Study on refined control and prediction model of district heating station based on support vector machine

Yuan, Jianjuan; Wang, Chendong; Zhou, Zhi‐Hua

doi:10.1016/j.energy.2019.116193

Cited by 39 publications

(9 citation statements)

References 28 publications

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“…For example, De Somer et al [41] proposed a reinforcement learningbased approach for optimal scheduling of the heating cycles of the domestic hot water. In another work [42], a prediction model of secondary supply temperature is built using indoor temperature and building thermal inertia to achieve more refined control. On the other hand, Potočnik et al [43] focuses on assessing the quality and condition of valves installed in district heating systems.…”

Section: Review Of Recent Research Articlesmentioning

confidence: 99%

Opportunities for Machine Learning in District Heating

et al. 2021

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The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.

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Section: Review Of Recent Research Articlesmentioning

confidence: 99%

Opportunities for Machine Learning in District Heating

et al. 2021

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“…SVM forms the hyperplane between the two classes and adjusts the boundary by expanding the distance between the two classes [57]. SVM uses the kernel functions [58] for the nonlinear and inseparable dataset cases. This study utilizes the RBF kernel function due to its higher robustness and infinite smoothness.…”

Section: Machine-learning Classifiersmentioning

confidence: 99%

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

2021

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Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

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“…In the case of linear non-separable data, SVM uses kernel functions, and these kernel functions have a significant influence on the performance of the model. The most commonly used kernel functions are as follows [51]:…”

Section: Svm Classifiermentioning

confidence: 99%

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

Khalid

Lim

Kim

et al. 2020

Sensors

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Boiler waterwall tube leakage is the most probable cause of failure in steam power plants (SPPs). The development of an intelligent tube leak detection system can increase the efficiency and reliability of modern power plants. The idea of e-maintenance based on multivariate algorithms was recently introduced for intelligent fault detection and diagnosis in SPPs. However, these multivariate algorithms are highly dependent on the number of input process variables (sensors). Therefore, this work proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler waterwall tube leakage. Finally, a real SPP test case is employed to validate the proposed model’s effectiveness. The results indicate that the proposed model can successfully detect waterwall tube leakage with improved accuracy vs. other comparable models.

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Study on refined control and prediction model of district heating station based on support vector machine

Cited by 39 publications

References 28 publications

Opportunities for Machine Learning in District Heating

Opportunities for Machine Learning in District Heating

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Intelligent Steam Power Plant Boiler Waterwall Tube Leakage Detection via Machine Learning-Based Optimal Sensor Selection

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