Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

Optimizacija reaktivne snage - Reactive power optimization (RPO) - osnovno je područje istraživanja u svrhu sigurnog i ekonomičnog rada energetskih sustava. RPO se može primijeniti za smanjenje gubitaka aktivne snage, reguliranje napona te za optimizaciju energetskih koeficijenata u energetskim sustavima. Funkcija ne-linearnog gubitka snage koristi se kao funkcija cilja koju treba smanjiti. U ovom se radu algoritam Kaotične umjetne kolonije pčela - Chaotic Artificial Bee Colony (CABC) - primjenjuje za smanjenje gubitka aktivne snage u energetskim sustavima. Rabe se kaotične mape kao što su logistička mapa i Henon mapa. CABS se primjenjuje na provjeravanim sustavima IEEE 6-sabirnice i IEEE 30-sabirnice i daju se rezultati. Provjerom rezultata ustanovilo se da primjena kritičnih vrijednosti stabilnosti dobivenih pomoću CABS može rezultirati dobrim potencijalnim rješenjima. Rezultati simulacije su obećavajući i pokazuju učinkovitost primijenjenog pristupa.Reactive power optimization (RPO) is a major field of study to ensure power systems for operating in a secure and economical manner. RPO can be used for decreasing of active power losses, voltage control, and for the optimization of the power coefficients in power systems. The non-linear power loss function is used as an object function that will be minimized. In this study Chaotic Artificial Bee Colony (CABC) algorithm is used to minimize the active power loss of power systems. Chaotic maps such as logistic map and Henon map are used against the random number generator. CABC is applied on the IEEE6-bus and IEEE 30-bus test systems and the results are shown. Accordingly, the results have been evaluated and observed that the stability critical values found by CABC can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach

Penentuan Letak Dan Kapasitas Optimal Bank Kapasitor Pada Jaring Transmisi 150 KV Sumatera Utara Menggunakan Artificial Bee Colony Algorithm

Listrik merupakan suatu kebutuhan mutlak yang harus dipenuhi untuk menjamin keberlangsungan hidup masyarakat masa kini. Kebutuhan ini terus meningkat seiring dengan pertumbuhan beban yang semakin bertambah dari tahun ke tahun. Pertumbuhan beban yang diikuti dengan peningkatan permintaan suplai daya reaktif akibat beban bersifat induktif meningkat menyebabkan perencanaan dan operasi dari sistem interkoneksi menjadi lebih kompleks sehingga kualitas sistem menjadi kurang dapat diandalkan. Aliran daya reaktif dapat menyebabkan drop tegangan dan kerugian daya dalam sistem transmisi. Untuk itu dilakukan penentuan letak dan kapasitas kapasitor shunt untuk mengurangi kerugian daya dengan menggunakan Newton-Raphson dan metode optimisasi Artificial Bee Colony Algorithm. Pada percobaan ini dilakukan pemasangan lima kapasitor dengan jumlah koloni sebesar 50 dan Max Cycle Number sebesar 150. Hasil simulasi menggunakan metode Artificial Bee Colony Algorithm menunjukkan bahwa pemasangan kapasitor pada Jaring Transmisi 150 kV Sumatera Utara dapat menurunkan kerugian daya aktif sebesar 8,37%. ===================================================================================================== Electricity is an absolute necessity that must be met to ensure the survival of communities. This necessity has improved along with the growth of load from year to year. The growth of load which followed by high reactive power demand due to high inductive load causes the planning and operation of the interconnection system becomes more complex so that the quality of the system becomes less reliable. Reactive power flow can cause a voltage drop and line losses in the transmission system. For that reason, the determination of the location and capacity of shunt capacitor is needed to reduce power losses using Newton-Raphson and Artificial Bee Colony Algorithm optimization method. In this experiment was installed five capacitors with 50 colony sizes and 150 of Max Cycle Number. Simulation results using Artificial Bee Colony Algorithm method showed that the installation of capacitors at 150 kV Sumatera Utara Transmission System can reduce 8,37 % active power losses

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods