Optimal placement of statcom controllers with metaheuristic algorithms for network power loss reduction and voltage profile deviation minimization.

Masters Degree. University of KwaZulu-Natal, Durban.Transmission system is a series of interconnected lines that enable the bulk movement of electrical power from a generating station to an electrical substation. This system suffers from unavoidable power losses and consequently voltage profile deviation which affects the overall efficiency of the system; hence the need to reduce these losses and voltage magnitude deviations. The existing methods of incorporation of static synchronous compensator (STATCOM) controllers to solve these problems suffer from incorrect location and sizing, which could bring about insignificant reduction in transmission network losses and voltage magnitude deviations. Hence, this research aims to reduce transmission network losses and voltage magnitude deviation in transmission network by suitable allocation of STATCOM controller using firefly algorithm (FA) and particle swarm optimization (PSO). A mathematical steady-state STATCOM power injection model was formulated from one voltage source representation to generate new set of equations, which was incorporated into the Newton-Raphson (NR) load flow solution algorithm and then optimized using PSO and FA. The approach was applied to IEEE 14-bus network and simulations were performed using MATLAB program. The results showed that the best STATCOM controller locations in the system after optimization were at bus 11 and 9 with the injection of shunt reactive power of 8.96 MVAr, and 9.54 MVAr with PSO and FA, respectively. The total active power loss for the network under consideration at steady state, with STATCOM only and STATCOM controller optimized using PSO and FA, were 6.251 MW, 6.075 MW, 5.819 MW and 5.581 MW, respectively. The corresponding reactive power were 14.256 MVAr, 13.857 MVAr, 12.954 MVAr and 12.156 MVAr, respectively. In addition, bus voltage profile improvement indicates the effectiveness of metaheuristic methods of STATCOM optimization. However, FA gave a better power loss and voltage magnitude deviations minimizations over PSO. The study concluded that FA is more effective as an optimization technique for suitably locating and sizing of STATCOM controller on a power transmission system.Publications listed on page iii

Simultaneous network reconfiguration and capacitor allocations using a novel dingo optimization algorithm

Power loss and voltage magnitude fluctuations are two major issues in distribution networks that have drawn a lot of attention. Numerous strategies have been put forward to provide remedies to lessen the undesirable effects of these issues. Combining two of these approaches and dealing with them simultaneously to get more effective outcomes is essential. Therefore, this study hybridizes the network reconfiguration and capacitor allocation strategies using a novel dingo optimization algorithm (DOA) to solve the optimization problems. The optimization problems for simultaneous network reconfiguration and capacitor allocations were formulated and solved using a novel DOA. To demonstrate its effectiveness, DOA’s results were contrasted with those of the other optimization techniques. The methodology was validated on the IEEE 33-bus network and implemented in the MATLAB program. The results demonstrated that the best network reconfiguration was accomplished with switches 7, 11, 17, 27, and 34 open, and buses 8, 29, and 30 were the best places for capacitors with ideal sizes of 512, 714, and 495 kVAr, respectively. The voltage profile was significantly improved, and the power losses were significantly decreased. When compared to some of the different methods, DOA came out on top

Power System Voltage Stability Margin Estimation Using Adaptive Neuro-Fuzzy Inference System Enhanced with Particle Swarm Optimization

In the current era of e-mobility and for the planning of sustainable grid infrastructures, developing new efficient tools for real-time grid performance monitoring is essential. Thus, this paper presents the prediction of the voltage stability margin (VSM) of power systems by the critical boundary index (CBI) approach using the machine learning technique. Prediction models are based on an adaptive neuro-fuzzy inference system (ANFIS) and its enhanced model with particle swarm optimization (PSO). Standalone ANFIS and PSO-ANFIS models are implemented using the fuzzy ‘c-means’ clustering method (FCM) to predict the expected values of CBI as a veritable tool for measuring the VSM of power systems under different loading conditions. Six vital power system parameters, including the transmission line and bus parameters, the power injection, and the system voltage derived from load flow analysis, are used as the ANFIS model implementation input. The performances of the two ANFIS models on the standard IEEE 30-bus and the Nigerian 28-bus systems are evaluated using error and regression analysis metrics. The performance metrics are the root mean square error (RMSE), mean absolute percentage error (MAPE), and Pearson correlation coefficient (R) analyses. For the IEEE 30-bus system, RMSE is estimated to be 0.5833 for standalone ANFIS and 0.1795 for PSO-ANFIS; MAPE is estimated to be 13.6002% for ANFIS and 5.5876% for PSO-ANFIS; and R is estimated to be 0.9518 and 0.9829 for ANFIS and PSO-ANFIS, respectively. For the NIGERIAN 28-bus system, the RMSE values for ANFIS and PSO-ANFIS are 5.5024 and 2.3247, respectively; MAPE is 19.9504% and 8.1705% for both ANFIS and PSO-ANFIS variants, respectively, and the R is estimated to be 0.9277 for ANFIS and 0.9519 for ANFIS-PSO, respectively. Thus, the PSO-ANFIS model shows a superior performance for both test cases, as indicated by the percentage reduction in prediction error, although at the cost of a higher simulation time

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around formulating an objective function that represented the cumulative active power losses that occurred across all branches of the network. The DOA was then effectively used to evaluate the most suitable capacities and positions for the PVDG units. To address the power flow challenges inherent to DNs, this study used the Newton–Raphson power flow method. To gauge the effectiveness of DOA in allocating PVDG units, it was rigorously compared to other metaheuristic optimization algorithms previously documented in the literature. The entire methodology was implemented using MATLAB and validated using the IEEE 33-bus DN. The performance of the network was scrutinized under normal, light, and heavy loading conditions. Subsequently, the approach was also applied to a practical Ajinde 62-bus DN. The research findings yielded crucial insights. For the IEEE 33-bus DN, it was determined that the optimal locations for PVDG units were buses 13, 25, and 33, with recommended capacities of 833, 532, and 866 kW, respectively. Similarly, in the context of the Ajinde 62-bus network, buses 17, 27, and 33 were identified as the prime locations for PVDGs, each with optimal sizes of 757, 150, and 1097 kW, respectively. Remarkably, the introduction of PVDGs led to substantial enhancements in network performance. For instance, in the IEEE 33-bus DN, the smallest voltage magnitude increased to 0.966 p.u. under normal loads, 0.9971 p.u. under light loads, and 0.96004 p.u. under heavy loads. These improvements translated into a significant reduction in active power losses—61.21% under normal conditions, 17.84% under light loads, and 33.31% under heavy loads. Similarly, in the case of the Ajinde 62-bus DN, the smallest voltage magnitude reached 0.9787 p.u., accompanied by an impressive 71.05% reduction in active power losses. In conclusion, the DOA exhibited remarkable efficacy in the strategic allocation of PVDGs, leading to substantial enhancements in DN performance across diverse loading conditions