Intelligent Power Distribution Research Articles

Drone image recognition and intelligent power distribution network equipment fault detection based on the transformer model and transfer learning

Drone image recognition and intelligent power distribution network equipment fault detection based on the transformer model and transfer learning

Studying the Intelligent Power Distribution and Consumption of the Power Grid and its Cost Structure Using Big Data Analysis

Based on the analysis of big data, this paper studies the impact of user behavior response on the cost structure of the microgrid system in the power grid system. The article first conducts in-depth research on distributed power generation and energy storage systems, focusing on the principles and output characteristics of smart power distribution and utilization in power grids, researches smart power distribution and utilization systems in power grids, and makes a more comprehensive discussion of the current situation. Secondly, a big data analysis platform was built, and distributed storage and computing were studied. The platform was used to perform distributed storage and regulation of electricity consumption data, and the electricity consumption information data was divided into the important load, controllable load and transferable load, constructed a microgrid system model based on electricity consumption behavior response, and analyzed a calculation example. After that, a micro-grid system was simulated based on HOMER software, and the optimal capacity configuration of the system was performed. Under this configuration, the micro-grid system has the highest economic efficiency. At the same time, a demand-side load control system was built. Introduced distributed power as a controllable load, integrated new energy access and load control technology, coordinated the contradiction between the power grid and distributed power, and completed a cost-benefit analysis. Finally, for demand-side management electricity price response, peak-valley time-of-use price, the most important implementation method, according to the cost structure theory, the peak-valley period is divided according to the membership function in fuzzy mathematics, and the user's response model to peak-valley time-of-use price is established. The experiment uses the original data to simulate, find the user response model parameters based on the load transfer rate, and complete the comparative analysis of the effect under the peak-valley time-of-use electricity price, which is of great significance to the implementation and improvement of the peak-valley time-of-use electricity price project. The analysis results of the calculation examples show that the method constructed in this paper can effectively realize the power quality analysis of the distribution network in the big data environment. The research results provide technical support for the management of the rural grid voltage deviation of the power company, and lay the foundation for improving the safe operation and management of the power grid.

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates

The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.

Deep learning anomaly detection in AI-powered intelligent power distribution systems

Introduction: Intelligent power distribution systems are vital in the modern power industry, tasked with managing power distribution efficiently. These systems, however, encounter challenges in anomaly detection, hampered by the complexity of data and limitations in model generalization.Methods: This study developed a Transformer-GAN model that combines Transformer architectures with GAN technology, efficiently processing complex data and enhancing anomaly detection. This model’s self-attention and generative capabilities allow for superior adaptability and robustness against dynamic data patterns and unknown anomalies.Results: The Transformer-GAN model demonstrated remarkable efficacy across multiple datasets, significantly outperforming traditional anomaly detection methods. Key highlights include achieving up to 95.18% accuracy and notably high recall and F1 scores across diverse power distribution scenarios. Its exceptional performance is further underscored by achieving the highest AUC of 96.64%, evidencing its superior ability to discern between normal and anomalous patterns, thereby reinforcing the model’s advantage in enhancing the security and stability of smart power systems.Discussion: The success of the Transformer-GAN model not only boosts the stability and security of smart power distribution systems but also finds potential applications in industrial automation and the Internet of Things. This research signifies a pivotal step in integrating artificial intelligence into the power sector, promising to advance the reliability and intelligent evolution of future power systems.

Virtual Synchronous Machine Control for Asynchronous Grid Connections

The reduced amount of large synchronous generators results in the need for fast, flexible, and intelligent power distribution devices to enhance the inertia in the modern power system. This paper proposes a new approach to control an asynchronous low-voltage grid connection, employing a virtual synchronous machine with frequency-based power control. The grid-forming converter, receiving the primary side frequency measurement, varies the fed grid frequency on the secondary side artificially, to interact with frequency-dependent resources. This enables the adjustment of the consumed or generated power in the fed grid without the need for additional communication infrastructure, and thus supports the frequency control of the mains. The performance of the proposed frequency control has been validated through simulation and using a newly developed double Power Hardware-in-the-Loop experimental test setup.

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

Key technologies of active distribution networks control

Active Distribution Networks (ADNs) are modern and intelligent power distribution systems that integrate renewable energy sources and advanced control technologies to provide reliable, secure, and efficient electricity supply. The traditional power distribution networks are designed to deliver electricity in a one-way flow from the transmission system to the customers. In contrast, ADN enables two-way power flow between the distribution network and customers, allowing them to become active participants in the electricity system. The key technology behind ADN includes advanced metering infrastructure (AMI), Distribution Automation (DA), and energy storage systems (ESS). AMI provides real-time data on electricity consumption, generation, and distribution, enabling efficient load management and demand response. Other key technologies of ADN include renewable energy sources such as solar photovoltaic (PV), Wind turbines and small-scale hydroelectricity. These sources are connected to the distribution network through power electronic interfaces that convert the DC output of the renewable sources into AC for distribution. Smart grid technologies such as microgrids, virtual power plants, and energy management systems are also integrated into ADN to improve grid stability and resilience. ADNs have the potential to transform the electricity system by enabling more efficient, sustainable, and reliable energy systems that can better serve the needs of consumers and society. Thus, ADN is expected to become a critical technology in achieving a cleaner and more sustainable energy future.

Short-term district power load self-prediction based on improved XGBoost model

Distributed generation and diversified loads increase the uncertainty of district power prediction. Useful prediction requires a highly accurate model, and there are several challenges facing the designers of a new power system with intelligent power distribution. To solve them, we improved an XGBoost model from three aspects: model, data, and performance. This paper proposes an XGBoost model with a windowed mechanism and random grid search (WR-XGBoost model) for self-prediction of short-term district power load. Specifically, a causal sliding window with different strides is introduced into the model optimization stage to process the training and test sets separately. In data optimization, the model initially processes the data organized in forms and then uses discrete difference data as input. Finally, in optimizing the performance, a random grid search method reduces the debugging of hyperparameters. The results show that the WR-XGBoost model outperforms five comparison models in terms of predictive power and generalization, using four datasets and seven statistical indicators.

Intelligent power distribution live‐line operation robot systems based on stereo camera

AbstractMaintenance tasks in distribution networks are often accompanied by hazards associated with high altitudes and high voltages. By utilising robots instead of human operators to perform these tasks, potential risks can be avoided, while productivity can be increased. This research proposes an intelligent power distribution live‐line operation robot (PDLOR) system based on a stereo camera to replace human to complete work. The PDLOR system consists of several key components, including dual manipulators, wireless tools, a visual perception system, an insulated bucket truck, and a ground control terminal. Once the task is confirmed, the real‐time vision system identification and positioning enable the adjustment of the insulated bucket to position the robot correctly for its intended work. The stereo camera plays a crucial role in accurately recognising and estimating the object's orientation. Additionally, a simplified reconstruction is performed within a virtual simulation environment, which aids in collision detection during path planning. After obtaining the optimal path, it is then communicated to the real manipulator for execution. To validate the feasibility of the PDLOR system, field experiments were conducted in actual distribution network scenarios. The results demonstrate that the PDLOR effectively completes single‐phase power‐line connection tasks within a remarkable 10‐min timeframe.

Methodology for determining the reactive parameters of aviation electrical energy consumers based on voltage and current data in transients

Modern trends in the aircraft development are associated with a significant increase in the level of its electrification. The emergence of new types of electricity consumers entails an increase in the power and number of consumers in the aircraft power supply system. At this stage of operation, direct control of the internal parameters (in particular, the parameters of reactive elements) of electricity consumers in the aircraft power supply system is not carried out. The aviation equipment control system is built taking into account the built-in system for monitoring the technical condition of the object. However, for greater efficiency, the built-in control can be supplemented with control capabilities through digital intelligent power distribution systems (local load control center). During the operation of aviation electrical equipment, the values of its parameters change relative to the nominal values. Deviations of equipment parameters from the nominal values should not exceed the specified values, which are determined by the operational technical documentation. Input impedances of secondary power supply sources can be investigated in a mode characterized by transient processes in the investigated component of aircraft equipment. Based on the experimental data of voltage and input current at the stage of transients in equivalent circuits of secondary power supplies, it is proposed to determine the values of the parameters of reactive elements. Changing the values of reactive elements gives information about the technical condition of the input stages of electrical energy receivers. This makes it possible to monitor the process of degradation of their properties or to diagnose a failure in the event of an abrupt change in reactive parameters. This method is proposed to be used for diagnosing electrical equipment during its operation on board the aircraft. This work is devoted to the development of methods for determining the parameters of secondary power sources of aircraft electrical equipment, characterizing its reactive properties.

Design and Implementation of Protocol Conversion of Intelligent Power Distribution Equipment for New Power System

A protocol conversion method of intelligent distribution equipment for a new power system is proposed to aim at the problems of short data reading distance and high data packet loss rate in the current protocol conversion process of intelligent distribution equipment. The protocol conversion process of the equipment is divided into two parts, the uplink channel, and the downlink channel. The multi-protocol conversion process of the intelligent distribution equipment is designed. A data-based protocol conversion scheme is set up to realize the bidirectional conversion of the protocol. The original protocol conversion gateway is replaced by the CAN-PROFINET protocol conversion gateway to control the network message throughput and realize the protocol conversion of the intelligent power distribution equipment. The experimental results show that the method can effectively extend the data reading distance and reduce the data packet loss rate to a certain extent, which provides a new development direction for the new power system network control technology.

Research on market trading strategy of multi-microgrid intelligent power distribution system based on Bi-level optimization

With the continuous promotion of the “dual carbon” idea, future power generation will rely heavily on renewable energy sources. As an effective utilization form of clean power sources, it is of positive significance to study the trading strategy of microgrids in the intelligent power distribution system under the influence of carbon quota. In this research, a bi-level optimization method is used to build a trading model of the distribution-side power market, with the upper-level planning aiming to reduce the cost of distribution system operators and the lower-level planning aiming to increase the revenue of microgrids. Secondly, the genetic algorithm and sequential quadratic planning algorithm are applied to the upper and bottom models to determine the optimal clearing strategy for the microgrid and the optimal scheduling scheme for the distribution system operator, respectively. Finally, a typical day is used as an example to analyze in detail the market trading strategy of a multi-microgrid intelligent distribution system under the influence of carbon quota, and the effectiveness of the method described in this paper is verified.

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

An abnormal identification method for operation and maintenance personnel of intelligent power distribution room based on monitoring video stream

Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System.

Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem is to build mechanisms to selectively send out communications, which allow distributors to send information at certain moments and key states. The distributors in the power grid are modeled as reinforcement learning agents, and the communication bandwidth in the power grid can be reduced by optimizing the communication frequency between agents. Therefore, in this paper, we propose a model for deciding whether to communicate based on the causal inference method, Causal Inference Communication Model (CICM). CICM regards whether to communicate as a binary intervention variable, and determines which intervention is more effective by estimating the individual treatment effect (ITE). It offers the optimal communication strategy about whether to send information while ensuring task completion. This method effectively reduces the communication frequency between grid distributors, and at the same time maximizes the power distribution effect. In addition, we test the method in StarCraft II and 3D environment habitation experiments, which fully proves the effectiveness of the method.

Firefly analytical hierarchy algorithm for optimal allocation and sizing of DG in distribution network

<span lang="EN-MY">Distributed generation (DG) can be beneficially allocated in distribution power systems to improve the power system's efficiency. However, specious DG's allocation and sizing may cause more power loss and voltage profile issues for distribution feeders. Therefore, optimization algorithms are vital for future intelligent power distribution network planning. Hence, this study proposes a multi-objective firefly analytical hierarchy algorithm (FAHA) for determining the optimal allocation and sizing of DG. The multi-objective function formulation is improved further by integrating analytical hierarchy process (AHP) with FA to obtain the weight of the coefficient factor (CF). The performance of the proposed approach is verified on the 118-bus radial distribution network with different bus voltage at DG location (VDG) as regulated PV-bus during load flow calculations. The calculated CF and impact of the unregulated voltage at the PV-bus on the objectives function have been analysed. The findings show that the proposed techniques could allocate the DG at the most voltage deviation while minimizing the power loss and improving the radial distribution’s voltage stability index (VSI). The experimental results indicate that the approach is able to improve the overall voltage profile, especially at PQ-buses, minimize the power loss while improving the network's stability index simultaneously.</span>

Big Data Application Technology and Prospect Analysis in Smart Distribution Network

Intelligent power distribution network system main application technology including the communications, computer and network, the modern electronic technology, etc., adapter power automation system main components for the master station, terminal automation monitoring system and communication system, has formed a complete collection, transmission and processing in the integration of information system, to realize the remote management of distribution network. The application of big data technology in smart distribution network makes it more intelligent and ADAPTS to the needs of social development. This paper analyzes the application technology and prospect of smart distribution network big data.

Identifying Topology of Low Voltage Systems via Incorporating Measurement Correlations

The management of intelligent power distribution systems is limited due to the lack of observability in low voltage (LV) systems. Ambiguous logical relations among branch units, topological unavailability of consumers increase the difficulty of topology identification (TI). To solve this problem, a three-step framework is proposed via measurement correlations. The first step applies correlations of active power features to identify the up-down and parallel, and phase connection of branch units; the second step specially calculates active power correlations between segments and non-vacant consumers, determines the phase connections, and identifies the connected lines of non-vacant consumers; the third step associates vacant consumers with non-vacant consumers by voltage profiles. Finally, robustness against errors and sample sizes is explored, and the results demonstrate that the proposed algorithm can accurately identify the LV topology based on limited samples of voltage magnitude and active power.

Интеллектуальные сети энергоснабжения: мониторинг качества электроэнергии

The issues of monitoring the normalized parameters of electricity, in smart grid networks, as a means of ensuring control of the quality of electricity, are considered. In the context of constantly growing consumption of electrical energy, the most important task is its rational distribution and use. State-of-the-art prototypes of intelligent power distribution networks include means of controlling power distribution and consumption processes to ensure that these processes comply with applicable regulations. The purpose of our study is to create a monitoring device for normalized power quality parameters, such as voltage, signal shape, harmonic coefficient, with the possibility of detecting deviations of these parameters from the norm, with the possibility of transmitting the results obtained via infocommunication networks. The developed device allows collecting, analyzing and transmitting data on power quality indicators in the distribution electrical network, with their further use in order to manage energy supply and energy consumption in smart power supply systems, and improve the energy efficiency of power supply systems through the introduction of smart grid technologies. The device can be integrated into energy systems based on the smart grid concept, which are a single energy-information complex, as part of a situation assessment system and emergency automation.

Intelligent Power Distribution Restoration Based on a Multi-Objective Bacterial Foraging Optimization Algorithm

The importance of power in society is indisputable. Virtually all economic activities depend on electricity. The electric power systems are complex, and move studies in different areas are motivated to make them more efficient and solve their operational problems. The smart grids emerged from this approach and aimed to improve the current systems and integrate electric power using alternative and renewable sources. Restoration techniques of these networks are being developed to reduce the impacts caused by the usual power supply interruptions due to failures in the distribution networks. This paper presents the development and evaluation of the performance of a multi-objective version of the Bacterial Foraging Optimization Algorithm for finding the minor handling switches that maximize the number of buses served, keeping the configuration radial system and within the limits of current in the conductors and bus voltage. An electrical system model was created, and routines were implemented for the network verification, which was used as a function of the Multi-Objective Bacterial Foraging Optimization Hybrid Algorithm. The proposed method has been applied in two distribution systems with 70 buses and 201 buses, respectively, and the algorithm’s effectiveness to solve the restoration problem is discussed.

Communication Bandwidth Prediction Technology for Smart Power Distribution Business in Smart Parks

Accurate prediction of power business communication bandwidth is the premise for the effectiveness of power communication planning and the fundamental guarantee for regular operation of power businesses. To solve the problem of scientifically and reasonably allocating bandwidth resources in smart parks, communication bandwidth prediction technology of intelligent power distribution service for smart parks is proposed in this paper. First, the characteristics of mixed service data arrival rate of power distribution and communication mixed services in smart parks were analyzed. Poisson process and interrupted Poisson process were used to simulate periodic and sudden business of smart parks to realize accurate simulation of the business arrival process. Then, a service arrival rate model based on the Markov modulation Poisson process was constructed. An active buffer management mechanism was used to dynamically discard data packets according to the set threshold and achieve accurate simulation of the packet loss rate during the arrival of smart park business. At the same time, considering the communication service quality index and bandwidth resource utilization, a business communication bandwidth prediction model of smart parks was established to improve the accuracy of business bandwidth prediction. Finally, a smart power distribution room in a smart park was used as an application scenario to quantitatively analyze the relationship between the communication service quality and bandwidth configuration. According to the predicted bandwidth, the reliability and effectiveness of the proposed method were verified by comparison with the elastic coefficient method.