Power Control Research Articles

ESG peer effects and corporate financial distress: An executive social network perspective

AbstractBased on the data of listed firms in China from 2009 to 2020, this study investigates whether environmental, society and governance (ESG) peer effects reduce the risk of corporate financial distress from an executive social network perspective. Using two‐stage least squares method, our empirical results suggest that the ESG peer effects exist in executive social networks, and the ESG peer effects can alleviate corporate financial distress. ESG subcategory analysis shows that the governance peer effect has the most obvious alleviating effect on financial distress. The negative impact of ESG peer effects on corporate financial distress is stronger when firms have high network power, network cohesion and network control in executive social networks. Our conclusions still hold after a series of robustness tests. Our research expands the literature on peer effects from the perspective of social relations, and sheds additional light on the critical role of ESG peer effects in financial risk management.

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Abstract To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

Summary statistics knockoffs inference with family-wise error rate control.

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks: An Aggregative Game-Theoretic Learning Approach

Joint Power and Coverage Control of Massive UAVs in Post-Disaster Emergency Networks: An Aggregative Game-Theoretic Learning Approach

Development of All Solid State Nanosecond Pulse Transformer Fault Detection Device Based on Marx Circuit

Abstract a nanosecond pulse generator with a fast leading edge for transformer fault diagnosis is developed. The device is composed of a full solid-state nanosecond pulse generator based on Marx circuit and with a tail cut switch and a field programmable logic gate array (FPGA) device. Among them, the nanosecond pulse source is mainly composed of DC power supply, control circuit and main circuit. The main circuit is a 9-level modular Marx circuit, using MOSFET as the main switch and tail cut switch; The control circuit generates a synchronous trigger pulse signal, which drives the MOSFET to work synchronously after being isolated through the optical fiber. The output nanosecond pulse voltage parameters are adjustable amplitude 0-8kv, pulse width 100˜1000ns, repetition frequency 1hz˜1000hz, rising edge 30ns and falling edge 40ns. The transformer fault diagnosis platform is built, and the transformer off-line and on-line fault diagnosis is carried out, which paves the way for further exploration of transformer fault diagnosis technology.

An Improved Constant Active Power Control for Three-Phase Buck Rectifier Under Unbalanced Input Voltages and Wide AC Input Frequency

An Improved Constant Active Power Control for Three-Phase Buck Rectifier Under Unbalanced Input Voltages and Wide AC Input Frequency

Research on Voltage Coordinated and Stability Control of Condensers Based on Reactive Power Replacement

Abstract In this research, we investigated condenser voltage coordination and stability control systems by analyzing voltage coordinated control mode among excitation, voltage coordination and stability as well as automatic-voltage reactive power control systems. By using dynamic excitation regulation and steady-state regional voltage source control replacement, dynamic reactive power quickly output under fault condition as well as optimized regional reactive power under steady state were realized. The correctness and practicability of the proposed strategy was verified through engineering application. Adopting the reactive power displacement of voltage coordination control system to solve local voltage instability problem after UHV DC blocking. The proposed method realized multi-time scale emergency, recovery and steady-state control of the system under steady and dynamic states.

Novel resistance control scheme for mitigating current sharing mismatches in parallel dual active bridge converters for DC fast charging stations

Abstract Dual Active Bridge (DAB) converters have gained popularity in electric vehicle charging stations due to their high efficiency and electrical isolation. As the demand for high-powered devices and large-capacity energy storage systems grows, charging systems that integrate multiple interconnected DAB modules are emerging as a promising solution. However, prolonged operation of these modules at high power levels can cause parameter deviations from the initial DAB circuit, resulting in power variations between modules. To overcome parameter deviations, this study presents an enhanced power control approach based on output resistance adjustment, intending to achieve consistent output capacity for multiple DAB modules. In the proposed enhanced power control method, the output resistance of the DAB module is considered to be controllable, and the current-sharing mismatches among DAB modules are fed back to tune the converter output resistance for mitigating current mismatches between modules. Thanks to the proposed control method, each DAB module can operate autonomously and balance the charging current between modules. When one DAB module is suddenly cut out of the system, the other DAB modules still maintain their stability with fully guaranteed load capacity. To demonstrate the feasibility of the enhanced control approach, the small signal model of the DAB system with three modules is derived together with its frequency-amplitude diagram. Then, the effect of virtual resistance on current balancing is comprehensively tested, and the proper control signal with virtual resistance is added to the DAB voltage control loop. The simulation results have demonstrated the reliability of the proposed control method with the ability to balance the charging current between modules and stabilize the system when a single DAB module fails.

AI-powered data center power control for sustainable development

AI-powered data center power control for sustainable development

End-to-End Delay Performance Analysis for Delay-Sensitive Services in Polymorphic Networks

Abstract In the polymorphic network for smart grid, the end-to-end latency performance analysis of delay-sensitive power control services such as differential protection and distributed power regulation is a crucial component in the intelligent control and management of power network slicing. In this paper, based on a detailed analysis of end-to-end delay factors of power control service, the distributed power regulation pilot project is taken as a typical application scenario of power control service, and the end-to-end delay of such service is theoretically derived and analyzed in detail by using network calculus theory. Numerical verification shows that the end-to-end delay performance of the power control service is positively correlated with the number of concurrent data streams and the number of subsystems and negatively correlated with the system service rate. Compared with the burst degree of the current data streams, the burst degree of other data streams significantly impacts the end-to-end delay calculation results of the current data flow.

Research on continuous commutation failure suppression strategy for multi-feed DC transmission system considering harmonic characteristic factors

Abstract Regarding the sustained issues of commutation failures in the hybrid double-fed DC system encompassing Line Commutated Converter High Voltage Direct Current (LCC-HVDC) and Modular Multilevel Converter High Voltage Direct Current (MMC-HVDC, from the perspective of harmonics, the harmonics in the voltage waveform of the AC bus on the inverter side are analyzed. Besides, a commutation failure suppression strategy for the hybrid doubly-fed DC transmission system based on the minimum shutdown area is designed, in which the setting value of the shutdown angle of the harmonic is considered in the minimum shutdown area. The reactive power is obtained by attaching the setting value to the reactive power control link of MMC-HVDC, and the simulation analysis in PSCAD verifies that the strategy can make the MMC-HVDC output more reactive power during the fault in the weak AC system. Increasing the shut-off margin of the manifold reduces the potential for continuous commutation failures in LCC-HVDC.

Dual virtual vector duty cycle model predicts power control of dual three-phase permanent magnet generator

Dual virtual vector duty cycle model predicts power control of dual three-phase permanent magnet generator

Review Paper on Street Light Automatic Intensity Controller

Abstract: This paper is planned for making more secure roadways with canny light framework to lessen power utilization. This framework has programmed road light power control dependent on the vehicular development and turning ON and OFF of road lights relying upon the light atmosphere. This will help in decreasing the power utilization during long periods of small street use. The road light module is introduced subsequently for each specific separation. This paper additionally targets lessening street mishaps by recognizing utilization of liquor by the driver. This can be actualized utilizing liquor sensor module which contains skin sensor, breath liquor sensor and nearness sensor. The skin sensor and broadness liquor sensor distinguish the nearness of liquor content and the nearness sensor helps in identifying any sort of misbehaviour. The oddity of this paper is to successfully lessen the vitality utilization of the road lights by controlling the road light's force, detecting both human just as vehicular development and injury and passing brought about by alcoholic driving can be forestalled by earlier detecting of the liquor content in drivers by a basic and affordable way..

Eliminating Harmonics and Voltage Ripples Using Optimal Constructive Recurrent Neural Network on Boost Rectifiers with Unbalanced Grid Sources

Various new methodologies and techniques have been proposed to improve power quality. One of the parameters that affect power quality is harmonics. Voltage and current unbalance is a frequent problem. Electronic devices such as rectifiers and power distribution networks are negatively affected by this imbalance. The current in the grid will be severely distorted. The DC bus voltage will fluctuate or ripple frequently, perhaps twice as often as the frequency. Direct power control (DPC) and voltage-oriented control (VOC) are two examples of control strategies developed for rectifiers. In classical VOC, the voltage and current in the synchronous frame of reference (dq) are controlled using a proportional-integral (PI) controller. However, the PI controller in controlling the VOC current has the disadvantage that when one variable changes, the other variables will also change. PI cannot control itself independently. it is difficult to achieve an accurate linear mathematical model of the system required for PI controllers. In addition, PI controllers often struggle to work satisfactorily on parameter variations, nonlinearities, and load disturbances. in transient conditions, the Optimal Constructive Recurrent Neural Network(OCRNN) controller takes faster time to reach a stable state. when there is a change in the reference dc voltage, PI has a higher overshot voltage and a lower voltage drop than OCRNN and takes longer than the OCRNN controller.

Cogs in the code: Applying labor process theory in algorithmic management of platform-mediated gig work

This article examines the evolving work landscape in the context of technological advancements, addressing a significant gap in understanding the foundational principles of algorithmic management. Employing labor process theory (LPT), this study focuses on power dynamics and control mechanisms within algorithmic management systems in platform-mediated work settings. By exploring managerial aspects such as task assignment, work organization, monitoring, surveillance, and performance evaluation under algorithmic management, this research utilizes LPT to meticulously explore the social relations of production, technological deskilling, division of labor, potential alienation and exploitation, and complex dynamics of control and resistance in the gig economy. By emphasizing the pivotal role of algorithms, this study reveals their influence on shaping the structural aspects of the gig economy, highlighting the intricate interplay between technological advancements and fundamental labor processes. This work also contributes to a deeper understanding of contemporary work dynamics by offering valuable insights into the evolving intersection of technology and labor in the modern workplace.

Preclinical prototype validation and characterization of a thermobrachytherapy system for interstitial hyperthermia and high-dose-rate brachytherapy

Background and purposeIntegrating simultaneous interstitial hyperthermia in high-dose-rate brachytherapy treatments (HDR-BT) is expected to lead to enhanced therapeutic effect. However, there is currently no device available for such an integration. In this study, we presented and validated the thermobrachytherapy (TBT) preclinical prototype system that is able to seamlessly integrate into the HDR-BT workflow. Materials and methodsThe TBT system consisted of an advanced radiofrequency power delivery and control system, dual-function interstitial applicators, and integrated connection and impedance matching system. The efficiency and minimum heating ability of the system was calculated performing calorimetric experiments. The effective-heating-length and heating pattern was evaluated using single-applicator split phantom experiments. The heating independence between applicators, the ability of the system to adaptable and predictable temperature steering was evaluated using multi-applicator split phantom experiments. ResultsThe system satisfied interstitial hyperthermia requirements. It demonstrated 50 % efficiency and ability to reach 6 °C temperature increase in 6 min. Effective-heating-length of the applicator was 43.7 mm, following the initial design. Heating pattern interference between applicators was lower than recommended. The system showed its ability to generate diverse heating patterns by adjusting the phase and amplitude settings of each electrode, aligning well with simulations (minimum agreement of 88 %). ConclusionsThe TBT preclinical prototype system complied with IHT requirements, and agreed well with design criteria and simulations, hence performing as expected. The preclinical prototype TBT system can now be scaled to an in-vivo validation prototype, including an adaptable impedance matching solution, appropriate number of channels, and ensuring biocompatibility and regulatory compliance.

A parallel power system linear model reduction method based on extended Krylov subspace

With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model.

PTC power control of electric vehicle thermal management system based on neural network feedforward

Thermal management system (TMS) plays a crucial role in improving driving range and passenger comfort for electric vehicles (EVs) in winter. However, EVTMS still faces unsolved challenges such as accurate modeling of time-delay, compensating for temperature variations, and achieving efficient control strategies, which are important for enhancing performance, safety, and energy efficiency of EVs. To address above issues, this study builds an integrated EVTMS model and corresponding controllers, aiming to obtain superior battery temperature and cabin comfort control performance through co-simulations. Uniquely, neural networks are set as control feedforward to predict the temperature changes because of their strong nonlinear mapping ability. The results reveal that, NN feedforward can well predict and compensate for the system time-delay and nonlinearity, significantly improving the performance of PI controller and MPC, reducing control overshoot, saving energy and extending component lifespan. The battery temperature and PMV fluctuations can be effectively reduced up to 32.50 % and 30.82 %, and the cabin and battery PTC consumption can be reduced by up to 6.03 % and 6.74 %. The results can provide valuable insights for TMS engineers, aiding in the selection of advanced control strategies.

Voltage control strategy of a high-permeability photovoltaic distribution network based on cluster division

The use of distributed photovoltaics (PVs) on a large scale often causes voltage over-limit problems in distribution networks. This paper proposes a distributed photovoltaic cluster collaborative optimization voltage control strategy based on an improved community algorithm to address the issue of centralized control being unable to respond quickly to the randomness of distributed photovoltaics and the difficulty of achieving overall coordination with local control. First, by improving the community algorithm, the division of reactive and active clusters, considering the power balance and node coupling degree, is realized. Then, the cluster-coordinated voltage control strategy is proposed by making full use of the power control ability of a photovoltaic inverter. Finally, a voltage regulation ability evaluation index is proposed to assess the node regulation ability within the cluster and select key nodes. This effectively reduces the number of control nodes. The simulation analysis of the improved IEEE 69 distribution network shows that the proposed voltage control strategy can mitigate the issue of voltage over-limit in high-permeability distributed photovoltaic access distribution and enhance the photovoltaic consumption capacity.

Initial estimate of AC optimal power flow with graph neural networks

Optimal power flow (OPF) is a crucial task in power system management and control; accurate and time-efficient solutions for OPF are necessary to ensure cost-efficient and reliable power system operation. We introduce a novel solution to solving alternating current OPF (ACOPF), a nonlinear and nonconvex optimization problem, by combining the speed of a trained deep learning model with the accuracy of iterative solvers. The proposed framework uses a graph neural network (GNN) to exploit the graph structure of a power system in conjunction with proximal policy optimization, a deep reinforcement learning algorithm, to compute initial guesses for an interior point solver (IPS), providing a warm start, allowing the solver to converge in fewer iterations. Existing literature that explores warm start ACOPF solutions using machine learning choose to compute initial guesses that are trained to be feasible and cost-minimizing. Our approach trains the GNN-based reinforcement learning agent to produce an output that minimizes IPS convergence time by designing a reward function that is a function of the IPS convergence time. We evaluate the proposed framework using IEEE test case environments, using PyPower’s IPS-based ACOPF solver and a GNN-based framework that computes ACOPF solutions directly as baselines, demonstrating significantly improved convergence times.