A maximum power point tracking (MPPT) technique plays an important role to ensure maximum photovoltaic (PV) output power is extracted under stochastic weather conditions. The research to date tends to focus on developing a standalone optimization MPPT algorithm rather than looking into a hybrid MPPT algorithm. This paper introduces particle swarm optimization (PSO) to optimize the maximum PV output power and to determine the best design variable for penalising the step size of the conventional methods namely the perturb and observe (PO) and the incremental conductance (IC). With the help of the hybrid MPPT algorithm (PSO+IC and PSO+PO), the step size is no longer fixed, and it is changing according to the solar irradiance. To evaluate the proposed hybrid algorithm, a single-stage grid connected PV system is designed for several different scenarios with various weather conditions. The performance of the hybrid MPPT algorithm and the conventional methods is compared. The results demonstrate that the hybrid MPPT algorithm is remarkably better than the conventional methods in terms of the output power ripple and the settling time.

The application of non-orthogonal multiple access (NOMA) to multi-layer multicast video streaming is envisioned to address the explosively growing demand for capacity which is dominated mostly by multimedia content. In this paper, a joint power allocation and subgrouping scheme is developed to enhance the performance of NOMA-based multi-layer multicast systems. An optimization problem is formulated to maximize the overall sum multicast rate whilst satisfying the maximum transmission power and proportional rate constraints. Due to the complexity of the optimization problem, we first derive two power allocation techniques for the 2-layer case considering arbitrary subgrouping which are based on the iterative implementation and closed-form analysis, respectively. We then generalize the low-complexity closed-form solution for the general multi-layer case. This scheme successively allocates power to each layer stream while assuring that the minimum target rate and proportionality are guaranteed, particularly for the transmission rate of the high-priority base layer stream. A sub-optimal joint power allocation and subgrouping scheme is also designed by incorporating the power allocation scheme into the proposed iterative subgrouping techniques. Simulation results show the effectiveness of the power allocation and subgrouping schemes in enhancing the sum multicast rate performance. In addition, the proposed power allocation scheme ensures substantially higher rates for the base layer stream, which is crucial in robust delivery of standard quality video to all users.

Coded multicasting is considered to be an effective approach to simultaneously serve multiple users in the same frequency/time/code resource, which is made possible by exploiting the information available in users’ caches. Recently, non-orthogonal multiple access (NOMA) has emerged as an alternative transmission technique for cache-aided network. In cache-aided NOMA system, cache-enabled interference cancellation (CIC) is employed to cancel the interference using the information in the cache, and thus enhance the user transmission rate, particularly for the weak users. Despite the enhanced spectral efficiency offered by both techniques, complexity issue arises from handling large number of users. Therefore, user pairing/clustering should be employed to limit the number of users served in the same time-frequency resource. However, the key question is which delivery technique performs better under different pairing scenarios. In this paper, the performance of NOMA and coded multicasting for two-user pairing are investigated in terms of probability of sum rate comparison and outage probability. In order to exploit the benefits of NOMA and coded multicasting, we also propose a hybrid delivery scheme, which select either NOMA or coded multicasting depending on the channel conditions of the paired users in each resource block (RB). A joint mode selection, power allocation and user pairing scheme is developed to enhance the performance of the hybrid scheme. Both analytical and simulation results demonstrate that NOMA outperforms coded multicasting when pairing users whose channel gains are highly distinctive, while coded multicasting is preferred when the paired users have similar channel gains. In addition, the hybrid scheme is demonstrated to offer enhanced sum rate performance in comparison to NOMA and coded multicasting.

Non-orthogonal multiple access (NOMA) scheme is emerging as an enabling technology for 5G wireless networks, which address the growing demand for capacity that is mostly made up of high quality video content. In this paper, by combining the principles of NOMA and layered coding, the sum rate is enhanced for multicast video streaming. An optimization problem for power allocation is formulated to maximize the overall sum rate whilst achieving the total transmission power constraints and target rates. A near-optimal scheme and a low-complexity closed form solution are proposed. Numerical simulation results show that the proposed schemes offer better sum rate performance than the existing schemes. The closed form solution incurs performance degradation relative to the near-optimal solution but with a much lower complexity. Furthermore, the proposed schemes provide a superior degree of fairness towards the users with poor channel conditions.

This paper introduces a novel Lithium-Ion Battery (LIB) State-of-Charge (SOC) estimation approach that integrates Akima–Savitzky–Golay curve reconstruction with a Bayesian-optimized, adaptive Extended Kalman Filter (EKF). The method addresses crucial SOC estimation challenges by means of three foundational advancements: (i) a refined open-circuit voltage (OCV)-SOC curve reconstruction grounded in Akima interpolation coupled with Savitzky–Golay filtering, (ii) an adaptive EKF weighting strategy, and (iii) systematic hyperparameter value optimization executed through Bayesian optimization. Comprehensive performance validation utilizes an extensive dataset collected from LG HG2 18650 cells across temperatures of −20 °C to 40 °C, incorporating multiple standard driving cycles—namely HPPC, UDDS, HWFET, LA92, and US06 cycles. The proposed method achieves an improved estimation accuracy with an average Root Mean Square Error (RMSE) of 2.65% over the different operating conditions and temperature variations. Notably, the method markedly enhances SOC estimation reliability in the critical mid-SOC range (20–80%), while preserving the computational overhead necessary for real-time integration into Battery Management Systems (BMSs). The adaptive weighting successfully compensates for the present physical limitations, thereby delivering a resilient SOC estimation tailored for Electric Vehicle (EV) battery applications.

In classical power systems, frequency measurements are transferred via a specialised communication channel, resulting in time delay. The time delay plays a major role in a power system, which can reduce the dynamic performance of the load–frequency control (LFC) system and can destabilise the system. The research to date has tended to focus on developing a new algorithm to determine the delay margin (DM) rather than looking into a hybrid algorithm which includes a nature-inspired metaheuristic optimisation technique. This paper introduces a novel method for computing the DM based on grey wolf optimisation (GWO), specifically for the constant time delay. In the proposed method, GWO is employed to optimise the minimum error of the spectral radius and to determine the best design variable of the crossing frequency. With the help of the proposed method, the sweeping range is no longer required, which improves the accuracy of the result. To evaluate the proposed method, a two-area network power system is considered as a case study. Furthermore, the effect of the PI controller gains on the DM is taken into account. The proposed method efficacy is demonstrated by comparing it with the most recently published methods. The results demonstrate that the proposed method is remarkably better than the existing methods found in the literature, where the smallest percentage inaccuracy using the simulation-based DM based on GWO is found to be 0.000%.