The operation of electric power system is a continuous process which demands coordination of various entities from power generating plants to distributing substations to render uninterrupted service still sticking to quality power delivery. Electric demand depends on external factors such as temperature, humidity, social activity pattern. Power grids are becoming complex due to integration of renewable energy sources. Thus, there is a need of electric energy forecasting. Efficiency of traditional forecasting approaches are less and existing many learning and ensembled models require high computational resources. To improve accuracy of prediction, a fast and efficient processing model which is suitable for real-time applications, light weight ensemble model is proposed in this paper. This research proposes a novel stacked light weight ensemble model that integrates the prowess of various weak base learners. The final prediction of the model is further improved by using extreme gradient boosting as a meta learner, which evolutionarily learns the predictions from individual learners and gives the final load forecast. Further the temporal nature of the exogeneous variables is preserved by a unique feature fusion technique which estimates the exponentially weighted moving average of the individual variable which are then aggregated. The efficacy of this model is validated by testing it on Panama electricity load forecasting dataset and the results are explored using important regression-based metrics. The analysis shows that the proposed method can vividly forecast the electricity load using the lightweight ensemble model in terms of Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE) and R2 values.

Electrostatic dielectric capacitors with high power density are the fundamental energy storage components in advanced electronic and electric power systems. However, simultaneously achieving ultrahigh energy density and efficiency poses a persistent challenge, preventing the capacitive applications towards miniaturization and low-energy consumption. Here we demonstrate giant energy storage properties in lead-free antiferroelectrics by designing hierarchical heterostructures to optimize polarization evolution paths. Through the design of antiferroelectric nanoclusters featuring interlocked polarization structure and fishbone polarization configuration, alongside order-disorder oxygen octahedral tilts, we increase polarization fluctuation and delay polarization saturation with nearly eliminated hysteresis under ultrahigh external electric fields. Leveraging this strategy, we achieve an ultrahigh energy density of 21.0 J cm-3 with an impressive efficiency of 90% in sodium niobate-based ceramics, underscoring the great potential of this methodology for designing high-performance dielectrics and other functional materials.

Over Current Relay (OCR) is a protection relay used to detect and cut off electric current when there is excess current (fault current). The definite time type OCR has constant trip time characteristics, regardless of the magnitude of the fault current. OCR is widely used in electric power systems to protect equipment from damage due to fault currents. This final report discusses the design of Arduino Uno-based definite time OCR with the use of PZEM sensor components, I2C LCD, relay module, and Arduino Uno. This system is designed to be able to measure the load current, display the current value on the LCD, and cut off the current when the load current exceeds the specified setting value.

An integrated fuel cell (FC) system simulator ‘FC-DynaMo’ was developed. It consists of physical and semi-empirical models of FC system components, such as a FC stack, air and H2 supply, thermal management, and electric power systems, and the controllers for them. It can reproduce the dynamic behaviors of the FC system used in 2nd-generation MIRAI (MIRAI-2), including the degradation of the Pt and carbon support in the cathode catalyst layer in remarkable computational speed. Overview of ‘FC-DynaMo’ ‘FC-DynaMo’ is an integrated fuel cell (FC) system simulator, which consists of physical or semi-empirical models of the comprehensive FC system components of the FC stack, air and H2 supply, thermal management, and electric power systems, and the controllers for them. It was designed to reproduce the configuration of the FC system used in MIRAI-2 shown in Fig. 1. It also includes a control system and can be utilized for the investigation of the control strategy as well as the hardware design. Fig. 2 shows a configuration of FC-DynaMo. The models and controllers are implemented on MATLAB/Simulink environment in modular program structure so that the users from different industries, such as automotive of passenger and commercial vehicles, railway, maritime, aviation, and construction and agricultural machinery, can customize the system configurations without significant efforts. The computational speed for a system-scale simulation is 40 times faster than the actual time with a normal laptop by combining physical and semi-empirical models and in-house numerical solvers which do not require iterative calculations to obtain converged solutions. FC-DynaMo has been delivered to more than 200 research institutes and industries in Japan and utilized for the advanced research and multi-purpose system development. Parameter determination and validation The parameters in the FC stack model are determined by a dataset of the polarization curves collected with a test-piece of an MEA under wide range of operating conditions of O2 fraction, humidity, and temperature. The parameters in the FC system component models are determined by datasets of single component tests. All the parameters in the FC stack and system component models can be determined before manufacturing expensive prototypes of FC systems and the specification of the hardware and controller can be optimized before prototyping. The accuracy of the developed simulator was validated with a considerable number of datasets collected with the actual system testbed and vehicle of MIRAI-2 under wide range of operating conditions of low to high load and temperature and steady-state and transient conditions. Simulation results Fig. 3 shows the simulation results of dynamic FC system behaviors in a dynamic operating pattern from low to high load and temperature. The impact of changes in the MEA specifications on the major system performance indicators were simulated: The cathode GDL was removed (case 1); The PEM thickness is decreased by half (case 2); the catalyst activity of the cathode catalyst layer is doubled (case 3). Figs. 3(a) and (b) show the overall heat generation rate transferred to the thermal management system and total H2 amount consumed during the system operation, respectively. The maximum heat generation rate was decreased by 22.0 % and the fuel economy was improved by 8.1 % between cases 1 and 3. The reduction of losses in activation and resistance overpotentials in cases 1 and 3 had considerable impacts as shown in Figs. 3(c) and (d), which accounted for the improvement of the FC system net efficiency by 9.0 % as shown in Fig. 3(e). It was demonstrated that FC-DynaMo can be utilized for the analysis of complex mechanisms among the specifications of the FC materials and system components, the FC reaction mechanisms, and overall system performance indicators, such as the thermal balance and fuel efficiency. Recent updates and future study In recent, FC-DynaMo has been updated with the models to estimate FC material degradation rates by Pt coarsening and carbon support corrosion. Similar as the methods described in the previous sections, the parameters in the degradation models were determined by the datasets collected with a test piece of MEA. The accuracy of the models was validated by the combined accelerated stress test (AST) data in literature [J. Electrochem. Soc., 169, 044523 (2022)], whose patterns are shown in Fig. 4, collected with a FC stack having 13 cells of MIRAI-2. The developed degradation models reproduced the actual polarization curves after ASTs with acceptable accuracy as shown in Fig. 5. The authors’ group is investigating new modeling methods to estimate PEM chemical degradation rate for extension FC-DynaMo and the operating strategy to optimize the FC system performance and durability. Figure 1

The model predictive control (MPC) method to optimize the balance of target tracking performance of the FC net power, fuel efficiency, and degradation rate was investigated. To evaluate control performance, an integrated FC system simulator ‘FC-DynaMo’, which consists of physical and semi-empirical models of FC system components, such as a FC stack including degradation models, air and H2 supply, thermal management, and electric power systems, and the controllers for them[1]-[5]. The original controllers in FC-DynaMo consist of rule-based controllers with feed-forward and feed-back controllers and are designed to maximize target tracking performance and fuel efficiency[6]. They were customized by replacing the original controllers with MPC which considers the optimization of the balance of target tracking performance, fuel economy, and degradation rates of Pt coarsening and carbon support corrosion. The following 3 cases were investigated: target tracking performance and Pt degradation rate are considered (case 1); target tracking performance and carbon support degradation rate are considered (case 2); target tracking performance and fuel economy are considered (case 3). In case 1, MPC maintained a comparable setpoint tracking performance to conventional control system even though the platinum catalyst degradation rate was reduced by 61%, as shown in Fig. 1. In case 2, MPC maintained a comparable setpoint tracking performance to conventional control system even though the carbon catalyst corrosion rate was reduced by 26%. In case 3, although the setpoint tracking error increased by a factor of 1.6, the hydrogen consumption was reduced by 6.4%. As the computational speed of the developed MPC is not acceptable to implement in a product engine control unit (ECU) due to iterative calculation in the numerical solver to obtain a converged solution, the authors will develop fast computational methods with similar control performance.

Abstract Arc fault detection is critical for ensuring the safety and reliability of electrical power systems, as it helps prevent fires and equipment damage. Data models demonstrate significant advantages across various fields and have become a focal point of arc fault detection. However, the generalization ability under small sample conditions and interpretability of these models remain key challenges in this field. This article presents an end-to-end arc fault detection method that integrates experience.First, based on current waveforms, three types of artificial experience are summarized. Then, a one-dimensional adaptive neural decision tree based arc fault detection method is explored by combining neural networks and decision tree to perform feature extracting, path selecting, and label classifying. Finally, by embedding experience into the one-dimensional adaptive neural decision tree to guide model training, the trained model is capable of effectively detecting arc faults across various load types. The results show that the proposed method achieves a high
 fault detection performance on datasets of different sizes, and is compared with other reported methods to verify its superiority.
 Moreover, it can visualize the decision-making process.

Power grids and their cyber infrastructure are classified as Critical Energy Infrastructure/Information (CEII) and are not publicly accessible. While realistic synthetic test cases for power systems have been developed in recent years, they often lack corresponding cyber network models. This work extends synthetic grid models by incorporating cyber-physical representations. To address the growing need for realistic and scalable models that integrate both cyber and physical layers in electric power systems, this paper presents the Scalable Automatic Model Generation Tool (SAM-GT). This tool creates large-scale cyber-physical topologies for power system models. The resulting cyber-physical network models include power system switches, routers, and firewalls while accounting for data flows and industrial communication protocols. Case studies demonstrate the tool’s application to synthetic grid models of 500, 2,000, and 10,000 buses, considering three distinct network topologies. Results from these case studies include network metrics on critical nodes, hops, and generation times, showcasing SAM-GT’s effectiveness, adaptability, and scalability.

Intelligent control system for compensation devices in the electric power system of a mining and processing plant

Probabilistic optimal co-planning of distributed series reactor and dynamic line thermal rating for congestion management of highly-renewable-penetrated electric power systems

A Novel Multi-Step Short-Term Power Forecasting Model for Electric Power Systems Based on Deep-Learning

The integration of solar photovoltaic (PV) and wind turbine (WT) systems into distribution electric power systems presents challenges in steady-state operational performance (i.e., voltage and thermal loading) and capacity management, particularly under varying generation patterns. This study quantifies the interplay between PV and WT systems by first analyzing their time-series signal correlation coefficients over a full year of operation. These coefficients provide a basis for identifying locations with distinct PV–WT interaction patterns. Using Quasi-Static Time Series power flow simulations across representative locations and varying Distributed Energy Resource (DER) penetration levels, hosting capacity (HC) simulations then quantify the combined impacts of PV and WT. The simulation results indicate that correlation coefficients can serve as indicators of potential line loading trends but are less effective in anticipating voltage violations, underscoring the complexity of steady-state HC analysis. As expected, non-concurrent generation—wind at night and solar during the day—mitigates operational stress, though this behavior occurs only in limited U.S. locations. The outcomes from this methodology, when applied to specific territories and feeders, provide a systematic means of identifying representative locations and quantifying combined PV–WT HC impacts. These results can directly support integration planning, resource siting, and DER dispatch strategies.

A modular three-switch integrated equalizer for ultracapacitor strings in low-carbon electric power systems

An Improved Droop Control Method of High Voltage DC Parallel Electric Power System for More Electric Aircraft

Metal Oxide Surge Arresters (MOSA) are connected in the system for protection of power system against over voltages. MOSA behaves as an insulator under normal working condition and offers conductive path under over voltage condition. Zinc oxide elements (ZnO Blocks) having non-linear voltage-current characteristics is used in construction of MOSA. Ageing effect under various operating conditions such as pollution, moisture ingress caused degradation of MOSA. Degradation of zinc oxide elements increase the leakage current flowing through it which can create thermal runaway conditions for MOSA. Once, it reaches to thermal runaway condition cannot return to normal working condition results in premature failure of MOSA. As MOSA constitutes a core protective device for electrical power system against transients. Condition monitoring of surge arresters should be done at periodic intervals. Online condition monitoring techniques are much popular. This paper presents the evaluation technique for MOSA based on the leakage current analysis. Maximum amplitude of total leakage current (IT), Maximum amplitude of fundamental resistive leakage current (IR) and maximum amplitude of third harmonic resistive leakage current (I3rd) have been analysed as indicators for surge arrester condition monitoring. The ratios of these leakage current has been evaluated for to access the condition of MOSA. The obtained results are validated with other condition monitoring tests.

Electricity expansion planning is inherently subject to uncertainty, shaped by climatic, regulatory, and economic risks. In Brazil, this challenge is compounded by recurrent crises that have repeatedly reduced electricity demand. This study proposes a complementary decision-support approach to make planning more resilient to such crises. Using Brazil’s official optimization models (NEWAVE), we introduce two analytical elements: (i) a regret-minimization screen for choosing between conservative and optimistic demand trajectories and (ii) a flexibility stress test that evaluates the cost impact of compulsory-dispatch shares in generation portfolios. Key findings show that conservative demand projections systematically minimize consumer-cost regret when crises occur, while portfolios with lower compulsory-dispatch shares reduce total system cost and improve adaptability across 2000 hydro inflow scenarios. These results highlight that crisis-robust planning requires combining cautious demand assumptions with flexible supply portfolios. Although grounded in the Brazilian context, the methodological contributions are generalizable and provide practical guidance for other electricity markets facing deep and recurrent uncertainty.

This study investigates the effectiveness of a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model for predicting the temperature of switchgear within electrical power systems. Given the critical importance of temperature monitoring for operational safety and stability, this research integrates CNNs and LSTMs to leverage their respective strengths in spatial feature extraction and temporal data processing. Utilizing a dataset from 2020 comprising hourly data points along with comprehensive environmental and operational variables, the model aims to deliver precise temperature predictions. Initial results indicate a high level of accuracy, with the CNN-LSTM model achieving an R2 score of 0.95 and a mean absolute error of 0.12 °C, highlighting its significant potential to enhance the monitoring and management of safety in power systems.

The energy crisis, from 2021 onward, has driven inflation and led to major reforms of energy policy. States in the Global North are increasingly moving away from neoliberal policies, introducing more strategic state coordination to “switch” capital investment into low-carbon electrical power systems. This article analyzes the energy crisis through the lens of the electricity capital accumulation, focusing on Great Britain, to show how the price crisis in Europe was an upward redistribution of wealth through the power system. It does so through a novel conceptualization of the circuit of electricity capital and how decarbonization is slowly shifting the accumulation regime for electricity to longer term time horizons while maintaining upward wealth redistribution. The article argues that the energy crisis marks a change in the accumulation regime from one that tended toward the underproduction of low-carbon energy to one contending with overaccumulation without devaluation in electricity capital. Absent greater public coordination and discipline for capital, overaccumulation is creating political instability and states are searching for a new accumulation regime for an “electro-capitalism” primarily organized around electricity capital rather than fossil capital.

The peculiarity of the functioning of modern electric power systems, caused by the presence of renewable energy sources, flexible control devices based on power electronics, and the reduction of the reserve of the transmission capacity of the electric network, increases the relevance of identifying and damping low-frequency oscillations (LFOs) of the electrical mode. This paper presents a comparative analysis of methods for estimating the parameters of low-frequency oscillations. Their applicability limits are shown as well as their peculiarity associated with low adaptability, and time costs in assessing the parameters of the electrical mode with low-frequency oscillations are revealed. A method for the accelerated evaluation of low-frequency oscillation parameters is proposed, the delay of which is ¼ of the oscillation cycle. The method was tested on both synthetic and physical signals. In the first case, the source of data was a four-machine mathematical model of a power system. In the second case, signals of transient processes occurring in a real power system were used as physical data. The accuracy of the proposed method was obtained by calculating the difference between the original and reconstructed signals. As a result, calculated error values were obtained, describing the accuracy and efficiency of the proposed method. The proposed algorithm for estimating LFO parameters displayed an error value not exceeding 0.8% for both synthetic and physical data.

Electricity theft is the illegal use of electrical power without proper authorization and payment. This act causes losses to the electricity provider and the public, poses safety hazards, and violates the law. Perusahaan Umum Daerah (PERUMDA) Tuah Sekata of Pelalawan Regency, as a regional-owned enterprise responsible for electricity distribution to the community, annually detects indications of electricity theft through field inspections. To simplify the identification process and reduce operational costs, an Internet of Things (IoT)-based device was designed to monitor and control customers’ electricity consumption. The device utilizes a power supply as a 5V DC voltage source, a PZEM-004T sensor to measure voltage, current, power, and energy, an LCD 16x2 to display measurement results, and a 5V relay to connect or disconnect the customer’s power supply. An ESP32 microcontroller processes the data and transmits it via Wi-Fi to the Blynk application, which serves as an online monitoring interface. The device developed in this study not only facilitates real-time monitoring of electricity consumption but also serves to detect and prevent potential electricity theft. The experimental results show that, for voltage measurements, the average percentage error obtained was 0.41% (Device IoT 1), 0.29% (Device IoT 2), and 0.38% (Device IoT 3). Meanwhile, for current measurements, the average error rates were 8.42% (Device IoT 1), 8.66% (Device IoT 2), and 8.60% (Device IoT 3). Based on the experimental results, it can be concluded that the three developed IoT devices have an error rate of less than 10% (classified as very good), making them feasible for use in monitoring electrical power consumption.

Numerous optimization techniques have recently been employed in the literature to enhance various electric power systems. Optimization algorithms help system operators determine the optimal location and capacity of any renewable energy source (RES) connected to a system, enabling them to achieve a specific goal and improve its performance. This study presents a novel statistical evaluation of 20 famous metaheuristic optimization techniques based on 10 performance measures. The performance measures comprise five power loss indices, three voltage profile indices, load flow calling frequency, and execution time. The evaluation involves 10 distribution systems of varying sizes to ensure an equitable comparison of the algorithm. The Friedman Ranking method evaluates algorithms based on performance metrics, yielding a specific score. Upon modeling all distribution systems, a composite ranking methodology is employed to categorize the algorithms into only four categories: excellent, very good, good, and fair. The study finalizes the ranking of all algorithms according to their overall assessment. The AEO, GWO, JS, PSO, MVO, BO, and GNDO algorithms attain ranks below 25%, thereby placing them in the highest category. The ALO, DA, FPA, SSA, YAYA, and SPO algorithms fall into the second category, with rankings ranging from 25 to 50%. The SMA and CGO algorithms are classified in the third group, with rankings between 50 and 75%. The analysis ultimately reveals that the algorithms CStA, HHO, AOA, GOA, and AOS are positioned in the lowest group, each achieving rankings beyond 75%. As comparison case studies, the proposed algorithms achieved a power loss of 87.164 kW for the 33-bus system, which is less than or equal to the published work. The same result is achieved with the 69-bus system, which has a power loss of 71.644 kW for most of the studied algorithms. Using the appropriate algorithms with distribution systems saves time and effort for the system operator, enhances performance, and increases the usability of optimization algorithms.