Partial discharges (PD) generate wideband radio frequency interference and, consequently, can be detected using radio receiving equipment. Due to the advances in ultra-high-speed sampling equipment, it is possible to accurately measure the propagation of the PD wavefront as it passes through a 4 element antenna array. From these measurements, the three-dimensional position of the PD source can be calculated using an iterative algorithm. The locating equipment is suitable for use within the vicinity of energized high-voltage plant and can locate sources up to 15 m from the array. Results are presented showing the location ability of the equipment under laboratory and field conditions. A significant advantage is the ability to detect PD sources in energized plant without the need for outages or electrical connections.

Partial discharges (PDs) generate wideband radio-frequency interference which can be used for noninvasive monitoring of discharges. This paper presents a novel method based on this principle for PD monitoring of substations. The significant advantage of this method lies in the ability to detect PD sources in energized equipment anywhere within a substation compound during normal operating conditions. The results obtained from the prototypes installed in the U.K. and U.S. substations are reported. Results include correlation with apparent charge and daily recordings obtained before, during, and after the failure of a 132-kV current transformer and 69-kV voltage transformer.

A novel approach to handover management for Long-Term Evolution (LTE) femtocells is presented. Within LTE, the use of self-organizing networks (SONs) is included as standard, and handover management is one of its use cases. Base stations can autonomously decide whether handover should take place and assign the values of relevant parameters. Due to the limited range of femtocells, handover requires more delicate attention in an indoor scenario to allow for efficient and seamless handover from indoor femtocells to outdoor macrocells. As a result of the complexity of the indoor radio environment, frequent ping-pong handovers between the femtocell and macrocell layers can occur. A novel approach requiring a small amount of additional processing using neural networks is presented. A modified self-organizing map (SOM) is used to allow a femtocell to learn the locations of the indoor environment from where handover requests have occurred and, based on previous experience, decide whether to permit or prohibit these handovers. Once the regions that coincide with unnecessary handovers have been detected, the algorithm can reduce the total number of handovers that occur by up to 70% while still permitting any necessary handover requests to proceed. By reducing the number of handovers, the overall efficiency of the system will improve as the consequence of a reduction in associated but unnecessary signaling. Using machine learning for this task complies with the plug-and-play functionality required from SONs in LTE systems.

Partial discharge (PD) is regarded as a precursor to plant failure and therefore, an effective indication of plant condition. Locating the source of PD before failure is key to efficient maintenance and improving reliability of power systems. This paper presents a low cost, autonomous partial discharge radiolocation mechanism to improve PD localization precision. The proposed radio frequency-based technique uses the wavelet packet transform (WPT) and machine learning ensemble methods to locate PDs. More specifically, the received signals are decomposed by the WPT and analyzed in order to identify localized PD signal patterns in the presence of noise. The regression tree algorithm, bootstrap aggregating method, and regression random forest are used to develop PD localization models based on the WPT-based PD features. The proposed PD localization scheme has been found to successfully locate PD with negligible error. Additionally, the principle of the PD location scheme has been validated using a separate test dataset. Numerical results demonstrate that the WPT-random forest PD localization scheme produced superior performance as a result of its robustness against noise.