Phase imbalance in the UK and European low voltage (415V, LV) distribution networks causes additional energy losses.A key barrier against understanding the imbalanceinduced energy losses is the absence of high-resolution time-series data for LV networks. It remains a challenge to estimate imbalance-induced energy losses in LV networks that only have the yearly average currents of the three phases. To address this insufficient data challenge, this paper proposes a new customized statistical approach, named as the CCRE (Clustering, Classification, and Range Estimation) approach. It finds a match between the network with only the yearly average phase currents (the data-scarce network) and a cluster of networks with time series of phase current data (data-rich networks). Then CCRE performs a range estimation of the imbalance-induced energy loss for the cluster of data-rich networks that resemble the data-scarce network. The Chebyshev's inequality is applied to narrow down this range, which represents the confidence interval of the imbalance-induced energy loss for the data-scarce network. Case studies reveal that, given such few data from the data-scarce networks, more than 80% of these networks are classified to the correct clusters and the confidence of the imbalance-induced energy loss estimation is 89%.

Phase unbalance is widespread in the distribution networks in the UK, continental Europe, US, China, and other countries. First, this paper reviews the mass scale of phase unbalance and its causes and consequences. Three challenges arise from phase rebalancing: the scalability, data scarcity, and adaptability (towards changing unbalance over time) challenges. Solutions to address the challenges are: 1) using retrofit-able, maintenance-free, automatic solutions to overcome the scalability challenge; 2) using data analytics to overcome the data-scarcity challenge; and 3) using phase balancers or other online phase rebalancing solutions to overcome the adaptability challenge. This paper categorizes existing phase rebalancing solutions into three classes: 1) load/lateral re-phasing; 2) using phase balancers; 3) controlling energy storage, electric vehicles, distributed generation, and micro-grids for phase rebalancing. Their advantages and limitations are analyzed and ways to overcome the limitations are recommended. Finally, this paper suggests future research topics: 1) long-term forecast of phase unbalance; 2) whole-system analysis of the unbalance-induced costs; 3) phase unbalance diagnosis for data-scarce LV networks; 4) technocommercial solutions to exploit the flexibility from large threephase customers for phase balancing; 5) the optimal placement of phase balancers; 6) the transition from single-phase customers to three-phase customers.

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The superconducting coils in winding of large-scale devices work in kind of harsh environment from both temperature – considering liquid hydrogen or gashouse helium as coolant – (thermal stress) and electro-magneto-mechanical stress, point of views. Reliable operation of the coils in winding is of vital importance for reliability of superconducting device and safety of the application that the device is used in. If superconducting coil confronts with a fault or an abnormal operation in laboratory-level operation, it is straightforward to test the coil by measuring its critical current, AC loss, and etc, to find whether it is damaged or not. However, there would be an urgent need to have faster and more intelligent approaches with a possibility to become fully autonomous and real-time, in large-scale power applications especially in sensitive applications such as in future cryo-electric aircraft, or in fusion industry. To reach such intelligent fault-finding approaches, artificial intelligence-based techniques have been foreseen to be a promising solution. In this paper, we have developed an intelligent fault detection technique for finding a faulty superconducting coil, named the frequency-temporal classification method. This method has two main steps: first, this paper utilizes the Discrete Fourier Transform and Independent Component Analysis to convert measurement signals of the healthy and faulty coils from 1) the time-series domain to the frequency domain; and 2) into time-series source signals. Second, this paper trains the support-vector machine using the derived frequency-components. This trained model is then used for making fault detection for other superconducting coils with voltage signal data only. The developed technique can classify a fault with 99.2% accuracy. The results of proposed method in this paper has been compared with some other techniques to prove its effectiveness.