# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Leakage Detection in DHC Networks  
Failures on district heating pipes are often caused by water leaks due to corrosion, mechanical impacts and insufficient or deteriorated performance of the thermal insulation solutions, as indicated by [PERSON] et al. (2012) [103]. However, some degree of leakage is impossible to be avoided during extended operation, since pipeline performance degrades over time. Therefore, an anticipated diagnosis of leakage occurrence is highly necessary to improve efficiency, reduce operating costs and protect the environment. In comparison with DHC networks, both oil/gas and water distribution networks have a longer industrial history. Thus, many established leak detection research results and applications were first applied to such applications. Some representative techniques for pipelines developed for specific fluids (oil, gas or water), different layout patterns, several lengths of pipelines as well as for a certain range of different operating conditions, can be found in [104]. As mentioned above, those methods found in literature are usually divided as \"internally-based\" (or \"software-based\") and \"externally-based\" (or \"hardware-based\"), as presented in the review of [PERSON] et al. (2018) [105] for leakage detection in DH networks. [PERSON] et al. (2020) [102] developed and compared \"software-based\" solutions, both model-based and data-driven, applied through a leakage detection algorithm, whereas a good example of a physical model-based algorithm can be found in [PERSON] et al. (2019) [106]. The latter includes a dynamic monitoring module (DMM) and a static testing module (STM): the DMM can detect larger leakages analyzing pressure waves through amplitude propagation and attenuation models; the STM, based on the pressure loss model, can detect micro-leakages, thus being able to act as an effective compensation for the DMM. As far as data-driven methods are concerned, an application can be found in [PERSON] et al. (2020) [107]. It consists of training a decision-tree-based ensemble ML algorithm called XGBoost, using data generated by a simplified physical model and using it to detect leakage in pipes through the collected data from pressure and flow sensors present in the DHC network and substations. A potential obstacle to replicate this approach is the fact that pressure sensors are not always available in the system. Two interesting proprietary \"hardware-based\" solutions deserve mentioning. One of them has been developed by the smart meter brand Kamstrup and consists of a leakage detection system based on the analysis of the signal coming from ultrasonic flowmeter installed in substations [108]. The other one is based on the well-known impedance method using sensing cables. When a leak takes place, the cable gets saturated with fluid, thus altering its impedance [109]. The advantages include high accuracy in determining leak location and easy configuration and maintenance. In contrast, the installation has very high costs and wiring requirements. In the field of image processing, infrared (IR) sensors are able to capture variations in the heat flow caused by underground fluid leaks, and then show them as hot spots in the DHC system route. This process can be accomplished on the ground, but the availability of high thermal sensitivity and spatial resolution thermal imaging systems mounted on an aerial platform has become the most effective procedure. For instance, data collection can be conducted by an aircraft or drone, which flies over the target area with a camera mounted to the airframe and looking straight down to the ground. This way, thermography reveals sources of heat and the relative differences in temperature from one object to another, as presented in [110]. In contrast, postprocessing of IR images may be computationally expensive, and their analysis could lead to false negatives because some color differences caused by a leakage could be almost inappreciable. Some authors such as [PERSON] et al. (2019) [111] and [PERSON] et al.