For the characteristics of a random distribution and a large number of buses in the power system, the authors introduce distributed compressed sensing to compress and reconstruct the power quality data. They built a distributed IEEE14 bus system in PSCAD. This model was used to analyse the correlation and sparsity of power quality data and to obtain the four types of data that should be used in the latter simulation, the power quality data used in this study are the voltage amplitude of each bus in a power system. To make the signal sparser, they constructed a distributed compressed sensing learning dictionary for power quality data. The simulation results show that the performance of the distributed compressed sensing learning dictionary constructed in this study is more suitable for power quality data. The application of distributed compressed sensing in a power system can ensure the accuracy of reconstructed data when the quantity of data is reduced by 1/3, which greatly reduces the system storage space. Additionally, the speed of reconstruction also increases by 3/5.

Comparison and analysis of the regularised zero forcing precoder, rapid numerical algorithms-based precoder and the truncated polynomial expansion-based precoder are done for massive multiple-input multiple-output wireless system for multicell scenario. The analysis was done for the imperfect channel covariance information. The achievable signal-to-interference-and-noise ratio, spectral efficiency and energy efficiency were investigated. The simulated outcome of the rapid numerical algorithms, regularised zero forcing and truncated polynomial expansion precoders for multicell massive MIMO system was analysed. The rapid numerical algorithms-based precoder gave the best performance followed by the regularised zero forcing precoder, and the truncated polynomial expansion-based precoder had the lowest performance for the multicell massive MIMO system. The increase in spectral efficiency per cell can be attributed to the fact that the pre-log factor reduces with the increased number of pilots. Also, this leads to increased instantaneous signal-to-interference-and-noise ratio as the channel estimates become better with reduced pilot contamination. Again, for truncated polynomial expansion precoding there is a reduction in spectral efficiency because improvement in approximation quality do not overshadow the reduction in pre-log factor. The performance is evaluated for uncoordinated and coordinated massive MIMO.

For the problem that mobile devices that are far away from the base station obtain limited wireless resources, which causes high transmission delays, the study proposes a multi-hop path to assist users who are far away from the base station to use edge computing resources. And it combines cloud radio access network and wireless self-organising network. The modelling aims to minimise the total energy consumption under the constraints of task delay considering the mobility of mobile devices. Meanwhile, it selects the joint optimisation problem of the path for data transmission and the virtual machine for calculation. This study also introduces the krill herd algorithm and analyses its advantages and disadvantages. The author enhances the global search ability of the algorithm by defining perturbation factors in the random diffusion behaviour and introduces a new strategy of elite selection and retention into the iterative process to improve the convergence accuracy. Finally, the improved krill herd algorithm is used to solve the joint optimisation problem and a better allocation result than the separate resource allocation of calculation and communication is obtained. The experiment proves that the selection algorithm combining virtual machine and routing proposed in this study can achieve the expected results.

This study investigates a small film DC-link capacitor inverter for a permanent magnet synchronous motor drive system. Two control methods, including a d–q-axis current control and a positive torque region current control, are originally proposed to improve the torque dynamics of the drive system. In addition, a damping compensation method is used to improve the input power factor and reduce the input current harmonics. A current-loop predictive controller and a speed-loop predictive controller are first proposed to improve the dynamic responses of the interior permanent magnet synchronous motor drive system. To implement the drive system, a digital signal processor, TMS 320LF2808, is used as a control centre to execute the control algorithms. Experimental results can validate the correctness and feasibility of the proposed methods.

Fault detection in hybrid AC–DC distribution networks is a challenging problem due to various sources of uncertainty and high degrees of complexity. A few well-known sources that instil uncertainty in the system are stochasticity of energy injected by distributed energy resources, noisy or corrupt data, heterogeneity of agents, problems with the automated mapping of equipment connectivity, and partial knowledge of the system. This study presents a distinctive approach that draws upon the use of Bayesian belief network to overcome uncertainties. The key advantage of Bayesian inference methodology is its capability to leverage both causal and correlational data in formulating a plausible conclusion. The proposed method uses state variables produced by distributed state estimation along with data collected from self-aware agents as the main sources of causal information. The rationale for using state estimation is its capability to overarch heterogeneity of AC and DC agents. It is shown that probabilistic graphical models can be employed successfully to detect faults in active hybrid distribution networks. An augmented version of IEEE 13-bus network is utilised to simulate and verify the suitability and effectiveness of the proposed technique.

The development of a smart grid electricity distribution network with advanced technology in smart metering will produce a massive amount of data. However, the limitation in communication network bandwidth makes it hard to transmit these data to the control center. Data compression is one of the best solutions to overcome this limitation. This study presents the use of multiresolution matrix factorisation (MMF) as a data compression technique for a smart distribution system. MMF will compress a data matrix into a core matrix with lower dimension via a series of orthogonal transformations. Experimental results gained from this study show that MMF is applicable in compressing large size data into lower dimension matrix with low error rates and high in speed. The MMF compression technique is able to reduce the volume of data to be transmitted through the communication network and thus save the bandwidth. Besides, MMF compression performs faster than the singular value decomposition method, especially with large size matrix. Findings from this study prove that MMF can serve as an alternative data compression technique for the smart distribution system, with a potential for an online application due to the high speed and high accuracy of the algorithm.