IEEE Open Access Journal of Power and Energy最新文献

{"title":"Reinforcement Learning-Based Symbolic Regression for Load Modeling","authors":"Ding Lin;Han GUO;Jianhui Wang;Meng Yue;Tianqiao Zhao","doi":"10.1109/OAJPE.2026.3672033","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3672033","url":null,"abstract":"With the growing demand variability and evolving grid control strategies, accurate and efficient load modeling has become a critical yet challenging task. Traditional methods, such as fixed-form parametric models and data-driven approaches, often struggle to balance accuracy, computational efficiency, and interpretability. This paper introduces a novel symbolic regression algorithm based on the Actor-Critic reinforcement learning framework, specifically tailored for dynamic load modeling. The algorithm employs a trainable expression tree with controlled depth and a predefined set of operators to generate compact and interpretable mathematical expressions. The Actor network probabilistically selects operators for the symbolic expression, while the Critic evaluates the resulting expression tree through a loss function. To further enhance performance, a candidate pool mechanism is implemented to store high-performing expressions, which are subsequently fine-tuned using gradient descent. By focusing on simplicity and precision, the proposed method significantly reduces computational complexity while preserving interpretability. Experimental results validate its superior performance compared to existing benchmarks, which offers a robust and scalable solution for dynamic load modeling and system analysis in modern power systems.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"250-261"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11424569","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Network-Assisted Model Predictive Control of a Modular Multilevel Converter","authors":"Milovan Majstorović;Bogdan Brković;Leposava Ristić;Aleksandra Lekić","doi":"10.1109/OAJPE.2026.3718999","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3718999","url":null,"abstract":"This paper describes an advanced algorithm for current control in a modular multilevel power converter. The proposed algorithm is based on deadbeat model predictive control, which is further enhanced by applying an artificial neural network. Rather than supplanting the existing predictive controller, the neural network is used to supplement it, thereby improving control performance. The reference current tracking capability is enhanced under all operating conditions, particularly in cases where the reactive elements of the converter exhibit a relatively high resistive component. By introducing the neural network, the inherent delay of model predictive control in such scenarios is virtually eliminated, which reduces the overall control error. The network is trained offline on a set of control inputs obtained by optimizing the current response in a wide range of operating modes. Algorithm verification is performed for a three-phase modular multilevel converter in different steady-state and transient conditions. A state-of-the-art high-fidelity real-time simulator is used in both the algorithm development and verification stages.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"619-627"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11638121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148748554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reversed Criterion for Impedance-Based Stability Analysis","authors":"Shahil Shah;Weihang Yan;Pengxiang Huang;Vahan Gevorgian","doi":"10.1109/OAJPE.2026.3735661","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3735661","url":null,"abstract":"The existing impedance-based stability criterion is effective for analyzing interactions between an inverter-based resource (IBR) and the grid at its terminal; however, it is difficult to scale the existing criterion to analyze wide-area interactions among numerous IBRs through a complex transmission network. The scaled version of the existing criterion requires the impedance response of each IBR in the system as well as of the network looking from all the IBRs. It is quite challenging to obtain all these impedance responses because of the computational effort and the requirement of scanning the impedance of the network in isolation from the IBRs. This paper presents a reversed criterion for impedance-based stability analysis to address these limitations. In contrast to the existing criterion, the reversed criterion analyzes the stability of a power system when an IBR is disconnected from the system. It estimates the impact of one IBR at a time on the frequency and damping of oscillation modes of a power system using the impedance scans of only the IBR and the grid at its terminal; hence, it can be applied sequentially at selected IBRs to individually evaluate their impact on the power system stability. The reversed criterion is also important for evaluating the stability improvement achieved by grid-forming IBRs. The application of the reversed criterion is presented using several case studies.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"837-850"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11698799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Waveform-Driven Framework for Pre-Installation Assessment of Series-Connected Passive Inductive Filter Performance in Industrial Power Systems","authors":"Linson Philip;Uchenna Johnpaul Aniekwensi;Jörg Bausch","doi":"10.1109/OAJPE.2026.3743650","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3743650","url":null,"abstract":"Nonlinear loads in industrial electrical systems cause harmonic distortion, reactive power losses, and reduced efficiency. While passive filters offer proven mitigation, accurately predicting their performance before installation remains challenging due to the cost and complexity of physical testing. Conventional analytical methods rely on simplified assumptions that poorly represent real facility conditions. This paper presents a digital replica-based simulation framework that predicts passive filter performance using measured voltage and current waveforms (<inline-formula> <tex-math>$le 80~mu $ </tex-math></inline-formula>s resolution) from target facilities. Developed in MATLAB Simulink with Simscape Electrical, the framework incorporates detailed transformer modelling derived from systematic parameter extraction and simulates both filter-active and bypass modes to calculate active power, reactive power, total harmonic distortion, power factor, and energy consumption. Validation across five real-world installations (1400 A to 3200 A ratings) demonstrated average absolute prediction error of 0.99 percentage points and average relative error of 0.42. For the detailed validation case, the simulated results predicted reductions in active power, reactive power, and THD, alongside power-factor improvements across all phases. Unlike analytical approaches requiring idealized load models, this data-driven methodology directly incorporates facility electrical signatures, reducing the uncertainty associated with constructing an explicit equivalent model of the facility load. The framework provides facility managers and filter suppliers with a scalable, non-invasive tool for quantifying energy-saving potential before capital investment, reducing financial risk and supporting evidence-based procurement decisions.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"943-952"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11738795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of AC Voltage Control and Reactive Power Control on Transient Stability of Grid-Forming Converters With Current Saturation Under Phase to Ground Faults and Phase Jumps","authors":"Yinru Chen;Xiao-Ping Zhang;Shuhao Yan","doi":"10.1109/OAJPE.2026.3685192","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3685192","url":null,"abstract":"Grid-forming (GFM) converters are increasingly deployed to support power system stability in low-inertia grids. However, the impact of voltage magnitude control strategies on transient stability under fault conditions remains insufficiently understood. We investigate the transient stability of a droop-controlled GFM converter with a circular current limiter, taking into account the choice of voltage magnitude control strategy. Mathematical models are derived for both normal operation and, more importantly, current saturation, and numerical examples are provided to illustrate the implications of the proposed models. In fault scenarios, voltage sag and phase jumps are analyzed separately. These theoretical results are further validated through simulations in Simulink. Our results necessitate the individual analysis of phase jumps and reveal that different voltage magnitude control strategies result in substantially different transient behaviors. In particular, reactive power control enables the converter to exit current saturation, whereas AC voltage control may prevent recovery. These findings provide insights for converter control design and enhance the robustness of GFM converters under grid disturbances. Furthermore, the analysis of phase jumps shows that, under AC voltage control, a larger phase jump angle does not necessarily lead to more adverse transient behaviors, and this reveals that the current Grid Code testing requirements for transient stability of GFM converters with respect to phase jumps, which rely on a maximum phase jump angle, are inadequate and should be revised.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"309-320"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11483189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147796270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Coordination of Practically Constrained Demand Response Program and Distribution Network Reconfiguration","authors":"Tharmini Thavaratnam;Bala Venkatesh","doi":"10.1109/OAJPE.2026.3711804","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3711804","url":null,"abstract":"Electric utilities and regulators are preparing for an electrified future with greater reliance on green energy. Building to peak demand is typical practice but becomes prohibitively expensive under deep electrification. Regulators are therefore contemplating methods to improve system utilization by reducing peak demand using flexibility tools such as demand response (DR) and distribution network reconfiguration (DNR). Practical DR programs for commercial and industrial users limit monthly activation frequency and duration, with higher costs beyond those limits. DNR causes additional wear on breakers and incurs costs. However, literature and utilities rarely address coordinated scheduling of DR and DNR, without coordination, DR may be used prematurely or sub optimally reducing long-term efficiency. The proposed framework co-optimizes DR activations and DNR switching decisions over a month-long horizon, using forecasted daily peak demand to minimize operational costs while respecting operational constraints, enabling a trend reversal in cumulative operational cost trajectories. Contribution 1 develops a DR model reflecting realistic utility constraints and consumer behavior. Contribution 2 formulates inter-temporal coordination of DR and DNR in a monthly mixed-integer nonlinear programming (MINLP) model. Studies on 12-Bus, IEEE 33-Bus and IEEE 69-Bus networks demonstrate that the co-optimization approach outperforms sequential daily optimizations, reducing operational costs by 47.56%, 29.77%, and 32.23% respectively, highlighting its effectiveness.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"570-582"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11602073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextual Robust State Estimation in Distribution Systems With Real-Time Unobservability and Scarce Data","authors":"José Gómez De la Varga;Juan Miguel Morales;Salvador Pineda","doi":"10.1109/OAJPE.2026.3733322","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3733322","url":null,"abstract":"Distribution system state estimation faces a double information shortage: real-time measurements are too sparse to guarantee observability, while historical data, however abundant in volume, is limited in its representativeness by the rapid evolution of network conditions. This paper proposes a data-driven methodology that addresses both challenges simultaneously. We first introduce a <inline-formula> <tex-math>$K$ </tex-math></inline-formula>-nearest neighbor estimator that approximates the conditional distribution of delayed measurements given the available real-time context, formulating state estimation as a weighted least squares problem over plausible pseudo-measurement scenarios. Building on this, we develop a Contextual Robust State Estimator (CR-SE) that hedges against the inherent statistical uncertainty of the pseudo-measurements by optimizing over an adversarial reweighting of the nearest neighbors, subject to contextual proximity and monotonicity constraints. CR-SE hedges against the statistical uncertainty inherent to the generation of the pseudo-measurements—i.e., the potential misspecification of their conditional distribution—rather than against gross errors or bad data. The resulting min-max problem admits an equivalent single-level reformulation of comparable tractability to the baseline estimator, and a data-driven procedure selects the robustness parameter online using the most recent training instance as a validation proxy. Numerical experiments on the IEEE 38-bus and a modified IEEE 123-bus radial networks, as well as on the meshed IEEE 30-bus active distribution network, demonstrate that CR-SE strictly and consistently outperforms the baseline across all tested configurations of measurement availability, training size, and load level, with tail error improvements of up to 9% and 11% in the radial networks and up to 18% in the meshed one, and the performance gap widening precisely in the configurations where the baseline is most vulnerable.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"788-799"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11690594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal-Aging-Constrained Distribution Transformer Sizing for Demand-Side Resource Integration","authors":"Bishal Rijal;Xin Fang;Wenbo Wang","doi":"10.1109/OAJPE.2026.3733304","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3733304","url":null,"abstract":"Distribution transformers (DTs) must accommodate rapidly changing net-load profiles driven by electric vehicles (EVs), rooftop photovoltaic (PV) systems, heat pumps (HPs), load growth, and ambient temperature. However, these factors are rarely considered jointly in long-term DT planning, particularly when thermal aging and utility-standard candidate ratings are included. This paper proposes a multiyear optimization framework that jointly determines DT rating and replacement timing. The mixed-integer linear programming model minimizes the present value of the investment, energy-loss, replacement, expected outage, and salvage-value components. Transformer loss of life is calculated using IEEE Std. C57.91, while Monte Carlo simulation represents heterogeneous residential EV charging. Scenario-based PV, HP, load-growth, and ambient-temperature profiles are then integrated into the planning model. In the reported comparison, the optimized plan reduced total present-value cost by approximately 16.5% relative to the conventional sizing rule while avoiding a later replacement. These results show how thermal aging and evolving demand-side resources can be incorporated into auditable, utility-relevant DT upgrade decisions.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"863-876"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11688095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapted Deep Reinforcement Learning for Demand Response of Interruptible Loads","authors":"Jian Zhang;Yigang He","doi":"10.1109/OAJPE.2025.3580689","DOIUrl":"https://doi.org/10.1109/OAJPE.2025.3580689","url":null,"abstract":"Interruptible loads (ILs) are important flexible resources which can be controlled to shave peak load demand and balance power in real-time when power system is under stress or emergency. However, physics based control of ILs requires complete and explicit information of distribution network, which is difficult to gather in practice. To address the above issue, in this article, a data-driven method is proposed to control ILs automatically under time of use (ToU) tariff. To minimize long-time accumulative operation costs, ILs control strategy is developed as a Markov decision process (MDP), where states, actions and costs are carefully formulated. To overcome dimensionality curses, an adapted deep deterministic policy gradient (DDPG) reinforcement learning with relaxation-prediction-correction procedures is proposed to solve it. Extensive numerical tests show that the proposed approach can reduce both peak power of loads and operation costs while regulate voltages magnitudes to the rated range. Moreover, it can achieve (near) optimum, using only minor current slot information of distribution systems. Further, the computing speed is significantly faster than traditional multi-slot physical conic model.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"930-942"},"PeriodicalIF":2.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11039830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy-Adaptive Rotating Sliding Surface for Super-Twisting Control of Stand-Alone Three-Phase Four-Leg Inverters","authors":"Erfan Rezapanah;Mohammad Pichan;Hossein Hafezi","doi":"10.1109/OAJPE.2026.3688376","DOIUrl":"https://doi.org/10.1109/OAJPE.2026.3688376","url":null,"abstract":"Three-phase four-leg inverters are widely used in stand-alone power applications to supply unbalanced/nonlinear loads providing a neutral connection. Ensuring stable and high-quality voltage regulation under such conditions requires advanced control strategies. In this paper, a robust control method based on second-order sliding mode control (SOSMC) and the super-twisting algorithm is proposed to regulate inverter output voltage in the stationary Clarke reference frame (<inline-formula> <tex-math>$alpha beta gamma $ </tex-math></inline-formula>). In addition, to overcome conventional drawbacks such as chattering, poor dynamic response, and undesirable THD<inline-formula> <tex-math>${}_{mathbf {V}}$ </tex-math></inline-formula>%, a fuzzy logic-based adaptive sliding surface is employed. This prominent mechanism dynamically adjusts the surface slope based on voltage error variations, significantly improving transient response, especially under sharp 100% load steps. Furthermore, this moving surface characteristic is performed in real-time with very low computational burden as the most distinguishing feature. Moreover, unlike conventional nonlinear controllers, the proposed approach maintains a fixed switching frequency, enabling seamless PWM integration, low output distortion, and efficient real-time implementation. The controller’s robustness and performance are validated through comprehensive simulations and experimental verifications on a 3kW inverter prototype. Results demonstrate precise voltage tracking, closed to zero steady-state error, and THD<inline-formula> <tex-math>${}_{mathbf {V}}$ </tex-math></inline-formula>% about 0.4% well below IEC 62040-3 limits.","PeriodicalId":56187,"journal":{"name":"IEEE Open Access Journal of Power and Energy","volume":"13 ","pages":"333-344"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11498353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147828937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}