Yanqiu Bi

Power load forecasting algorithm based on wavelet packet analysis

This paper investigates the application of wavelet packet in power load forecasting, and it proposes a novel forecasting algorithm based on wavelet packet decomposition and reconstruction. The algorithm uses the biorthogonal wavelet that has linear phase to decompose the load data to extract load components of different frequencies, and then neural network is used to predict the load component of each wavelet packet space. Finally, the load forecasting values of all the spaces are added up to produce the load forecasting result. It is advantageous in analyzing the load characteristics in each time-frequency zone to achieve accurate modeling and forecasting. Case study shows that the proposed algorithm improves forecasting accuracy and it is superior to traditional back-propagation neural network.

Power load forecasting based on grey neural network

To improve the accuracy of power load forecasting, this paper analyzes the defects as well as merits of artificial neural network (ANN) and grey prediction method, and it combines the two methods to propose a novel forecasting method called grey neural network (GNN). GNN utilizes the accumulation generation operation (AGO) of grey prediction to transform the original load data to first order AGO data which has better regularity, making it easier for ANN to model and forecast. At the same time the theoretical error of traditional grey prediction method is avoided. GNN is suitable for middle and long term load forecasting, and case study shows that its forecasting accuracy is better than that of ANN and grey prediction method. The paper also reveals the importance of data transformation in load forecasting process, and it further investigates the effect of inverse transformation on forecasting error.

Communication network of Wide Area Protection System using OPNET simulator

The paper investigates an architecture and communication network of Wide Area Protection System (WAPS). The features of three WAPS architectures, including centralized, distributed and networked, are discussed respectively in detail, and the networked structure is considered to be the best due to its fast response time and open standard. Different communication networks of WAPS are modeled using OPNET simulator, and their performances for relay protection are compared. The results show the networked architecture could satisfy the relay protection requirements of WAPS, and it has less communication delay than the centralized architecture. In order to deal with burst data transmission, the 100Mbps Ethernet is preferable to ensure communication performance.

Single-phase-to-ground fault feeder detection based on transient current and wavelet packet

Single-phase-to-ground fault is the most frequently occurred short-circuit fault in distribution network, and it is urgent to develop new methods to detect fault feeder correctly. The paper compares the fault-feeder selection theories using transient signals with those of using steady signals, and the application of transient signal is emphasized. By analyzing the characteristics of the Peterson-coil-grounded system, the paper proposes a novel algorithm to select fault feeder based on the transient signals and wavelet packet. It records the post-fault transient zero-sequence current of each feeder, and wavelet packet is used to extract the supply fundamental frequency and high frequency components. Finally the current and its components are used to detect fault feeder. This method is accurate and adaptive for the Peterson-coil-grounded system. Case study validates the effectiveness of the new algorithm for various fault modes in neural ineffectively grounded systems.

A new data compression algorithm for power quality online monitoring

The increasing application of power quality data acquisition devices produces large amount of data that should be compressed. The paper investigates the characteristics of power quality data and delta modulation technique, and it proposes a lossless power data compression algorithm based on high-order delta modulation. The compression algorithm carries on multiple differential operations on power quality data, so it could reduce the magnitude of data and requires fewer bits for coding. The proposed algorithm has high compression ratio and little computation requirements. Furthermore, it is suitable for dealing with the power quality data measured at high sampling frequency, and it can work well with traditional compression methods such as Huffman coding. Simulations for several kinds of power quality data confirm its effectiveness and advantages over traditional Huffman coding method.

Impact of interharmonics on the measurement error of AC sampling

Interharmonics exists in power system and it brings negative effect to power quality. The problem of interharmonics has received more and more attention. The paper investigates the impact of interharmonics on measurement error of AC sampling, which is widely used in power data acquisition system. The research reveals that interharmonics, even if its amplitude is small, will bring considerable error, and makes it impossible for data acquisition instruments to assure acceptable accuracy. Traditional one-period sampling method could do little to reduce the measurement error, even if it traces the supply fundamental frequency and increases the sampling rate. The paper suggests adopt multiperiod sampling to reduce the negative effect of interharmonics and measurement error. Simulation validates the effectiveness of the proposed method.

Design and initialization algorithm based on modulus maxima of wavelet transform for wavelet neural network

Wavelet neural network (WNN) has many advantages and it receives wide attention in power system. Based on the problems encountered in application, the paper investigates the defects of WNN. It points out that the characteristics of activation functions of the continuous WNN and the back-propagation (BP) neural network have great differences, while the current continuous WNN uses the random initialization and back-propagation algorithm of BP network. Thus the continuous WNN has poor convergence performance. Referring to the design method of discrete WNN and signal edge detection and reconstruction theory, the paper proposes a design and initialization algorithm for single-input single-output continuous WNN. The novel algorithm utilizes the known data to search the modulus maxima of wavelet transform, then the number of hidden nodes and the initial parameters of continuous WNN can be obtained, which speeds up the training process as well as improves the convergence performance. Case study validates the effectiveness of the proposed method.

Influence of hexadecimal character and decimal character for power waveform data compression

Data compression becomes an increasingly important problem in power quality online monitoring network due to the increasing quantity of sample data. Huffman code algorithm and Huffman code combined with wavelet transform are widely used in data compression. However, the compression ratio (CR) is not high enough because the characteristic of power quality data is not considered. Hence, efficient compression algorithm is required. In this paper, the characteristic of electrical power quality data is described. Through comparing the difference expression between the decimal code and the hexadecimal code, the paper discusses their influence on the compression ratio of power quality data. The algorithm with hexadecimal code expression is better than that with decimal one. Under the same condition the compression ratio using hexadecimal character compression is 3.8% higher than that using decimal character compression. The simulation analysis validates that the algorithm is efficient and feasible.