HVAC 시스템의 중복고장 검출을 위한 실험적 연구

조성환,홍영주,양훈철,안병천 대한설비공학회 2004 설비공학 논문집 Vol.16 No.10

The objective of this study is to detect the multi-fault of HVAC system using a new pattern classification technique. To classify the effect of single-fault in determining the pattern, supply air temperature, OA-damper, supply fan, and air flowrate were chosen as experimental parameters. The combination of supply temperature, flow rate, supply fan and OA- damper were chosen as multi-fault conditions. Three kinds of patterns were introduced in the analysis of multi-fault problem. To solve multi-fault problem, the new pattern classification technique using residual ratio analysis was introduced to detect the multi-fault as well as single- fault. The residual ratio could diagnose single-fault or multi-fault into several patterns.

MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines

김형민,Park Chan Hee,Suh Chaehyun,채민석,Yoon Heonjun,Youn Byeng D. 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.2

Multi-scale convolutional neural network structures consisting of parallel convolution paths with different kernel sizes have been developed to extract features from multiple temporal scales and applied for fault diagnosis of rotating machines. However, when the extracted features are used to the same extent regardless of the temporal scale inside the network, good diagnostic performance may not be guaranteed due to the influence of the features of certain temporal scale less related to faults. Considering this issue, this paper presents a novel architecture called a multi-scale path attention residual network to further enhance the feature representational ability of a multi-scale structure. Multi-scale path attention residual network adopts a path attention module after a multi-scale dilated convolution layer, assigning different weights to features from different convolution paths. In addition, the network is composed of a stacked multi-scale attention residual block structure to continuously extract meaningful multi-scale characteristics and relationships between scales. The effectiveness of the proposed method is verified by examining its application to a helical gearbox vibration dataset and a permanent magnet synchronous motor current dataset. The results show that the proposed multi-scale path attention residual network can improve the feature learning ability of the multi-scale structure and achieve better fault diagnosis performance.

Numerical simulations on fault behaviors of heat pump systems with multi-indoor coils

K. Han 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11

A series of heat pump system simulations are carried out to develop a fault detection method, especially for a system having multi-indoor coils. Five major faults like refrigerant overcharge, undercharge, reduced indoor coil air flow, reduced outdoor coil air flow, and superheat control are systematically varied in three distinctive levels. The system behavior is described with evaporation temperature, compressor discharge temperature, saturated condensing temperature, subcooling, indoor air side temperature rise, and outdoor air side temperature drop by imposing individual and simultaneous faults. The multi-fault behavior cannot be explained with individual fault characteristics. Simple fault prediction models are presented for both single and multi-indoor systems, respectively.

Fault-tolerant Bipartite Output Regulation of Linear Multi-agent Systems with Loss-of-effectiveness Actuator Faults

Jie Zhang,Da-Wei Ding,Xinmiao Sun,Xiangpeng Xie 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.5

In this paper, the fault-tolerant bipartite output regulation problem of linear multi-agent systems with two antagonistic subgroups and loss-of-effectiveness actuator faults has been solved by utilizing the output regulation theory. The aim of our study is to design a fault-tolerant controller such that bipartite tracking for the output of exosystem (also seen as the leader) in the presence of actuator faults can be achieved under a directed signed graph, where both collaboration and competition coexist in a multi-agent environment. Firstly, a simultaneous state and fault estimator is designed based upon local output estimation errors such that loss of actuator effectiveness of all followers can be accurately compensated. Then, a distributed adaptive exosystem observer is provided to obtain the information of exosystem since not all followers can directly access exosystem. By using the estimations of state, fault and exosystem, a new cooperative fault-tolerant bipartite output regulation framework with competitive interactions is eventually presented. Finally, we provide a numerical example to demonstrate the effectiveness of the designed fault-tolerant control law.

A Novel Online Multi-section Weighed Fault Matching and Detecting Algorithm Based on Wide-area Information

Xiaoyang Tong,Wenchao Lian,Hongbin Wang 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.6

The large-scale power system blackouts have indicated that conventional protection relays that based on local signals cannot fit for modern power grids with complicated setting or heavily loaded-flow transfer. In order to accurately detect various faulted lines and improve the fault-tolerance of wide-area protection, a novel multi-section weighed fault matching and detecting algorithm is proposed. The real protection vector (RPV) and expected section protection vectors (ESPVs) for five fault sections are constructed respectively. The function of multi-section weighed fault matching is established to calculate the section fault matching degrees between RPV and five ESPVs. Then the fault degree of protected line based on five section fault degrees can be obtained. Two fault detecting criterions are given to support the higher accuracy rate of detecting fault. With the enumerating method, the simulation tests illustrate the correctness and fault-tolerance of proposed algorithm. It can reach the target of 100% accuracy rate under 5 bits error of wide-area protections. The influence factors of fault-tolerance are analyzed, which include the choosing of wide-area protections, as well as the topological structures of power grid and fault threshold.

3실형 멀티 열펌프 시스템의 고장감지 및 진단에 관한 연구

한융희(Yung Hee Han),이일환(Il Hwan Lee),김민수(Min Soo Kim) 대한기계학회 2008 대한기계학회 춘추학술대회 Vol.2008 No.5

Nowadays, multi type heat pump systems that have one outdoor unit and several indoor units have been developed. Indoor units are inter-connected together, thus change of one indoor unit influences the performance of other indoor units. Capacity control with PI multi-loop controller was carried out for multi type heat pump system. Saturation pressure in the evaporator was suggested for the control variable of compressor speed modulation. An experimental study was performed in this study to develop a method for fault detection and diagnosis with multi type heat pump system. Three major faults were investigated which are indoor fan fault, outdoor fan fault and refrigerant leakage. Rule-based fault classifier was used in order to detect and diagnose the system. The variation of residuals between reference data and measured data were used for the detection of faults. As a result, faults can be detected and diagnosed by measuring evaporation temperature and compressor discharge temperature.

A Decision Tree Based Ultra-high-speed Protection Scheme for Meshed MMC-MTDC Grids with Hybrid Lines

Gaballah Amr,Abu-Elanien Ahmed E. B.,Megahed Ashraf I. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.2

The reliable operation of modular multi-level converter based multi-terminal high voltage direct current (MMC-MTDC) grids requires high-speed and selective DC line protection. A single-ended protection technique is proposed for MTDC grids with ofshore wind farms and hybrid lines that involve overhead lines and submarine cables in series. Positive and negative poles’ currents at one end of each line section are analyzed using discrete wavelet analysis (DWA). To classify fault type and identify fault zone, a fne decision tree is supplied with an energy index and the envelope slop of detail 1 coefcient (D1) obtained from DWA. Only 0.2 ms following the fault inception are needed for calculating the energy index and the envelope slope to perform relay functions. The approach was tested on a three-terminal two-poles ± 400 kV MMC-MTDC model. The simulation results validate the efectiveness of the suggested protection technique under various fault scenarios, even with up to 200 Ω fault resistance. The proposed technique is not only able to detect the faulty line, but also it identifes overhead line faults and submarine cable faults for hybrid type lines. Moreover, the proposed technique is not afected by wind power injection changes, AC faults, or data noise. The simulated model was produced in the MATLAB/Simulink platform

Fault-Tolerant and Reconfiguration Control for Boost Multi-level NPC Converter Fed Doubly Fed Induction Machines

Anto Joseph,Yeongsu Bak,Sze Sing Lee,Kyo-Beum Lee 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5

Double fed induction machine (DFIM) is preferred in various industrial applications, as it provides variable speed operation with reduced power converter rating and high dynamic stability. On rotor side, back-toback voltage source converter (VSC) is act as excitation system and controls real and reactive power of the machine. Boost multi-level neutral point clamped (NPC) VSC is emerging in industrial applications due to the high dc link voltage utilization and less current THD. Full power converter redundancy in large rated VSC fed DFIM is challengeable and it is not yet employed in any practical applications such wind power generation and pumped-hydro power units. Therefore, fault-tolerant control (FTC) of VSC is mandatory for the continuous operation of unit by the project authorities of the units. This paper presents the fault tolerant and reconfiguration control approach for a boost NPC converter fed DFIM unit at open circuit faults in all controlled switches/devices. FTC during open-switch faults are investigated with the help of converter output voltage. Fault detection and isolation is achieved through the rotor currents and dc link voltage. A 2 MW DFIM is simulated in Matlab/Simulink environment to verify the operation of the proposed fault tolerant control.

Fault-Line Selection Method for Small-Current Grounded System Based on Multi-classifier

Su Xianxin,Wei Hua,Wei Hongbo,Lyu Zhongliang,Zhang Xuan,Gao Wei 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2

In a small-current grounded system, the fault characteristics are very obscure when a single-phase ground fault occurs; therefore, the faulty line must be selected to remove it. This paper proposes a fault-line selection (FLS) method based on multi-classifier, which transforms FLS into a multi-classification problem. It solves the problems in traditional methods, such as low accuracy and high equipment cost. Multi-classifiers based on denoising Autoencoder(DAE) are used to reduce the dimension of historical dispatching data and extract single-phase ground-fault features. Firstly, the dispatching data are preprocessed to eliminate useless data and fill in vacancies. Then, the fault segments are marked and labeled samples containing steady-state and transient information of single-phase ground faults are obtained. Finally, a multi-classifier based on DAE is built, and this model is trained with labeled fault samples to obtain a high-accuracy FLS model. The experiments show that the accuracy of the proposed method exceeds 97%, which is much better than other data-driven models and traditional methods. The proposed method has been operating for over two years in a real power system south of China. The excellent performance of the proposed method for FLS in practice and simulation indicates a vast application potential.

Diagnosis of Ship Generator Optimized Neural Network Based on Multi-population Chaos Genetic Algorithm

Ming Yang,Wei-feng SHI 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11

In view of fault diagnosis of the ship generator , the paper proposes improved fault diagnosis method of ship generator ,which is Optimized Neural Network based on Multi-population Chaos Genetic Algorithm. The results prove that the method effectively solves low precision,slow constringency and local minimum of neural network and improves global search ability, optimizes the rate and precision of fault diagnosis. The method has a certain application prospect for the ship power system generator fault diagnosis.