ý.. The method used for forecasting the load demand in UTP is Fuzzy Logic. Based on the analysis of the load pattern, a load forecasting model is then developed using Fuzzy L[r]

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ABSTRACT: In today’s world, load **forecasting** is an important part of planning in **power** **system** for developing countries, to utilise **power** more efficiently. In developing countries like India, Ethiopia, Ghana, large amount of **power** is wasted due to inaccurate generation, transmission, and distribution. It is due to lack of accuracy in **forecasting** the daily load. **Forecasting** of **electricity** demand is a fundamental process for planning periodical operations and future load growth in the **electricity** sector. Thus, accurate load **forecasting** plays a vital role in reducing the generation cost and optimising the spinning reserve capacity. Various authors have conducted research on the analysis of load **forecasting** and methods to improve the accuracy and efficiency in prediction. This paper presents a review of research and development in load **forecasting** methods for improving the forecasted error **using** **fuzzy** **logic**. The accuracy of the prediction model constructed **using** **fuzzy** **logic** is better than the **forecasting** models based on classical prediction methods.

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learning (RL). Some efforts are addressed in [3, 4, 5, 7, 16, 17]. RL based controllers learn and are adjusted to keep the area control error **small** enough in each sampling time of a LFC cycle. Since, these controllers are based on learning methods; they are independent of environment conditions and can learn a wide range of operating conditions. The RL based frequency control design is a model-free design and can easily scalable for large **scale** systems and suitable for frequency variation caused by wind turbine fluctuation. **Using** conventional linear control methodologies for the LFC design in a modern **power** **system** is not more efficient, because they are only suitable for a specific operating point in a traditional structure. If the dynamic/structure of **system** varies; they may not perform as expected. Most of conventional control strategies provide model based controllers that are highly dependent to the specific models, and are not useable for large-**scale** **power** systems concerning the integration of RES units with nonlinearities, undefined parameters and uncertain models. If the dimensions of the **power** **system** increase, then these control design may become more different as the number of the state variables also increases, significantly. Therefore, design of intelligent controllers that are more adaptive and flexible than conventional controllers is become an appealing approach. When WTGs are introduced to the **power** **system**, as they generate a part of **power** **system** loads, much portion of conventional nominal **power** can be available for **using** in supplementary control. However, as the variable wind farms **power** output may or may not be available during peak demand and abnormal periods, due to unpredictable nature

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Generally speaking, load **forecasting** is a piece of technology used by utility corporations to determine the equilibrium point between demand and supply of **electricity**, and the beneficiaries include banks, companies, businesses, industries, governments, private residences, and many others. The importance of this technology is very vast as it serves as a function of pricing and evaluation in the energy industry and it also provides the companies and stakeholders with the accurate forecast in the operation and management of the industry and decision making [1,2]. It is the white blood cell of the energy companies by ensuring security, reliability, and cost-effectiveness under which a deregulation spurs aggressive competitiveness and determination of rates versus investment analysis. To forecast a load, there are many factors by which the companies must take into account if they are to arrive at a near-accurate estimation, even though in such handling of information estimations are not always 100 % perfect [3]. The companies take into account the time, the weather, the classes of customers, the temperature range, humidity, and population and other relevant information. There are three ways of conducting this analysis: the short-term, medium- term, and the long-term load forecast. In brief, the short term is aligned to a short period, and it contains the forecast for one hour and a week. The medium term extends from one week to the maximum of a year ahead and in long-term forecast, the time frame starts from one year too many[4,5]. Certainly, many methodologies dominate the whole process of load **forecasting** ranging from regression method, exponential smoothing, stochastic process, ARMA, to data mining; et cetera. However, the most recent and shared

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Energy is the basic necessity for the economic development of a country. Agricultural and industrial production increases due to large amount of energy. The subject of load **forecasting** has been in existence for decades to forecast the future demand. There is a close relationship between the energy used per person and their standard of living. Load **forecasting** helps an electric utility to make important decisions including decisions on purchasing and generating electric **power**, load switching, and infrastructure development. More the population, more will be the per capita consumption of energy in a country, as higher will be the standard of living of the people. There are different forms of energy and most common form is electrical energy. **Power** demand of different consumers varies according to their activities. The growing tendency of **electricity** **system** is continually confronting the different sectors of the industry, with increasing demand on planning management and operations of the network. We know that energy cannot be stored but can be changed from one form to other. Similarly electrical **power** cannot be stored. Therefore the **power** station must produce **power** as and when required by the consumers. Since there is no “inventory” or “buffer” from generation to end users, ideally, **power** systems have to be build to meet the maximum demand, so called peak load, to ensure that sufficient **power** can be delivered to the customers whenever they need it. [16]

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The proposed model follows the object-oriented approach, which is the most suitable for the modelling of complex knowledge bases, as is the case of inspection planning. Three kinds of abstraction mechanisms are used: classification, composition and generalization. Furthermore, it uses three kinds of relationship between classes: aggregation, inheritance and association relationships. Three are also semantic constraints associated with these relationships. A class is the descriptor for a set of objects with similar structure, behaviour and relationships. A composite object represents a high-level object made of tightly bound parts. This is an instance of a composite class, which implies the composition aggregation between the class and its parts. Generalization allows the taxonomic relationship between a more general element (the parent) and a more specific element (the child) that is fully consistent with the first element and that additional information. An aggregation relationship implies a logical or physical relationship between the objects of the related classes. There is also an inheritance relationship and an association relationship, which imply a semantic relationship between the objects of the related classes. Each relationship may be seen in two perspectives according to the two classes it connects. In each perspective, one class is the source class while the other is the target class. However, in the case of association relationships the interpretation is the same for both perspectives. In order to visualize and document the aforementioned mechanisms the notation offered by the unified modelling language is used. Unified modelling language is a graphical language that offers a standard way to write a system’s blueprints, including conceptual things such as business processes and **system** functions as well as concrete things such as programming language statements, database schemas and reusable software components.

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(e.g. reduce number of tap operations, satisfy the desired consumers demand or maximise utility of DGs), and a given context (e.g. decision according to severity or economical targets). This context established whether to satisfy or not the local goals. The way the context works not differs much from a selection priority index [119] which maps the requirements with the control budget and decides which action to deploy (i.e. Table 4.2). For example, the budget is the sum of local sensitivities that is used as an approximation as actions can cancel each other or flows within the network may change affecting voltage levels. Thus, all these inputs are mapped into crisp actions as in [59] and applied and validated **using** a Matpower network model [45]. This process is continuously re-run until the violation is solved according to the network model, there no changes in the solution or until a maximum number of iterations is reached. Oscillations in voltage magnitude, but also interactions between devices may appear during the solution calculation due to the fact that removing one violation could cause another one to occur. Sequential validation can help avoid trailing

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systems: (a) the primary speed control and (b) supplementary or secondary speed control actions. The former performs the initial vulgar readjustment of the frequency by which generators in the control area track a load variation and share it in proportion to their capacities. This process typically takes place within 2–20 s. The latter takes over the fine adjustment of the frequency by resetting the frequency error to zero through an integral action. The relationship between the speed and load can be adjusted by changing a load reference set point input. In practice, the adjustment of the load reference set point is accomplished by operating the speed changer motor. The output of each unit at a given **system** frequency can be varied only by changing its load reference, which in effect moves the speed

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ABSTRACT: High-**power** LED drivers with high **power** factors require large capacitances to limit the low frequency LED current ripples. Electrolytic capacitors are used oftenly because they are the only capacitors with required energy density toaccommodate high-**power** applications. Thus, the LED **power** regulation with high **power** efficiency and a long lifetime of LED operation can be simultaneously performed by multi-stage switching circuits. The LED **power**, **power** factor can be calculated and controlled by micro controller unit (MCU). Our paper proposes a bipolar ripple cancellation method with two different full-bridge **power** structures to cancel the low-frequency ac ripple in the LED current and minimize the output capacitance. Our proposed prototype has achieved a peak **power** factor of 0.92-0.93 benefiting from the conventional method.

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Contingency and pattern of generation resulted from heavy flows tend to incur greater losses and has threaten stability and security. This ultimately makes certain generation patterns economically undesirable [1]. **Electricity** can be delivered to the load centres without violating operation limits such as voltage limit and thermal limit. In order to achieve secure and smooth delivery of **electricity** to the consumers, proper planning needs to be in place. Compensation process can be one of the possible choices to alleviate **power** disturbances and insecure **power** dispatch. On the other hand, load shedding and other schemes can be the other options especially when contingencies are experienced by the **system**. These days, the advancement of computational intelligence has been made use to alleviate **power** **system** problems especially when optimization process or decision making process are of urgency. Various techniques and schemes have been reported in these few decades. Various studies involving optimization techniques have been reported in [2-10]. These techniques apparently managed to identify the optimal sizing of the compensation devices, or even the optimal location in making sure that the compensation schemes are worth. In addition to optimization technique, utilization of index is also incorporated together so that other issues can be concurrently addressed. For instance, location optimization for the UPFC was conducted **using** voltage stability index and the voltage change index in [2]. In [1] line loss sensitivity indices, total **system** loss sensitivity indices, real **power** flow PI sensitivity indices were proposed for placement of TCSC and TCPAR. The optimal placement of TCSCs to minimise congestion cost based on the sensitivity factor approach was analysed in [3]. The extended voltage phasor approach was proposed in [4] for placement of FACTS controller from voltage stability viewpoint. In [5] contingency severity index was

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In this paper design of self tuned **fuzzy** set theory based PI controller is incorporated in typical FACTS device DSTATCOM. Its effects are tested in **power** systems. The modeling and the controller block diagram for DSTATCOM with detailed design of self tuned **fuzzy** **logic** controller is presented. The performance of proposed **fuzzy** **logic** DSTATCOM has been simulated for current balancing and harmonic compensation for both linear and non-linear loads. The results show the capability of proposed model in enhancing the dynamic behavior of interconnected systems. The simulation is carried out in MATLAB SIMULINK and the results shows the results confirm the feasibility of proposed **system**.

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The Static Var Compensator is basically a shunt connected variable Var generator whose output is adjusted to exchange capacitive or inductive current to the **system**. One of the most widely used configurations of the SVC is the FC- TCR type in which a Fixed Capacitor (FC) is connected in parallel with Thyristor Controlled Reactor (TCR). The magnitude of the SVC is inductive admittance BL (α) is a function of the firing angle α and is given by.

Dhurvey et al. [13] have examined the relative effectiveness of IPFC control signals on linearized **power** sys- tem model of single machine infinite bus **system** (SMIB) **system** for analyzing performance comparison of IPFC in coordination with **Power** Oscillation Damping Controller [POD] and **Power** **System** Stabilizer [PSS]. Menniti et al. [14] have discussed the structure of proposed IPFC **fuzzy** controller obtained on a test **power** **system** **using** ATP EMTP as programming environment. However, they have not applied strategy on multimachine **system** for analysis and POD damping signals for improved damping performance. Dash et al. [15] present a combination of both TS-**fuzzy** scheme and RBFN is adopted for nonlinear control of TCSC and IPFC by combining intelli- gent techniques. However, they have not presented independent **fuzzy** **logic** control and not included any sup- plementary damping signal. They have presented **fuzzy** interference with more complicated strategy. However, this paper delineates the simple design strategy for **fuzzy** **logic** control in comparison with PI based controller. Naresh Babu et al. [16] present the Newton-Raphson (NR) **power** flow solution method to study the effects of IPFC parameters for steady state analysis. N. Yadaiah et al. [17] present the survey of various techniques for li- nearisation of multi-machine **power** **system** dynamics and designing of controllers for the transient stability problem. Mansour-Khalilian et al. [18] designed a **fuzzy** **logic** controller to use the DSSC for enhancing tran- sient stability in a two-machine, two-area **power** **system**. Mohan P. Thakre et al. [19] propose a powerful sub- synchronous component based (SSC) controller to mitigate the subsynchronous resonance (SSR) with statics synchronous series compensator (SSSC).

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In the majority of **electricity** price **forecasting** studies, especially for the hourly price case, only one model is normally utilized to forecast the next 24 hourly prices. However, it is a rather difficult task to associate all the characteristics of 24 different hourly prices by a single model. Thus, the model may become under-fitting for some hourly predictions, while at the same time, it may become over-fitting for some others, which eventually leads to unsatisfactory results. An obvious disadvantage of such approach is related to the high complexity of the network structure (i.e. a **system** with 24 output nodes) in terms of training time and performance. Alternatively, a recurrent structure could provide similar characteristics, however in practice its performance would be deteriorated due to the feedback error accumulation. An alternative approach has been proposed in recent past [10] and it has been adopted also in this paper. The core of the proposed modular **forecasting** **system** is the 24 multi-input-single-output (MISO) modeling blocks. One of the advantages of the proposed modular **system** is its possible use also for long- range **forecasting** schemes.

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The concept of shunt active filtering was first introduced by Gyugyi and Strycula in 1976. Nowadays, a shunt active filter is not a dream but a reality, and many shunt active filters are in commercial operation all over the world. Their controllers determine in real time the compensating current reference, and force a **power** converter to synthesize it accurately. In this way, the active filtering can be selective and adaptive. In other words, a shunt active filter can compensate only for the harmonic current of a selected nonlinear load, and can continuously track changes in its harmonic content The shunt active **power** filter, with a self-controlled dc bus, has a topology similar to that of a static compensator (STATCOM) used for reactive **power** compensation in **power** transmission systems. Shunt active **power** filters compensate load current harmonics by injecting equal but opposite harmonic compensating current. In this case the shunt active **power** filter operates as a current source injecting the harmonic components generated by the load but phase shifted by 180 0

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ABSTRACT: Now a days our **power** **system** is facing many **power** qualityissues. The various reasons for these **power** quality problems are voltage fluctuations, harmonics, transients and reactive **power** demands. All these **power** quality problems are due to changing trend of our **power** demand. This paper proposes a unified **power** quality conditioner (UPQC) which is implemented **using** **fuzzy** **logic** controller (FLC). The results are analyzed **using** MATLAB Simulink software.

Fig.8. the universal bridge is controlled with a predictive control strategy.it requires the measurement of the grid voltage and current at PCC and the inverter DC link voltage. The measurement of the load current and the injected inverter current are not required. The inverter references current is extracted **using** DC-link capacitor voltage control method. The DC-link voltage, , are subtracted from the reference voltage, . A PI controller acts on the resultant error the DC –link voltage is maintained constant and the **power** balance between the grid, inverter ,and the load is achieved as the capacitor compensate instantaneously the difference between the grid and the load **power**[26]-[29]. Multiplication of the PI controller output with PCC per unit voltage forms the grid current reference. Ideal voltage is assumed. The reference and measured grid current and the PCC voltage are used to predict the inverter reference voltage required to force the actual current to track its reference. The predictive current controller presented in [27] is used to control the interfacing.

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Abstract. **Forecasting** energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-**Fuzzy** Inference **System** (ANFIS) for a mid-term forecast of **electricity** consumption. The model comprises of three meteorological variables as inputs and **electricity** consumption as output. Two ANFIS models with two clustering techniques (**Fuzzy** c-Means (FCM) and Grid Partitioning (GP) were developed (ANFIS-FCM and ANFIS- GP) to forecast monthly energy consumption based on meteorological variables. The performance of each model was determined **using** known statistical metrics. This compares the predicted **electricity** consumption with the observed and a statistical significance between the two reported. ANFIS-FCM model recorded a better mean absolute deviation (MAD), root mean square (RMSE), and mean absolute percentage error (MAPE) values of 0.396, 0.738, and 8.613 respectively compared to the ANFIS-GP model, which has MAD, RMSE, and MAPE values of 0.450, 0.762, and 9.430 values respectively. The study established that FCM is a good clustering technique in ANFIS compared to GP and recommended a comparison between the two techniques on hybrid ANFIS model.

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