Huo-Ching Sun

Fault Diagnosis of Power Transformers Using Rough Set Theory

This paper has presented an effective and efficient approach to extract diagnosis rules from inconsistent and redundant data set of power transformers using rough set theory. The extracted diagnosis rules can effectively reduce space of input attributes and simplify knowledge representation for fault diagnosis. The fault diagnosis decision table is first built through discretized attributes. Next, the genetic algorithm based optimization process is used to obtain the minimal reduct of symptom attributes. Finally, the rule simplification process is adapted to achieve the maximal generalized decision rules derived from inconsistent and redundant information. Experimental results demonstrate that the proposed approach has remarkable diagnosis accuracy than the existing method.

Data mining for oil-insulated power transformers: an advanced literature survey

Knowledge discovery in database and data mining (DM) have emerged as high profile, rapidly evolving, urgently needed, and highly practical approaches to use dissolved gas analysis (DGA) data to monitor conditions and faults in oil‐immersed power transformers. This study reviews different DM approaches to oil‐immersed power transformer maintenance by discussing historical developments and presenting state‐of‐the‐art DM methods. Relevant publications covering a broad range of artificial intelligence methods are reviewed. Current approaches to the latter method are discussed in the field of DM for oil‐immersed power transformers. In this paper, various DM approaches are discussed, including expert systems, fuzzy logic, neural networks, classification and decision, and hybrid intelligent‐based diagnostic systems that apply the DGA database.

A New Algorithm for Power System Scheduling Problems

This paper presents a parallel-refined simulated annealing (PRSA) approach for the generator maintenance scheduling (GMS) problem to maximize profit objective function. The merit of simulated annealing (SA) is preserved and the deficiency is pruned away in the proposed PRSA. The proposed PRSA is an effective method because the multiple search trajectories parallel and refined in each stage. The near optimal solution for each trajectory in current state can be derived from the local optimal solutions by using the parallel optimization process. The proposed parallel searching strategy can obtain optimal or near-optimal solution for the GMS problem. This paper has demonstrated the effectiveness and feasibility of applying the proposed approach for the 50-unit GMS problem.

Vibration Fault Diagnosis of Rotating Machinery in Power Plants

This paper presents a novel data mining approach for fault diagnosis of turbine-generator units. The proposed rough set theory based approach generates the diagnosis rules from inconsistent and redundant information using genetic algorithm and process of rule generalization. In this paper, a fault diagnosis decision table is obtained from discretization of continuous symptom attributes in the data set. Then, the proposed genetic algorithm is used to achieve the minimal reduct from the discretized symptom attributes. In addition, a set of maximal generalized decision rules is obtained from the proposed rule generalization process.

Applications of Simulated Annealing-Based Approaches to Electric Power Systems

In the last decade, many heuristic methods have evolved for solving optimization problems that were previously difficult or impossible to solve. These methods include simulated annealing (SA), tabu search (TS), genetic algorithm (GA), differential evolution (DE), evolutionary programming (EP), evolutionary strategy (ES), ant colony optimization (ACO), and particle swarm optimization (PSO). This chapter reviews papers in international journals that present the SA-based methods for electric power system applications.

Intelligent Data Mining Approach for Fault Diagnosis

This paper presents wavelet analysis and statistical techniques for assessing the insulation condition of power cables. A specific fault is made and placed on the terminal joint of a 25 kV power cable, and the deterioration phenomena is accelerated by the overvoltage method. The deterioration phenomena of the internal insulation material are explained by wavelet analysis and statistical techniques using partial discharge (PD) current pulse waveforms. The PD value reaches its maximum level, and average discharge level rises, before insulation breakdown. However, the discharge numbers and the equivalent time-length of partial discharge current pulse waveforms fall, causing a current pulse with a large amplitude, and a short time period in the final stage of PD. The proposed method is demonstrated to be effective and feasible.

Energy Management Technologies for Smart Home Applications

This paper reviews previous and recent trends in energy management systems (EMS) and energy information communication technologies (EICT) for smart home applications. Relevant EMS and EICT publications on smart homes are reviewed. This paper first analyzes different energy management approaches for smart home applications, including fuzzy logic, neural networks, heuristic methods, and evolution-based approaches. Then, various EICT approaches are surveyed to evaluate the feasibility of smart home applications by discussing historical developments and introducing advanced EICT methods. Importantly, this paper contributes to efforts to further advanced energy management technologies for smart home applications.

Fault diagnosis using hybrid artificial intelligent methods

This paper presents genetic-based neural networks (GNNs) for fault diagnosis of power transformers. The GNNs automatically tune the network parameters, connection weights and bias terms of the neural networks, to yield the best model according to the proposed genetic algorithm. Due to the global search capabilities of the genetic algorithm and the highly nonlinear mapping nature of the neural networks, the GNNs can identify complicated relationships among dissolved gas contents in transformer oil and corresponding fault types. The proposed GNNs have been tested on the Taipower Company diagnostic records and compared with the fuzzy logic diagnosis system, artificial neural networks and the conventional method. The test results show that the proposed GNNs improve the diagnosis accuracy and the learning speed of the existing approaches.