Because of the severe global energy problems, every country has been putting forward to the construction of smart grid. To keep the smart grid operation under safety and reliability, it is necessary to real-time acquire, transfer, storage and analyze the data flow in the power grid. Data analysis that is defined as the theory method of data processing and analyzing is an important technical support for smart grid. It pushes forward the development of smart grid by the ways of digging out the information hidden in the power grid and the deep-level analysis of power data. The most common analytic methods include data mining, data analysis and optimization theory. These analytic methods can be used in the fields, such as load forecast, demand response, power grid disaster warning, electrical equipment condition monitoring, etc..
Because I used to particpate in the PhD joint program, electrical equipment and commercial building load are the research points at the Southeast University and North Carolina State University (NCSU), respecrtively. Electrical equipment is the hardware support of smart grid, of which the major forms of data analysis are condition assessment and fault diagnosis. This paper selects transformer and high voltage circuit breaker (HVCB) to be discussed. The former transforms voltages and transmits power energy while the latter plays an important role in control and protection of power transmission system. These two equipments are essential to the operation of smart grid so that it is necessary to do research on intelligent fault diagnosis. Through automatic analysis and self-study of electrical equipment data, intelligent fault diagnosis could improve the reliability of electrical equipments and promote the development of smart grid.
There exists lots of electrical appliances and distributed energy resources on the commercial buildings so that data analysis of commercial building, such as load forecast, load dispatch, can enhance the energy efficiency and economy of power system. This paper selects load interval forecast, demand response and load dispatch to be discussed. Load interval forecast describes the fluctuating range of predicted results, helping managers master the load uncertainty in future. Demand response refers to a tariff or program coordinated with power market conditions for motivating changes in electricity consumptions by end-use customers. With the centralized control of energy terminal, load dispatch strengthens the stability of smart grid and the ability of power resources allocation. The followings are the content of this paper
In order to solve the problems, which arise in three ratio method applied in transformer fault diagnosis, this paper proposes a comprehensive fault diagnosis based on SVM multi-class probability output and evidence theory. Evidence theory is suitable for uncertainty analysis, fusing various criterions into one uniform conclusion by means of evidence fusion formula. With the help of SVM multi-class probabilistic output, SVM output turns from hard decision to soft decision, providing evidential theory with basic probability assignment. Case study based on dissolved gas validates the reliability, universality and accuracy of the proposed method.
The current research on high voltage circuit break (HVCB) fault diagnosis lacks of unknown fault detection and real-time model classification updating. This paper proposes an adaptive fault diagnosis based on particle swarm optimization (PSO), support vector domain (SVDD) and fuzzy kernel clustering algorithm (KFCM). In the proposed method, PSO-SVDD can detect the unknown fault sample while P-KFCM and modified partition coefficient (MPC) cluster validity analysis are used in known sample category recognition and new fault learning. The simulation results based on HVCB closing coil current demonstrate the effectiveness of the proposed method, compared with the existing algorithms.
At present, the most of short term load forecast (STLF) belong to point load forecast and cannot give the fluctuation range of predicted load. To solve the above problems, this paper proposes a day-ahead commercial load interval forecast based on similar day and kernel density estimation. Similar day unit decomposes point load forecast into daily average load prediction and pre-unit curve search and provides each time node with multiple predictive values. Then, these multiple predictive values are synthesized into load interval by kernel density estimation unit. The decomposition of point load forecast ensures that the two decomposition modules will not interfere with each other. The reason for selecting kernel density estimation is due to its free-form and independence of the sample distribution. The simulation results collected from centennial campus at NCSU demonstrate the universality and strong robustness of the proposed method.
Few load scheduling strategies focus on minimizing the monthly electrical bill under the time-of-use (TOU) rate, and this has received wide attentions from commercial building managers. We present a plug-and-play demand response algorithm for day-ahead commercial building load scheduling on the basis of distributed energy resources and building end-uses. Three daily optimal modes are designed for minimizing the monthly peak load during on-peak hours with consideration of optimizing the daily energy charge. When implementing this scheduling method for everyday in one month, one suitable mode is selected for each day. The use of switching and cooperation among modes can save money both in terms of the monthly charge of energy and demand. Moreover, plug-and-play DR uses a penalty function to adjust the amount of building end-use loads to be shifted before or after the targeted hours. The proposed DR, which is resource agnostic, can be as a generic control method for enhancing building energy management systems. Data collected from the Centennial Campus at North Carolina State University and the Duke Energy commercial TOU rate are used for the case studies. The simulation results show that the proposed method is satisfactory and robust.