Analysis of Power Load Components Based on Neural Network

With the development of load modeling technology, the accuracy of load model is increasingly required. In addition, as the end user of power, the accuracy of load model has a great impact on the simulation of power system. In the load modeling of large power grids, 220KV or 110KV substations are generally used as load nodes, and enterprise load modeling is rarely carried out. In view of the shortcomings of the current comprehensive load model in the enterprise load grid structure, due to the large proportion of the enterprise motor load, this paper uses the comprehensive statistical method based on statistical data to divide the motors with different load characteristics into multiple motor groups, and then aggregate the motor clusters into equivalent motors. Then, according to the characteristics of motors and the influence of distribution network impedance, the motors in the enterprise are aggregated into two or more motor groups to realize the integrated load modeling of the enterprise load grid.

Dry-coupling ultrasonic technology is applied to detecting oil-level of oil-filled equipment in power grid. Vinyl silicone rubber is used to substitute liquid coupling agent to fabricate dry-coupling ultrasonic probe, which makes the ultrasonic directly enter the oil-filled equipment through the special structure of dry-coupling probe and detect oil-level of oil-filled equipment accurately and rapidly. Omitting the step that smearing liquid coupling agent on the probe can avoid the contamination on the surface of equipment and significantly improve the detection efficiency.

Practice shows that the frequency characteristics of the load have important influence on analysising the safety and stability of the power grid. In order to improve the accuracy of the load model, a comprehensive load model considering load frequency characteristics is established based on the static load frequency factor and the frequency dependent part of induction motor model. For the parameter of the load model, a new composite modeling method based on the combination of statistical comprehensive method and measurement-based modeling approach is proposed to calculate and check the parameters of the model. Finally, a simulation example is given to illustrate the effectiveness of the proposed composite modeling method considering load frequency characteristics

The purpose of this paper is to improve the fault early warning effect of smart grid equipment through multi-sensor information fusion technology. Therefore, based on the analytical model of power grid fault diagnosis, this paper considers the influence of distributed generation in distribution network on fault diagnosis, as well as the misoperation or refusal of protection and switch, and the false alarm or leakage of alarm signal. At the same time, in order to display the results of fault diagnosis accurately and intuitively, an analytical model of fault diagnosis of distribution network based on multi-source information fusion is proposed. Finally, this paper verifies the effectiveness of this method through an example application. This article uses the PEDL dataset for experimental research, Through the comparison of fault data, it can be seen that compared with existing methods, the method proposed in this paper achieves the highest goodness of fit for warning, indicating the best fault warning effect.When there is enough training set, the prediction accuracy of the fault set can reach over 99%, Based on experimental analysis, it can be concluded that the proposed power grid equipment model has higher accuracy and reliability compared to traditional models. And the model in this article integrates the real-time monitoring function of power grid equipment and the equipment fault warning function, which improves the practicality of the power grid equipment monitoring system.

State grid Tianjin electric power company carries out lean grid management research based on grid management statistical analysis based on power grid equipment operation efficiency analysis platform. Firstly, this paper introduces the construction process of power grid equipment operation efficiency analysis platform. Secondly, this paper introduces lean management strategy of power grid, based on the constructed operation efficiency analysis platform of power grid equipment, which deepens its application in power grid management. Finally, this paper analyzes the effect of the project results, from the aspects of economic benefit and social benefit. This project can realize lean analysis of grid equipment running efficiency and asset efficiency, and to play a better role for data statistics, on supporting effect of operation and management decision making.

With the integration of power grid developing further, some demand such as power grid analyses decisions centrally has become more and more urgent. At present, the information resources for the use of Power grid equipment monitoring analysis is becoming abundant, but these information, which exists in every professional system independently, is lack of connection and meets the demand of big data analysis on power equipment monitoring hardly. In this paper, a unified information model, modeled by IEC61970 common information model and IEC61850 hierarchical model, is proposed for the analysis of big data monitoring in power grid equipment, which can establish the first and second Equipment Association within the scope of the grid equipment monitoring data, as well as realize centralized access to all measurement information in the method of extending measurement model. This is significant to promote the process of deepening application of power grid equipment monitoring.

With the application and popularization of distributed power supply and power electronic equipment in power grid, AC distribution is faced with problems such as low efficiency and low power quality. At the same time, due to the advantages of large power supply capacity, long power supply radius, high efficiency, good controllability and adaptability to renewable energy access, DC power distribution has become a crucial development direction of the future distribution system. Reasonable and systematic analysis of power quality problems of DC distribution network plays an important role in its construction and application. Firstly, this paper introduces the background, significance and status of the research on DC distribution network and its power quality. Then introduces the three main topology of DC distribution network structure and connection mode. After selecting pseudo bipolar structure to build the simulation circuit, the power quality phenomena such as voltage sag are simulated and analyzed by PSCAD. The results obtained are consistent with theoretical analysis.

With the increasing number of power electronic devices and renewable energy units in the power network, wide frequency oscillation accidents occur frequently in China and abroad. The oscillation frequency covers several hertz to several thousand hertz, which causes great harm to the safe and stable operation of the units. As the most important equipment in power grid, the safety and stability of synchronous generator under wide frequency is very important. In this paper, the response of synchronous generator as load to wide frequency oscillation is studied, and the formula for calculating the impedance of synchronous generator is deduced and verified. Then the reason of shaft torsional vibration caused by synchronous generator as oscillation source is introduced, and the equivalent impedance response characteristic of synchronous generator at this time is given.

Aiming at the problem that the traditional fault monitoring method of mine power supply equipment is single and can not effectively reflect the real-time working state of equipment and cables, this project studies a comprehensive intelligent fault diagnosis and monitoring early warning technology method. Through the research of two key technologies, power quality analysis technology based on harmonic method and insulation condition monitoring technology of electrical equipment, an intelligent diagnosis system for the health condition of mine power supply equipment is designed and formed. Through real-time monitoring of power grid harmonic parameters, power grid operation parameters, insulation parameters, and temperature parameters, the system can comprehensively analyze and judge the working status of various equipment and connecting cables in the underground power grid to comprehensively judge the faults of high explosive switches, transformers, and connecting cables, and achieve the purpose of fault location and early warning.

One well-adopted power grid simulation methodology is to factorize matrix once and perform only backward/forward substitution with a deliberately chosen step size along the simulation. Since the required simulation time is usually long for the power grid design, the costly factorization is amortized. However, such fixed step size cannot exploit larger step size for the low frequency response in the power grid to speedup the simulation. In this work, we utilize the matrix exponential method with the rational Krylov subspace approximation to enable adaptive step size in the power grid simulation. The kernel operation in our method only demands one factorization and backward/forward substitutions. Moreover, the rational Krylov subspace approximation can relax the stiffness constraint of the previous works [12][13]. The cheap computation of adaptivity in our method could exploit the long low-frequency response in a power grid and significantly accelerate the simulation. The experimental results show that our method achieves up to 18X speedup over the trapezoidal method with fixed step size.

As the size and complexity of current VLSI circuits grows, faster power grid simulation is becoming more and more desirable. In this article, we present a parallel iterative method for static VLSI power grid simulation. In the proposed enlarged-partition-based preconditioned conjugate gradient (EPPCG) power grid solver, the power grid is divided into disjoint partitions that are subsequently enlarged to obtain accurate solution within each partition. The global solution obtained by solving enlarged partition problems concurrently acts as a highly effective parallel preconditioner. The combination of effective preconditioning and efficient parallelization helps achieve very high performance. The experiments show that our parallel implementation can achieve significant speed improvement [61X--142X] over a state-of-the-art direct solver.

Over the last decade, the Common Information Model (CIM), as specified by IEC 61970 / 61968, has become an important document format for the storage of power grid data. Its object-oriented design makes it easily maintainable and extensible for many use cases in the energy sector. As a result, more and more power grid analysis and simulation tools allow the import and export of CIM based power grid data. Unfortunately, many of them are proprietary and therefore not convenient in the research area since their component models and numerical back-ends often cannot be modified by the user. Thus, open-source alternatives are in demand, such as simulation environments based on the popular modeling language Modelica. Therefore, this paper presents our approach of a template based CIM to Modelica converter. The usage of templates makes it easily adaptable for the generation of Modelica system models targeting arbitrary Modelica libraries. The presented approach is implemented in an open-source project called CIMverter, evaluated on a real-world case with two Modelica power system libraries, and validated against a proprietary simulation tool.

A methodology for measurement-based load modeling based on integrating dynamic models is presented in this paper. A composite load model (CLM) and an exponential recovery load (ERL) model are combined and used as a model structure for load representation coupled with a heuristic criterion. An improved particle swarm optimization (IPSO) algorithm that considers a speed update modification to enhance its global search capabilities is used to identify the model's parameters. Several simulations are performed in two test systems, using load response measurements after small and severe disturbances, considering noise measurement to simulate monitoring devices. Results verify that the proposed load model has a better performance than the individual load models, and also IPSO algorithm is adequate for parameter identification.

Electric power has become an essential part of daily life: we plug our electronic devices in, switch our lights on, and expect to have power. As the availability of power is usually taken for granted in modern societies, we mostly feel annoyed at its absence and perceive the importance of power during outages which have severe effects on the public order. Blackouts have had disastrous consequences for many countries and they continue to occur frequently. Such examples demonstrate the necessity for careful analysis and planning of power grids, to ultimately increase the reliability of power grids. The power grids have evolved due to economic, environmental and human-caused factors. In addition to the contingency analysis, nowadays, the operation and planning of power grids are facing many other challenges (such as demand growth, targeted attacks, cascading failures, and renewable energy integration). Thus, many questions arise, including: which buses (nodes) to connect with a new line (link)? What are the impacts of malicious attacks on power grids? How may an initial failure result in a cascade of failures? How to prepare for the integration of renewable energy? Answering such questions requires developing new concepts and tools for analysing and planning of power grids. Power grids are one of the largest and the most complex man-made systems on earth. The complex nature of power grids and its underlying structure make it possible to analyse power grids relying on network science. The applications of network science on power grids have shown the promising potential to capture the interdependencies between components and to understand the collective emergent behaviour of complex power grids. This thesis is motivated by the increasing need of reliable power grids and the merits of network science on the investigation of power grids. In this context, relying on network science, we model and analyse the power grid and its near-future challenges in terms of line removals/additions, malicious attacks, cascading failures, and renewable integration.

Pervasive computing emphasizes the integration of computing and the environment. In this computing model, people can get information and process knowledge at any time, in any way. But the traditional knowledge representation methods can not be applied to pervasive computing directly. Agent technology is originated from the research of distribution in artificial intelligence, and the BDI theoretical model of Agent is considered one of the foundations of distributed artificial intelligence theories. BDI agent can sense environment, get information, store, use, exchange knowledge. This paper proposed a agent-based knowledge representation, focusing on basic concepts, basic framework and the key points of the agent-based knowledge representation. Taking power grid as an illustration, it describes how to use agent-based knowledge representation to represent the diverse equipments in power grid, applying the knowledge to pervasive computing. We can find that the agent-based knowledge representation has advantages in the pervasive computing.