Communication plays a huge role in the operation of modern power systems. It permits a real-time monitoring coordination and control of the transmission, generation and distribution of electrical energy. As the modern grid grows towards an increased reliance on communication systems for the protection, metering and monitoring for as well as data acquisition for planning; there is a need to understand the challenge in the powers’ system communication and their impact on the uninterrupted supply of electrical energy. Communication delays are one of the challenges that might affect the performance of the power system and lead to power losses and equipment damage, it is important to investigate the causes and the mitigation options available. Thus, this paper the state of arts on the cause, the effect and mitigation of communication delays in the power system. Furthermore, in this paper an analysis of different causes of the delays for different network configurations and communication systems used; a comparative analysis of different latency mitigation methods and system performance simulations of a given compensation algorithm is tested against the existing methods. The pros and cons of these control strategies are illustrated in this paper. The summary and assessment of those methods of control in this review offer scholars and utilities valuable direction-finding to design superior communication energy control systems in the future.

Demand estimation in a water distribution network provides crucial data for monitoring and controlling systems. Because of budgetary and physical constraints, there is a need to estimate water demand from a limited number of sensor measurements. The demand estimation problem is underdetermined because of the limited sensor data and the implicit relationships between nodal demands and pressure heads. A simulation optimization technique using the water distribution network hydraulic model and an evolutionary algorithm is a potential solution to the demand estimation problem. This paper presents a detailed process simulation model for water demand estimation using the particle swarm optimization (PSO) algorithm. Nodal water demands and pipe flows are estimated when the number of estimated parameters is more than the number of measured values. The water demand at each node is determined by using the PSO algorithm to identify a corresponding demand multiplier. The demand multipliers are encoded with varying step sizes and the optimization algorithm particles are also discretized in order to improve the computation time. The sensitivity of the estimated water demand to uncertainty in demand multiplier discrete values and uncertainty in measured parameters is investigated. The sensor placement locations are selected using an analysis of the sensitivity of measured nodal heads and pipe flows to the change in the water demand. The results show that nodal demands and pipe flows can be accurately determined from a limited number of sensors.

Due to increase in integration of renewable energy into the grid and power quality issues arising from it, there is need for analysis and power improvement of such networks. This paper presents voltage profile, Q-V sensitivity analysis and Q-V curves analysis for a grid that is highly penetrated by renewable energy sources; solar PV, wind power and small hydro systems. Analysis is done on IEEE 39 bus test system with Wind power injection alone, PV power injection alone, with PV and wind power injection and with PV, wind and micro hydro power injection to the grid. The analysis is used to determine the buses where voltage stability improvement is needed. From the results, it was concluded that injection of the modeled wind power alone helped in stabilizing the voltage levels as determined from voltage profiles and reactive power margins. Replacing some of the conventional sources with PV power led to reduction of voltages for weak buses below the required standards. Injection of power from more than one renewable energy source helped in slightly improving the voltage levels. Distribution Static compensators (D-STATCOMs) were used to improve the voltage levels of the buses that were below the required standards.

A particle swarm optimized Takagi-Sugeno (T-S) fuzzy logic controller for maximum power point tracking in a photovoltaic (PV) system is presented. The method proposed automates the tuning of fuzzy logic controller (FLC) rules and membership functions as opposed to the trial-and-error approach. Expert knowledge used for tuning the FLC is extracted from an improved PV module model under varying solar radiation, temperature, and load conditions. The proposed optimized FLC provides fast and accurate tracking of the maximum power point (MPP) under varying operating conditions. The formulation, implementation, and simulation results are presented. Results obtained has shown that the optimized FLC gives a better performance compared with the conventional FLC tuned using trial and error.

This paper presents a technique for implementing population based metaheuristic algorithms during dynamic optimisation of a non–linear system with time varying inputs. The system dynamics due to the presence of multiple inputs and large signal variations are simulated when designing controller parameters. The proposed method is used to implement the particle swarm optimisation (PSO) algorithm and used to optimise fuzzy logic controllers for photovoltaic (PV) array maximum power point tracking. Controller optimisation is carried out using a large signal average model of the dc–dc converter. A rule firing analysis technique for interpretation of fuzzy logic controller rule participation at run–time is formulated. The rule inference parameters used for analysis are firing frequency, firing strength, and contribution to the control effort. The performance of optimised fuzzy logic controllers consisting of 9, 25, and 49 rules is analysed at run–time. Simulation results show that the fuzzy logic controller rules centred on the equilibrium point have the most significant contribution to the control effort. A fuzzy logic controller (FLC) with 9 rules can therefore give a good performance. The robustness of the 9–rule FLC is verified using the Lyapunov stability theory.

One of the most common power problems today is communication and control delays. This can adversely affect decision interaction in grid security management. This paper focuses on communication signal delays and how to identify and address communication system failure issues in the context of grid monitoring and control, with emphasis on communication signal delay. An application to solve this problem uses a thyristor switch capacitor (TSC) and a thyristor-controlled reactor (TCR) to improve the power quality of the Rwandan National Grid (RNG) with synchronous and PV generators. It is to counteract the negative effects of time delays. To this end, the TSC and TCR architectures use two methods: the fuzzy logic controller (FLC) method and the modified predictor method (MPM). The experiment was performed using the Simulink MATLAB tool. The power quality of the system was assessed using two indicators: the voltage index and total harmonic distortion. The FLC-based performance was shown to outperform the MPM for temporary or permanent failures if the correct outcome was found. As a result, we are still unsure if TSC and TCR can continue to provide favorable results in the event of a network cyber-attack.