Author(s): Linus Idoko, Olimpo Anaya-Lara, David Campos-Gaona

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use.

The energy industry faces a variety of challenges as a result of the growing demand for electricity. The emphasis is shifting to optimizing energy use in residential settings so as to achieve sustainable alternatives. The escalating demand for sustainable energy practices in residential environments gives rise to innovative approaches to home energy management. In order to significantly reduce home energy use and contribute to a more sustainable future, this paper proposes an optimization model for home energy management that combines Model Predictive Control (MPC) with Demand Response (DR) strategy to reduce energy consumption. The study used several types of data, such as the hourly load demand of a house and solar irradiance data. Load demand profile, derived from historical electricity usage records, provided hourly energy consumption over a 24-hour period, serving as essential input for predicting future energy needs using the MPC algorithm. Solar irradiance data and PV system specifications were utilized to model the power generated by PV panels, while information about the battery energy storage system, including its capacity, efficiency, and state of charge (SOC) limits, was essential for modelling the behavior of the battery in storing and discharging energy. The model encompasses mathematical models and optimization tools for the efficient usage of photovoltaic (PV) panels, battery energy storage systems (BESS), and grid power. With the aid of MATLAB/Simulink simulations, the study demonstrated that MPC effectively predicts energy demand and allocates power sources effectively, achieving a 41% reduction in energy costs compared to grid-only scenarios. Considering the results obtained, this paper suggests areas of further research work, such as integrating dynamic pricing models in countries like Nigeria and exploring hybrid renewable energy systems. This will build on the findings obtained in this work and further improve household energy efficiency and sustainability.

For power systems to be stable and reliable, an accurate prediction of electrical load demand is crucial. Electric utilities rely on short-term load forecasting to effectively manage the generation, transmission, and distribution of power to satisfy customer demand. Artificial Neuro-Fuzzy Inference System (ANFIS) is utilized in this work for short-term load forecasting due to its propensity to manage non-linear relationships and uncertainty. ANFIS method has been used in the past for short-term load forecasting, there are certain conditions and issues that need to be resolved. Data normalization, choice of optimization technique, and choice of membership function can greatly influence short-term load forecast using ANFIS. Thus, this paper explored the different choices available and recommended the best choices based on the results obtained. Hourly electrical load data from 24 ^th November 2021 to 4 ^th January 2022, sourced from University of Lagos Power Station, and relevant temperature data within the same range sourced from National Solar Irradiation Database (NSRDB) were used to train and test the various ANFIS architectures. Different ANFIS models, including different membership functions and sets of input data, were simulated on MATLAB, and the performance of the models was evaluated using standard matrices such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The result of this study showed that the inclusion of temperature as an exogenous variable and the use of Gaussian membership function yielded a higher forecast accuracy with MAPE 0.05754% and RMSE 0.56614. This implies that using temperature as an input variable and Gaussian membership functions can improve forecasting accuracy.

The increase in harmonic distortion in power networks due to non-linear loads is now a major challenge for utilities, and utilities are finding ways to reduce these harmonics by imposing penalties on customers that generate them. This research aimed to design a fairer harmonic penalty model for customers who inject harmonics into power systems, accounting for harmonics injected from both the customer and utility sides. The harmonics injected from the customer and utility sides of the PCC (Point of Common Coupling) were evaluated, and the resulting Total Harmonic Distortions (THD) were used to assess the penalty and compensation due to the customer. The customers electricity bill was also evaluated using this penalty model. The application of this penalty model results in a fairer model in which utility-penalised customers are compensated for harmonics injected from the network that exceed the limits, as well as for harmonics injected from the utility side. This research offers a substantial improvement over existing penalty models, as customers were treated fairly by considering the harmonics generated by their networks and the utility networks.