Mukwanga W. Siti

Reconfiguration and Load Balancing in the LV and MV Distribution Networks for Optimal Performance

To get the distribution network to operate at its optimum performance in an automated distribution system reconfiguration was been proposed and researched. Considering, however, that optimum performance implies minimum loss, no overloading of transformers and cables, correct voltage profile, and absence of phase voltage and current imbalances, network reconfiguration alone is insufficient. It has to be complemented with techniques for phase rearrangement between the distribution transformer banks and the specific primary feeder with a radial structure and dynamic phase and load balancing along a feeder with a radial structure. This paper contributes such a technique at the low-voltage and medium-voltage levels of a distribution network simultaneously with reconfiguration at both levels. While the neural network is adopted for the network reconfiguration problem, this paper introduces a heuristic method for the phase balancing/loss minimization problem. A comparison of the heuristic algorithm with that of the neural network shows the former to be more robust. The approach proposed here, therefore for the combined problem, uses the neural network in conjunction with a heuristic method which enables different reconfiguration switches to be turned on/off and connected consumers to be switched between different phases to keep the phases balanced. An application example of the proposed method using real data is presented.

Particle swarm optimization for power system state estimation

Abstract The electrical network measurements are usually sent to the control centers using specific communication protocols. However, these measurements contain uncertainties due to the meters and communication errors (noise), incomplete metering or unavailability of some of these measurements. The aim of state estimation is to estimate the state variables of the power system by minimizing all measurement errors available at the control center. In the past, many traditional algorithms, based on gradient approach, have been used for this purpose. This paper discusses the application of an artificial intelligence (AI) algorithm, the particle swarm optimization (PSO), to solve the state estimation problem within a power system. Two objective functions are formulated: the weighted least square (WLS) and weighted least absolute value (WLAV). The effectiveness of PSO over another AI optimization algorithm, genetic algorithm (GA), is shown by comparing both two solutions to the true state variable values obtained using Newton–Raphson (NR) algorithm.

Phase load balancing in the secondary distribution network using fuzzy logic

The electrical network system is to ensure that an adequate supply is available to meet the estimated load of the consumers in both the near and more distant future. This must of course, be done for minimum possible cost consistent with satisfactory reliability and quality of the supply. In order to avoid excessive voltage drop and minimize loss, it may be economical to install apparatus to balance or partially balance the loads. It is believed that the technology to achieve an automatic load balancing lends itself readily for the implementation of different types of algorithms for automatically rearranging the connection of consumers on the low voltage and of a feeder for optimal performance. In this paper the fuzzy logic is been implemented to balance the load in the secondary part of the transformer.

Feeder load balancing using combinatorial optimization-based heuristic method

The distribution system problems, such as planning, loss minimization, and energy restoration, usually involve the phase balancing or network reconfiguration procedures. The determination of an optimal phase balance is, in general a combinatorial optimization problem. Several Algorithms have been proposed and had achieved good result. However, the running time may be very high or the algorithm can not work with a complex network. This paper proposes a combinatorial optimization-based fast heuristic method to balance the phase currents. It can provide an efficient and fast alternative solution for the load balancing task.

Optimal Control of Active Power of Two Micro-grids Interconnected with Two AC Tie-Lines

Abstract Active power balance and frequency control are important tasks in the daily management of a power system. With the integration of several microgrids in the modern power system, the balancing of sources of energy has become a major concern for electrical power engineers and researchers worldwide. New power systems require more flexibility in optimization and control design to ensure their ability to maintain the balance between generation and load of the system. The present paper discusses an optimal control design for minimization of the power flow in tie-lines and frequency deviations in the microgrid, which will lead to power balance between the generation and load demand of the system. The system being studied consists of two microgrids, each made up of a wind farm, conventional power system (large hydro or thermal plant), photovoltaic system, battery energy storage system and the system load. Optimal control theory is applied to control the power flow between two microgrids. The Matlab environment is used for simulations.

Weighted least squares and iteratively reweighted least squares comparison using Particle Swarm Optimization algorithm in solving power system state estimation

Measurements from the electrical network are generally transmitted towards the control centres using special communication links. These measurements allow determining the state of the network in real time. However, these measurements often contain uncertainties due to the meter and communication errors, incomplete metering or unavailability of some of these measurements, etc. This paper presents the application of the Particle Swarm Optimization (PSO) algorithm in minimizing the raw measurement errors in order to identify or estimate the optimal operating state of the power system. Two different objective function formulations are assessed by PSO. The first formulation is the Weighted Least Square (WLS) and the second one is the Iteratively Reweighted Least Squares (IRLS) implementation of the Weighted Least Absolute Value (WLAV). Both solutions are compared with a Newton-Raphson (NR) power flow solution using an IEEE 6-bus test system.

Feeder's load balancing using an expert system

The electrical distribution system is to ensure that an adequate supply is available to meet the estimated load of the consumers in both the near and more distant future. This must of course, be done for minimum possible cost consistent with satisfactory reliability and quality of the supply. In order to avoid excessive voltage drop and minimize technical loss, it may be economical to install apparatus to balance or partially balance the loads. It is believed that the technology to achieve an automatic load balancing lends itself readily for the implementation of different types of algorithms for automatically reconfiguring a distribution network system for optimal performance

The use of multi agent system for monitoring and control of the smelting process in the mining metallurgical sector

This paper is focusing on the development of intelligent agent technology and application of that technology to the mining and metallurgical sector. We have shown how networks of communicating and cooperating intelligent software agents can be used to implement complex distributed systems. In the mining and metallurgical industries there are processes which cannot tolerate interruption, such as some sequential line chemical processes. Most of the time large production losses may occur due to voltage dips. Poor quality issues have been a major concern for the mine and metallurgical sectors. There is a need for equipment that supports production units to ride through the voltage dips so that the electrical networks are controllable and the voltage dips can be monitored. The paper proposes an application of the intelligent agent technology in creating an efficient data acquisition and expert fault tolerant system in the smelting process