the network reinforcements, reduce the investment of ESSs, and improve the reliability, flexibility, and efficiency of distribution grids. In order to investigate the optimal ESS configuration and to solve voltage fluctuations brought by the increased penetration of PV, in this study a two-stage heuristic planning strategy has been proposed, which considers both the economic operation and the lifetime of the distributed ESSs, to determine the optimal sitting and sizing of the ESSs, in the distribution grids. The first stage decides the optimal installation site and the economic scheduling of the ESSs, aiming to minimize the fabricating cost of the distributed ESSs and the network losses. Based on the output of the first stage, the second stage planning is further delivered to achieve the optimal ESS capacity, considering the Life-Cycle Cost (LCC) minimization. Finally, the feasibility and effectiveness of the proposed method is verified on a typical distribution case study network.