Jianguo Zhao

Bio: Jianguo Zhao is an academic researcher from Shandong University. The author has contributed to research in topics: Thyristor & Fault (power engineering). The author has an hindex of 7, co-authored 13 publications receiving 150 citations.

Distribution network reconfiguration for load balancing using binary particle swarm optimization

Abstract: In this paper, a method based on modified binary particle swarm optimization (BPSO) is proposed for distribution network reconfiguration with the objective of load balancing. A novel model to simplify distribution network is presented. The feeder reconfiguration problem is formulated as a non-linear optimization problem, and BPSO is used to find the optimal solution. According to the characteristics of distribution network, some modifications are done to retain the radial structure and reduce searching requirement. Test results based on a sample network have shown that the proposed feeder reconfiguration method can effectively keep load balancing, and the BPSO technique is efficient in searching for the optimal solution.

Power load forecasting algorithm based on wavelet packet analysis

Abstract: 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.

A new data compression algorithm for power quality online monitoring

Abstract: 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 evaluation of IEC 61850 process bus architecture on numerical protection systems

Abstract: The deployment of IEC 61850 process bus in substations calls for an impact evaluation of possible process bus architecture scenarios on the performance of numerical protection systems. Six alternative system architectures are proposed based on a component redundancy perspective. The functionality of each system is assessed using reliability block diagrams based on a success-oriented network. System reliability is calculated using the minimal tie sets method determined by the connection matrix. Sensitivity analysis is then carried out on the results using two approaches. One highlights those components with the greatest impact on system reliability and the other the importance of the task performed by each component in terms of how it affects the functionality of the system.

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

Abstract: 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.

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Abstract: Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks

Abstract: The paper addresses the problem of predicting hourly load demand using adaptive artificial neural networks (ANNs). A particle swarm optimization (PSO) algorithm is employed to adjust the network's weights in the training phase of the ANNs. The advantage of using a PSO algorithm over other conventional training algorithms such as the back-propagation (BP) is that potential solutions will be flown through the problem hyperspace with accelerated movement towards the best solution. Thus the training phase should result in obtaining the weights configuration associated with the minimum output error. Data are wavelet transformed during the preprocessing stage and then inserted into the neural network to extract redundant information from the load curve. This results in better load characterization which creates a more reliable forecasting model. The transformed data of historical load and weather information were trained and tested over various periods of time. The generalized error estimation is done by using the reverse part of the data as a ldquotestrdquo set. The results were compared with traditional BP algorithm and offered a high forecasting precision.

Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization

Abstract: Interruptible loads represent highly valuable demand side resources within the electricity industry. However, maximizing their potential value in terms of system security and scheduling is a considerable challenge because of their widely varying and potentially complex operational characteristics. This paper investigates the use of binary particle swarm optimization (BPSO) to schedule a significant number of varied interruptible loads over 16 h. The scheduling objective is to achieve a system requirement of total hourly curtailments while satisfying the operational constraints of the available interruptible loads, minimizing the total payment to them and minimizing the frequency of interruptions imposed upon them. This multiobjective optimization problem was simplified by using a single aggregate objective function. The BPSO algorithm proved capable of achieving near-optimal solutions in manageable computational time-frames for this relatively complex, nonlinear and noncontinuous problem. The effectiveness of the approach was further improved by dividing the swarm into several subswarms. The proposed scheduling technique demonstrated useful performance for a relatively challenging scheduling task, and would seem to offer some potential advantages in scheduling significant numbers of widely varied and technically complex interruptible loads.

An AIS-ACO Hybrid Approach for Multi-Objective Distribution System Reconfiguration

Abstract: This paper proposes a hybrid algorithm based on artificial immune systems and ant colony optimization for distribution system reconfiguration, which is formulated as a multi-objective optimization problem The algorithm maintains a population of candidate solutions called antibodies The search space is explored by means of the hypermutation operator that perturbs existing antibodies to produce new ones A table of pheromones is used to reinforce better edges during hypermutation An added innovation is the use of the pheromones to obtain quick solutions to restore the distribution system under contingency situations The hybrid approach has been successfully implemented on two test networks The results obtained demonstrate the efficacy of the algorithm

Robust Distribution Network Reconfiguration

Abstract: We propose a two-stage robust optimization model for the distribution network reconfiguration problem with load uncertainty. The first-stage decision is to configure the radial distribution network and the second-stage decision is to find the optimal a/c power flow of the reconfigured network for given demand realization. We solve the two-stage robust model by using a column-and-constraint generation algorithm, where the master problem and subproblem are formulated as mixed-integer second-order cone programs. Computational results for 16, 33, 70, and 94-bus test cases are reported. We find that the configuration from the robust model does not compromise much the power loss under the nominal load scenario compared to the configuration from the deterministic model, yet it provides the reliability of the distribution system for all scenarios in the uncertainty set.