Operation Of Systems Research Articles

Applicable International Standardized Basis in Defining Data for Security Systems Development

Creating an intelligent security environment is one of the main trends in the activity of scientific organizations, where security technologies are a significant part of the product range. Organizations using these technologies, including critical infrastructure with built-in security systems, provide increased operational efficiency, guaranteeing readiness for action in emergency situations. For this purpose, standardized approaches are applied to determine the requirements for the development of security systems, according to the specifics of the organization, ensuring the prevention of any level of potential consequences of possible risk events, both in the structural objects of the organization and for the population in the adjacent territories. The author's intention is through this publication to present his experience in the application of modern standardized approaches ensuring the construction and operation of critical infrastructure security systems developed by scientific organizations in the conditions of key partnerships of the Competence Center.

Interpretable machine learning-based prediction and analysis of supercritical fluid heat transfer characteristics at different boundary conditions

The study of the heat transfer characteristics of supercritical fluids is crucial for the safe and economical operation of supercritical energy systems. This study presents a new method to study the heat transfer characteristics of supercritical fluids, employing interpretable machine learning. Two back propagation neural network models are proposed for the prediction of heat transfer to supercritical fluid for the cases with given heat flux and given wall temperature, respectively. The particle swarm optimization algorithm was employed to search for the optimal hyperparameters, and the resulting accuracy was evaluated in comparison with that of the traditional empirical correlation and a number of machine learning models. Combining the SHAP interpretable algorithm with prediction models, the supercritical heat transfer mechanism is explored from the perspective of global and local prediction behaviors based on explainable models. The results demonstrate that the average error of two established neural network models on the test set is 0.47 % and 0.69 %. Furthermore, for vertical upward flow, the buoyancy and acceleration effects are of greater feature importance in heat transfer deterioration. They are the primary factors contributing to heat transfer deterioration behavior. The research method proposed in this study has certain reference significance for further study of the heat transfer characteristics of supercritical fluids.

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

Demand side management (DSM) and demand response programs (DRPs) are widely used economic strategies that insist customers shift or curtail loads to economize the generation cost of the distribution system. These policies often involve tedious calculation processes or optimization tools to calculate the apt amount of load to be shredded or shifted to get a new modified load profile. This paper proposes an order characterized load-shifting policy (OCLSP), which is very easy to calculate and yields a considerable decrease in the peak demand, eventually improving the distribution system's load factor, also known as the peak-to-average ratio (PAR). Unlike other DSM and DR strategies, the proposed approach requires no parameters except the forecasted load demand. The proposed OCLSP is implemented on ten distribution systems from the literature which consists of a 3-, 6-, 7-, 10-, 15- units system, one islanded microgrid (MG) system and four grid-connected MG systems. Initially, the load demand of these test systems is restructured using OCLSP, and thereafter, dynamic economic load dispatch (DELD) is performed to minimize the generation cost of the system. A latest circle search algorithm (CSA) is utilized as the optimization tool. Results show that the generation cost of all ten test systems has been decreased by a considerable amount when their load demand is restructured using the proposed OCLSP. The minimum percentage improvement in the PAR of the ten systems was 2 % and maximum improvement was 60 %. Likewise, the percentage decrement in the peak demand was within 2 % to 37.5 % for the ten test systems and the decrement in generation cost rose up to 16 %. All these parameters were in comparison with the base load demand. Numerical results also corroborate to the efficiency and robustness of proposed CSA.

Calculation method of renewable maximum penetration considering frequency response of renewable energy

The calculation of maximum penetration is crucial for planning renewable energy integration and ensuring the safe operation of power systems. This paper proposes a method for calculating maximum penetration under kinetic energy constraints. In scenarios where renewable energy lacks additional frequency control, we construct a frequency response model to reveal the energy change mechanism during active disturbances. By converting the frequency constraint into a kinetic energy constraint, we derive an analytical expression for maximum penetration under these constraints. Key influencing factors were quantitatively analyzed, revealing nonlinear relationships between system capacity, frequency constraints, and maximum penetration, while also calculating the minimum operational generator capacity. When renewable energy participates in frequency control, its frequency regulation can significantly enhance maximum penetration. However, frequency oscillations caused by time delays in current source frequency control become the primary constraint. The proposed method and conclusions are validated using actual sending-end and receiving-end grid data, providing valuable guidance for grid operation planning.

Effect of background noise characteristics on early warning indicators of thermoacoustic instability

In this work, we investigate the effects of background noise characteristics — specifically noise color (or correlation time) and intensity — arising from velocity-coupled and additive noise sources, on the early warning indicators (EWIs) of thermoacoustic instability. We employ a nonlinear reduced-order combustion dynamics model for our investigation. In the absence of noise, the nonlinear model undergoes a subcritical Hopf bifurcation, and our focus lies within the linearly stable region of the system (subthreshold region). The studied class of EWIs encompasses those derived from time series (variance), spectral analysis (coherence factor), Hurst exponent, and nonlinear methods (permutation entropy). We find that when the background noise is purely multiplicative, the trends in EWIs are primarily influenced by noise characteristics rather than the control parameter. Further, the EWIs cannot be estimated at low noise levels for large correlation times. In case of purely additive noise driven system, the coherence factor and variance are reliable EWIs across all range of investigated noise characteristics. The Hurst exponent can serve as effective EWI when the system features large noise correlation times, while permutation entropy is effective only when the system features small noise correlation time, i.e., where white noise assumption is acceptable. When the background noise includes contributions from both multiplicative and additive sources, coherence factor and variance emerges as the most reliable EWIs. These results provide insights for selecting appropriate EWIs to be employed in practical systems, considering potential variations in noise characteristics with simultaneous changes in combustor operating conditions.Novelty and significanceThis study conclusively shows that background noise (multiplicative and additive) characteristics – noise correlation time (color) and intensity – can significantly change the trends in early warning indicators (EWIs) for predicting the impending thermoacoustic oscillations. In gas turbine combustors, where thermoacoustic instability poses a critical challenge, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. The development of effective EWIs to foresee the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of combustion systems. Previous works on stochastic dynamics of combustors simplify noise with the additive white noise assumption. Thus, our results demonstrated on a nonlinear combustion dynamics model advance the state-of-the-art with respect to the early prediction and control of thermoacoustic instability. The results provide valuable insights for selecting appropriate EWIs for engine monitoring in the absence of information on noise properties and their variation with operating conditions. This practical information is of direct relevance and interest to any gas turbine manufacturer/user.

Model-free aperiodic tracking for discrete-time systems using hierarchical reinforcement learning

In the operation of practical systems, it is often a challenging task to deal with limited knowledge of the system dynamics, therefore, it is needed to obtain a framework for the simultaneous learning and control of dynamic systems, able to cope with uncertain dynamics. This paper proposes a model-free aperiodic tracking control method based on MAXQ hierarchical reinforcement learning for unknown dynamics in discrete-time systems. Firstly, a MAXQ hierarchical framework is developed for aperiodic tracking control that decomposes the task into pre-control and accurate control subtasks. The former achieves sub-optimal tracking which prompts the implementation of accurate control. The latter implements aperiodic tracking based on the pre-control result to reduce resource occupation. The structure of the MAXQ framework simplifies the complexity of the tracking task, and the incorporation of pre-control accelerates the learning process in the formal control stage. Secondly, considering the disparities between the control inputs and the control update instants, pre-control is decomposed to learn the triggering strategy and control policy, respectively. Meanwhile, the control policy learns optimal control inputs by minimizing the cost function. Similarly, the triggering strategy and control policy are also separately learned in accurate control. In contrast to traditional aperiodic triggering mechanisms, in accurate control stage, the triggering strategy is developed based on the cumulative error of the pre-control result, thus avoiding the influence of accidental factors and the cumulative effects of errors, enhancing system robustness. Thirdly, the proposed tracking control is derived by using only the input, output, and reference signal data from the system, without relying on system dynamics. It is applicable to both linear and nonlinear systems, demonstrating strong generalizability. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed method.

Feasibility and safety study of advanced prostate biopsy robot system based on MR-TRUS Image flexible fusion technology in animal experiments

The advanced prostate biopsy robot system has broad application prospects in clinical practice, but due to the deformation and distortion between MR-TRUS (magnetic resonance transrectal ultrasound) images, it poses challenges in biopsy accuracy and safety. The study utilized an advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology and conducted experiments on animal models. Retrospective analysis of the puncture accuracy of 12 animal experiments undergoing prostate puncture using MR-TRUS flexible registration technology from May 2022 to October 2023, and observation of intraoperative and 7-day postoperative complications. The study obtained MR-TRUS images and utilized image processing algorithms for registration to reduce image deformation and distortion. Then, precise positioning and operation are carried out through the robot system to execute the prostate biopsy program. The experimental results indicate that the advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology has demonstrated good feasibility and safety in animal experiments. Image registration technology has successfully reduced image distortion and deformation, improving biopsy accuracy. The precise positioning and operation of robot systems play a crucial role in the biopsy process, reducing the occurrence of complications.

Novel glassbox based explainable boosting machine for fault detection in electrical power transmission system.

The reliable operation of electrical power transmission systems is crucial for ensuring consumer's stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method's detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

Multi-objective optimization for economic load distribution and emission reduction with wind energy integration

In today’s power systems operation, the dual challenge of optimizing economic load distribution while minimizing power plant emissions is pivotal. This challenge is accentuated by the pressing environmental concerns and the finite nature of fossil fuel reserves. In this context, renewable energy sources, notably wind power, have emerged as indispensable alternatives due to their cost-effectiveness and environmental compatibility. However, the inherent variability of wind velocity introduces uncertainty into power output, necessitating innovative approaches to address this complexity. To tackle this issue, we propose a scenario-based probabilistic approach that dynamically considers the slope rate of power output. By leveraging the Blue Whale multi-objective algorithm and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) criterion, we identify significant solutions from the Pareto set across a spectrum of scenarios. Our method is rigorously evaluated across various systems and operational contexts, revealing its superiority over alternative algorithms. Specifically, our approach achieves lower objective function values, reduced standard deviation, and superior overall performance. These findings underscore the critical importance of efficient power system management in balancing environmental sustainability and economic viability. By embracing innovative methodologies, we can navigate the evolving energy landscape and contribute towards a more sustainable future.

A Novel Contact Resistance Model for the Spherical–Planar Joint Interface Based on Three Dimensional Fractal Theory

In order to obtain the contact resistance of relay contacts more accurately, a novel contact resistance model for the spherical–planar joint interface is constructed based on the three-dimensional fractal theory. In this model, three-dimensional fractal theory is adopted to generate a rough surface at microscopic scale. Then, using contact mechanics theory, the deformation mechanism of asperities on rough surfaces is explored. Combined with the distribution of asperities, a contact resistance model for the planar joint interface is established. Furthermore, by introducing the surface contact coefficient, cross-scale coupling between the macro-geometric configuration and micro-surface topography is achieved, and a contact resistance model for the spherical–planar joint interface is constructed. After that, experiments are conducted to verify the accuracy of the proposed model, and the maximum relative error of the proposed model is 8.44%. Ultimately, combining numerical simulation analysis, the patterns of variation in contact resistance influenced by factors such as macroscopic configuration and microscopic topography are discussed, thereby revealing the influence mechanism of the contact resistance for the spherical–planar joint interface. The proposed model provides a solid theoretical foundation for the optimization of relay contact structures and improvements in manufacturing processes, which is of great significance for ensuring the safe and stable operation of power systems and electronic equipment.

Performance analysis of IoT-enabled hydro-photovoltaic power systems considering electrical power mission chains

Synergistically and complementarily managing the operation of hydropower plants and other weather-dependent renewable energy generation plants (e.g., solar energy plants) provides an effective measure to improve the performance efficiency (PE) of renewable energy. However, environmental uncertainty and physical system unavailability pose challenges in assessing the PE of the power generation. This paper proposes an approach to synergistically and complementarily managing the operation of hydro-photovoltaic (HPV) power systems in IoT under uncertain environments and develops a mission chain-based PE model for quantifying the capacity of the fulfilment of a power supply mission to satisfy users’ demand for power. An importance measure-based restoration strategy is then proposed to enhance the PE of an HPV power system (PEPS). The approach is examined by taking a large-scale HPV in China in the case study. The results show that, in winters and in summers, the PEPS is higher than PE of a photovoltaic power system and a hydropower system, respectively, and the PEPS with the importance measure-based restoration strategy is higher than that under the random restoration strategy, respectively. The findings not only lay the foundation for the complementary operation of hybrid renewable energy power systems but also provide a reference for the restoration in case of power cuts.

Numerical Simulation of Corona Discharge Plasma Affecting the Surface Behavior of Polymer Insulators

Corona discharge is a significant problem in the operation of high-voltage transmission and distribution systems, particularly for polymer insulators. Numerical simulation has become an effective tool for investigating the underlying physical mechanisms and optimizing the design of insulators. In this paper, we present a two-dimensional numerical simulation study on corona discharge plasma affecting the surface behavior of polymer insulators. The simulation was performed with the Comsol Multiphysic software and is based on the finite element method and the fluid plasma model, which considers ionization, recombination, and the transport of plasma species. The numerical results are analyzed to study the spatial and temporal characteristics of the corona discharge and its effect on the surface behavior of polymer insulators. The results show that the electric field is affected not only by the volume charge density but also by the surface charge density, which in turn depends on the densities of the charge carriers migrating on the insulator surface. However, the electric field drops drastically when one or two grading rings are installed. But one grading ring is not enough to limit the discharge.

Machine Learning-Enhanced Benders Decomposition Approach for the Multi-Stage Stochastic Transmission Expansion Planning Problem

The necessary decarbonization efforts in energy sectors entail integrating flexible assets and increased levels of uncertainty for the planning and operation of power systems. To cope with this in a cost-effective manner, transmission expansion planning (TEP) models need to incorporate progressively more details to represent potential long-term system developments and the operation of power grids with intermittent renewable generation. However, the increased modeling complexities of TEP exercises can easily lead to computationally intractable optimization problems. Currently, most techniques that address computational intractability alter the original problem, thus neglecting critical modeling aspects or affecting the structure of the optimal solution. In this paper, we propose an alternative approach to significantly alleviate the computational burden of large-scale TEP problems. Our approach integrates machine learning (ML) with the well-established Benders decomposition to manage the problem size while preserving solution quality. The proposed ML-enhanced Multicut Benders Decomposition algorithm improves computational efficiency by identifying effective and ineffective optimality cuts via supervised learning techniques. We illustrate the benefits of the proposed methodology by solving multi-stage TEP problems of different sizes based on the IEEE24 and IEEE118 test systems, while also considering energy storage investment options..

Delivering holistic energy-saving operation for heterogeneous all-parallel chiller plant systems through two-stage optimization assisted by input convex neural networks

With the rapid growth of building cooling demand and energy consumption, the potential for energy-saving operations gave rise to the popularity of implementing heterogeneous all-parallel chiller plant systems in large-scale public buildings. Meanwhile, heterogeneity, multi-dimensionality, and tight coupling interactions considerably complicate the holistically optimal operation of the system. Although artificial neural networks (ANN) have achieved great success in complex system identification, the endogenous non-convexity of classical feedforward neural networks (FNN), which are widely applied in model-based optimization for chiller plant operation, makes conventional optimizers prone to falling into local optima, forcing previous methodologies to sacrifice model precision for computational tractability. In this study, a special type of ANN named input convex neural network (ICNN) was leveraged to identify five individual convex models for energy-consuming units in the chiller plant system, providing convex input-output mappings while preserving high-resolution representations. Then, a two-stage optimizer with a sequential integration of a global searcher and a local optimizer was developed to effectively solve the holistic energy-saving problem by optimizing ten decision variables including chilled water supply temperature, partial load ratio of chillers, speed ratios of pumps and cooling tower fans, cooling water flow rate of cooling towers, and on/off states of devices. Finally, the methodology was deployed to a case study using on-site operation data and was compared with conventional candidates. Average holistic energy-saving ratios of 13.37% and 9.14% were achieved with average runtimes of 1.12 s and 1.07 s by the proposed optimizer in the high-load and light-load scenarios, respectively, showcasing more advantageous in resolving the operation optimization of complicated chiller plant systems.

Dynamic management of ground thermal response uncertainty through temporal analysis of parameter sensitivity

Accurately estimating the dimensionless ground thermal response factor (g-function) is critical for the design and operation of geothermal energy systems. This process, however, is fraught with uncertainties stemming from the difficulty in controlling on-site geometric parameters and estimating thermal properties through field experiments. The relative contributions of these parameters to response uncertainty change temporally as the response progresses. Thus, understanding this temporal sensitivity variation is crucial for effective response uncertainty management. To address this, we conducted transient global sensitivity analyses. Regardless of borefield setups, ground thermal properties were key factors in thermal response uncertainty. However, their influence decreased from the late-mid term as borehole radius and borehole distance became more significant in single-borehole and multi-borehole setups, respectively. This time-varying relative sensitivity suggests that prioritizing parameters to reduce response uncertainty depends on the temporal characteristics of the ground load. To demonstrate this, we systematically halved the uncertainty for each parameter and examined the resultant change in the fluid temperature uncertainty for a dynamic ground load scenario. Notably, prioritizing parameters with high early-response sensitivity significantly reduced fluid temperature uncertainty. These findings highlight the need for dynamic uncertainty management strategies in geothermal system design and operation. For dynamic load profiles utilizing the ground as a heat source or sink, prioritizing parameters with high sensitivity in the early- to mid-term is crucial. Conversely, for long-term storage applications, focusing on parameters with high sensitivity in the mid- to long-term response becomes essential.

Numerical analysis on the flow and heat transfer characteristics of heat exchanger with cruciform tube

The heat exchanger (HE) is widely used in energy and power industries, which has a vital impact on the stability and efficient operation of relevant industrial systems. Different shaped heat exchange tubes are often designed specially to improve the thermal–hydraulic efficiency of HEs. In the study, two new geometries named the cruciform twisted tube and cruciform straight tube are introduced, and the heat & mass transfer properties of liquid lead (Pb) and supercritical carbon dioxide (s-CO2) in the HEs are numerically studied to assess the thermal–hydraulic efficiency and application prospect of two kinds of cruciform tube HEs. The results demonstrate that the unique cross-section geometry of the cruciform tube improves the uniformity of the flow field, and the spiral structure of the cruciform twisted tube effectively improves the blending and turbulence of working fluid. The cruciform tube can improve the comprehensive performance of the HE by more than 12%.

FDIA localization and classification detection in smart grids using multi-modal data and deep learning technique

False data injection attacks (FDIA) exploit the vulnerabilities of bad data detection in energy management systems to maliciously tamper with state estimation results, seriously jeopardizing the safe and reliable operation of power systems. In order to promptly detect FDIA, recent studies have used machine learning techniques to extract attack characteristics and detect FDIA based on changes in these characteristics. Current research predominantly focuses on detecting a single type of FDIA attack model and topology. However, the increasing diversification of FDIA construction methods and the variability of topological structures in power systems can reduce or even invalidate detection effectiveness. To cope with the abovementioned difficulties, this study introduces a multimodal deep learning detection model based on variational graph auto-encoders (VGAE), temporal convolutional networks (TCN), and gated recurrent units (GRU). The topological features of power systems are obtained through VGAE to adapt to the detection needs of various topological structures and performance enhancement. These features are then integrated with preprocessed measurement data via BDD to form multimodal data. Furthermore, this multimodal data is used to locate and classify FDIA with complete information, FDIA with incomplete information, and topology attack after applying a multi-label classification algorithm based on TCN-GRU for temporal feature extraction. Experiments carried out on the IEEE 14 and IEEE 118 bus systems show that the proposed method is robust and has high detection performance. The results indicate that the proposed detection method achieves AUC values that are, on average, 0.085, 0.145, and 0.04 higher than those of CNN, LSTM, and CNN-LSTM, respectively. Moreover, under various noise and attack intensities, recall, precision, F1 score, and RACC remain above 0.859, 0.877, 0.867, and 0.818, respectively, with a classification accuracy greater than 0.912. This study provides a unique perspective on detecting FDIA across various attack models and power system topologies.

Adsorption and sensing properties of TiO2-modified MoSe2 monolayer for SF6 decomposition components based on the first-principles

As crucial components in power systems, SF6 gas-insulated equipment inevitably develops insulation defects and faults over long-term operation, leading to the decomposition of SF6 gas. Therefore, detecting SF6 decomposition components is essential for assessing the operational status of SF6 gas-insulated equipment and ensuring the safe and stable operation of power systems. Based on first principles, the modification mechanism of TiO2 on MoSe2 was explored, and optimal adsorption models for gases SO2, H2S, SOF2, and SO2F2 on this composite material were established. The adsorption characteristics of each system were analyzed through parameters such as adsorption energy, adsorption distance, charge transfer, electron density difference, and density of states, and the sensing properties of the systems were discussed using parameters including changes in frontier molecular orbitals, desorption time, and sensitivity. The results indicate that TiO2–MoSe2 has difficulty capturing SO2F2, but exhibits chemisorption for SO2, H2S, and SOF2 with adsorption energies of −1.25 eV, −0.99 eV, and −3.40 eV, respectively. Upon adsorption of SO2, TiO2–MoSe2 shows a significant change in electrical conductivity, a desorption time of 4.51 s (at 498 K), and a strong sensitivity response. However, the response to H2S is relatively weak, with lower sensitivity. Therefore, this material is suitable for detecting SO2 but not H2S. TiO2–MoSe2 demonstrates a significant response and excellent sensitivity to SOF2; however, the adsorption process lacks repeatability. Therefore, it can be used as a disposable gas sensor or solid adsorbent for SOF2 detection. The results of this study provide theoretical support for the application of MoSe2-based sensors in the monitoring and fault diagnosis of SF6 gas-insulated equipment.

Enhancing Distribution Networks with Optimal BESS Sitting and Operation: A Weekly Horizon Optimization Approach

The optimal sitting and operation of Battery Energy Storage Systems (BESS) plays a key role in energy transition and sustainability. This paper presents an optimization framework based on a Multi-period Optimal Power Flow (MOPF) for the optimal sitting and operation of BESS alongside PV in active distribution grids. The model was implemented in AMPL (A Mathematical Programming Language) and solved using the Knitro solver to minimize power losses over one week, divided into hourly intervals. To demonstrate the applicability of the proposed model, various analyses were conducted on a benchmark 33-bus distribution network considering 1, 2 and 3 BESS. Along with the reduction in power losses of up to 17.95%, 26% and 29%, respectively. In all cases, there was an improvement in the voltage profile and a more uniform generation curve at the substation. An additional study showed that operating over a one-week horizon results in an energy gain of 1.08 MWh per day compared to single daily operations. The findings suggest that the proposed model for optimal sitting and operation of BESS in the presence of Renewable Energy Sources (RES) applies to real-world scenarios.

An electropermanent magnet valve for the onboard control of multi-degree of freedom pneumatic soft robots

To achieve coordinated functions, fluidic soft robots typically rely on multiple input lines for the independent inflation and deflation of each actuator. Fluidic actuators are controlled by rigid electronic pneumatic valves, restricting the mobility and compliance of the soft robot. Recent developments in soft valve designs have shown the potential to achieve a more integrated robotic system, but are limited by high energy consumption and slow response time. In this work, we present an electropermanent magnet (EPM) valve for electronic control of pneumatic soft actuators that is activated through microsecond electronic pulses. The valve incorporates a thin channel made from thermoplastic films. The proposed valve (3 × 3 × 0.8 cm, 2.9 g) can block pressure up to 146 kPa and negative pressures up to –100 kPa with a response time of less than 1 s. Using the EPM valves, we demonstrate the ability to switch between multiple operation sequences in real time through the control of a six-DoF robot capable of grasping and hopping with a single pressure input. Our proposed onboard control strategy simplifies the operation of multi-pressure systems, enabling the development of dynamically programmable soft fluid-driven robots that are versatile in responding to different tasks.