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Concrete semi-submersible floating wind turbines (FWTs) have generated increasing interest in the offshore wind energy industry due to their superiority over steel structures in aspects of construc...

When nuclear power plants (NPPs) are in a state of failure, they may release radioactive material into the environment. The safety of NPPs must thus be maintained at a high standard. Online monitoring and fault detection and diagnosis (FDD) are important in helping NPP operators understand the state of the system and provide online guidance in a timely manner. Here, to mitigate the shortcomings of process monitoring in NPPs, five-level threshold, qualitative trend analysis (QTA), and signed directed graph (SDG) inference are combined to improve the veracity and sensitivity of process monitoring and FDD. First, a three-level threshold is used for process monitoring to ensure the accuracy of an alarm signal, and candidate faults are determined based on SDG backward inference from the alarm parameters. According to the candidate faults, SDG forward inference is applied to obtain candidate parameters. Second, a five-level threshold and QTA are combined to determine the qualitative trend of candidate parameters to be utilized for FDD. Finally, real faults are identified by SDG forward inference on the basis of alarm parameters and the qualitative trend of candidate parameters. To verify the validity of the method, we have conducted simulation experiments, which comprise loss of coolant accident, steam generator tube rupture, loss of feed water, main steam line break, and station black-out. This case study shows that the proposed method is superior to the conventional SDG method and can diagnose faults more quickly and accurately.

Modern science and technology is a weapon against natural disasters. Shenzhen is a city of technology and innovation and we should endeavor to address flood control, drought relief and wind mitigation (named “three-prevention” in this context). In particular, we should vigorously promote the improvement of technology, and make use of modern information technologies such as big data, internet of things, cloud computing and artificial intelligence to create intelligent three-prevention system. This paper described in detail the characteristics and the construction status of the intelligent three-prevention system as well as the future development direction, in order to achieve the construction goals of the overall situation probing of the three-prevention, real-time decision-making assistance, flat command and control and urban resilience development. Since the intelligent three-prevention system’s launch in April 2020, it has started more than 20 times of emergency response against typhoon and flood. The whole process functions of front-end intelligent perception, fine dynamic simulation, real-time forecast and early warning, emergency command and post-disaster assessment have been preliminarily realized.

It is of great significance for load monitoring to monitor illegal use of electricity. Load monitoring can provide supervision for government and improve residents' awareness of safety. Compared with the intrusive load monitoring, non-intrusive load monitoring has the advantages of good economy, high reliability, and quickly realizing the electricity decomposition. Many scholars have carried out this research, but there still exists the problems: it is difficult to obtain key information from big data and the diagnostic results are inaccuracy. Therefore, load monitoring is conducted in this paper. To overcome the above shortcomings, firstly this paper obtains key information of sample based on harmonic analysis. Secondly, random forest based on multiple decision trees has better accuracy in recognition. So the random forest algorithm is applied to machine learning and pattern recognition. Finally, the proposed method is identified and analyzed. The results show that the accuracy of the online electrical detection based on the harmonic analysis and random forest algorithm is greater than 86%, which shows the effectiveness of the method.

When nuclear power plants (NPPs) are in failure state, it may release radioactive substance into the environment. Thus, the safety of NPPs is put forward a high standard. Fault detection and diagnosis (FDD) are significant for NPPs to help operator timely know the state of system and provide the online guidance. Fault diagnosis can improve the safety of nuclear power plants, but current fault diagnosis methods pay too much attention to accuracy of diagnostic results. As a complex industrial system, how to explain the causes of faults in NPPs becomes more important. Although there are many studies on the knowledge graph, there is no detailed description on the failure process (consider timing). This paper proposed a three-layer structure for FDD in NPPs. Each layer represents the stage of the accident, it can give the operator a clear cognitive process to faults. The three-layer structure includes “smooth layer”, “threshold layer” and “fault layer”. The three layers indicate the reason of faults, the response of the parameters at each stage, and clearly showing the accident process. The smooth layer uses the stability analysis to analyze whether the current NPP is operating abnormally; the threshold layer uses the thresholds of the NPP to monitor which parameters have exceeded the upper limit or the lower limit; the fault layer reflects what is happening in the current operation and accidents are explained using signed directed graph. This paper takes the loss of coolant accident as an example, three-layer structure is analyzed, which shows the feasibility of the method. The case shows that the proposed method is superior to the conventional SDG method, can diagnose the faults, and give the reason of diagnosis results.

Fault tree analysis (FTA) is one of the most important methods of probabilistic risk assessment (PRA). The fault state of the system is taken. While traditional FTA is based on static failure model. FTA is not applicable for systems that include redundant, sequence-related systems. At the same time, nuclear power plants (NPPs) contains a large number of redundant equipment, and FTA is difficult to solve these dynamic problems. Therefore, it is necessary to use dynamic fault tree analysis (DFTA) for PRA. In DFTA research, the modular analysis method was first proposed. The modular method divides the dynamic fault tree into a dynamic fault tree and a static fault tree. Among them, the dynamic fault tree is analyzed using a Markov chain, and the static fault tree is studied using a binary decision diagrams method. However, the shortcomings are that when the system is complicated, the information explosion in the Markov chain is appeared. To solve this problem, a dynamic fault tree is transformed into a Bayesian network. At the same time, to verify the feasibility of the method, Monte Carlo random sampling was used to evaluate the method. Other methods are relatively infrequently studied. In this paper, firstly, status of dynamic fault trees has been investigated. Secondly, each method is analyzed and the problems of dynamic fault tree are described. Finally, a survey and analysis on the dynamic fault tree is finished, and the main problems of the dynamic fault tree are: information explosion; the lack of commercial software to apply to engineering. Through this review, we hope to play a certain guiding role in the subsequent research on dynamic fault trees.

A procedure for identifying the dynamic parameter of offshore platform is presented. The present procedure consists of two key features. First uses random decrement (RD) technology to extract free vibration signal in strong noise environment in which it may not white noise. Second technology which called autoregressive moving average (ARMA) was used to model the data treated by the random decrement method. In order to get rid of the color noise in the output signal response from the offshore platform an imaginary system is added in RD system and make the course of extracting performed under the state of color input by choosing the breakover condition and lead time. For eliminating multi-values of parameters identified, an updating moving average method is used. The dynamic parameters of structure under arbitrary input are identified. Example of the method as applied to a scale-model offshore platform was used to evaluate the technology of efficiency and the value of on-line.