Qingyan Wu

Bio: Qingyan Wu is an academic researcher. The author has contributed to research in topics: Engineering & Partial discharge. The author has an hindex of 2, co-authored 2 publications receiving 24 citations.

The study of Archimedean spiral antenna for partial discharge measurement

Abstract: Partial discharge (PD) is an important indicator for electrical equipment's insulation property in power industry. Through the measurement of partial discharge we can have a good understanding of the equipment's performance. Ultra High Frequency (UHF) detection techniques are being recently a more and more important method to detect partial discharges. In this paper, a wide bandwidth Archimedean spiral antenna was designed. Its main parameters were analyzed by computer simulation software. A partial discharge test platform was built and three typical partial discharge models were designed. We used the designed Archimedean spiral antenna to detect the three models. The experiment results show that the designed Archimedean spiral antenna has good performance to measure electrical equipment partial discharge signals.

A DDoS attack detection method based on AE network in the internet of vehicles

Abstract: With the rapid development of 5G technology, the Intelligent and Connected Vehicle (ICV) technology is also evolving and expanding its application scenarios. In order to achieve lower latency and reduce the network load caused by massive data reflow in ICV, MEC (Mobile Edge Computing) technology is introduced to support ICV communication. While MEC technology brings a good experience to users, more and more attacks against Telematics come along, the most common of which is DDoS attacks, which can bring huge losses to telematics systems. Based on this, this paper proposes a DDoS attack detection method based on SAE neural network. The method uses the stacked Auto-encoder-based model proposed in the paper to detect network traffic in the telematics network, feeds the traffic data into the test model, and determines whether the automotive network system is under DDOS attack based on a threshold value. The DDoS attack is detected using the method proposed in the paper, with high detection rates in the training and test sets and stable models. Better experimental results were also obtained by later changing the number of hidden layers in the SAE network to detect DDoS attacks. Comparing the method in this paper with the SVM and CNN methods, the experimental results show that the DDoS attack detection method based on SAE networks works best.

Optimisation of a sensor for onsite detection of partial discharges in power transformers by the UHF method

Abstract: Among the different solutions which allow onsite partial discharge measurement in energized power transformers, the UHF technique is gaining general interest. In order to apply this method in existing transformers, it is considered advantageous to design a UHF sensor to be fitted inside the transformer tank. This paper describes the constraints affecting the development of such sensor and the process followed to optimize its design. During this process, different solutions for broadband UHF antennas were analysed. The selection was based on computer simulation and experimental results. Computer simulation was used to evaluate differences in radiation pattern, antenna impedance, gain and effective area. Measurements of the antenna response to different partial discharge sources in oil were performed using an oil dielectric breakdown test set in an electromagnetic shielded laboratory. A group of selected antennas were then tested in a power transformer simulating the broadband signals generated by partial discharges by injecting controlled voltage pulses in the transformer. Finally, a sensor based on a conical monopole antenna structure was built and attached to a specially designed housing to be fitted inside the transformer tank through the drain valve. This final prototype was benchmarked during a high voltage factory test of a power transformer which showed a significant partial discharge activity.

On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals

Abstract: The article presents a novel on-line partial discharge (PD) monitoring system for power transformers, whose functioning is based on the simultaneous use of three unconventional methods of PD detection: high-frequency (HF), ultra-high frequency (UHF), and acoustic emission (AE) It is the first monitoring system equipped in an active dielectric window (ADW), which is a combined ultrasonic and electromagnetic PD sensor The article discusses in detail the process of designing and building individual modules of hardware and software layers of the system, wherein the most attention was paid to the PD sensors, ie, meandered planar inverted-F antenna (MPIFA), high-frequency current transformer (HFCT), and active dielectric window with ultrasonic transducer, which were optimized for detection of PDs occurring in oil-paper insulation The prototype of the hybrid monitoring system was first checked on a 330 MVA large power transformer during the induced voltage test with partial discharge measurement (IVPD) Next, it was installed on a 315 MVA substation power transformer and integrated according to the standard IEC 61850 with SCADA (Supervisory Control and Data Acquisition) system registering voltage, active power, and oil temperature of the monitored unit The obtained results showed high sensitivity of the manufactured PD sensors as well as the advantages of the simultaneous use of three techniques of PD detection and the possibility of discharge parameter correlation with other power transformer parameters

Visual ultrasonic detection method for partial discharge failure detection

Abstract: The invention relates to a visual ultrasonic detection method for partial discharge failure detection. The method is characterized in that an ultrasonic sensor array is firstly adopted to acquire ultrasonic signals generated by power equipment, and ultrasonic intensity space distribution is evaluated; and meanwhile, video signals are acquired on the scene, and the ultrasonic intensity space distribution is visualized on the basis of the acquired ultrasonic signals and video signals, thereby visually displaying the space position of the partial discharge point as an ultrasonic source. By researching the partial discharge failure detection of the power equipment and designing the visual ultrasonic detection according to the distribution characteristics of the generated ultrasonic signals in the time domain and frequency domain, the system can detect the partial discharge phenomenon in the power equipment at high precision.

Quantum genetic algorithm-based converter transformer partial-discharge ultrasonic location method

Abstract: The invention provides a quantum genetic algorithm-based converter transformer partial-discharge ultrasonic location method. Many ultrasonic sensors at different positions of a transformer are adopted to receive ultrasonic signals that are sent by a partial discharge source, and a distance solving model is established by a Cartesian coordinate system. Relevant parameters of the quantum genetic algorithm are initialized. Chromosome is coded, and population Q(t) is initialized. Every individual of the initialized population is measured and a state P(t) is obtained. The fitness to each state is calculated. The optimal individual and its fitness value are recorded. The result is directly output if the termination condition is satisfied. If the termination condition is not satisfied, then t=t+1 is set and every individual of the initialized population is measured to obtain the state P(t). The fitness to each state is calculated, the population individuals are updated by means of quantum rotation gate operation and quantum non-gate to obtain a progeny population Q(t+1), and the optimal individual and its fitness are recorded, until the terminal condition is satisfied. The method of the invention has the characteristics that the iteration frequency is low and the location precision is high under the condition of a small population size, premature convergence is avoided, and rapid convergence is achieved to get the globally optimal solution.